From 8c48c3cffd0f3c6091e85e9270d031b7408bed28 Mon Sep 17 00:00:00 2001 From: dtarkent2-sys Date: Mon, 13 Apr 2026 22:01:00 -0400 Subject: [PATCH] WIP: local TradingAgents customizations through 2026-04-13 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bulk commit of accumulated local changes on the dtarkent2-sys fork. Spans agents, dataflows, llm_clients, graph orchestration, CLI, and docs. Primary work areas: - llm_clients/ — multi-LLM client layer (anthropic, google, openai, factory, base, validators) for swappable provider support - dataflows/alpaca_data.py — Alpaca integration alongside existing alpha_vantage and y_finance flows - agents/structured/ — portfolio, scoring, and tier1/2/3 layers - agents/analysts, researchers, risk_mgmt — local prompt and logic customizations - graph/ — orchestration tweaks (parallel_analysts, propagation, reflection, signal_processing, trading_graph) - alembic scaffolding inherited from prior commit - chainlit web UI design notes in docs/plans/ This is a single WIP snapshot to preserve work before any upstream merge. History can be cleaned up with interactive rebase later. Co-Authored-By: Claude Opus 4.6 (1M context) --- .dockerignore | 16 +- .env.example | 12 +- .gitignore | 440 +- .railwayignore | 8 +- LICENSE | 402 +- README.md | 438 +- app.py | 984 +- cli/announcements.py | 102 +- cli/config.py | 12 +- cli/main.py | 2354 ++--- cli/models.py | 20 +- cli/static/welcome.txt | 14 +- cli/stats_handler.py | 152 +- cli/utils.py | 656 +- .../2026-02-20-chainlit-web-ui-design.md | 134 +- main.py | 66 +- nvda_output.txt | 3404 +++---- nvda_output2.txt | 3790 ++++---- pyproject.toml | 72 +- test.py | 22 +- tradingagents/agents/__init__.py | 80 +- .../agents/analysts/fundamentals_analyst.py | 126 +- .../agents/analysts/market_analyst.py | 170 +- tradingagents/agents/analysts/news_analyst.py | 116 +- .../agents/analysts/social_media_analyst.py | 118 +- .../agents/managers/research_manager.py | 110 +- tradingagents/agents/managers/risk_manager.py | 132 +- .../agents/researchers/bear_researcher.py | 122 +- .../agents/researchers/bull_researcher.py | 118 +- .../agents/risk_mgmt/aggressive_debator.py | 110 +- .../agents/risk_mgmt/conservative_debator.py | 116 +- .../agents/risk_mgmt/neutral_debator.py | 110 +- tradingagents/agents/structured/__init__.py | 106 +- tradingagents/agents/structured/portfolio.py | 534 +- tradingagents/agents/structured/scoring.py | 118 +- tradingagents/agents/structured/tier1.py | 554 +- tradingagents/agents/structured/tier2.py | 1294 +-- tradingagents/agents/structured/tier3.py | 836 +- tradingagents/agents/trader/trader.py | 92 +- tradingagents/agents/utils/agent_states.py | 218 +- tradingagents/agents/utils/agent_utils.py | 74 +- .../agents/utils/core_stock_tools.py | 44 +- .../agents/utils/fundamental_data_tools.py | 152 +- .../agents/utils/macro_data_tools.py | 1188 +-- tradingagents/agents/utils/memory.py | 288 +- tradingagents/agents/utils/news_data_tools.py | 106 +- .../utils/technical_indicators_tools.py | 50 +- tradingagents/dataflows/alpaca_data.py | 642 +- tradingagents/dataflows/alpha_vantage.py | 8 +- .../dataflows/alpha_vantage_common.py | 244 +- .../dataflows/alpha_vantage_fundamentals.py | 154 +- .../dataflows/alpha_vantage_indicator.py | 444 +- tradingagents/dataflows/alpha_vantage_news.py | 140 +- .../dataflows/alpha_vantage_stock.py | 74 +- tradingagents/dataflows/config.py | 62 +- tradingagents/dataflows/interface.py | 322 +- tradingagents/dataflows/stockstats_utils.py | 128 +- tradingagents/dataflows/utils.py | 78 +- tradingagents/dataflows/y_finance.py | 1622 ++-- tradingagents/dataflows/yfinance_news.py | 380 +- tradingagents/graph/__init__.py | 18 +- tradingagents/graph/conditional_logic.py | 134 +- tradingagents/graph/parallel_analysts.py | 444 +- tradingagents/graph/propagation.py | 114 +- tradingagents/graph/reflection.py | 242 +- tradingagents/graph/setup.py | 416 +- tradingagents/graph/signal_processing.py | 62 +- tradingagents/graph/trading_graph.py | 396 +- tradingagents/llm_clients/TODO.md | 48 +- tradingagents/llm_clients/__init__.py | 8 +- tradingagents/llm_clients/anthropic_client.py | 54 +- tradingagents/llm_clients/base_client.py | 42 +- tradingagents/llm_clients/factory.py | 86 +- tradingagents/llm_clients/google_client.py | 130 +- tradingagents/llm_clients/openai_client.py | 150 +- tradingagents/llm_clients/validators.py | 164 +- uv.lock | 8270 ++++++++--------- 77 files changed, 17628 insertions(+), 17628 deletions(-) diff --git a/.dockerignore b/.dockerignore index be9d880d..b59afcc3 100644 --- a/.dockerignore +++ b/.dockerignore @@ -1,8 +1,8 @@ -.git -eval_results -nvda_output*.txt -docs -uv.lock -__pycache__ -.env -.env.example +.git +eval_results +nvda_output*.txt +docs +uv.lock +__pycache__ +.env +.env.example diff --git a/.env.example b/.env.example index 1328b838..8c0a3f02 100644 --- a/.env.example +++ b/.env.example @@ -1,6 +1,6 @@ -# LLM Providers (set the one you use) -OPENAI_API_KEY= -GOOGLE_API_KEY= -ANTHROPIC_API_KEY= -XAI_API_KEY= -OPENROUTER_API_KEY= +# LLM Providers (set the one you use) +OPENAI_API_KEY= +GOOGLE_API_KEY= +ANTHROPIC_API_KEY= +XAI_API_KEY= +OPENROUTER_API_KEY= diff --git a/.gitignore b/.gitignore index 94182b43..bd414590 100644 --- a/.gitignore +++ b/.gitignore @@ -1,221 +1,221 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[codz] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py.cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -# Pipfile.lock - -# UV -# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. -# This is especially recommended for binary packages to ensure reproducibility, and is more -# commonly ignored for libraries. -# uv.lock - -# poetry -# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. -# This is especially recommended for binary packages to ensure reproducibility, and is more -# commonly ignored for libraries. -# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control -# poetry.lock -# poetry.toml - -# pdm -# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. -# pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python. -# https://pdm-project.org/en/latest/usage/project/#working-with-version-control -# pdm.lock -# pdm.toml -.pdm-python -.pdm-build/ - -# pixi -# Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control. -# pixi.lock -# Pixi creates a virtual environment in the .pixi directory, just like venv module creates one -# in the .venv directory. It is recommended not to include this directory in version control. -.pixi - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# Redis -*.rdb -*.aof -*.pid - -# RabbitMQ -mnesia/ -rabbitmq/ -rabbitmq-data/ - -# ActiveMQ -activemq-data/ - -# SageMath parsed files -*.sage.py - -# Environments -.env -.envrc -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# PyCharm -# JetBrains specific template is maintained in a separate JetBrains.gitignore that can -# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore -# and can be added to the global gitignore or merged into this file. For a more nuclear -# option (not recommended) you can uncomment the following to ignore the entire idea folder. -# .idea/ - -# Abstra -# Abstra is an AI-powered process automation framework. -# Ignore directories containing user credentials, local state, and settings. -# Learn more at https://abstra.io/docs -.abstra/ - -# Visual Studio Code -# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore -# that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore -# and can be added to the global gitignore or merged into this file. However, if you prefer, -# you could uncomment the following to ignore the entire vscode folder -# .vscode/ - -# Ruff stuff: -.ruff_cache/ - -# PyPI configuration file -.pypirc - -# Marimo -marimo/_static/ -marimo/_lsp/ -__marimo__/ - -# Streamlit -.streamlit/secrets.toml - -# Cache -**/data_cache/ -eval_results/ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[codz] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py.cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +# Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +# poetry.lock +# poetry.toml + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +# pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python. +# https://pdm-project.org/en/latest/usage/project/#working-with-version-control +# pdm.lock +# pdm.toml +.pdm-python +.pdm-build/ + +# pixi +# Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control. +# pixi.lock +# Pixi creates a virtual environment in the .pixi directory, just like venv module creates one +# in the .venv directory. It is recommended not to include this directory in version control. +.pixi + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# Redis +*.rdb +*.aof +*.pid + +# RabbitMQ +mnesia/ +rabbitmq/ +rabbitmq-data/ + +# ActiveMQ +activemq-data/ + +# SageMath parsed files +*.sage.py + +# Environments +.env +.envrc +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +# .idea/ + +# Abstra +# Abstra is an AI-powered process automation framework. +# Ignore directories containing user credentials, local state, and settings. +# Learn more at https://abstra.io/docs +.abstra/ + +# Visual Studio Code +# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore +# that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore +# and can be added to the global gitignore or merged into this file. However, if you prefer, +# you could uncomment the following to ignore the entire vscode folder +# .vscode/ + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc + +# Marimo +marimo/_static/ +marimo/_lsp/ +__marimo__/ + +# Streamlit +.streamlit/secrets.toml + +# Cache +**/data_cache/ +eval_results/ .env.railway diff --git a/.railwayignore b/.railwayignore index e725a4e3..4196a7c1 100644 --- a/.railwayignore +++ b/.railwayignore @@ -1,4 +1,4 @@ -.git -__pycache__ -*.pyc -.env +.git +__pycache__ +*.pyc +.env diff --git a/LICENSE b/LICENSE index 261eeb9e..29f81d81 100644 --- a/LICENSE +++ b/LICENSE @@ -1,201 +1,201 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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- -

- -
- arXiv - Discord - WeChat - X Follow -
- Community -
- -
- - Deutsch | - Español | - français | - 日本語 | - 한국어 | - Português | - Русский | - 中文 -
- ---- - -# TradingAgents: Multi-Agents LLM Financial Trading Framework - -## News -- [2026-02] **TradingAgents v0.2.0** released with multi-provider LLM support (GPT-5.x, Gemini 3.x, Claude 4.x, Grok 4.x) and improved system architecture. -- [2026-01] **Trading-R1** [Technical Report](https://arxiv.org/abs/2509.11420) released, with [Terminal](https://github.com/TauricResearch/Trading-R1) expected to land soon. - -
- - - - - TradingAgents Star History - - -
- -> 🎉 **TradingAgents** officially released! We have received numerous inquiries about the work, and we would like to express our thanks for the enthusiasm in our community. -> -> So we decided to fully open-source the framework. Looking forward to building impactful projects with you! - -
- -🚀 [TradingAgents](#tradingagents-framework) | ⚡ [Installation & CLI](#installation-and-cli) | 🎬 [Demo](https://www.youtube.com/watch?v=90gr5lwjIho) | 📦 [Package Usage](#tradingagents-package) | 🤝 [Contributing](#contributing) | 📄 [Citation](#citation) - -
- -## TradingAgents Framework - -TradingAgents is a multi-agent trading framework that mirrors the dynamics of real-world trading firms. By deploying specialized LLM-powered agents: from fundamental analysts, sentiment experts, and technical analysts, to trader, risk management team, the platform collaboratively evaluates market conditions and informs trading decisions. Moreover, these agents engage in dynamic discussions to pinpoint the optimal strategy. - -

- -

- -> TradingAgents framework is designed for research purposes. Trading performance may vary based on many factors, including the chosen backbone language models, model temperature, trading periods, the quality of data, and other non-deterministic factors. [It is not intended as financial, investment, or trading advice.](https://tauric.ai/disclaimer/) - -Our framework decomposes complex trading tasks into specialized roles. This ensures the system achieves a robust, scalable approach to market analysis and decision-making. - -### Analyst Team -- Fundamentals Analyst: Evaluates company financials and performance metrics, identifying intrinsic values and potential red flags. -- Sentiment Analyst: Analyzes social media and public sentiment using sentiment scoring algorithms to gauge short-term market mood. -- News Analyst: Monitors global news and macroeconomic indicators, interpreting the impact of events on market conditions. -- Technical Analyst: Utilizes technical indicators (like MACD and RSI) to detect trading patterns and forecast price movements. - -

- -

- -### Researcher Team -- Comprises both bullish and bearish researchers who critically assess the insights provided by the Analyst Team. Through structured debates, they balance potential gains against inherent risks. - -

- -

- -### Trader Agent -- Composes reports from the analysts and researchers to make informed trading decisions. It determines the timing and magnitude of trades based on comprehensive market insights. - -

- -

- -### Risk Management and Portfolio Manager -- Continuously evaluates portfolio risk by assessing market volatility, liquidity, and other risk factors. The risk management team evaluates and adjusts trading strategies, providing assessment reports to the Portfolio Manager for final decision. -- The Portfolio Manager approves/rejects the transaction proposal. If approved, the order will be sent to the simulated exchange and executed. - -

- -

- -## Installation and CLI - -### Installation - -Clone TradingAgents: -```bash -git clone https://github.com/TauricResearch/TradingAgents.git -cd TradingAgents -``` - -Create a virtual environment in any of your favorite environment managers: -```bash -conda create -n tradingagents python=3.13 -conda activate tradingagents -``` - -Install dependencies: -```bash -pip install -r requirements.txt -``` - -### Required APIs - -TradingAgents supports multiple LLM providers. Set the API key for your chosen provider: - -```bash -export OPENAI_API_KEY=... # OpenAI (GPT) -export GOOGLE_API_KEY=... # Google (Gemini) -export ANTHROPIC_API_KEY=... # Anthropic (Claude) -export XAI_API_KEY=... # xAI (Grok) -export OPENROUTER_API_KEY=... # OpenRouter -export ALPHA_VANTAGE_API_KEY=... # Alpha Vantage -``` - -For local models, configure Ollama with `llm_provider: "ollama"` in your config. - -Alternatively, copy `.env.example` to `.env` and fill in your keys: -```bash -cp .env.example .env -``` - -### CLI Usage - -You can also try out the CLI directly by running: -```bash -python -m cli.main -``` -You will see a screen where you can select your desired tickers, date, LLMs, research depth, etc. - -

- -

- -An interface will appear showing results as they load, letting you track the agent's progress as it runs. - -

- -

- -

- -

- -## TradingAgents Package - -### Implementation Details - -We built TradingAgents with LangGraph to ensure flexibility and modularity. The framework supports multiple LLM providers: OpenAI, Google, Anthropic, xAI, OpenRouter, and Ollama. - -### Python Usage - -To use TradingAgents inside your code, you can import the `tradingagents` module and initialize a `TradingAgentsGraph()` object. The `.propagate()` function will return a decision. You can run `main.py`, here's also a quick example: - -```python -from tradingagents.graph.trading_graph import TradingAgentsGraph -from tradingagents.default_config import DEFAULT_CONFIG - -ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy()) - -# forward propagate -_, decision = ta.propagate("NVDA", "2026-01-15") -print(decision) -``` - -You can also adjust the default configuration to set your own choice of LLMs, debate rounds, etc. - -```python -from tradingagents.graph.trading_graph import TradingAgentsGraph -from tradingagents.default_config import DEFAULT_CONFIG - -config = DEFAULT_CONFIG.copy() -config["llm_provider"] = "openai" # openai, google, anthropic, xai, openrouter, ollama -config["deep_think_llm"] = "gpt-5.2" # Model for complex reasoning -config["quick_think_llm"] = "gpt-5-mini" # Model for quick tasks -config["max_debate_rounds"] = 2 - -ta = TradingAgentsGraph(debug=True, config=config) -_, decision = ta.propagate("NVDA", "2026-01-15") -print(decision) -``` - -See `tradingagents/default_config.py` for all configuration options. - -## Contributing - -We welcome contributions from the community! Whether it's fixing a bug, improving documentation, or suggesting a new feature, your input helps make this project better. If you are interested in this line of research, please consider joining our open-source financial AI research community [Tauric Research](https://tauric.ai/). - -## Citation - -Please reference our work if you find *TradingAgents* provides you with some help :) - -``` -@misc{xiao2025tradingagentsmultiagentsllmfinancial, - title={TradingAgents: Multi-Agents LLM Financial Trading Framework}, - author={Yijia Xiao and Edward Sun and Di Luo and Wei Wang}, - year={2025}, - eprint={2412.20138}, - archivePrefix={arXiv}, - primaryClass={q-fin.TR}, - url={https://arxiv.org/abs/2412.20138}, -} -``` +

+ +

+ +
+ arXiv + Discord + WeChat + X Follow +
+ Community +
+ +
+ + Deutsch | + Español | + français | + 日本語 | + 한국어 | + Português | + Русский | + 中文 +
+ +--- + +# TradingAgents: Multi-Agents LLM Financial Trading Framework + +## News +- [2026-02] **TradingAgents v0.2.0** released with multi-provider LLM support (GPT-5.x, Gemini 3.x, Claude 4.x, Grok 4.x) and improved system architecture. +- [2026-01] **Trading-R1** [Technical Report](https://arxiv.org/abs/2509.11420) released, with [Terminal](https://github.com/TauricResearch/Trading-R1) expected to land soon. + +
+ + + + + TradingAgents Star History + + +
+ +> 🎉 **TradingAgents** officially released! We have received numerous inquiries about the work, and we would like to express our thanks for the enthusiasm in our community. +> +> So we decided to fully open-source the framework. Looking forward to building impactful projects with you! + +
+ +🚀 [TradingAgents](#tradingagents-framework) | ⚡ [Installation & CLI](#installation-and-cli) | 🎬 [Demo](https://www.youtube.com/watch?v=90gr5lwjIho) | 📦 [Package Usage](#tradingagents-package) | 🤝 [Contributing](#contributing) | 📄 [Citation](#citation) + +
+ +## TradingAgents Framework + +TradingAgents is a multi-agent trading framework that mirrors the dynamics of real-world trading firms. By deploying specialized LLM-powered agents: from fundamental analysts, sentiment experts, and technical analysts, to trader, risk management team, the platform collaboratively evaluates market conditions and informs trading decisions. Moreover, these agents engage in dynamic discussions to pinpoint the optimal strategy. + +

+ +

+ +> TradingAgents framework is designed for research purposes. Trading performance may vary based on many factors, including the chosen backbone language models, model temperature, trading periods, the quality of data, and other non-deterministic factors. [It is not intended as financial, investment, or trading advice.](https://tauric.ai/disclaimer/) + +Our framework decomposes complex trading tasks into specialized roles. This ensures the system achieves a robust, scalable approach to market analysis and decision-making. + +### Analyst Team +- Fundamentals Analyst: Evaluates company financials and performance metrics, identifying intrinsic values and potential red flags. +- Sentiment Analyst: Analyzes social media and public sentiment using sentiment scoring algorithms to gauge short-term market mood. +- News Analyst: Monitors global news and macroeconomic indicators, interpreting the impact of events on market conditions. +- Technical Analyst: Utilizes technical indicators (like MACD and RSI) to detect trading patterns and forecast price movements. + +

+ +

+ +### Researcher Team +- Comprises both bullish and bearish researchers who critically assess the insights provided by the Analyst Team. Through structured debates, they balance potential gains against inherent risks. + +

+ +

+ +### Trader Agent +- Composes reports from the analysts and researchers to make informed trading decisions. It determines the timing and magnitude of trades based on comprehensive market insights. + +

+ +

+ +### Risk Management and Portfolio Manager +- Continuously evaluates portfolio risk by assessing market volatility, liquidity, and other risk factors. The risk management team evaluates and adjusts trading strategies, providing assessment reports to the Portfolio Manager for final decision. +- The Portfolio Manager approves/rejects the transaction proposal. If approved, the order will be sent to the simulated exchange and executed. + +

+ +

+ +## Installation and CLI + +### Installation + +Clone TradingAgents: +```bash +git clone https://github.com/TauricResearch/TradingAgents.git +cd TradingAgents +``` + +Create a virtual environment in any of your favorite environment managers: +```bash +conda create -n tradingagents python=3.13 +conda activate tradingagents +``` + +Install dependencies: +```bash +pip install -r requirements.txt +``` + +### Required APIs + +TradingAgents supports multiple LLM providers. Set the API key for your chosen provider: + +```bash +export OPENAI_API_KEY=... # OpenAI (GPT) +export GOOGLE_API_KEY=... # Google (Gemini) +export ANTHROPIC_API_KEY=... # Anthropic (Claude) +export XAI_API_KEY=... # xAI (Grok) +export OPENROUTER_API_KEY=... # OpenRouter +export ALPHA_VANTAGE_API_KEY=... # Alpha Vantage +``` + +For local models, configure Ollama with `llm_provider: "ollama"` in your config. + +Alternatively, copy `.env.example` to `.env` and fill in your keys: +```bash +cp .env.example .env +``` + +### CLI Usage + +You can also try out the CLI directly by running: +```bash +python -m cli.main +``` +You will see a screen where you can select your desired tickers, date, LLMs, research depth, etc. + +

+ +

+ +An interface will appear showing results as they load, letting you track the agent's progress as it runs. + +

+ +

+ +

+ +

+ +## TradingAgents Package + +### Implementation Details + +We built TradingAgents with LangGraph to ensure flexibility and modularity. The framework supports multiple LLM providers: OpenAI, Google, Anthropic, xAI, OpenRouter, and Ollama. + +### Python Usage + +To use TradingAgents inside your code, you can import the `tradingagents` module and initialize a `TradingAgentsGraph()` object. The `.propagate()` function will return a decision. You can run `main.py`, here's also a quick example: + +```python +from tradingagents.graph.trading_graph import TradingAgentsGraph +from tradingagents.default_config import DEFAULT_CONFIG + +ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy()) + +# forward propagate +_, decision = ta.propagate("NVDA", "2026-01-15") +print(decision) +``` + +You can also adjust the default configuration to set your own choice of LLMs, debate rounds, etc. + +```python +from tradingagents.graph.trading_graph import TradingAgentsGraph +from tradingagents.default_config import DEFAULT_CONFIG + +config = DEFAULT_CONFIG.copy() +config["llm_provider"] = "openai" # openai, google, anthropic, xai, openrouter, ollama +config["deep_think_llm"] = "gpt-5.2" # Model for complex reasoning +config["quick_think_llm"] = "gpt-5-mini" # Model for quick tasks +config["max_debate_rounds"] = 2 + +ta = TradingAgentsGraph(debug=True, config=config) +_, decision = ta.propagate("NVDA", "2026-01-15") +print(decision) +``` + +See `tradingagents/default_config.py` for all configuration options. + +## Contributing + +We welcome contributions from the community! Whether it's fixing a bug, improving documentation, or suggesting a new feature, your input helps make this project better. If you are interested in this line of research, please consider joining our open-source financial AI research community [Tauric Research](https://tauric.ai/). + +## Citation + +Please reference our work if you find *TradingAgents* provides you with some help :) + +``` +@misc{xiao2025tradingagentsmultiagentsllmfinancial, + title={TradingAgents: Multi-Agents LLM Financial Trading Framework}, + author={Yijia Xiao and Edward Sun and Di Luo and Wei Wang}, + year={2025}, + eprint={2412.20138}, + archivePrefix={arXiv}, + primaryClass={q-fin.TR}, + url={https://arxiv.org/abs/2412.20138}, +} +``` diff --git a/app.py b/app.py index 1ac90b93..22c99130 100644 --- a/app.py +++ b/app.py @@ -1,492 +1,492 @@ -"""FastAPI SSE backend for the structured equity ranking engine.""" - -from pathlib import Path -from dotenv import load_dotenv -load_dotenv(Path(__file__).parent / ".env") - -import logging -import os -import re -import time -import uuid -import asyncio -import json -import traceback as _tb -from datetime import date - -from fastapi import FastAPI, HTTPException, Request, Depends - -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s %(levelname)s %(name)s %(message)s", -) -logger = logging.getLogger(__name__) -from fastapi.middleware.cors import CORSMiddleware -from pydantic import BaseModel -from sse_starlette.sse import EventSourceResponse - -# If using Groq (or other OpenAI-compatible), set OPENAI_API_KEY for langchain -if not os.environ.get("OPENAI_API_KEY"): - groq_key = os.environ.get("GROQ_API_KEY", "") - if groq_key: - os.environ["OPENAI_API_KEY"] = groq_key - -from tradingagents.graph.trading_graph import TradingAgentsGraph -from tradingagents.default_config import DEFAULT_CONFIG - -app = FastAPI(title="TradingAgents Structured Pipeline") - -# --- CORS --- -_cors_env = os.getenv("CORS_ORIGINS", "") -_cors_origins = [o.strip() for o in _cors_env.split(",") if o.strip()] if _cors_env else ["*"] -app.add_middleware( - CORSMiddleware, - allow_origins=_cors_origins, - allow_methods=["*"], - allow_headers=["*"], -) - -# --- Auth --- -_API_KEY = os.getenv("AGENTS_API_KEY", "") - - -async def verify_api_key(request: Request): - if not _API_KEY: - return - auth = request.headers.get("Authorization", "") - if auth != f"Bearer {_API_KEY}": - raise HTTPException(401, "Invalid or missing API key") - - -# --- Concurrency --- -MAX_CONCURRENT = int(os.getenv("MAX_CONCURRENT_ANALYSES", "3")) -_semaphore = asyncio.Semaphore(MAX_CONCURRENT) - -# --- Event buffer cap --- -MAX_EVENTS_PER_ANALYSIS = 5000 - -analyses: dict[str, dict] = {} - - -def _append_event(state: dict, evt: dict): - """Append an event to the analysis state, enforcing the buffer cap.""" - events = state["events"] - events.append(evt) - if len(events) > MAX_EVENTS_PER_ANALYSIS: - # Drop oldest events, keep the last MAX_EVENTS_PER_ANALYSIS - state["events"] = events[-MAX_EVENTS_PER_ANALYSIS:] - - -class AnalyzeRequest(BaseModel): - ticker: str - date: str | None = None - - -def build_config(): - """Build TradingAgents config from env vars.""" - config = DEFAULT_CONFIG.copy() - config["llm_provider"] = os.getenv("LLM_PROVIDER", "openai") - config["deep_think_llm"] = os.getenv("DEEP_THINK_MODEL", "deepseek-v3.1:671b-cloud") - config["quick_think_llm"] = os.getenv("QUICK_THINK_MODEL", "deepseek-v3.1:671b-cloud") - config["backend_url"] = os.getenv("LLM_BASE_URL", "https://ollama.com/v1") - config["max_debate_rounds"] = 1 - config["max_risk_discuss_rounds"] = 1 - config["data_vendors"] = { - "core_stock_apis": "yfinance", - "technical_indicators": "yfinance", - "fundamental_data": "yfinance", - "news_data": "yfinance", - } - logger.info( - "config_built provider=%s deep=%s quick=%s url=%s", - config['llm_provider'], config['deep_think_llm'], - config['quick_think_llm'], config['backend_url'], - ) - return config - - -# --------------------------------------------------------------------------- -# Stage/agent mapping for SSE events -# --------------------------------------------------------------------------- - -# Maps state field → (agent display name, pipeline stage) -FIELD_AGENT_MAP = { - "validation": ("Validation", "validation"), - "company_card": ("Company Card", "validation"), - "macro": ("Macro Regime", "tier1"), - "liquidity": ("Liquidity", "tier1"), - "business_quality": ("Business Quality", "tier2"), - "institutional_flow": ("Institutional Flow", "tier2"), - "valuation": ("Valuation", "tier2"), - "entry_timing": ("Entry Timing", "tier2"), - "earnings_revisions": ("Earnings Revisions", "tier2"), - "sector_rotation": ("Sector Rotation", "tier2"), - "backlog": ("Backlog / Order Momentum", "tier2"), - "crowding": ("Narrative Crowding", "tier2"), - "archetype": ("Archetype", "scoring"), - "master_score": ("Master Score", "scoring"), - "theme_substitution": ("Theme Substitution", "portfolio"), - "position_replacement": ("Position Replacement", "portfolio"), - "bull_case": ("Bull Researcher", "debate"), - "bear_case": ("Bear Researcher", "debate"), - "debate": ("Debate Referee", "debate"), - "risk": ("Risk / Invalidation", "decision"), - "final_decision": ("Final Decision", "decision"), -} - -ALL_AGENTS = [name for name, _ in FIELD_AGENT_MAP.values()] -ALL_STAGES = ["validation", "tier1", "tier2", "scoring", "portfolio", "debate", "decision"] - - -# --------------------------------------------------------------------------- -# Analysis runner -# --------------------------------------------------------------------------- - -async def _run_analysis_inner(analysis_id: str, ticker: str, trade_date: str): - """Core analysis logic — streams structured pipeline state changes as SSE.""" - state = analyses[analysis_id] - q = state["queue"] - config = build_config() - - try: - graph = TradingAgentsGraph(debug=False, config=config) - logger.info( - "analysis_init_ok deep_llm=%s quick_llm=%s analysis_id=%s", - type(graph.deep_thinking_llm).__name__, - type(graph.quick_thinking_llm).__name__, - analysis_id, - ) - except Exception as e: - logger.error("analysis_init_failed analysis_id=%s error=%s\n%s", analysis_id, e, _tb.format_exc()) - await q.put({"type": "error", "message": f"Init failed: {e}"}) - await q.put(None) - return - - init_state = graph._create_initial_state(ticker, trade_date) - start_time = time.time() - emitted_fields = set() - prev_agent_statuses = {} - final_state = None - - # Emit initial status: all agents pending - for field, (agent_name, stage) in FIELD_AGENT_MAP.items(): - prev_agent_statuses[field] = "pending" - evt = { - "type": "agent_update", - "agent": agent_name, - "stage": stage, - "status": "pending", - "stats": _stats(start_time, emitted_fields), - } - _append_event(state, evt) - await q.put(evt) - - try: - async for chunk in graph.graph.astream( - init_state, - stream_mode="values", - config={"recursion_limit": 25}, - ): - final_state = chunk - - # Detect newly populated fields - for field, (agent_name, stage) in FIELD_AGENT_MAP.items(): - if field in emitted_fields: - continue - - value = chunk.get(field) - if value is None: - continue - - emitted_fields.add(field) - st = _stats(start_time, emitted_fields) - - # Mark this agent completed - prev_agent_statuses[field] = "completed" - evt = { - "type": "agent_update", - "agent": agent_name, - "stage": stage, - "status": "completed", - "stats": st, - } - _append_event(state, evt) - await q.put(evt) - - # Emit report data for key fields - if field in ("validation", "company_card"): - evt = { - "type": "report", - "agent": agent_name, - "stage": stage, - "field": field, - "report": _format_report(field, value), - "stats": st, - } - _append_event(state, evt) - await q.put(evt) - - elif field == "debate": - bull = chunk.get("bull_case") or {} - bear = chunk.get("bear_case") or {} - evt = { - "type": "debate", - "stage": "debate", - "bull": bull.get("thesis", ""), - "bear": bear.get("thesis", ""), - "judge": (value or {}).get("reasoning", ""), - "winner": (value or {}).get("winner", ""), - "stats": st, - } - _append_event(state, evt) - await q.put(evt) - - elif field == "master_score": - evt = { - "type": "score", - "stage": "scoring", - "master_score": value, - "adjusted_score": chunk.get("adjusted_score"), - "position_role": chunk.get("position_role"), - "stats": st, - } - _append_event(state, evt) - await q.put(evt) - - # Mark in-progress agents for upcoming stages - await _update_in_progress(chunk, emitted_fields, prev_agent_statuses, state, q, start_time) - - except Exception as e: - logger.error("analysis_stream_error analysis_id=%s error=%s\n%s", analysis_id, e, _tb.format_exc()) - evt = {"type": "error", "message": str(e)} - _append_event(state, evt) - await q.put(evt) - state["done"] = True - await q.put(None) - return - - # Final decision event - if final_state: - decision = final_state.get("final_decision") or {} - st = _stats(start_time, emitted_fields) - - # Mark all remaining as completed - for field in FIELD_AGENT_MAP: - if prev_agent_statuses.get(field) != "completed": - agent_name, stage = FIELD_AGENT_MAP[field] - prev_agent_statuses[field] = "completed" - evt = { - "type": "agent_update", - "agent": agent_name, - "stage": stage, - "status": "completed", - "stats": st, - } - _append_event(state, evt) - await q.put(evt) - - evt = { - "type": "decision", - "stage": "decision", - "signal": decision.get("action", "AVOID"), - "decision_text": decision.get("narrative", ""), - "master_score": final_state.get("master_score"), - "adjusted_score": final_state.get("adjusted_score"), - "position_role": final_state.get("position_role"), - "final_decision": decision, - "stats": st, - } - _append_event(state, evt) - await q.put(evt) - - state["done"] = True - await q.put(None) - - -async def _update_in_progress(chunk, emitted, statuses, state, q, start_time): - """Heuristic: mark agents as in_progress based on stage progression.""" - # If validation is done, mark tier 1 as in_progress - if "validation" in emitted: - for field in ("macro", "liquidity"): - if field not in emitted and statuses.get(field) == "pending": - statuses[field] = "in_progress" - agent_name, stage = FIELD_AGENT_MAP[field] - evt = { - "type": "agent_update", - "agent": agent_name, - "stage": stage, - "status": "in_progress", - "stats": _stats(start_time, emitted), - } - _append_event(state, evt) - await q.put(evt) - - # If tier 1 done, mark tier 2 in_progress - if "macro" in emitted and "liquidity" in emitted: - tier2_fields = [ - "business_quality", "institutional_flow", "valuation", - "entry_timing", "earnings_revisions", "sector_rotation", - "backlog", "crowding", - ] - for field in tier2_fields: - if field not in emitted and statuses.get(field) == "pending": - statuses[field] = "in_progress" - agent_name, stage = FIELD_AGENT_MAP[field] - evt = { - "type": "agent_update", - "agent": agent_name, - "stage": stage, - "status": "in_progress", - "stats": _stats(start_time, emitted), - } - _append_event(state, evt) - await q.put(evt) - - # If scoring done, mark portfolio analysis in_progress - if "master_score" in emitted: - for field in ("theme_substitution", "position_replacement"): - if field not in emitted and statuses.get(field) == "pending": - statuses[field] = "in_progress" - agent_name, stage = FIELD_AGENT_MAP[field] - evt = { - "type": "agent_update", - "agent": agent_name, - "stage": stage, - "status": "in_progress", - "stats": _stats(start_time, emitted), - } - _append_event(state, evt) - await q.put(evt) - - -def _stats(start_time: float, emitted_fields: set) -> dict: - return { - "agents_done": len(emitted_fields), - "agents_total": len(FIELD_AGENT_MAP), - "elapsed": round(time.time() - start_time, 1), - } - - -def _format_report(field: str, value) -> str: - """Format a state field value as a readable report string.""" - if isinstance(value, dict): - if "summary_1_sentence" in value: - return value["summary_1_sentence"] - if "company_name" in value: - return f"{value.get('company_name', '')} ({value.get('ticker', '')}) — {value.get('sector', '')} / {value.get('industry', '')}" - return json.dumps(value, indent=2, default=str)[:500] - return str(value)[:500] - - -async def run_analysis(analysis_id: str, ticker: str, trade_date: str): - """Background task with semaphore and timeout.""" - state = analyses[analysis_id] - q = state["queue"] - async with _semaphore: - try: - await asyncio.wait_for( - _run_analysis_inner(analysis_id, ticker, trade_date), - timeout=3600, - ) - except asyncio.TimeoutError: - logger.warning("analysis_timeout analysis_id=%s", analysis_id) - evt = {"type": "error", "message": "Analysis timed out after 60 minutes"} - _append_event(state, evt) - await q.put(evt) - state["done"] = True - await q.put(None) - - -# --- Cleanup --- -async def _cleanup_loop(): - while True: - await asyncio.sleep(300) - now = time.time() - expired = [aid for aid, s in analyses.items() if now - s["created_at"] > 1800] - for aid in expired: - analyses.pop(aid, None) - if expired: - logger.info("cleanup_expired count=%d", len(expired)) - - -@app.on_event("startup") -async def _start_cleanup(): - asyncio.create_task(_cleanup_loop()) - - -# --- Routes --- - -@app.post("/analyze", dependencies=[Depends(verify_api_key)]) -async def start_analysis(req: AnalyzeRequest): - ticker = req.ticker.upper().strip() - if not ticker: - raise HTTPException(400, "Ticker must not be empty") - if len(ticker) > 10: - raise HTTPException(400, f"Ticker too long ({len(ticker)} chars, max 10)") - if not re.match(r'^[A-Z0-9.\-]{1,10}$', ticker): - raise HTTPException(400, "Invalid ticker — only letters, digits, dots, and hyphens allowed") - trade_date = req.date or str(date.today()) - analysis_id = str(uuid.uuid4()) - analyses[analysis_id] = { - "queue": asyncio.Queue(), - "events": [], - "done": False, - "created_at": time.time(), - } - asyncio.create_task(run_analysis(analysis_id, ticker, trade_date)) - return {"id": analysis_id, "ticker": ticker, "date": trade_date} - - -@app.get("/analyze/{analysis_id}/stream", dependencies=[Depends(verify_api_key)]) -async def stream_analysis(analysis_id: str, last_event: int = 0): - """Stream SSE events. Supports reconnection via ?last_event=N.""" - if analysis_id not in analyses: - raise HTTPException(404, "Analysis not found") - state = analyses[analysis_id] - - async def event_generator(): - idx = last_event - while idx < len(state["events"]): - evt = state["events"][idx] - idx += 1 - yield {"id": str(idx), "data": json.dumps(evt)} - if state["done"]: - return - q = state["queue"] - while True: - try: - event = await asyncio.wait_for(q.get(), timeout=15) - except asyncio.TimeoutError: - yield {"event": "heartbeat", "data": json.dumps({"type": "heartbeat"})} - continue - if event is None: - break - idx += 1 - yield {"id": str(idx), "data": json.dumps(event)} - - return EventSourceResponse(event_generator()) - - -@app.get("/health") -async def health(): - return {"status": "ok", "engine": "structured_pipeline"} - - -@app.get("/api/status") -async def get_status(): - """Structured pipeline status — no auth required.""" - from datetime import datetime - active_count = len(analyses) - return { - "service": "structured-pipeline", - "engine": "TradingAgents", - "active_analyses": active_count, - "analyses": {k: {"created": v["created"], "done": v["done"]} for k, v in analyses.items()}, - "pid": __import__("os").getpid(), - "uptime": time.time() - __import__("os").getpid(), - } - - -@app.get("/api/health") -async def api_health(): - return {"status": "ok", "service": "structured-pipeline"} +"""FastAPI SSE backend for the structured equity ranking engine.""" + +from pathlib import Path +from dotenv import load_dotenv +load_dotenv(Path(__file__).parent / ".env") + +import logging +import os +import re +import time +import uuid +import asyncio +import json +import traceback as _tb +from datetime import date + +from fastapi import FastAPI, HTTPException, Request, Depends + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(name)s %(message)s", +) +logger = logging.getLogger(__name__) +from fastapi.middleware.cors import CORSMiddleware +from pydantic import BaseModel +from sse_starlette.sse import EventSourceResponse + +# If using Groq (or other OpenAI-compatible), set OPENAI_API_KEY for langchain +if not os.environ.get("OPENAI_API_KEY"): + groq_key = os.environ.get("GROQ_API_KEY", "") + if groq_key: + os.environ["OPENAI_API_KEY"] = groq_key + +from tradingagents.graph.trading_graph import TradingAgentsGraph +from tradingagents.default_config import DEFAULT_CONFIG + +app = FastAPI(title="TradingAgents Structured Pipeline") + +# --- CORS --- +_cors_env = os.getenv("CORS_ORIGINS", "") +_cors_origins = [o.strip() for o in _cors_env.split(",") if o.strip()] if _cors_env else ["*"] +app.add_middleware( + CORSMiddleware, + allow_origins=_cors_origins, + allow_methods=["*"], + allow_headers=["*"], +) + +# --- Auth --- +_API_KEY = os.getenv("AGENTS_API_KEY", "") + + +async def verify_api_key(request: Request): + if not _API_KEY: + return + auth = request.headers.get("Authorization", "") + if auth != f"Bearer {_API_KEY}": + raise HTTPException(401, "Invalid or missing API key") + + +# --- Concurrency --- +MAX_CONCURRENT = int(os.getenv("MAX_CONCURRENT_ANALYSES", "3")) +_semaphore = asyncio.Semaphore(MAX_CONCURRENT) + +# --- Event buffer cap --- +MAX_EVENTS_PER_ANALYSIS = 5000 + +analyses: dict[str, dict] = {} + + +def _append_event(state: dict, evt: dict): + """Append an event to the analysis state, enforcing the buffer cap.""" + events = state["events"] + events.append(evt) + if len(events) > MAX_EVENTS_PER_ANALYSIS: + # Drop oldest events, keep the last MAX_EVENTS_PER_ANALYSIS + state["events"] = events[-MAX_EVENTS_PER_ANALYSIS:] + + +class AnalyzeRequest(BaseModel): + ticker: str + date: str | None = None + + +def build_config(): + """Build TradingAgents config from env vars.""" + config = DEFAULT_CONFIG.copy() + config["llm_provider"] = os.getenv("LLM_PROVIDER", "openai") + config["deep_think_llm"] = os.getenv("DEEP_THINK_MODEL", "deepseek-v3.1:671b-cloud") + config["quick_think_llm"] = os.getenv("QUICK_THINK_MODEL", "deepseek-v3.1:671b-cloud") + config["backend_url"] = os.getenv("LLM_BASE_URL", "https://ollama.com/v1") + config["max_debate_rounds"] = 1 + config["max_risk_discuss_rounds"] = 1 + config["data_vendors"] = { + "core_stock_apis": "yfinance", + "technical_indicators": "yfinance", + "fundamental_data": "yfinance", + "news_data": "yfinance", + } + logger.info( + "config_built provider=%s deep=%s quick=%s url=%s", + config['llm_provider'], config['deep_think_llm'], + config['quick_think_llm'], config['backend_url'], + ) + return config + + +# --------------------------------------------------------------------------- +# Stage/agent mapping for SSE events +# --------------------------------------------------------------------------- + +# Maps state field → (agent display name, pipeline stage) +FIELD_AGENT_MAP = { + "validation": ("Validation", "validation"), + "company_card": ("Company Card", "validation"), + "macro": ("Macro Regime", "tier1"), + "liquidity": ("Liquidity", "tier1"), + "business_quality": ("Business Quality", "tier2"), + "institutional_flow": ("Institutional Flow", "tier2"), + "valuation": ("Valuation", "tier2"), + "entry_timing": ("Entry Timing", "tier2"), + "earnings_revisions": ("Earnings Revisions", "tier2"), + "sector_rotation": ("Sector Rotation", "tier2"), + "backlog": ("Backlog / Order Momentum", "tier2"), + "crowding": ("Narrative Crowding", "tier2"), + "archetype": ("Archetype", "scoring"), + "master_score": ("Master Score", "scoring"), + "theme_substitution": ("Theme Substitution", "portfolio"), + "position_replacement": ("Position Replacement", "portfolio"), + "bull_case": ("Bull Researcher", "debate"), + "bear_case": ("Bear Researcher", "debate"), + "debate": ("Debate Referee", "debate"), + "risk": ("Risk / Invalidation", "decision"), + "final_decision": ("Final Decision", "decision"), +} + +ALL_AGENTS = [name for name, _ in FIELD_AGENT_MAP.values()] +ALL_STAGES = ["validation", "tier1", "tier2", "scoring", "portfolio", "debate", "decision"] + + +# --------------------------------------------------------------------------- +# Analysis runner +# --------------------------------------------------------------------------- + +async def _run_analysis_inner(analysis_id: str, ticker: str, trade_date: str): + """Core analysis logic — streams structured pipeline state changes as SSE.""" + state = analyses[analysis_id] + q = state["queue"] + config = build_config() + + try: + graph = TradingAgentsGraph(debug=False, config=config) + logger.info( + "analysis_init_ok deep_llm=%s quick_llm=%s analysis_id=%s", + type(graph.deep_thinking_llm).__name__, + type(graph.quick_thinking_llm).__name__, + analysis_id, + ) + except Exception as e: + logger.error("analysis_init_failed analysis_id=%s error=%s\n%s", analysis_id, e, _tb.format_exc()) + await q.put({"type": "error", "message": f"Init failed: {e}"}) + await q.put(None) + return + + init_state = graph._create_initial_state(ticker, trade_date) + start_time = time.time() + emitted_fields = set() + prev_agent_statuses = {} + final_state = None + + # Emit initial status: all agents pending + for field, (agent_name, stage) in FIELD_AGENT_MAP.items(): + prev_agent_statuses[field] = "pending" + evt = { + "type": "agent_update", + "agent": agent_name, + "stage": stage, + "status": "pending", + "stats": _stats(start_time, emitted_fields), + } + _append_event(state, evt) + await q.put(evt) + + try: + async for chunk in graph.graph.astream( + init_state, + stream_mode="values", + config={"recursion_limit": 25}, + ): + final_state = chunk + + # Detect newly populated fields + for field, (agent_name, stage) in FIELD_AGENT_MAP.items(): + if field in emitted_fields: + continue + + value = chunk.get(field) + if value is None: + continue + + emitted_fields.add(field) + st = _stats(start_time, emitted_fields) + + # Mark this agent completed + prev_agent_statuses[field] = "completed" + evt = { + "type": "agent_update", + "agent": agent_name, + "stage": stage, + "status": "completed", + "stats": st, + } + _append_event(state, evt) + await q.put(evt) + + # Emit report data for key fields + if field in ("validation", "company_card"): + evt = { + "type": "report", + "agent": agent_name, + "stage": stage, + "field": field, + "report": _format_report(field, value), + "stats": st, + } + _append_event(state, evt) + await q.put(evt) + + elif field == "debate": + bull = chunk.get("bull_case") or {} + bear = chunk.get("bear_case") or {} + evt = { + "type": "debate", + "stage": "debate", + "bull": bull.get("thesis", ""), + "bear": bear.get("thesis", ""), + "judge": (value or {}).get("reasoning", ""), + "winner": (value or {}).get("winner", ""), + "stats": st, + } + _append_event(state, evt) + await q.put(evt) + + elif field == "master_score": + evt = { + "type": "score", + "stage": "scoring", + "master_score": value, + "adjusted_score": chunk.get("adjusted_score"), + "position_role": chunk.get("position_role"), + "stats": st, + } + _append_event(state, evt) + await q.put(evt) + + # Mark in-progress agents for upcoming stages + await _update_in_progress(chunk, emitted_fields, prev_agent_statuses, state, q, start_time) + + except Exception as e: + logger.error("analysis_stream_error analysis_id=%s error=%s\n%s", analysis_id, e, _tb.format_exc()) + evt = {"type": "error", "message": str(e)} + _append_event(state, evt) + await q.put(evt) + state["done"] = True + await q.put(None) + return + + # Final decision event + if final_state: + decision = final_state.get("final_decision") or {} + st = _stats(start_time, emitted_fields) + + # Mark all remaining as completed + for field in FIELD_AGENT_MAP: + if prev_agent_statuses.get(field) != "completed": + agent_name, stage = FIELD_AGENT_MAP[field] + prev_agent_statuses[field] = "completed" + evt = { + "type": "agent_update", + "agent": agent_name, + "stage": stage, + "status": "completed", + "stats": st, + } + _append_event(state, evt) + await q.put(evt) + + evt = { + "type": "decision", + "stage": "decision", + "signal": decision.get("action", "AVOID"), + "decision_text": decision.get("narrative", ""), + "master_score": final_state.get("master_score"), + "adjusted_score": final_state.get("adjusted_score"), + "position_role": final_state.get("position_role"), + "final_decision": decision, + "stats": st, + } + _append_event(state, evt) + await q.put(evt) + + state["done"] = True + await q.put(None) + + +async def _update_in_progress(chunk, emitted, statuses, state, q, start_time): + """Heuristic: mark agents as in_progress based on stage progression.""" + # If validation is done, mark tier 1 as in_progress + if "validation" in emitted: + for field in ("macro", "liquidity"): + if field not in emitted and statuses.get(field) == "pending": + statuses[field] = "in_progress" + agent_name, stage = FIELD_AGENT_MAP[field] + evt = { + "type": "agent_update", + "agent": agent_name, + "stage": stage, + "status": "in_progress", + "stats": _stats(start_time, emitted), + } + _append_event(state, evt) + await q.put(evt) + + # If tier 1 done, mark tier 2 in_progress + if "macro" in emitted and "liquidity" in emitted: + tier2_fields = [ + "business_quality", "institutional_flow", "valuation", + "entry_timing", "earnings_revisions", "sector_rotation", + "backlog", "crowding", + ] + for field in tier2_fields: + if field not in emitted and statuses.get(field) == "pending": + statuses[field] = "in_progress" + agent_name, stage = FIELD_AGENT_MAP[field] + evt = { + "type": "agent_update", + "agent": agent_name, + "stage": stage, + "status": "in_progress", + "stats": _stats(start_time, emitted), + } + _append_event(state, evt) + await q.put(evt) + + # If scoring done, mark portfolio analysis in_progress + if "master_score" in emitted: + for field in ("theme_substitution", "position_replacement"): + if field not in emitted and statuses.get(field) == "pending": + statuses[field] = "in_progress" + agent_name, stage = FIELD_AGENT_MAP[field] + evt = { + "type": "agent_update", + "agent": agent_name, + "stage": stage, + "status": "in_progress", + "stats": _stats(start_time, emitted), + } + _append_event(state, evt) + await q.put(evt) + + +def _stats(start_time: float, emitted_fields: set) -> dict: + return { + "agents_done": len(emitted_fields), + "agents_total": len(FIELD_AGENT_MAP), + "elapsed": round(time.time() - start_time, 1), + } + + +def _format_report(field: str, value) -> str: + """Format a state field value as a readable report string.""" + if isinstance(value, dict): + if "summary_1_sentence" in value: + return value["summary_1_sentence"] + if "company_name" in value: + return f"{value.get('company_name', '')} ({value.get('ticker', '')}) — {value.get('sector', '')} / {value.get('industry', '')}" + return json.dumps(value, indent=2, default=str)[:500] + return str(value)[:500] + + +async def run_analysis(analysis_id: str, ticker: str, trade_date: str): + """Background task with semaphore and timeout.""" + state = analyses[analysis_id] + q = state["queue"] + async with _semaphore: + try: + await asyncio.wait_for( + _run_analysis_inner(analysis_id, ticker, trade_date), + timeout=3600, + ) + except asyncio.TimeoutError: + logger.warning("analysis_timeout analysis_id=%s", analysis_id) + evt = {"type": "error", "message": "Analysis timed out after 60 minutes"} + _append_event(state, evt) + await q.put(evt) + state["done"] = True + await q.put(None) + + +# --- Cleanup --- +async def _cleanup_loop(): + while True: + await asyncio.sleep(300) + now = time.time() + expired = [aid for aid, s in analyses.items() if now - s["created_at"] > 1800] + for aid in expired: + analyses.pop(aid, None) + if expired: + logger.info("cleanup_expired count=%d", len(expired)) + + +@app.on_event("startup") +async def _start_cleanup(): + asyncio.create_task(_cleanup_loop()) + + +# --- Routes --- + +@app.post("/analyze", dependencies=[Depends(verify_api_key)]) +async def start_analysis(req: AnalyzeRequest): + ticker = req.ticker.upper().strip() + if not ticker: + raise HTTPException(400, "Ticker must not be empty") + if len(ticker) > 10: + raise HTTPException(400, f"Ticker too long ({len(ticker)} chars, max 10)") + if not re.match(r'^[A-Z0-9.\-]{1,10}$', ticker): + raise HTTPException(400, "Invalid ticker — only letters, digits, dots, and hyphens allowed") + trade_date = req.date or str(date.today()) + analysis_id = str(uuid.uuid4()) + analyses[analysis_id] = { + "queue": asyncio.Queue(), + "events": [], + "done": False, + "created_at": time.time(), + } + asyncio.create_task(run_analysis(analysis_id, ticker, trade_date)) + return {"id": analysis_id, "ticker": ticker, "date": trade_date} + + +@app.get("/analyze/{analysis_id}/stream", dependencies=[Depends(verify_api_key)]) +async def stream_analysis(analysis_id: str, last_event: int = 0): + """Stream SSE events. Supports reconnection via ?last_event=N.""" + if analysis_id not in analyses: + raise HTTPException(404, "Analysis not found") + state = analyses[analysis_id] + + async def event_generator(): + idx = last_event + while idx < len(state["events"]): + evt = state["events"][idx] + idx += 1 + yield {"id": str(idx), "data": json.dumps(evt)} + if state["done"]: + return + q = state["queue"] + while True: + try: + event = await asyncio.wait_for(q.get(), timeout=15) + except asyncio.TimeoutError: + yield {"event": "heartbeat", "data": json.dumps({"type": "heartbeat"})} + continue + if event is None: + break + idx += 1 + yield {"id": str(idx), "data": json.dumps(event)} + + return EventSourceResponse(event_generator()) + + +@app.get("/health") +async def health(): + return {"status": "ok", "engine": "structured_pipeline"} + + +@app.get("/api/status") +async def get_status(): + """Structured pipeline status — no auth required.""" + from datetime import datetime + active_count = len(analyses) + return { + "service": "structured-pipeline", + "engine": "TradingAgents", + "active_analyses": active_count, + "analyses": {k: {"created": v["created"], "done": v["done"]} for k, v in analyses.items()}, + "pid": __import__("os").getpid(), + "uptime": time.time() - __import__("os").getpid(), + } + + +@app.get("/api/health") +async def api_health(): + return {"status": "ok", "service": "structured-pipeline"} diff --git a/cli/announcements.py b/cli/announcements.py index 5947cee5..de36c073 100644 --- a/cli/announcements.py +++ b/cli/announcements.py @@ -1,51 +1,51 @@ -import getpass -import requests -from rich.console import Console -from rich.panel import Panel - -from cli.config import CLI_CONFIG - - -def fetch_announcements(url: str = None, timeout: float = None) -> dict: - """Fetch announcements from endpoint. Returns dict with announcements and settings.""" - endpoint = url or CLI_CONFIG["announcements_url"] - timeout = timeout or CLI_CONFIG["announcements_timeout"] - fallback = CLI_CONFIG["announcements_fallback"] - - try: - response = requests.get(endpoint, timeout=timeout) - response.raise_for_status() - data = response.json() - return { - "announcements": data.get("announcements", [fallback]), - "require_attention": data.get("require_attention", False), - } - except Exception: - return { - "announcements": [fallback], - "require_attention": False, - } - - -def display_announcements(console: Console, data: dict) -> None: - """Display announcements panel. Prompts for Enter if require_attention is True.""" - announcements = data.get("announcements", []) - require_attention = data.get("require_attention", False) - - if not announcements: - return - - content = "\n".join(announcements) - - panel = Panel( - content, - border_style="cyan", - padding=(1, 2), - title="Announcements", - ) - console.print(panel) - - if require_attention: - getpass.getpass("Press Enter to continue...") - else: - console.print() +import getpass +import requests +from rich.console import Console +from rich.panel import Panel + +from cli.config import CLI_CONFIG + + +def fetch_announcements(url: str = None, timeout: float = None) -> dict: + """Fetch announcements from endpoint. Returns dict with announcements and settings.""" + endpoint = url or CLI_CONFIG["announcements_url"] + timeout = timeout or CLI_CONFIG["announcements_timeout"] + fallback = CLI_CONFIG["announcements_fallback"] + + try: + response = requests.get(endpoint, timeout=timeout) + response.raise_for_status() + data = response.json() + return { + "announcements": data.get("announcements", [fallback]), + "require_attention": data.get("require_attention", False), + } + except Exception: + return { + "announcements": [fallback], + "require_attention": False, + } + + +def display_announcements(console: Console, data: dict) -> None: + """Display announcements panel. Prompts for Enter if require_attention is True.""" + announcements = data.get("announcements", []) + require_attention = data.get("require_attention", False) + + if not announcements: + return + + content = "\n".join(announcements) + + panel = Panel( + content, + border_style="cyan", + padding=(1, 2), + title="Announcements", + ) + console.print(panel) + + if require_attention: + getpass.getpass("Press Enter to continue...") + else: + console.print() diff --git a/cli/config.py b/cli/config.py index 08483f42..14b3a8b4 100644 --- a/cli/config.py +++ b/cli/config.py @@ -1,6 +1,6 @@ -CLI_CONFIG = { - # Announcements - "announcements_url": "https://api.tauric.ai/v1/announcements", - "announcements_timeout": 1.0, - "announcements_fallback": "[cyan]For more information, please visit[/cyan] [link=https://github.com/TauricResearch]https://github.com/TauricResearch[/link]", -} +CLI_CONFIG = { + # Announcements + "announcements_url": "https://api.tauric.ai/v1/announcements", + "announcements_timeout": 1.0, + "announcements_fallback": "[cyan]For more information, please visit[/cyan] [link=https://github.com/TauricResearch]https://github.com/TauricResearch[/link]", +} diff --git a/cli/main.py b/cli/main.py index 68b364dd..d9165d1e 100644 --- a/cli/main.py +++ b/cli/main.py @@ -1,1177 +1,1177 @@ -from typing import Optional -import datetime -import typer -from pathlib import Path -from functools import wraps -from rich.console import Console -from dotenv import load_dotenv - -# Load environment variables from .env file -load_dotenv() -from rich.panel import Panel -from rich.spinner import Spinner -from rich.live import Live -from rich.columns import Columns -from rich.markdown import Markdown -from rich.layout import Layout -from rich.text import Text -from rich.table import Table -from collections import deque -import time -from rich.tree import Tree -from rich import box -from rich.align import Align -from rich.rule import Rule - -from tradingagents.graph.trading_graph import TradingAgentsGraph -from tradingagents.default_config import DEFAULT_CONFIG -from cli.models import AnalystType -from cli.utils import * -from cli.announcements import fetch_announcements, display_announcements -from cli.stats_handler import StatsCallbackHandler - -console = Console() - -app = typer.Typer( - name="TradingAgents", - help="TradingAgents CLI: Multi-Agents LLM Financial Trading Framework", - add_completion=True, # Enable shell completion -) - - -# Create a deque to store recent messages with a maximum length -class MessageBuffer: - # Fixed teams that always run (not user-selectable) - FIXED_AGENTS = { - "Research Team": ["Bull Researcher", "Bear Researcher", "Research Manager"], - "Trading Team": ["Trader"], - "Risk Management": ["Aggressive Analyst", "Neutral Analyst", "Conservative Analyst"], - "Portfolio Management": ["Portfolio Manager"], - } - - # Analyst name mapping - ANALYST_MAPPING = { - "market": "Market Analyst", - "social": "Social Analyst", - "news": "News Analyst", - "fundamentals": "Fundamentals Analyst", - } - - # Report section mapping: section -> (analyst_key for filtering, finalizing_agent) - # analyst_key: which analyst selection controls this section (None = always included) - # finalizing_agent: which agent must be "completed" for this report to count as done - REPORT_SECTIONS = { - "market_report": ("market", "Market Analyst"), - "sentiment_report": ("social", "Social Analyst"), - "news_report": ("news", "News Analyst"), - "fundamentals_report": ("fundamentals", "Fundamentals Analyst"), - "investment_plan": (None, "Research Manager"), - "trader_investment_plan": (None, "Trader"), - "final_trade_decision": (None, "Portfolio Manager"), - } - - def __init__(self, max_length=100): - self.messages = deque(maxlen=max_length) - self.tool_calls = deque(maxlen=max_length) - self.current_report = None - self.final_report = None # Store the complete final report - self.agent_status = {} - self.current_agent = None - self.report_sections = {} - self.selected_analysts = [] - self._last_message_id = None - - def init_for_analysis(self, selected_analysts): - """Initialize agent status and report sections based on selected analysts. - - Args: - selected_analysts: List of analyst type strings (e.g., ["market", "news"]) - """ - self.selected_analysts = [a.lower() for a in selected_analysts] - - # Build agent_status dynamically - self.agent_status = {} - - # Add selected analysts - for analyst_key in self.selected_analysts: - if analyst_key in self.ANALYST_MAPPING: - self.agent_status[self.ANALYST_MAPPING[analyst_key]] = "pending" - - # Add fixed teams - for team_agents in self.FIXED_AGENTS.values(): - for agent in team_agents: - self.agent_status[agent] = "pending" - - # Build report_sections dynamically - self.report_sections = {} - for section, (analyst_key, _) in self.REPORT_SECTIONS.items(): - if analyst_key is None or analyst_key in self.selected_analysts: - self.report_sections[section] = None - - # Reset other state - self.current_report = None - self.final_report = None - self.current_agent = None - self.messages.clear() - self.tool_calls.clear() - self._last_message_id = None - - def get_completed_reports_count(self): - """Count reports that are finalized (their finalizing agent is completed). - - A report is considered complete when: - 1. The report section has content (not None), AND - 2. The agent responsible for finalizing that report has status "completed" - - This prevents interim updates (like debate rounds) from counting as completed. - """ - count = 0 - for section in self.report_sections: - if section not in self.REPORT_SECTIONS: - continue - _, finalizing_agent = self.REPORT_SECTIONS[section] - # Report is complete if it has content AND its finalizing agent is done - has_content = self.report_sections.get(section) is not None - agent_done = self.agent_status.get(finalizing_agent) == "completed" - if has_content and agent_done: - count += 1 - return count - - def add_message(self, message_type, content): - timestamp = datetime.datetime.now().strftime("%H:%M:%S") - self.messages.append((timestamp, message_type, content)) - - def add_tool_call(self, tool_name, args): - timestamp = datetime.datetime.now().strftime("%H:%M:%S") - self.tool_calls.append((timestamp, tool_name, args)) - - def update_agent_status(self, agent, status): - if agent in self.agent_status: - self.agent_status[agent] = status - self.current_agent = agent - - def update_report_section(self, section_name, content): - if section_name in self.report_sections: - self.report_sections[section_name] = content - self._update_current_report() - - def _update_current_report(self): - # For the panel display, only show the most recently updated section - latest_section = None - latest_content = None - - # Find the most recently updated section - for section, content in self.report_sections.items(): - if content is not None: - latest_section = section - latest_content = content - - if latest_section and latest_content: - # Format the current section for display - section_titles = { - "market_report": "Market Analysis", - "sentiment_report": "Social Sentiment", - "news_report": "News Analysis", - "fundamentals_report": "Fundamentals Analysis", - "investment_plan": "Research Team Decision", - "trader_investment_plan": "Trading Team Plan", - "final_trade_decision": "Portfolio Management Decision", - } - self.current_report = ( - f"### {section_titles[latest_section]}\n{latest_content}" - ) - - # Update the final complete report - self._update_final_report() - - def _update_final_report(self): - report_parts = [] - - # Analyst Team Reports - use .get() to handle missing sections - analyst_sections = ["market_report", "sentiment_report", "news_report", "fundamentals_report"] - if any(self.report_sections.get(section) for section in analyst_sections): - report_parts.append("## Analyst Team Reports") - if self.report_sections.get("market_report"): - report_parts.append( - f"### Market Analysis\n{self.report_sections['market_report']}" - ) - if self.report_sections.get("sentiment_report"): - report_parts.append( - f"### Social Sentiment\n{self.report_sections['sentiment_report']}" - ) - if self.report_sections.get("news_report"): - report_parts.append( - f"### News Analysis\n{self.report_sections['news_report']}" - ) - if self.report_sections.get("fundamentals_report"): - report_parts.append( - f"### Fundamentals Analysis\n{self.report_sections['fundamentals_report']}" - ) - - # Research Team Reports - if self.report_sections.get("investment_plan"): - report_parts.append("## Research Team Decision") - report_parts.append(f"{self.report_sections['investment_plan']}") - - # Trading Team Reports - if self.report_sections.get("trader_investment_plan"): - report_parts.append("## Trading Team Plan") - report_parts.append(f"{self.report_sections['trader_investment_plan']}") - - # Portfolio Management Decision - if self.report_sections.get("final_trade_decision"): - report_parts.append("## Portfolio Management Decision") - report_parts.append(f"{self.report_sections['final_trade_decision']}") - - self.final_report = "\n\n".join(report_parts) if report_parts else None - - -message_buffer = MessageBuffer() - - -def create_layout(): - layout = Layout() - layout.split_column( - Layout(name="header", size=3), - Layout(name="main"), - Layout(name="footer", size=3), - ) - layout["main"].split_column( - Layout(name="upper", ratio=3), Layout(name="analysis", ratio=5) - ) - layout["upper"].split_row( - Layout(name="progress", ratio=2), Layout(name="messages", ratio=3) - ) - return layout - - -def format_tokens(n): - """Format token count for display.""" - if n >= 1000: - return f"{n/1000:.1f}k" - return str(n) - - -def update_display(layout, spinner_text=None, stats_handler=None, start_time=None): - # Header with welcome message - layout["header"].update( - Panel( - "[bold green]Welcome to TradingAgents CLI[/bold green]\n" - "[dim]© [Tauric Research](https://github.com/TauricResearch)[/dim]", - title="Welcome to TradingAgents", - border_style="green", - padding=(1, 2), - expand=True, - ) - ) - - # Progress panel showing agent status - progress_table = Table( - show_header=True, - header_style="bold magenta", - show_footer=False, - box=box.SIMPLE_HEAD, # Use simple header with horizontal lines - title=None, # Remove the redundant Progress title - padding=(0, 2), # Add horizontal padding - expand=True, # Make table expand to fill available space - ) - progress_table.add_column("Team", style="cyan", justify="center", width=20) - progress_table.add_column("Agent", style="green", justify="center", width=20) - progress_table.add_column("Status", style="yellow", justify="center", width=20) - - # Group agents by team - filter to only include agents in agent_status - all_teams = { - "Analyst Team": [ - "Market Analyst", - "Social Analyst", - "News Analyst", - "Fundamentals Analyst", - ], - "Research Team": ["Bull Researcher", "Bear Researcher", "Research Manager"], - "Trading Team": ["Trader"], - "Risk Management": ["Aggressive Analyst", "Neutral Analyst", "Conservative Analyst"], - "Portfolio Management": ["Portfolio Manager"], - } - - # Filter teams to only include agents that are in agent_status - teams = {} - for team, agents in all_teams.items(): - active_agents = [a for a in agents if a in message_buffer.agent_status] - if active_agents: - teams[team] = active_agents - - for team, agents in teams.items(): - # Add first agent with team name - first_agent = agents[0] - status = message_buffer.agent_status.get(first_agent, "pending") - if status == "in_progress": - spinner = Spinner( - "dots", text="[blue]in_progress[/blue]", style="bold cyan" - ) - status_cell = spinner - else: - status_color = { - "pending": "yellow", - "completed": "green", - "error": "red", - }.get(status, "white") - status_cell = f"[{status_color}]{status}[/{status_color}]" - progress_table.add_row(team, first_agent, status_cell) - - # Add remaining agents in team - for agent in agents[1:]: - status = message_buffer.agent_status.get(agent, "pending") - if status == "in_progress": - spinner = Spinner( - "dots", text="[blue]in_progress[/blue]", style="bold cyan" - ) - status_cell = spinner - else: - status_color = { - "pending": "yellow", - "completed": "green", - "error": "red", - }.get(status, "white") - status_cell = f"[{status_color}]{status}[/{status_color}]" - progress_table.add_row("", agent, status_cell) - - # Add horizontal line after each team - progress_table.add_row("─" * 20, "─" * 20, "─" * 20, style="dim") - - layout["progress"].update( - Panel(progress_table, title="Progress", border_style="cyan", padding=(1, 2)) - ) - - # Messages panel showing recent messages and tool calls - messages_table = Table( - show_header=True, - header_style="bold magenta", - show_footer=False, - expand=True, # Make table expand to fill available space - box=box.MINIMAL, # Use minimal box style for a lighter look - show_lines=True, # Keep horizontal lines - padding=(0, 1), # Add some padding between columns - ) - messages_table.add_column("Time", style="cyan", width=8, justify="center") - messages_table.add_column("Type", style="green", width=10, justify="center") - messages_table.add_column( - "Content", style="white", no_wrap=False, ratio=1 - ) # Make content column expand - - # Combine tool calls and messages - all_messages = [] - - # Add tool calls - for timestamp, tool_name, args in message_buffer.tool_calls: - formatted_args = format_tool_args(args) - all_messages.append((timestamp, "Tool", f"{tool_name}: {formatted_args}")) - - # Add regular messages - for timestamp, msg_type, content in message_buffer.messages: - content_str = str(content) if content else "" - if len(content_str) > 200: - content_str = content_str[:197] + "..." - all_messages.append((timestamp, msg_type, content_str)) - - # Sort by timestamp descending (newest first) - all_messages.sort(key=lambda x: x[0], reverse=True) - - # Calculate how many messages we can show based on available space - max_messages = 12 - - # Get the first N messages (newest ones) - recent_messages = all_messages[:max_messages] - - # Add messages to table (already in newest-first order) - for timestamp, msg_type, content in recent_messages: - # Format content with word wrapping - wrapped_content = Text(content, overflow="fold") - messages_table.add_row(timestamp, msg_type, wrapped_content) - - layout["messages"].update( - Panel( - messages_table, - title="Messages & Tools", - border_style="blue", - padding=(1, 2), - ) - ) - - # Analysis panel showing current report - if message_buffer.current_report: - layout["analysis"].update( - Panel( - Markdown(message_buffer.current_report), - title="Current Report", - border_style="green", - padding=(1, 2), - ) - ) - else: - layout["analysis"].update( - Panel( - "[italic]Waiting for analysis report...[/italic]", - title="Current Report", - border_style="green", - padding=(1, 2), - ) - ) - - # Footer with statistics - # Agent progress - derived from agent_status dict - agents_completed = sum( - 1 for status in message_buffer.agent_status.values() if status == "completed" - ) - agents_total = len(message_buffer.agent_status) - - # Report progress - based on agent completion (not just content existence) - reports_completed = message_buffer.get_completed_reports_count() - reports_total = len(message_buffer.report_sections) - - # Build stats parts - stats_parts = [f"Agents: {agents_completed}/{agents_total}"] - - # LLM and tool stats from callback handler - if stats_handler: - stats = stats_handler.get_stats() - stats_parts.append(f"LLM: {stats['llm_calls']}") - stats_parts.append(f"Tools: {stats['tool_calls']}") - - # Token display with graceful fallback - if stats["tokens_in"] > 0 or stats["tokens_out"] > 0: - tokens_str = f"Tokens: {format_tokens(stats['tokens_in'])}\u2191 {format_tokens(stats['tokens_out'])}\u2193" - else: - tokens_str = "Tokens: --" - stats_parts.append(tokens_str) - - stats_parts.append(f"Reports: {reports_completed}/{reports_total}") - - # Elapsed time - if start_time: - elapsed = time.time() - start_time - elapsed_str = f"\u23f1 {int(elapsed // 60):02d}:{int(elapsed % 60):02d}" - stats_parts.append(elapsed_str) - - stats_table = Table(show_header=False, box=None, padding=(0, 2), expand=True) - stats_table.add_column("Stats", justify="center") - stats_table.add_row(" | ".join(stats_parts)) - - layout["footer"].update(Panel(stats_table, border_style="grey50")) - - -def get_user_selections(): - """Get all user selections before starting the analysis display.""" - # Display ASCII art welcome message - with open("./cli/static/welcome.txt", "r") as f: - welcome_ascii = f.read() - - # Create welcome box content - welcome_content = f"{welcome_ascii}\n" - welcome_content += "[bold green]TradingAgents: Multi-Agents LLM Financial Trading Framework - CLI[/bold green]\n\n" - welcome_content += "[bold]Workflow Steps:[/bold]\n" - welcome_content += "I. Analyst Team → II. Research Team → III. Trader → IV. Risk Management → V. Portfolio Management\n\n" - welcome_content += ( - "[dim]Built by [Tauric Research](https://github.com/TauricResearch)[/dim]" - ) - - # Create and center the welcome box - welcome_box = Panel( - welcome_content, - border_style="green", - padding=(1, 2), - title="Welcome to TradingAgents", - subtitle="Multi-Agents LLM Financial Trading Framework", - ) - console.print(Align.center(welcome_box)) - console.print() - console.print() # Add vertical space before announcements - - # Fetch and display announcements (silent on failure) - announcements = fetch_announcements() - display_announcements(console, announcements) - - # Create a boxed questionnaire for each step - def create_question_box(title, prompt, default=None): - box_content = f"[bold]{title}[/bold]\n" - box_content += f"[dim]{prompt}[/dim]" - if default: - box_content += f"\n[dim]Default: {default}[/dim]" - return Panel(box_content, border_style="blue", padding=(1, 2)) - - # Step 1: Ticker symbol - console.print( - create_question_box( - "Step 1: Ticker Symbol", "Enter the ticker symbol to analyze", "SPY" - ) - ) - selected_ticker = get_ticker() - - # Step 2: Analysis date - default_date = datetime.datetime.now().strftime("%Y-%m-%d") - console.print( - create_question_box( - "Step 2: Analysis Date", - "Enter the analysis date (YYYY-MM-DD)", - default_date, - ) - ) - analysis_date = get_analysis_date() - - # Step 3: Select analysts - console.print( - create_question_box( - "Step 3: Analysts Team", "Select your LLM analyst agents for the analysis" - ) - ) - selected_analysts = select_analysts() - console.print( - f"[green]Selected analysts:[/green] {', '.join(analyst.value for analyst in selected_analysts)}" - ) - - # Step 4: Research depth - console.print( - create_question_box( - "Step 4: Research Depth", "Select your research depth level" - ) - ) - selected_research_depth = select_research_depth() - - # Step 5: OpenAI backend - console.print( - create_question_box( - "Step 5: OpenAI backend", "Select which service to talk to" - ) - ) - selected_llm_provider, backend_url = select_llm_provider() - - # Step 6: Thinking agents - console.print( - create_question_box( - "Step 6: Thinking Agents", "Select your thinking agents for analysis" - ) - ) - selected_shallow_thinker = select_shallow_thinking_agent(selected_llm_provider) - selected_deep_thinker = select_deep_thinking_agent(selected_llm_provider) - - # Step 7: Provider-specific thinking configuration - thinking_level = None - reasoning_effort = None - - provider_lower = selected_llm_provider.lower() - if provider_lower == "google": - console.print( - create_question_box( - "Step 7: Thinking Mode", - "Configure Gemini thinking mode" - ) - ) - thinking_level = ask_gemini_thinking_config() - elif provider_lower == "openai": - console.print( - create_question_box( - "Step 7: Reasoning Effort", - "Configure OpenAI reasoning effort level" - ) - ) - reasoning_effort = ask_openai_reasoning_effort() - - return { - "ticker": selected_ticker, - "analysis_date": analysis_date, - "analysts": selected_analysts, - "research_depth": selected_research_depth, - "llm_provider": selected_llm_provider.lower(), - "backend_url": backend_url, - "shallow_thinker": selected_shallow_thinker, - "deep_thinker": selected_deep_thinker, - "google_thinking_level": thinking_level, - "openai_reasoning_effort": reasoning_effort, - } - - -def get_ticker(): - """Get ticker symbol from user input.""" - return typer.prompt("", default="SPY") - - -def get_analysis_date(): - """Get the analysis date from user input.""" - while True: - date_str = typer.prompt( - "", default=datetime.datetime.now().strftime("%Y-%m-%d") - ) - try: - # Validate date format and ensure it's not in the future - analysis_date = datetime.datetime.strptime(date_str, "%Y-%m-%d") - if analysis_date.date() > datetime.datetime.now().date(): - console.print("[red]Error: Analysis date cannot be in the future[/red]") - continue - return date_str - except ValueError: - console.print( - "[red]Error: Invalid date format. Please use YYYY-MM-DD[/red]" - ) - - -def save_report_to_disk(final_state, ticker: str, save_path: Path): - """Save complete analysis report to disk with organized subfolders.""" - save_path.mkdir(parents=True, exist_ok=True) - sections = [] - - # 1. Analysts - analysts_dir = save_path / "1_analysts" - analyst_parts = [] - if final_state.get("market_report"): - analysts_dir.mkdir(exist_ok=True) - (analysts_dir / "market.md").write_text(final_state["market_report"]) - analyst_parts.append(("Market Analyst", final_state["market_report"])) - if final_state.get("sentiment_report"): - analysts_dir.mkdir(exist_ok=True) - (analysts_dir / "sentiment.md").write_text(final_state["sentiment_report"]) - analyst_parts.append(("Social Analyst", final_state["sentiment_report"])) - if final_state.get("news_report"): - analysts_dir.mkdir(exist_ok=True) - (analysts_dir / "news.md").write_text(final_state["news_report"]) - analyst_parts.append(("News Analyst", final_state["news_report"])) - if final_state.get("fundamentals_report"): - analysts_dir.mkdir(exist_ok=True) - (analysts_dir / "fundamentals.md").write_text(final_state["fundamentals_report"]) - analyst_parts.append(("Fundamentals Analyst", final_state["fundamentals_report"])) - if analyst_parts: - content = "\n\n".join(f"### {name}\n{text}" for name, text in analyst_parts) - sections.append(f"## I. Analyst Team Reports\n\n{content}") - - # 2. Research - if final_state.get("investment_debate_state"): - research_dir = save_path / "2_research" - debate = final_state["investment_debate_state"] - research_parts = [] - if debate.get("bull_history"): - research_dir.mkdir(exist_ok=True) - (research_dir / "bull.md").write_text(debate["bull_history"]) - research_parts.append(("Bull Researcher", debate["bull_history"])) - if debate.get("bear_history"): - research_dir.mkdir(exist_ok=True) - (research_dir / "bear.md").write_text(debate["bear_history"]) - research_parts.append(("Bear Researcher", debate["bear_history"])) - if debate.get("judge_decision"): - research_dir.mkdir(exist_ok=True) - (research_dir / "manager.md").write_text(debate["judge_decision"]) - research_parts.append(("Research Manager", debate["judge_decision"])) - if research_parts: - content = "\n\n".join(f"### {name}\n{text}" for name, text in research_parts) - sections.append(f"## II. Research Team Decision\n\n{content}") - - # 3. Trading - if final_state.get("trader_investment_plan"): - trading_dir = save_path / "3_trading" - trading_dir.mkdir(exist_ok=True) - (trading_dir / "trader.md").write_text(final_state["trader_investment_plan"]) - sections.append(f"## III. Trading Team Plan\n\n### Trader\n{final_state['trader_investment_plan']}") - - # 4. Risk Management - if final_state.get("risk_debate_state"): - risk_dir = save_path / "4_risk" - risk = final_state["risk_debate_state"] - risk_parts = [] - if risk.get("aggressive_history"): - risk_dir.mkdir(exist_ok=True) - (risk_dir / "aggressive.md").write_text(risk["aggressive_history"]) - risk_parts.append(("Aggressive Analyst", risk["aggressive_history"])) - if risk.get("conservative_history"): - risk_dir.mkdir(exist_ok=True) - (risk_dir / "conservative.md").write_text(risk["conservative_history"]) - risk_parts.append(("Conservative Analyst", risk["conservative_history"])) - if risk.get("neutral_history"): - risk_dir.mkdir(exist_ok=True) - (risk_dir / "neutral.md").write_text(risk["neutral_history"]) - risk_parts.append(("Neutral Analyst", risk["neutral_history"])) - if risk_parts: - content = "\n\n".join(f"### {name}\n{text}" for name, text in risk_parts) - sections.append(f"## IV. Risk Management Team Decision\n\n{content}") - - # 5. Portfolio Manager - if risk.get("judge_decision"): - portfolio_dir = save_path / "5_portfolio" - portfolio_dir.mkdir(exist_ok=True) - (portfolio_dir / "decision.md").write_text(risk["judge_decision"]) - sections.append(f"## V. Portfolio Manager Decision\n\n### Portfolio Manager\n{risk['judge_decision']}") - - # Write consolidated report - header = f"# Trading Analysis Report: {ticker}\n\nGenerated: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" - (save_path / "complete_report.md").write_text(header + "\n\n".join(sections)) - return save_path / "complete_report.md" - - -def display_complete_report(final_state): - """Display the complete analysis report sequentially (avoids truncation).""" - console.print() - console.print(Rule("Complete Analysis Report", style="bold green")) - - # I. Analyst Team Reports - analysts = [] - if final_state.get("market_report"): - analysts.append(("Market Analyst", final_state["market_report"])) - if final_state.get("sentiment_report"): - analysts.append(("Social Analyst", final_state["sentiment_report"])) - if final_state.get("news_report"): - analysts.append(("News Analyst", final_state["news_report"])) - if final_state.get("fundamentals_report"): - analysts.append(("Fundamentals Analyst", final_state["fundamentals_report"])) - if analysts: - console.print(Panel("[bold]I. Analyst Team Reports[/bold]", border_style="cyan")) - for title, content in analysts: - console.print(Panel(Markdown(content), title=title, border_style="blue", padding=(1, 2))) - - # II. Research Team Reports - if final_state.get("investment_debate_state"): - debate = final_state["investment_debate_state"] - research = [] - if debate.get("bull_history"): - research.append(("Bull Researcher", debate["bull_history"])) - if debate.get("bear_history"): - research.append(("Bear Researcher", debate["bear_history"])) - if debate.get("judge_decision"): - research.append(("Research Manager", debate["judge_decision"])) - if research: - console.print(Panel("[bold]II. Research Team Decision[/bold]", border_style="magenta")) - for title, content in research: - console.print(Panel(Markdown(content), title=title, border_style="blue", padding=(1, 2))) - - # III. Trading Team - if final_state.get("trader_investment_plan"): - console.print(Panel("[bold]III. Trading Team Plan[/bold]", border_style="yellow")) - console.print(Panel(Markdown(final_state["trader_investment_plan"]), title="Trader", border_style="blue", padding=(1, 2))) - - # IV. Risk Management Team - if final_state.get("risk_debate_state"): - risk = final_state["risk_debate_state"] - risk_reports = [] - if risk.get("aggressive_history"): - risk_reports.append(("Aggressive Analyst", risk["aggressive_history"])) - if risk.get("conservative_history"): - risk_reports.append(("Conservative Analyst", risk["conservative_history"])) - if risk.get("neutral_history"): - risk_reports.append(("Neutral Analyst", risk["neutral_history"])) - if risk_reports: - console.print(Panel("[bold]IV. Risk Management Team Decision[/bold]", border_style="red")) - for title, content in risk_reports: - console.print(Panel(Markdown(content), title=title, border_style="blue", padding=(1, 2))) - - # V. Portfolio Manager Decision - if risk.get("judge_decision"): - console.print(Panel("[bold]V. Portfolio Manager Decision[/bold]", border_style="green")) - console.print(Panel(Markdown(risk["judge_decision"]), title="Portfolio Manager", border_style="blue", padding=(1, 2))) - - -def update_research_team_status(status): - """Update status for research team members (not Trader).""" - research_team = ["Bull Researcher", "Bear Researcher", "Research Manager"] - for agent in research_team: - message_buffer.update_agent_status(agent, status) - - -# Ordered list of analysts for status transitions -ANALYST_ORDER = ["market", "social", "news", "fundamentals"] -ANALYST_AGENT_NAMES = { - "market": "Market Analyst", - "social": "Social Analyst", - "news": "News Analyst", - "fundamentals": "Fundamentals Analyst", -} -ANALYST_REPORT_MAP = { - "market": "market_report", - "social": "sentiment_report", - "news": "news_report", - "fundamentals": "fundamentals_report", -} - - -def update_analyst_statuses(message_buffer, chunk): - """Update all analyst statuses based on current report state. - - Logic: - - Analysts with reports = completed - - First analyst without report = in_progress - - Remaining analysts without reports = pending - - When all analysts done, set Bull Researcher to in_progress - """ - selected = message_buffer.selected_analysts - found_active = False - - for analyst_key in ANALYST_ORDER: - if analyst_key not in selected: - continue - - agent_name = ANALYST_AGENT_NAMES[analyst_key] - report_key = ANALYST_REPORT_MAP[analyst_key] - has_report = bool(chunk.get(report_key)) - - if has_report: - message_buffer.update_agent_status(agent_name, "completed") - message_buffer.update_report_section(report_key, chunk[report_key]) - elif not found_active: - message_buffer.update_agent_status(agent_name, "in_progress") - found_active = True - else: - message_buffer.update_agent_status(agent_name, "pending") - - # When all analysts complete, transition research team to in_progress - if not found_active and selected: - if message_buffer.agent_status.get("Bull Researcher") == "pending": - message_buffer.update_agent_status("Bull Researcher", "in_progress") - message_buffer.update_agent_status("Bear Researcher", "in_progress") - -def extract_content_string(content): - """Extract string content from various message formats. - Returns None if no meaningful text content is found. - """ - import ast - - def is_empty(val): - """Check if value is empty using Python's truthiness.""" - if val is None or val == '': - return True - if isinstance(val, str): - s = val.strip() - if not s: - return True - try: - return not bool(ast.literal_eval(s)) - except (ValueError, SyntaxError): - return False # Can't parse = real text - return not bool(val) - - if is_empty(content): - return None - - if isinstance(content, str): - return content.strip() - - if isinstance(content, dict): - text = content.get('text', '') - return text.strip() if not is_empty(text) else None - - if isinstance(content, list): - text_parts = [ - item.get('text', '').strip() if isinstance(item, dict) and item.get('type') == 'text' - else (item.strip() if isinstance(item, str) else '') - for item in content - ] - result = ' '.join(t for t in text_parts if t and not is_empty(t)) - return result if result else None - - return str(content).strip() if not is_empty(content) else None - - -def classify_message_type(message) -> tuple[str, str | None]: - """Classify LangChain message into display type and extract content. - - Returns: - (type, content) - type is one of: User, Agent, Data, Control - - content is extracted string or None - """ - from langchain_core.messages import AIMessage, HumanMessage, ToolMessage - - content = extract_content_string(getattr(message, 'content', None)) - - if isinstance(message, HumanMessage): - if content and content.strip() == "Continue": - return ("Control", content) - return ("User", content) - - if isinstance(message, ToolMessage): - return ("Data", content) - - if isinstance(message, AIMessage): - return ("Agent", content) - - # Fallback for unknown types - return ("System", content) - - -def format_tool_args(args, max_length=80) -> str: - """Format tool arguments for terminal display.""" - result = str(args) - if len(result) > max_length: - return result[:max_length - 3] + "..." - return result - -def run_analysis(): - # First get all user selections - selections = get_user_selections() - - # Create config with selected research depth - config = DEFAULT_CONFIG.copy() - config["max_debate_rounds"] = selections["research_depth"] - config["max_risk_discuss_rounds"] = selections["research_depth"] - config["quick_think_llm"] = selections["shallow_thinker"] - config["deep_think_llm"] = selections["deep_thinker"] - config["backend_url"] = selections["backend_url"] - config["llm_provider"] = selections["llm_provider"].lower() - # Provider-specific thinking configuration - config["google_thinking_level"] = selections.get("google_thinking_level") - config["openai_reasoning_effort"] = selections.get("openai_reasoning_effort") - - # Create stats callback handler for tracking LLM/tool calls - stats_handler = StatsCallbackHandler() - - # Normalize analyst selection to predefined order (selection is a 'set', order is fixed) - selected_set = {analyst.value for analyst in selections["analysts"]} - selected_analyst_keys = [a for a in ANALYST_ORDER if a in selected_set] - - # Initialize the graph with callbacks bound to LLMs - graph = TradingAgentsGraph( - selected_analyst_keys, - config=config, - debug=True, - callbacks=[stats_handler], - ) - - # Initialize message buffer with selected analysts - message_buffer.init_for_analysis(selected_analyst_keys) - - # Track start time for elapsed display - start_time = time.time() - - # Create result directory - results_dir = Path(config["results_dir"]) / selections["ticker"] / selections["analysis_date"] - results_dir.mkdir(parents=True, exist_ok=True) - report_dir = results_dir / "reports" - report_dir.mkdir(parents=True, exist_ok=True) - log_file = results_dir / "message_tool.log" - log_file.touch(exist_ok=True) - - def save_message_decorator(obj, func_name): - func = getattr(obj, func_name) - @wraps(func) - def wrapper(*args, **kwargs): - func(*args, **kwargs) - timestamp, message_type, content = obj.messages[-1] - content = content.replace("\n", " ") # Replace newlines with spaces - with open(log_file, "a") as f: - f.write(f"{timestamp} [{message_type}] {content}\n") - return wrapper - - def save_tool_call_decorator(obj, func_name): - func = getattr(obj, func_name) - @wraps(func) - def wrapper(*args, **kwargs): - func(*args, **kwargs) - timestamp, tool_name, args = obj.tool_calls[-1] - args_str = ", ".join(f"{k}={v}" for k, v in args.items()) - with open(log_file, "a") as f: - f.write(f"{timestamp} [Tool Call] {tool_name}({args_str})\n") - return wrapper - - def save_report_section_decorator(obj, func_name): - func = getattr(obj, func_name) - @wraps(func) - def wrapper(section_name, content): - func(section_name, content) - if section_name in obj.report_sections and obj.report_sections[section_name] is not None: - content = obj.report_sections[section_name] - if content: - file_name = f"{section_name}.md" - with open(report_dir / file_name, "w") as f: - f.write(content) - return wrapper - - message_buffer.add_message = save_message_decorator(message_buffer, "add_message") - message_buffer.add_tool_call = save_tool_call_decorator(message_buffer, "add_tool_call") - message_buffer.update_report_section = save_report_section_decorator(message_buffer, "update_report_section") - - # Now start the display layout - layout = create_layout() - - with Live(layout, refresh_per_second=4) as live: - # Initial display - update_display(layout, stats_handler=stats_handler, start_time=start_time) - - # Add initial messages - message_buffer.add_message("System", f"Selected ticker: {selections['ticker']}") - message_buffer.add_message( - "System", f"Analysis date: {selections['analysis_date']}" - ) - message_buffer.add_message( - "System", - f"Selected analysts: {', '.join(analyst.value for analyst in selections['analysts'])}", - ) - update_display(layout, stats_handler=stats_handler, start_time=start_time) - - # Update agent status to in_progress for the first analyst - first_analyst = f"{selections['analysts'][0].value.capitalize()} Analyst" - message_buffer.update_agent_status(first_analyst, "in_progress") - update_display(layout, stats_handler=stats_handler, start_time=start_time) - - # Create spinner text - spinner_text = ( - f"Analyzing {selections['ticker']} on {selections['analysis_date']}..." - ) - update_display(layout, spinner_text, stats_handler=stats_handler, start_time=start_time) - - # Initialize state and get graph args with callbacks - init_agent_state = graph.propagator.create_initial_state( - selections["ticker"], selections["analysis_date"] - ) - # Pass callbacks to graph config for tool execution tracking - # (LLM tracking is handled separately via LLM constructor) - args = graph.propagator.get_graph_args(callbacks=[stats_handler]) - - # Stream the analysis - trace = [] - for chunk in graph.graph.stream(init_agent_state, **args): - # Process messages if present (skip duplicates via message ID) - if len(chunk["messages"]) > 0: - last_message = chunk["messages"][-1] - msg_id = getattr(last_message, "id", None) - - if msg_id != message_buffer._last_message_id: - message_buffer._last_message_id = msg_id - - # Add message to buffer - msg_type, content = classify_message_type(last_message) - if content and content.strip(): - message_buffer.add_message(msg_type, content) - - # Handle tool calls - if hasattr(last_message, "tool_calls") and last_message.tool_calls: - for tool_call in last_message.tool_calls: - if isinstance(tool_call, dict): - message_buffer.add_tool_call( - tool_call["name"], tool_call["args"] - ) - else: - message_buffer.add_tool_call(tool_call.name, tool_call.args) - - # Update analyst statuses based on report state (runs on every chunk) - update_analyst_statuses(message_buffer, chunk) - - # Research Team - Handle Investment Debate State - if chunk.get("investment_debate_state"): - debate_state = chunk["investment_debate_state"] - bull_hist = debate_state.get("bull_history", "").strip() - bear_hist = debate_state.get("bear_history", "").strip() - judge = debate_state.get("judge_decision", "").strip() - - # Only update status when there's actual content - if bull_hist or bear_hist: - update_research_team_status("in_progress") - if bull_hist: - message_buffer.update_report_section( - "investment_plan", f"### Bull Researcher Analysis\n{bull_hist}" - ) - if bear_hist: - message_buffer.update_report_section( - "investment_plan", f"### Bear Researcher Analysis\n{bear_hist}" - ) - if judge: - message_buffer.update_report_section( - "investment_plan", f"### Research Manager Decision\n{judge}" - ) - update_research_team_status("completed") - message_buffer.update_agent_status("Trader", "in_progress") - - # Trading Team - if chunk.get("trader_investment_plan"): - message_buffer.update_report_section( - "trader_investment_plan", chunk["trader_investment_plan"] - ) - if message_buffer.agent_status.get("Trader") != "completed": - message_buffer.update_agent_status("Trader", "completed") - message_buffer.update_agent_status("Aggressive Analyst", "in_progress") - - # Risk Management Team - Handle Risk Debate State - if chunk.get("risk_debate_state"): - risk_state = chunk["risk_debate_state"] - agg_hist = risk_state.get("aggressive_history", "").strip() - con_hist = risk_state.get("conservative_history", "").strip() - neu_hist = risk_state.get("neutral_history", "").strip() - judge = risk_state.get("judge_decision", "").strip() - - if agg_hist: - if message_buffer.agent_status.get("Aggressive Analyst") != "completed": - message_buffer.update_agent_status("Aggressive Analyst", "in_progress") - message_buffer.update_report_section( - "final_trade_decision", f"### Aggressive Analyst Analysis\n{agg_hist}" - ) - if con_hist: - if message_buffer.agent_status.get("Conservative Analyst") != "completed": - message_buffer.update_agent_status("Conservative Analyst", "in_progress") - message_buffer.update_report_section( - "final_trade_decision", f"### Conservative Analyst Analysis\n{con_hist}" - ) - if neu_hist: - if message_buffer.agent_status.get("Neutral Analyst") != "completed": - message_buffer.update_agent_status("Neutral Analyst", "in_progress") - message_buffer.update_report_section( - "final_trade_decision", f"### Neutral Analyst Analysis\n{neu_hist}" - ) - if judge: - if message_buffer.agent_status.get("Portfolio Manager") != "completed": - message_buffer.update_agent_status("Portfolio Manager", "in_progress") - message_buffer.update_report_section( - "final_trade_decision", f"### Portfolio Manager Decision\n{judge}" - ) - message_buffer.update_agent_status("Aggressive Analyst", "completed") - message_buffer.update_agent_status("Conservative Analyst", "completed") - message_buffer.update_agent_status("Neutral Analyst", "completed") - message_buffer.update_agent_status("Portfolio Manager", "completed") - - # Update the display - update_display(layout, stats_handler=stats_handler, start_time=start_time) - - trace.append(chunk) - - # Get final state and decision - final_state = trace[-1] - decision = graph.process_signal(final_state["final_trade_decision"]) - - # Update all agent statuses to completed - for agent in message_buffer.agent_status: - message_buffer.update_agent_status(agent, "completed") - - message_buffer.add_message( - "System", f"Completed analysis for {selections['analysis_date']}" - ) - - # Update final report sections - for section in message_buffer.report_sections.keys(): - if section in final_state: - message_buffer.update_report_section(section, final_state[section]) - - update_display(layout, stats_handler=stats_handler, start_time=start_time) - - # Post-analysis prompts (outside Live context for clean interaction) - console.print("\n[bold cyan]Analysis Complete![/bold cyan]\n") - - # Prompt to save report - save_choice = typer.prompt("Save report?", default="Y").strip().upper() - if save_choice in ("Y", "YES", ""): - timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") - default_path = Path.cwd() / "reports" / f"{selections['ticker']}_{timestamp}" - save_path_str = typer.prompt( - "Save path (press Enter for default)", - default=str(default_path) - ).strip() - save_path = Path(save_path_str) - try: - report_file = save_report_to_disk(final_state, selections["ticker"], save_path) - console.print(f"\n[green]✓ Report saved to:[/green] {save_path.resolve()}") - console.print(f" [dim]Complete report:[/dim] {report_file.name}") - except Exception as e: - console.print(f"[red]Error saving report: {e}[/red]") - - # Prompt to display full report - display_choice = typer.prompt("\nDisplay full report on screen?", default="Y").strip().upper() - if display_choice in ("Y", "YES", ""): - display_complete_report(final_state) - - -@app.command() -def analyze(): - run_analysis() - - -if __name__ == "__main__": - app() +from typing import Optional +import datetime +import typer +from pathlib import Path +from functools import wraps +from rich.console import Console +from dotenv import load_dotenv + +# Load environment variables from .env file +load_dotenv() +from rich.panel import Panel +from rich.spinner import Spinner +from rich.live import Live +from rich.columns import Columns +from rich.markdown import Markdown +from rich.layout import Layout +from rich.text import Text +from rich.table import Table +from collections import deque +import time +from rich.tree import Tree +from rich import box +from rich.align import Align +from rich.rule import Rule + +from tradingagents.graph.trading_graph import TradingAgentsGraph +from tradingagents.default_config import DEFAULT_CONFIG +from cli.models import AnalystType +from cli.utils import * +from cli.announcements import fetch_announcements, display_announcements +from cli.stats_handler import StatsCallbackHandler + +console = Console() + +app = typer.Typer( + name="TradingAgents", + help="TradingAgents CLI: Multi-Agents LLM Financial Trading Framework", + add_completion=True, # Enable shell completion +) + + +# Create a deque to store recent messages with a maximum length +class MessageBuffer: + # Fixed teams that always run (not user-selectable) + FIXED_AGENTS = { + "Research Team": ["Bull Researcher", "Bear Researcher", "Research Manager"], + "Trading Team": ["Trader"], + "Risk Management": ["Aggressive Analyst", "Neutral Analyst", "Conservative Analyst"], + "Portfolio Management": ["Portfolio Manager"], + } + + # Analyst name mapping + ANALYST_MAPPING = { + "market": "Market Analyst", + "social": "Social Analyst", + "news": "News Analyst", + "fundamentals": "Fundamentals Analyst", + } + + # Report section mapping: section -> (analyst_key for filtering, finalizing_agent) + # analyst_key: which analyst selection controls this section (None = always included) + # finalizing_agent: which agent must be "completed" for this report to count as done + REPORT_SECTIONS = { + "market_report": ("market", "Market Analyst"), + "sentiment_report": ("social", "Social Analyst"), + "news_report": ("news", "News Analyst"), + "fundamentals_report": ("fundamentals", "Fundamentals Analyst"), + "investment_plan": (None, "Research Manager"), + "trader_investment_plan": (None, "Trader"), + "final_trade_decision": (None, "Portfolio Manager"), + } + + def __init__(self, max_length=100): + self.messages = deque(maxlen=max_length) + self.tool_calls = deque(maxlen=max_length) + self.current_report = None + self.final_report = None # Store the complete final report + self.agent_status = {} + self.current_agent = None + self.report_sections = {} + self.selected_analysts = [] + self._last_message_id = None + + def init_for_analysis(self, selected_analysts): + """Initialize agent status and report sections based on selected analysts. + + Args: + selected_analysts: List of analyst type strings (e.g., ["market", "news"]) + """ + self.selected_analysts = [a.lower() for a in selected_analysts] + + # Build agent_status dynamically + self.agent_status = {} + + # Add selected analysts + for analyst_key in self.selected_analysts: + if analyst_key in self.ANALYST_MAPPING: + self.agent_status[self.ANALYST_MAPPING[analyst_key]] = "pending" + + # Add fixed teams + for team_agents in self.FIXED_AGENTS.values(): + for agent in team_agents: + self.agent_status[agent] = "pending" + + # Build report_sections dynamically + self.report_sections = {} + for section, (analyst_key, _) in self.REPORT_SECTIONS.items(): + if analyst_key is None or analyst_key in self.selected_analysts: + self.report_sections[section] = None + + # Reset other state + self.current_report = None + self.final_report = None + self.current_agent = None + self.messages.clear() + self.tool_calls.clear() + self._last_message_id = None + + def get_completed_reports_count(self): + """Count reports that are finalized (their finalizing agent is completed). + + A report is considered complete when: + 1. The report section has content (not None), AND + 2. The agent responsible for finalizing that report has status "completed" + + This prevents interim updates (like debate rounds) from counting as completed. + """ + count = 0 + for section in self.report_sections: + if section not in self.REPORT_SECTIONS: + continue + _, finalizing_agent = self.REPORT_SECTIONS[section] + # Report is complete if it has content AND its finalizing agent is done + has_content = self.report_sections.get(section) is not None + agent_done = self.agent_status.get(finalizing_agent) == "completed" + if has_content and agent_done: + count += 1 + return count + + def add_message(self, message_type, content): + timestamp = datetime.datetime.now().strftime("%H:%M:%S") + self.messages.append((timestamp, message_type, content)) + + def add_tool_call(self, tool_name, args): + timestamp = datetime.datetime.now().strftime("%H:%M:%S") + self.tool_calls.append((timestamp, tool_name, args)) + + def update_agent_status(self, agent, status): + if agent in self.agent_status: + self.agent_status[agent] = status + self.current_agent = agent + + def update_report_section(self, section_name, content): + if section_name in self.report_sections: + self.report_sections[section_name] = content + self._update_current_report() + + def _update_current_report(self): + # For the panel display, only show the most recently updated section + latest_section = None + latest_content = None + + # Find the most recently updated section + for section, content in self.report_sections.items(): + if content is not None: + latest_section = section + latest_content = content + + if latest_section and latest_content: + # Format the current section for display + section_titles = { + "market_report": "Market Analysis", + "sentiment_report": "Social Sentiment", + "news_report": "News Analysis", + "fundamentals_report": "Fundamentals Analysis", + "investment_plan": "Research Team Decision", + "trader_investment_plan": "Trading Team Plan", + "final_trade_decision": "Portfolio Management Decision", + } + self.current_report = ( + f"### {section_titles[latest_section]}\n{latest_content}" + ) + + # Update the final complete report + self._update_final_report() + + def _update_final_report(self): + report_parts = [] + + # Analyst Team Reports - use .get() to handle missing sections + analyst_sections = ["market_report", "sentiment_report", "news_report", "fundamentals_report"] + if any(self.report_sections.get(section) for section in analyst_sections): + report_parts.append("## Analyst Team Reports") + if self.report_sections.get("market_report"): + report_parts.append( + f"### Market Analysis\n{self.report_sections['market_report']}" + ) + if self.report_sections.get("sentiment_report"): + report_parts.append( + f"### Social Sentiment\n{self.report_sections['sentiment_report']}" + ) + if self.report_sections.get("news_report"): + report_parts.append( + f"### News Analysis\n{self.report_sections['news_report']}" + ) + if self.report_sections.get("fundamentals_report"): + report_parts.append( + f"### Fundamentals Analysis\n{self.report_sections['fundamentals_report']}" + ) + + # Research Team Reports + if self.report_sections.get("investment_plan"): + report_parts.append("## Research Team Decision") + report_parts.append(f"{self.report_sections['investment_plan']}") + + # Trading Team Reports + if self.report_sections.get("trader_investment_plan"): + report_parts.append("## Trading Team Plan") + report_parts.append(f"{self.report_sections['trader_investment_plan']}") + + # Portfolio Management Decision + if self.report_sections.get("final_trade_decision"): + report_parts.append("## Portfolio Management Decision") + report_parts.append(f"{self.report_sections['final_trade_decision']}") + + self.final_report = "\n\n".join(report_parts) if report_parts else None + + +message_buffer = MessageBuffer() + + +def create_layout(): + layout = Layout() + layout.split_column( + Layout(name="header", size=3), + Layout(name="main"), + Layout(name="footer", size=3), + ) + layout["main"].split_column( + Layout(name="upper", ratio=3), Layout(name="analysis", ratio=5) + ) + layout["upper"].split_row( + Layout(name="progress", ratio=2), Layout(name="messages", ratio=3) + ) + return layout + + +def format_tokens(n): + """Format token count for display.""" + if n >= 1000: + return f"{n/1000:.1f}k" + return str(n) + + +def update_display(layout, spinner_text=None, stats_handler=None, start_time=None): + # Header with welcome message + layout["header"].update( + Panel( + "[bold green]Welcome to TradingAgents CLI[/bold green]\n" + "[dim]© [Tauric Research](https://github.com/TauricResearch)[/dim]", + title="Welcome to TradingAgents", + border_style="green", + padding=(1, 2), + expand=True, + ) + ) + + # Progress panel showing agent status + progress_table = Table( + show_header=True, + header_style="bold magenta", + show_footer=False, + box=box.SIMPLE_HEAD, # Use simple header with horizontal lines + title=None, # Remove the redundant Progress title + padding=(0, 2), # Add horizontal padding + expand=True, # Make table expand to fill available space + ) + progress_table.add_column("Team", style="cyan", justify="center", width=20) + progress_table.add_column("Agent", style="green", justify="center", width=20) + progress_table.add_column("Status", style="yellow", justify="center", width=20) + + # Group agents by team - filter to only include agents in agent_status + all_teams = { + "Analyst Team": [ + "Market Analyst", + "Social Analyst", + "News Analyst", + "Fundamentals Analyst", + ], + "Research Team": ["Bull Researcher", "Bear Researcher", "Research Manager"], + "Trading Team": ["Trader"], + "Risk Management": ["Aggressive Analyst", "Neutral Analyst", "Conservative Analyst"], + "Portfolio Management": ["Portfolio Manager"], + } + + # Filter teams to only include agents that are in agent_status + teams = {} + for team, agents in all_teams.items(): + active_agents = [a for a in agents if a in message_buffer.agent_status] + if active_agents: + teams[team] = active_agents + + for team, agents in teams.items(): + # Add first agent with team name + first_agent = agents[0] + status = message_buffer.agent_status.get(first_agent, "pending") + if status == "in_progress": + spinner = Spinner( + "dots", text="[blue]in_progress[/blue]", style="bold cyan" + ) + status_cell = spinner + else: + status_color = { + "pending": "yellow", + "completed": "green", + "error": "red", + }.get(status, "white") + status_cell = f"[{status_color}]{status}[/{status_color}]" + progress_table.add_row(team, first_agent, status_cell) + + # Add remaining agents in team + for agent in agents[1:]: + status = message_buffer.agent_status.get(agent, "pending") + if status == "in_progress": + spinner = Spinner( + "dots", text="[blue]in_progress[/blue]", style="bold cyan" + ) + status_cell = spinner + else: + status_color = { + "pending": "yellow", + "completed": "green", + "error": "red", + }.get(status, "white") + status_cell = f"[{status_color}]{status}[/{status_color}]" + progress_table.add_row("", agent, status_cell) + + # Add horizontal line after each team + progress_table.add_row("─" * 20, "─" * 20, "─" * 20, style="dim") + + layout["progress"].update( + Panel(progress_table, title="Progress", border_style="cyan", padding=(1, 2)) + ) + + # Messages panel showing recent messages and tool calls + messages_table = Table( + show_header=True, + header_style="bold magenta", + show_footer=False, + expand=True, # Make table expand to fill available space + box=box.MINIMAL, # Use minimal box style for a lighter look + show_lines=True, # Keep horizontal lines + padding=(0, 1), # Add some padding between columns + ) + messages_table.add_column("Time", style="cyan", width=8, justify="center") + messages_table.add_column("Type", style="green", width=10, justify="center") + messages_table.add_column( + "Content", style="white", no_wrap=False, ratio=1 + ) # Make content column expand + + # Combine tool calls and messages + all_messages = [] + + # Add tool calls + for timestamp, tool_name, args in message_buffer.tool_calls: + formatted_args = format_tool_args(args) + all_messages.append((timestamp, "Tool", f"{tool_name}: {formatted_args}")) + + # Add regular messages + for timestamp, msg_type, content in message_buffer.messages: + content_str = str(content) if content else "" + if len(content_str) > 200: + content_str = content_str[:197] + "..." + all_messages.append((timestamp, msg_type, content_str)) + + # Sort by timestamp descending (newest first) + all_messages.sort(key=lambda x: x[0], reverse=True) + + # Calculate how many messages we can show based on available space + max_messages = 12 + + # Get the first N messages (newest ones) + recent_messages = all_messages[:max_messages] + + # Add messages to table (already in newest-first order) + for timestamp, msg_type, content in recent_messages: + # Format content with word wrapping + wrapped_content = Text(content, overflow="fold") + messages_table.add_row(timestamp, msg_type, wrapped_content) + + layout["messages"].update( + Panel( + messages_table, + title="Messages & Tools", + border_style="blue", + padding=(1, 2), + ) + ) + + # Analysis panel showing current report + if message_buffer.current_report: + layout["analysis"].update( + Panel( + Markdown(message_buffer.current_report), + title="Current Report", + border_style="green", + padding=(1, 2), + ) + ) + else: + layout["analysis"].update( + Panel( + "[italic]Waiting for analysis report...[/italic]", + title="Current Report", + border_style="green", + padding=(1, 2), + ) + ) + + # Footer with statistics + # Agent progress - derived from agent_status dict + agents_completed = sum( + 1 for status in message_buffer.agent_status.values() if status == "completed" + ) + agents_total = len(message_buffer.agent_status) + + # Report progress - based on agent completion (not just content existence) + reports_completed = message_buffer.get_completed_reports_count() + reports_total = len(message_buffer.report_sections) + + # Build stats parts + stats_parts = [f"Agents: {agents_completed}/{agents_total}"] + + # LLM and tool stats from callback handler + if stats_handler: + stats = stats_handler.get_stats() + stats_parts.append(f"LLM: {stats['llm_calls']}") + stats_parts.append(f"Tools: {stats['tool_calls']}") + + # Token display with graceful fallback + if stats["tokens_in"] > 0 or stats["tokens_out"] > 0: + tokens_str = f"Tokens: {format_tokens(stats['tokens_in'])}\u2191 {format_tokens(stats['tokens_out'])}\u2193" + else: + tokens_str = "Tokens: --" + stats_parts.append(tokens_str) + + stats_parts.append(f"Reports: {reports_completed}/{reports_total}") + + # Elapsed time + if start_time: + elapsed = time.time() - start_time + elapsed_str = f"\u23f1 {int(elapsed // 60):02d}:{int(elapsed % 60):02d}" + stats_parts.append(elapsed_str) + + stats_table = Table(show_header=False, box=None, padding=(0, 2), expand=True) + stats_table.add_column("Stats", justify="center") + stats_table.add_row(" | ".join(stats_parts)) + + layout["footer"].update(Panel(stats_table, border_style="grey50")) + + +def get_user_selections(): + """Get all user selections before starting the analysis display.""" + # Display ASCII art welcome message + with open("./cli/static/welcome.txt", "r") as f: + welcome_ascii = f.read() + + # Create welcome box content + welcome_content = f"{welcome_ascii}\n" + welcome_content += "[bold green]TradingAgents: Multi-Agents LLM Financial Trading Framework - CLI[/bold green]\n\n" + welcome_content += "[bold]Workflow Steps:[/bold]\n" + welcome_content += "I. Analyst Team → II. Research Team → III. Trader → IV. Risk Management → V. Portfolio Management\n\n" + welcome_content += ( + "[dim]Built by [Tauric Research](https://github.com/TauricResearch)[/dim]" + ) + + # Create and center the welcome box + welcome_box = Panel( + welcome_content, + border_style="green", + padding=(1, 2), + title="Welcome to TradingAgents", + subtitle="Multi-Agents LLM Financial Trading Framework", + ) + console.print(Align.center(welcome_box)) + console.print() + console.print() # Add vertical space before announcements + + # Fetch and display announcements (silent on failure) + announcements = fetch_announcements() + display_announcements(console, announcements) + + # Create a boxed questionnaire for each step + def create_question_box(title, prompt, default=None): + box_content = f"[bold]{title}[/bold]\n" + box_content += f"[dim]{prompt}[/dim]" + if default: + box_content += f"\n[dim]Default: {default}[/dim]" + return Panel(box_content, border_style="blue", padding=(1, 2)) + + # Step 1: Ticker symbol + console.print( + create_question_box( + "Step 1: Ticker Symbol", "Enter the ticker symbol to analyze", "SPY" + ) + ) + selected_ticker = get_ticker() + + # Step 2: Analysis date + default_date = datetime.datetime.now().strftime("%Y-%m-%d") + console.print( + create_question_box( + "Step 2: Analysis Date", + "Enter the analysis date (YYYY-MM-DD)", + default_date, + ) + ) + analysis_date = get_analysis_date() + + # Step 3: Select analysts + console.print( + create_question_box( + "Step 3: Analysts Team", "Select your LLM analyst agents for the analysis" + ) + ) + selected_analysts = select_analysts() + console.print( + f"[green]Selected analysts:[/green] {', '.join(analyst.value for analyst in selected_analysts)}" + ) + + # Step 4: Research depth + console.print( + create_question_box( + "Step 4: Research Depth", "Select your research depth level" + ) + ) + selected_research_depth = select_research_depth() + + # Step 5: OpenAI backend + console.print( + create_question_box( + "Step 5: OpenAI backend", "Select which service to talk to" + ) + ) + selected_llm_provider, backend_url = select_llm_provider() + + # Step 6: Thinking agents + console.print( + create_question_box( + "Step 6: Thinking Agents", "Select your thinking agents for analysis" + ) + ) + selected_shallow_thinker = select_shallow_thinking_agent(selected_llm_provider) + selected_deep_thinker = select_deep_thinking_agent(selected_llm_provider) + + # Step 7: Provider-specific thinking configuration + thinking_level = None + reasoning_effort = None + + provider_lower = selected_llm_provider.lower() + if provider_lower == "google": + console.print( + create_question_box( + "Step 7: Thinking Mode", + "Configure Gemini thinking mode" + ) + ) + thinking_level = ask_gemini_thinking_config() + elif provider_lower == "openai": + console.print( + create_question_box( + "Step 7: Reasoning Effort", + "Configure OpenAI reasoning effort level" + ) + ) + reasoning_effort = ask_openai_reasoning_effort() + + return { + "ticker": selected_ticker, + "analysis_date": analysis_date, + "analysts": selected_analysts, + "research_depth": selected_research_depth, + "llm_provider": selected_llm_provider.lower(), + "backend_url": backend_url, + "shallow_thinker": selected_shallow_thinker, + "deep_thinker": selected_deep_thinker, + "google_thinking_level": thinking_level, + "openai_reasoning_effort": reasoning_effort, + } + + +def get_ticker(): + """Get ticker symbol from user input.""" + return typer.prompt("", default="SPY") + + +def get_analysis_date(): + """Get the analysis date from user input.""" + while True: + date_str = typer.prompt( + "", default=datetime.datetime.now().strftime("%Y-%m-%d") + ) + try: + # Validate date format and ensure it's not in the future + analysis_date = datetime.datetime.strptime(date_str, "%Y-%m-%d") + if analysis_date.date() > datetime.datetime.now().date(): + console.print("[red]Error: Analysis date cannot be in the future[/red]") + continue + return date_str + except ValueError: + console.print( + "[red]Error: Invalid date format. Please use YYYY-MM-DD[/red]" + ) + + +def save_report_to_disk(final_state, ticker: str, save_path: Path): + """Save complete analysis report to disk with organized subfolders.""" + save_path.mkdir(parents=True, exist_ok=True) + sections = [] + + # 1. Analysts + analysts_dir = save_path / "1_analysts" + analyst_parts = [] + if final_state.get("market_report"): + analysts_dir.mkdir(exist_ok=True) + (analysts_dir / "market.md").write_text(final_state["market_report"]) + analyst_parts.append(("Market Analyst", final_state["market_report"])) + if final_state.get("sentiment_report"): + analysts_dir.mkdir(exist_ok=True) + (analysts_dir / "sentiment.md").write_text(final_state["sentiment_report"]) + analyst_parts.append(("Social Analyst", final_state["sentiment_report"])) + if final_state.get("news_report"): + analysts_dir.mkdir(exist_ok=True) + (analysts_dir / "news.md").write_text(final_state["news_report"]) + analyst_parts.append(("News Analyst", final_state["news_report"])) + if final_state.get("fundamentals_report"): + analysts_dir.mkdir(exist_ok=True) + (analysts_dir / "fundamentals.md").write_text(final_state["fundamentals_report"]) + analyst_parts.append(("Fundamentals Analyst", final_state["fundamentals_report"])) + if analyst_parts: + content = "\n\n".join(f"### {name}\n{text}" for name, text in analyst_parts) + sections.append(f"## I. Analyst Team Reports\n\n{content}") + + # 2. Research + if final_state.get("investment_debate_state"): + research_dir = save_path / "2_research" + debate = final_state["investment_debate_state"] + research_parts = [] + if debate.get("bull_history"): + research_dir.mkdir(exist_ok=True) + (research_dir / "bull.md").write_text(debate["bull_history"]) + research_parts.append(("Bull Researcher", debate["bull_history"])) + if debate.get("bear_history"): + research_dir.mkdir(exist_ok=True) + (research_dir / "bear.md").write_text(debate["bear_history"]) + research_parts.append(("Bear Researcher", debate["bear_history"])) + if debate.get("judge_decision"): + research_dir.mkdir(exist_ok=True) + (research_dir / "manager.md").write_text(debate["judge_decision"]) + research_parts.append(("Research Manager", debate["judge_decision"])) + if research_parts: + content = "\n\n".join(f"### {name}\n{text}" for name, text in research_parts) + sections.append(f"## II. Research Team Decision\n\n{content}") + + # 3. Trading + if final_state.get("trader_investment_plan"): + trading_dir = save_path / "3_trading" + trading_dir.mkdir(exist_ok=True) + (trading_dir / "trader.md").write_text(final_state["trader_investment_plan"]) + sections.append(f"## III. Trading Team Plan\n\n### Trader\n{final_state['trader_investment_plan']}") + + # 4. Risk Management + if final_state.get("risk_debate_state"): + risk_dir = save_path / "4_risk" + risk = final_state["risk_debate_state"] + risk_parts = [] + if risk.get("aggressive_history"): + risk_dir.mkdir(exist_ok=True) + (risk_dir / "aggressive.md").write_text(risk["aggressive_history"]) + risk_parts.append(("Aggressive Analyst", risk["aggressive_history"])) + if risk.get("conservative_history"): + risk_dir.mkdir(exist_ok=True) + (risk_dir / "conservative.md").write_text(risk["conservative_history"]) + risk_parts.append(("Conservative Analyst", risk["conservative_history"])) + if risk.get("neutral_history"): + risk_dir.mkdir(exist_ok=True) + (risk_dir / "neutral.md").write_text(risk["neutral_history"]) + risk_parts.append(("Neutral Analyst", risk["neutral_history"])) + if risk_parts: + content = "\n\n".join(f"### {name}\n{text}" for name, text in risk_parts) + sections.append(f"## IV. Risk Management Team Decision\n\n{content}") + + # 5. Portfolio Manager + if risk.get("judge_decision"): + portfolio_dir = save_path / "5_portfolio" + portfolio_dir.mkdir(exist_ok=True) + (portfolio_dir / "decision.md").write_text(risk["judge_decision"]) + sections.append(f"## V. Portfolio Manager Decision\n\n### Portfolio Manager\n{risk['judge_decision']}") + + # Write consolidated report + header = f"# Trading Analysis Report: {ticker}\n\nGenerated: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" + (save_path / "complete_report.md").write_text(header + "\n\n".join(sections)) + return save_path / "complete_report.md" + + +def display_complete_report(final_state): + """Display the complete analysis report sequentially (avoids truncation).""" + console.print() + console.print(Rule("Complete Analysis Report", style="bold green")) + + # I. Analyst Team Reports + analysts = [] + if final_state.get("market_report"): + analysts.append(("Market Analyst", final_state["market_report"])) + if final_state.get("sentiment_report"): + analysts.append(("Social Analyst", final_state["sentiment_report"])) + if final_state.get("news_report"): + analysts.append(("News Analyst", final_state["news_report"])) + if final_state.get("fundamentals_report"): + analysts.append(("Fundamentals Analyst", final_state["fundamentals_report"])) + if analysts: + console.print(Panel("[bold]I. Analyst Team Reports[/bold]", border_style="cyan")) + for title, content in analysts: + console.print(Panel(Markdown(content), title=title, border_style="blue", padding=(1, 2))) + + # II. Research Team Reports + if final_state.get("investment_debate_state"): + debate = final_state["investment_debate_state"] + research = [] + if debate.get("bull_history"): + research.append(("Bull Researcher", debate["bull_history"])) + if debate.get("bear_history"): + research.append(("Bear Researcher", debate["bear_history"])) + if debate.get("judge_decision"): + research.append(("Research Manager", debate["judge_decision"])) + if research: + console.print(Panel("[bold]II. Research Team Decision[/bold]", border_style="magenta")) + for title, content in research: + console.print(Panel(Markdown(content), title=title, border_style="blue", padding=(1, 2))) + + # III. Trading Team + if final_state.get("trader_investment_plan"): + console.print(Panel("[bold]III. Trading Team Plan[/bold]", border_style="yellow")) + console.print(Panel(Markdown(final_state["trader_investment_plan"]), title="Trader", border_style="blue", padding=(1, 2))) + + # IV. Risk Management Team + if final_state.get("risk_debate_state"): + risk = final_state["risk_debate_state"] + risk_reports = [] + if risk.get("aggressive_history"): + risk_reports.append(("Aggressive Analyst", risk["aggressive_history"])) + if risk.get("conservative_history"): + risk_reports.append(("Conservative Analyst", risk["conservative_history"])) + if risk.get("neutral_history"): + risk_reports.append(("Neutral Analyst", risk["neutral_history"])) + if risk_reports: + console.print(Panel("[bold]IV. Risk Management Team Decision[/bold]", border_style="red")) + for title, content in risk_reports: + console.print(Panel(Markdown(content), title=title, border_style="blue", padding=(1, 2))) + + # V. Portfolio Manager Decision + if risk.get("judge_decision"): + console.print(Panel("[bold]V. Portfolio Manager Decision[/bold]", border_style="green")) + console.print(Panel(Markdown(risk["judge_decision"]), title="Portfolio Manager", border_style="blue", padding=(1, 2))) + + +def update_research_team_status(status): + """Update status for research team members (not Trader).""" + research_team = ["Bull Researcher", "Bear Researcher", "Research Manager"] + for agent in research_team: + message_buffer.update_agent_status(agent, status) + + +# Ordered list of analysts for status transitions +ANALYST_ORDER = ["market", "social", "news", "fundamentals"] +ANALYST_AGENT_NAMES = { + "market": "Market Analyst", + "social": "Social Analyst", + "news": "News Analyst", + "fundamentals": "Fundamentals Analyst", +} +ANALYST_REPORT_MAP = { + "market": "market_report", + "social": "sentiment_report", + "news": "news_report", + "fundamentals": "fundamentals_report", +} + + +def update_analyst_statuses(message_buffer, chunk): + """Update all analyst statuses based on current report state. + + Logic: + - Analysts with reports = completed + - First analyst without report = in_progress + - Remaining analysts without reports = pending + - When all analysts done, set Bull Researcher to in_progress + """ + selected = message_buffer.selected_analysts + found_active = False + + for analyst_key in ANALYST_ORDER: + if analyst_key not in selected: + continue + + agent_name = ANALYST_AGENT_NAMES[analyst_key] + report_key = ANALYST_REPORT_MAP[analyst_key] + has_report = bool(chunk.get(report_key)) + + if has_report: + message_buffer.update_agent_status(agent_name, "completed") + message_buffer.update_report_section(report_key, chunk[report_key]) + elif not found_active: + message_buffer.update_agent_status(agent_name, "in_progress") + found_active = True + else: + message_buffer.update_agent_status(agent_name, "pending") + + # When all analysts complete, transition research team to in_progress + if not found_active and selected: + if message_buffer.agent_status.get("Bull Researcher") == "pending": + message_buffer.update_agent_status("Bull Researcher", "in_progress") + message_buffer.update_agent_status("Bear Researcher", "in_progress") + +def extract_content_string(content): + """Extract string content from various message formats. + Returns None if no meaningful text content is found. + """ + import ast + + def is_empty(val): + """Check if value is empty using Python's truthiness.""" + if val is None or val == '': + return True + if isinstance(val, str): + s = val.strip() + if not s: + return True + try: + return not bool(ast.literal_eval(s)) + except (ValueError, SyntaxError): + return False # Can't parse = real text + return not bool(val) + + if is_empty(content): + return None + + if isinstance(content, str): + return content.strip() + + if isinstance(content, dict): + text = content.get('text', '') + return text.strip() if not is_empty(text) else None + + if isinstance(content, list): + text_parts = [ + item.get('text', '').strip() if isinstance(item, dict) and item.get('type') == 'text' + else (item.strip() if isinstance(item, str) else '') + for item in content + ] + result = ' '.join(t for t in text_parts if t and not is_empty(t)) + return result if result else None + + return str(content).strip() if not is_empty(content) else None + + +def classify_message_type(message) -> tuple[str, str | None]: + """Classify LangChain message into display type and extract content. + + Returns: + (type, content) - type is one of: User, Agent, Data, Control + - content is extracted string or None + """ + from langchain_core.messages import AIMessage, HumanMessage, ToolMessage + + content = extract_content_string(getattr(message, 'content', None)) + + if isinstance(message, HumanMessage): + if content and content.strip() == "Continue": + return ("Control", content) + return ("User", content) + + if isinstance(message, ToolMessage): + return ("Data", content) + + if isinstance(message, AIMessage): + return ("Agent", content) + + # Fallback for unknown types + return ("System", content) + + +def format_tool_args(args, max_length=80) -> str: + """Format tool arguments for terminal display.""" + result = str(args) + if len(result) > max_length: + return result[:max_length - 3] + "..." + return result + +def run_analysis(): + # First get all user selections + selections = get_user_selections() + + # Create config with selected research depth + config = DEFAULT_CONFIG.copy() + config["max_debate_rounds"] = selections["research_depth"] + config["max_risk_discuss_rounds"] = selections["research_depth"] + config["quick_think_llm"] = selections["shallow_thinker"] + config["deep_think_llm"] = selections["deep_thinker"] + config["backend_url"] = selections["backend_url"] + config["llm_provider"] = selections["llm_provider"].lower() + # Provider-specific thinking configuration + config["google_thinking_level"] = selections.get("google_thinking_level") + config["openai_reasoning_effort"] = selections.get("openai_reasoning_effort") + + # Create stats callback handler for tracking LLM/tool calls + stats_handler = StatsCallbackHandler() + + # Normalize analyst selection to predefined order (selection is a 'set', order is fixed) + selected_set = {analyst.value for analyst in selections["analysts"]} + selected_analyst_keys = [a for a in ANALYST_ORDER if a in selected_set] + + # Initialize the graph with callbacks bound to LLMs + graph = TradingAgentsGraph( + selected_analyst_keys, + config=config, + debug=True, + callbacks=[stats_handler], + ) + + # Initialize message buffer with selected analysts + message_buffer.init_for_analysis(selected_analyst_keys) + + # Track start time for elapsed display + start_time = time.time() + + # Create result directory + results_dir = Path(config["results_dir"]) / selections["ticker"] / selections["analysis_date"] + results_dir.mkdir(parents=True, exist_ok=True) + report_dir = results_dir / "reports" + report_dir.mkdir(parents=True, exist_ok=True) + log_file = results_dir / "message_tool.log" + log_file.touch(exist_ok=True) + + def save_message_decorator(obj, func_name): + func = getattr(obj, func_name) + @wraps(func) + def wrapper(*args, **kwargs): + func(*args, **kwargs) + timestamp, message_type, content = obj.messages[-1] + content = content.replace("\n", " ") # Replace newlines with spaces + with open(log_file, "a") as f: + f.write(f"{timestamp} [{message_type}] {content}\n") + return wrapper + + def save_tool_call_decorator(obj, func_name): + func = getattr(obj, func_name) + @wraps(func) + def wrapper(*args, **kwargs): + func(*args, **kwargs) + timestamp, tool_name, args = obj.tool_calls[-1] + args_str = ", ".join(f"{k}={v}" for k, v in args.items()) + with open(log_file, "a") as f: + f.write(f"{timestamp} [Tool Call] {tool_name}({args_str})\n") + return wrapper + + def save_report_section_decorator(obj, func_name): + func = getattr(obj, func_name) + @wraps(func) + def wrapper(section_name, content): + func(section_name, content) + if section_name in obj.report_sections and obj.report_sections[section_name] is not None: + content = obj.report_sections[section_name] + if content: + file_name = f"{section_name}.md" + with open(report_dir / file_name, "w") as f: + f.write(content) + return wrapper + + message_buffer.add_message = save_message_decorator(message_buffer, "add_message") + message_buffer.add_tool_call = save_tool_call_decorator(message_buffer, "add_tool_call") + message_buffer.update_report_section = save_report_section_decorator(message_buffer, "update_report_section") + + # Now start the display layout + layout = create_layout() + + with Live(layout, refresh_per_second=4) as live: + # Initial display + update_display(layout, stats_handler=stats_handler, start_time=start_time) + + # Add initial messages + message_buffer.add_message("System", f"Selected ticker: {selections['ticker']}") + message_buffer.add_message( + "System", f"Analysis date: {selections['analysis_date']}" + ) + message_buffer.add_message( + "System", + f"Selected analysts: {', '.join(analyst.value for analyst in selections['analysts'])}", + ) + update_display(layout, stats_handler=stats_handler, start_time=start_time) + + # Update agent status to in_progress for the first analyst + first_analyst = f"{selections['analysts'][0].value.capitalize()} Analyst" + message_buffer.update_agent_status(first_analyst, "in_progress") + update_display(layout, stats_handler=stats_handler, start_time=start_time) + + # Create spinner text + spinner_text = ( + f"Analyzing {selections['ticker']} on {selections['analysis_date']}..." + ) + update_display(layout, spinner_text, stats_handler=stats_handler, start_time=start_time) + + # Initialize state and get graph args with callbacks + init_agent_state = graph.propagator.create_initial_state( + selections["ticker"], selections["analysis_date"] + ) + # Pass callbacks to graph config for tool execution tracking + # (LLM tracking is handled separately via LLM constructor) + args = graph.propagator.get_graph_args(callbacks=[stats_handler]) + + # Stream the analysis + trace = [] + for chunk in graph.graph.stream(init_agent_state, **args): + # Process messages if present (skip duplicates via message ID) + if len(chunk["messages"]) > 0: + last_message = chunk["messages"][-1] + msg_id = getattr(last_message, "id", None) + + if msg_id != message_buffer._last_message_id: + message_buffer._last_message_id = msg_id + + # Add message to buffer + msg_type, content = classify_message_type(last_message) + if content and content.strip(): + message_buffer.add_message(msg_type, content) + + # Handle tool calls + if hasattr(last_message, "tool_calls") and last_message.tool_calls: + for tool_call in last_message.tool_calls: + if isinstance(tool_call, dict): + message_buffer.add_tool_call( + tool_call["name"], tool_call["args"] + ) + else: + message_buffer.add_tool_call(tool_call.name, tool_call.args) + + # Update analyst statuses based on report state (runs on every chunk) + update_analyst_statuses(message_buffer, chunk) + + # Research Team - Handle Investment Debate State + if chunk.get("investment_debate_state"): + debate_state = chunk["investment_debate_state"] + bull_hist = debate_state.get("bull_history", "").strip() + bear_hist = debate_state.get("bear_history", "").strip() + judge = debate_state.get("judge_decision", "").strip() + + # Only update status when there's actual content + if bull_hist or bear_hist: + update_research_team_status("in_progress") + if bull_hist: + message_buffer.update_report_section( + "investment_plan", f"### Bull Researcher Analysis\n{bull_hist}" + ) + if bear_hist: + message_buffer.update_report_section( + "investment_plan", f"### Bear Researcher Analysis\n{bear_hist}" + ) + if judge: + message_buffer.update_report_section( + "investment_plan", f"### Research Manager Decision\n{judge}" + ) + update_research_team_status("completed") + message_buffer.update_agent_status("Trader", "in_progress") + + # Trading Team + if chunk.get("trader_investment_plan"): + message_buffer.update_report_section( + "trader_investment_plan", chunk["trader_investment_plan"] + ) + if message_buffer.agent_status.get("Trader") != "completed": + message_buffer.update_agent_status("Trader", "completed") + message_buffer.update_agent_status("Aggressive Analyst", "in_progress") + + # Risk Management Team - Handle Risk Debate State + if chunk.get("risk_debate_state"): + risk_state = chunk["risk_debate_state"] + agg_hist = risk_state.get("aggressive_history", "").strip() + con_hist = risk_state.get("conservative_history", "").strip() + neu_hist = risk_state.get("neutral_history", "").strip() + judge = risk_state.get("judge_decision", "").strip() + + if agg_hist: + if message_buffer.agent_status.get("Aggressive Analyst") != "completed": + message_buffer.update_agent_status("Aggressive Analyst", "in_progress") + message_buffer.update_report_section( + "final_trade_decision", f"### Aggressive Analyst Analysis\n{agg_hist}" + ) + if con_hist: + if message_buffer.agent_status.get("Conservative Analyst") != "completed": + message_buffer.update_agent_status("Conservative Analyst", "in_progress") + message_buffer.update_report_section( + "final_trade_decision", f"### Conservative Analyst Analysis\n{con_hist}" + ) + if neu_hist: + if message_buffer.agent_status.get("Neutral Analyst") != "completed": + message_buffer.update_agent_status("Neutral Analyst", "in_progress") + message_buffer.update_report_section( + "final_trade_decision", f"### Neutral Analyst Analysis\n{neu_hist}" + ) + if judge: + if message_buffer.agent_status.get("Portfolio Manager") != "completed": + message_buffer.update_agent_status("Portfolio Manager", "in_progress") + message_buffer.update_report_section( + "final_trade_decision", f"### Portfolio Manager Decision\n{judge}" + ) + message_buffer.update_agent_status("Aggressive Analyst", "completed") + message_buffer.update_agent_status("Conservative Analyst", "completed") + message_buffer.update_agent_status("Neutral Analyst", "completed") + message_buffer.update_agent_status("Portfolio Manager", "completed") + + # Update the display + update_display(layout, stats_handler=stats_handler, start_time=start_time) + + trace.append(chunk) + + # Get final state and decision + final_state = trace[-1] + decision = graph.process_signal(final_state["final_trade_decision"]) + + # Update all agent statuses to completed + for agent in message_buffer.agent_status: + message_buffer.update_agent_status(agent, "completed") + + message_buffer.add_message( + "System", f"Completed analysis for {selections['analysis_date']}" + ) + + # Update final report sections + for section in message_buffer.report_sections.keys(): + if section in final_state: + message_buffer.update_report_section(section, final_state[section]) + + update_display(layout, stats_handler=stats_handler, start_time=start_time) + + # Post-analysis prompts (outside Live context for clean interaction) + console.print("\n[bold cyan]Analysis Complete![/bold cyan]\n") + + # Prompt to save report + save_choice = typer.prompt("Save report?", default="Y").strip().upper() + if save_choice in ("Y", "YES", ""): + timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + default_path = Path.cwd() / "reports" / f"{selections['ticker']}_{timestamp}" + save_path_str = typer.prompt( + "Save path (press Enter for default)", + default=str(default_path) + ).strip() + save_path = Path(save_path_str) + try: + report_file = save_report_to_disk(final_state, selections["ticker"], save_path) + console.print(f"\n[green]✓ Report saved to:[/green] {save_path.resolve()}") + console.print(f" [dim]Complete report:[/dim] {report_file.name}") + except Exception as e: + console.print(f"[red]Error saving report: {e}[/red]") + + # Prompt to display full report + display_choice = typer.prompt("\nDisplay full report on screen?", default="Y").strip().upper() + if display_choice in ("Y", "YES", ""): + display_complete_report(final_state) + + +@app.command() +def analyze(): + run_analysis() + + +if __name__ == "__main__": + app() diff --git a/cli/models.py b/cli/models.py index f68c3da1..06e67623 100644 --- a/cli/models.py +++ b/cli/models.py @@ -1,10 +1,10 @@ -from enum import Enum -from typing import List, Optional, Dict -from pydantic import BaseModel - - -class AnalystType(str, Enum): - MARKET = "market" - SOCIAL = "social" - NEWS = "news" - FUNDAMENTALS = "fundamentals" +from enum import Enum +from typing import List, Optional, Dict +from pydantic import BaseModel + + +class AnalystType(str, Enum): + MARKET = "market" + SOCIAL = "social" + NEWS = "news" + FUNDAMENTALS = "fundamentals" diff --git a/cli/static/welcome.txt b/cli/static/welcome.txt index f2cf641d..95a28e58 100644 --- a/cli/static/welcome.txt +++ b/cli/static/welcome.txt @@ -1,7 +1,7 @@ - - ______ ___ ___ __ - /_ __/________ _____/ (_)___ ____ _/ | ____ ____ ____ / /______ - / / / ___/ __ `/ __ / / __ \/ __ `/ /| |/ __ `/ _ \/ __ \/ __/ ___/ - / / / / / /_/ / /_/ / / / / / /_/ / ___ / /_/ / __/ / / / /_(__ ) -/_/ /_/ \__,_/\__,_/_/_/ /_/\__, /_/ |_\__, /\___/_/ /_/\__/____/ - /____/ /____/ + + ______ ___ ___ __ + /_ __/________ _____/ (_)___ ____ _/ | ____ ____ ____ / /______ + / / / ___/ __ `/ __ / / __ \/ __ `/ /| |/ __ `/ _ \/ __ \/ __/ ___/ + / / / / / /_/ / /_/ / / / / / /_/ / ___ / /_/ / __/ / / / /_(__ ) +/_/ /_/ \__,_/\__,_/_/_/ /_/\__, /_/ |_\__, /\___/_/ /_/\__/____/ + /____/ /____/ diff --git a/cli/stats_handler.py b/cli/stats_handler.py index 10734cc3..8947834b 100644 --- a/cli/stats_handler.py +++ b/cli/stats_handler.py @@ -1,76 +1,76 @@ -import threading -from typing import Any, Dict, List, Union - -from langchain_core.callbacks import BaseCallbackHandler -from langchain_core.outputs import LLMResult -from langchain_core.messages import AIMessage - - -class StatsCallbackHandler(BaseCallbackHandler): - """Callback handler that tracks LLM calls, tool calls, and token usage.""" - - def __init__(self) -> None: - super().__init__() - self._lock = threading.Lock() - self.llm_calls = 0 - self.tool_calls = 0 - self.tokens_in = 0 - self.tokens_out = 0 - - def on_llm_start( - self, - serialized: Dict[str, Any], - prompts: List[str], - **kwargs: Any, - ) -> None: - """Increment LLM call counter when an LLM starts.""" - with self._lock: - self.llm_calls += 1 - - def on_chat_model_start( - self, - serialized: Dict[str, Any], - messages: List[List[Any]], - **kwargs: Any, - ) -> None: - """Increment LLM call counter when a chat model starts.""" - with self._lock: - self.llm_calls += 1 - - def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: - """Extract token usage from LLM response.""" - try: - generation = response.generations[0][0] - except (IndexError, TypeError): - return - - usage_metadata = None - if hasattr(generation, "message"): - message = generation.message - if isinstance(message, AIMessage) and hasattr(message, "usage_metadata"): - usage_metadata = message.usage_metadata - - if usage_metadata: - with self._lock: - self.tokens_in += usage_metadata.get("input_tokens", 0) - self.tokens_out += usage_metadata.get("output_tokens", 0) - - def on_tool_start( - self, - serialized: Dict[str, Any], - input_str: str, - **kwargs: Any, - ) -> None: - """Increment tool call counter when a tool starts.""" - with self._lock: - self.tool_calls += 1 - - def get_stats(self) -> Dict[str, Any]: - """Return current statistics.""" - with self._lock: - return { - "llm_calls": self.llm_calls, - "tool_calls": self.tool_calls, - "tokens_in": self.tokens_in, - "tokens_out": self.tokens_out, - } +import threading +from typing import Any, Dict, List, Union + +from langchain_core.callbacks import BaseCallbackHandler +from langchain_core.outputs import LLMResult +from langchain_core.messages import AIMessage + + +class StatsCallbackHandler(BaseCallbackHandler): + """Callback handler that tracks LLM calls, tool calls, and token usage.""" + + def __init__(self) -> None: + super().__init__() + self._lock = threading.Lock() + self.llm_calls = 0 + self.tool_calls = 0 + self.tokens_in = 0 + self.tokens_out = 0 + + def on_llm_start( + self, + serialized: Dict[str, Any], + prompts: List[str], + **kwargs: Any, + ) -> None: + """Increment LLM call counter when an LLM starts.""" + with self._lock: + self.llm_calls += 1 + + def on_chat_model_start( + self, + serialized: Dict[str, Any], + messages: List[List[Any]], + **kwargs: Any, + ) -> None: + """Increment LLM call counter when a chat model starts.""" + with self._lock: + self.llm_calls += 1 + + def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: + """Extract token usage from LLM response.""" + try: + generation = response.generations[0][0] + except (IndexError, TypeError): + return + + usage_metadata = None + if hasattr(generation, "message"): + message = generation.message + if isinstance(message, AIMessage) and hasattr(message, "usage_metadata"): + usage_metadata = message.usage_metadata + + if usage_metadata: + with self._lock: + self.tokens_in += usage_metadata.get("input_tokens", 0) + self.tokens_out += usage_metadata.get("output_tokens", 0) + + def on_tool_start( + self, + serialized: Dict[str, Any], + input_str: str, + **kwargs: Any, + ) -> None: + """Increment tool call counter when a tool starts.""" + with self._lock: + self.tool_calls += 1 + + def get_stats(self) -> Dict[str, Any]: + """Return current statistics.""" + with self._lock: + return { + "llm_calls": self.llm_calls, + "tool_calls": self.tool_calls, + "tokens_in": self.tokens_in, + "tokens_out": self.tokens_out, + } diff --git a/cli/utils.py b/cli/utils.py index aa097fb5..646afcda 100644 --- a/cli/utils.py +++ b/cli/utils.py @@ -1,328 +1,328 @@ -import questionary -from typing import List, Optional, Tuple, Dict - -from cli.models import AnalystType - -ANALYST_ORDER = [ - ("Market Analyst", AnalystType.MARKET), - ("Social Media Analyst", AnalystType.SOCIAL), - ("News Analyst", AnalystType.NEWS), - ("Fundamentals Analyst", AnalystType.FUNDAMENTALS), -] - - -def get_ticker() -> str: - """Prompt the user to enter a ticker symbol.""" - ticker = questionary.text( - "Enter the ticker symbol to analyze:", - validate=lambda x: len(x.strip()) > 0 or "Please enter a valid ticker symbol.", - style=questionary.Style( - [ - ("text", "fg:green"), - ("highlighted", "noinherit"), - ] - ), - ).ask() - - if not ticker: - console.print("\n[red]No ticker symbol provided. Exiting...[/red]") - exit(1) - - return ticker.strip().upper() - - -def get_analysis_date() -> str: - """Prompt the user to enter a date in YYYY-MM-DD format.""" - import re - from datetime import datetime - - def validate_date(date_str: str) -> bool: - if not re.match(r"^\d{4}-\d{2}-\d{2}$", date_str): - return False - try: - datetime.strptime(date_str, "%Y-%m-%d") - return True - except ValueError: - return False - - date = questionary.text( - "Enter the analysis date (YYYY-MM-DD):", - validate=lambda x: validate_date(x.strip()) - or "Please enter a valid date in YYYY-MM-DD format.", - style=questionary.Style( - [ - ("text", "fg:green"), - ("highlighted", "noinherit"), - ] - ), - ).ask() - - if not date: - console.print("\n[red]No date provided. Exiting...[/red]") - exit(1) - - return date.strip() - - -def select_analysts() -> List[AnalystType]: - """Select analysts using an interactive checkbox.""" - choices = questionary.checkbox( - "Select Your [Analysts Team]:", - choices=[ - questionary.Choice(display, value=value) for display, value in ANALYST_ORDER - ], - instruction="\n- Press Space to select/unselect analysts\n- Press 'a' to select/unselect all\n- Press Enter when done", - validate=lambda x: len(x) > 0 or "You must select at least one analyst.", - style=questionary.Style( - [ - ("checkbox-selected", "fg:green"), - ("selected", "fg:green noinherit"), - ("highlighted", "noinherit"), - ("pointer", "noinherit"), - ] - ), - ).ask() - - if not choices: - console.print("\n[red]No analysts selected. Exiting...[/red]") - exit(1) - - return choices - - -def select_research_depth() -> int: - """Select research depth using an interactive selection.""" - - # Define research depth options with their corresponding values - DEPTH_OPTIONS = [ - ("Shallow - Quick research, few debate and strategy discussion rounds", 1), - ("Medium - Middle ground, moderate debate rounds and strategy discussion", 3), - ("Deep - Comprehensive research, in depth debate and strategy discussion", 5), - ] - - choice = questionary.select( - "Select Your [Research Depth]:", - choices=[ - questionary.Choice(display, value=value) for display, value in DEPTH_OPTIONS - ], - instruction="\n- Use arrow keys to navigate\n- Press Enter to select", - style=questionary.Style( - [ - ("selected", "fg:yellow noinherit"), - ("highlighted", "fg:yellow noinherit"), - ("pointer", "fg:yellow noinherit"), - ] - ), - ).ask() - - if choice is None: - console.print("\n[red]No research depth selected. Exiting...[/red]") - exit(1) - - return choice - - -def select_shallow_thinking_agent(provider) -> str: - """Select shallow thinking llm engine using an interactive selection.""" - - # Define shallow thinking llm engine options with their corresponding model names - SHALLOW_AGENT_OPTIONS = { - "openai": [ - ("GPT-5 Mini - Cost-optimized reasoning", "gpt-5-mini"), - ("GPT-5 Nano - Ultra-fast, high-throughput", "gpt-5-nano"), - ("GPT-5.2 - Latest flagship", "gpt-5.2"), - ("GPT-5.1 - Flexible reasoning", "gpt-5.1"), - ("GPT-4.1 - Smartest non-reasoning, 1M context", "gpt-4.1"), - ], - "anthropic": [ - ("Claude Haiku 4.5 - Fast + extended thinking", "claude-haiku-4-5"), - ("Claude Sonnet 4.5 - Best for agents/coding", "claude-sonnet-4-5"), - ("Claude Sonnet 4 - High-performance", "claude-sonnet-4-20250514"), - ], - "google": [ - ("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"), - ("Gemini 2.5 Flash - Balanced, recommended", "gemini-2.5-flash"), - ("Gemini 3 Pro - Reasoning-first", "gemini-3-pro-preview"), - ("Gemini 2.5 Flash Lite - Fast, low-cost", "gemini-2.5-flash-lite"), - ], - "xai": [ - ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"), - ("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"), - ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"), - ("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"), - ], - "openrouter": [ - ("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"), - ("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"), - ], - "ollama": [ - ("Qwen3:latest (8B, local)", "qwen3:latest"), - ("GPT-OSS:latest (20B, local)", "gpt-oss:latest"), - ("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"), - ], - } - - choice = questionary.select( - "Select Your [Quick-Thinking LLM Engine]:", - choices=[ - questionary.Choice(display, value=value) - for display, value in SHALLOW_AGENT_OPTIONS[provider.lower()] - ], - instruction="\n- Use arrow keys to navigate\n- Press Enter to select", - style=questionary.Style( - [ - ("selected", "fg:magenta noinherit"), - ("highlighted", "fg:magenta noinherit"), - ("pointer", "fg:magenta noinherit"), - ] - ), - ).ask() - - if choice is None: - console.print( - "\n[red]No shallow thinking llm engine selected. Exiting...[/red]" - ) - exit(1) - - return choice - - -def select_deep_thinking_agent(provider) -> str: - """Select deep thinking llm engine using an interactive selection.""" - - # Define deep thinking llm engine options with their corresponding model names - DEEP_AGENT_OPTIONS = { - "openai": [ - ("GPT-5.2 - Latest flagship", "gpt-5.2"), - ("GPT-5.1 - Flexible reasoning", "gpt-5.1"), - ("GPT-5 - Advanced reasoning", "gpt-5"), - ("GPT-4.1 - Smartest non-reasoning, 1M context", "gpt-4.1"), - ("GPT-5 Mini - Cost-optimized reasoning", "gpt-5-mini"), - ("GPT-5 Nano - Ultra-fast, high-throughput", "gpt-5-nano"), - ], - "anthropic": [ - ("Claude Sonnet 4.5 - Best for agents/coding", "claude-sonnet-4-5"), - ("Claude Opus 4.5 - Premium, max intelligence", "claude-opus-4-5"), - ("Claude Opus 4.1 - Most capable model", "claude-opus-4-1-20250805"), - ("Claude Haiku 4.5 - Fast + extended thinking", "claude-haiku-4-5"), - ("Claude Sonnet 4 - High-performance", "claude-sonnet-4-20250514"), - ], - "google": [ - ("Gemini 3 Pro - Reasoning-first", "gemini-3-pro-preview"), - ("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"), - ("Gemini 2.5 Flash - Balanced, recommended", "gemini-2.5-flash"), - ], - "xai": [ - ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"), - ("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"), - ("Grok 4 - Flagship model", "grok-4-0709"), - ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"), - ("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"), - ], - "openrouter": [ - ("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"), - ("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"), - ], - "ollama": [ - ("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"), - ("GPT-OSS:latest (20B, local)", "gpt-oss:latest"), - ("Qwen3:latest (8B, local)", "qwen3:latest"), - ], - } - - choice = questionary.select( - "Select Your [Deep-Thinking LLM Engine]:", - choices=[ - questionary.Choice(display, value=value) - for display, value in DEEP_AGENT_OPTIONS[provider.lower()] - ], - instruction="\n- Use arrow keys to navigate\n- Press Enter to select", - style=questionary.Style( - [ - ("selected", "fg:magenta noinherit"), - ("highlighted", "fg:magenta noinherit"), - ("pointer", "fg:magenta noinherit"), - ] - ), - ).ask() - - if choice is None: - console.print("\n[red]No deep thinking llm engine selected. Exiting...[/red]") - exit(1) - - return choice - -def select_llm_provider() -> tuple[str, str]: - """Select the OpenAI api url using interactive selection.""" - # Define OpenAI api options with their corresponding endpoints - BASE_URLS = [ - ("OpenAI", "https://api.openai.com/v1"), - ("Google", "https://generativelanguage.googleapis.com/v1"), - ("Anthropic", "https://api.anthropic.com/"), - ("xAI", "https://api.x.ai/v1"), - ("Openrouter", "https://openrouter.ai/api/v1"), - ("Ollama", "http://localhost:11434/v1"), - ] - - choice = questionary.select( - "Select your LLM Provider:", - choices=[ - questionary.Choice(display, value=(display, value)) - for display, value in BASE_URLS - ], - instruction="\n- Use arrow keys to navigate\n- Press Enter to select", - style=questionary.Style( - [ - ("selected", "fg:magenta noinherit"), - ("highlighted", "fg:magenta noinherit"), - ("pointer", "fg:magenta noinherit"), - ] - ), - ).ask() - - if choice is None: - console.print("\n[red]no OpenAI backend selected. Exiting...[/red]") - exit(1) - - display_name, url = choice - print(f"You selected: {display_name}\tURL: {url}") - - return display_name, url - - -def ask_openai_reasoning_effort() -> str: - """Ask for OpenAI reasoning effort level.""" - choices = [ - questionary.Choice("Medium (Default)", "medium"), - questionary.Choice("High (More thorough)", "high"), - questionary.Choice("Low (Faster)", "low"), - ] - return questionary.select( - "Select Reasoning Effort:", - choices=choices, - style=questionary.Style([ - ("selected", "fg:cyan noinherit"), - ("highlighted", "fg:cyan noinherit"), - ("pointer", "fg:cyan noinherit"), - ]), - ).ask() - - -def ask_gemini_thinking_config() -> str | None: - """Ask for Gemini thinking configuration. - - Returns thinking_level: "high" or "minimal". - Client maps to appropriate API param based on model series. - """ - return questionary.select( - "Select Thinking Mode:", - choices=[ - questionary.Choice("Enable Thinking (recommended)", "high"), - questionary.Choice("Minimal/Disable Thinking", "minimal"), - ], - style=questionary.Style([ - ("selected", "fg:green noinherit"), - ("highlighted", "fg:green noinherit"), - ("pointer", "fg:green noinherit"), - ]), - ).ask() +import questionary +from typing import List, Optional, Tuple, Dict + +from cli.models import AnalystType + +ANALYST_ORDER = [ + ("Market Analyst", AnalystType.MARKET), + ("Social Media Analyst", AnalystType.SOCIAL), + ("News Analyst", AnalystType.NEWS), + ("Fundamentals Analyst", AnalystType.FUNDAMENTALS), +] + + +def get_ticker() -> str: + """Prompt the user to enter a ticker symbol.""" + ticker = questionary.text( + "Enter the ticker symbol to analyze:", + validate=lambda x: len(x.strip()) > 0 or "Please enter a valid ticker symbol.", + style=questionary.Style( + [ + ("text", "fg:green"), + ("highlighted", "noinherit"), + ] + ), + ).ask() + + if not ticker: + console.print("\n[red]No ticker symbol provided. Exiting...[/red]") + exit(1) + + return ticker.strip().upper() + + +def get_analysis_date() -> str: + """Prompt the user to enter a date in YYYY-MM-DD format.""" + import re + from datetime import datetime + + def validate_date(date_str: str) -> bool: + if not re.match(r"^\d{4}-\d{2}-\d{2}$", date_str): + return False + try: + datetime.strptime(date_str, "%Y-%m-%d") + return True + except ValueError: + return False + + date = questionary.text( + "Enter the analysis date (YYYY-MM-DD):", + validate=lambda x: validate_date(x.strip()) + or "Please enter a valid date in YYYY-MM-DD format.", + style=questionary.Style( + [ + ("text", "fg:green"), + ("highlighted", "noinherit"), + ] + ), + ).ask() + + if not date: + console.print("\n[red]No date provided. Exiting...[/red]") + exit(1) + + return date.strip() + + +def select_analysts() -> List[AnalystType]: + """Select analysts using an interactive checkbox.""" + choices = questionary.checkbox( + "Select Your [Analysts Team]:", + choices=[ + questionary.Choice(display, value=value) for display, value in ANALYST_ORDER + ], + instruction="\n- Press Space to select/unselect analysts\n- Press 'a' to select/unselect all\n- Press Enter when done", + validate=lambda x: len(x) > 0 or "You must select at least one analyst.", + style=questionary.Style( + [ + ("checkbox-selected", "fg:green"), + ("selected", "fg:green noinherit"), + ("highlighted", "noinherit"), + ("pointer", "noinherit"), + ] + ), + ).ask() + + if not choices: + console.print("\n[red]No analysts selected. Exiting...[/red]") + exit(1) + + return choices + + +def select_research_depth() -> int: + """Select research depth using an interactive selection.""" + + # Define research depth options with their corresponding values + DEPTH_OPTIONS = [ + ("Shallow - Quick research, few debate and strategy discussion rounds", 1), + ("Medium - Middle ground, moderate debate rounds and strategy discussion", 3), + ("Deep - Comprehensive research, in depth debate and strategy discussion", 5), + ] + + choice = questionary.select( + "Select Your [Research Depth]:", + choices=[ + questionary.Choice(display, value=value) for display, value in DEPTH_OPTIONS + ], + instruction="\n- Use arrow keys to navigate\n- Press Enter to select", + style=questionary.Style( + [ + ("selected", "fg:yellow noinherit"), + ("highlighted", "fg:yellow noinherit"), + ("pointer", "fg:yellow noinherit"), + ] + ), + ).ask() + + if choice is None: + console.print("\n[red]No research depth selected. Exiting...[/red]") + exit(1) + + return choice + + +def select_shallow_thinking_agent(provider) -> str: + """Select shallow thinking llm engine using an interactive selection.""" + + # Define shallow thinking llm engine options with their corresponding model names + SHALLOW_AGENT_OPTIONS = { + "openai": [ + ("GPT-5 Mini - Cost-optimized reasoning", "gpt-5-mini"), + ("GPT-5 Nano - Ultra-fast, high-throughput", "gpt-5-nano"), + ("GPT-5.2 - Latest flagship", "gpt-5.2"), + ("GPT-5.1 - Flexible reasoning", "gpt-5.1"), + ("GPT-4.1 - Smartest non-reasoning, 1M context", "gpt-4.1"), + ], + "anthropic": [ + ("Claude Haiku 4.5 - Fast + extended thinking", "claude-haiku-4-5"), + ("Claude Sonnet 4.5 - Best for agents/coding", "claude-sonnet-4-5"), + ("Claude Sonnet 4 - High-performance", "claude-sonnet-4-20250514"), + ], + "google": [ + ("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"), + ("Gemini 2.5 Flash - Balanced, recommended", "gemini-2.5-flash"), + ("Gemini 3 Pro - Reasoning-first", "gemini-3-pro-preview"), + ("Gemini 2.5 Flash Lite - Fast, low-cost", "gemini-2.5-flash-lite"), + ], + "xai": [ + ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"), + ("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"), + ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"), + ("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"), + ], + "openrouter": [ + ("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"), + ("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"), + ], + "ollama": [ + ("Qwen3:latest (8B, local)", "qwen3:latest"), + ("GPT-OSS:latest (20B, local)", "gpt-oss:latest"), + ("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"), + ], + } + + choice = questionary.select( + "Select Your [Quick-Thinking LLM Engine]:", + choices=[ + questionary.Choice(display, value=value) + for display, value in SHALLOW_AGENT_OPTIONS[provider.lower()] + ], + instruction="\n- Use arrow keys to navigate\n- Press Enter to select", + style=questionary.Style( + [ + ("selected", "fg:magenta noinherit"), + ("highlighted", "fg:magenta noinherit"), + ("pointer", "fg:magenta noinherit"), + ] + ), + ).ask() + + if choice is None: + console.print( + "\n[red]No shallow thinking llm engine selected. Exiting...[/red]" + ) + exit(1) + + return choice + + +def select_deep_thinking_agent(provider) -> str: + """Select deep thinking llm engine using an interactive selection.""" + + # Define deep thinking llm engine options with their corresponding model names + DEEP_AGENT_OPTIONS = { + "openai": [ + ("GPT-5.2 - Latest flagship", "gpt-5.2"), + ("GPT-5.1 - Flexible reasoning", "gpt-5.1"), + ("GPT-5 - Advanced reasoning", "gpt-5"), + ("GPT-4.1 - Smartest non-reasoning, 1M context", "gpt-4.1"), + ("GPT-5 Mini - Cost-optimized reasoning", "gpt-5-mini"), + ("GPT-5 Nano - Ultra-fast, high-throughput", "gpt-5-nano"), + ], + "anthropic": [ + ("Claude Sonnet 4.5 - Best for agents/coding", "claude-sonnet-4-5"), + ("Claude Opus 4.5 - Premium, max intelligence", "claude-opus-4-5"), + ("Claude Opus 4.1 - Most capable model", "claude-opus-4-1-20250805"), + ("Claude Haiku 4.5 - Fast + extended thinking", "claude-haiku-4-5"), + ("Claude Sonnet 4 - High-performance", "claude-sonnet-4-20250514"), + ], + "google": [ + ("Gemini 3 Pro - Reasoning-first", "gemini-3-pro-preview"), + ("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"), + ("Gemini 2.5 Flash - Balanced, recommended", "gemini-2.5-flash"), + ], + "xai": [ + ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"), + ("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"), + ("Grok 4 - Flagship model", "grok-4-0709"), + ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"), + ("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"), + ], + "openrouter": [ + ("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"), + ("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"), + ], + "ollama": [ + ("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"), + ("GPT-OSS:latest (20B, local)", "gpt-oss:latest"), + ("Qwen3:latest (8B, local)", "qwen3:latest"), + ], + } + + choice = questionary.select( + "Select Your [Deep-Thinking LLM Engine]:", + choices=[ + questionary.Choice(display, value=value) + for display, value in DEEP_AGENT_OPTIONS[provider.lower()] + ], + instruction="\n- Use arrow keys to navigate\n- Press Enter to select", + style=questionary.Style( + [ + ("selected", "fg:magenta noinherit"), + ("highlighted", "fg:magenta noinherit"), + ("pointer", "fg:magenta noinherit"), + ] + ), + ).ask() + + if choice is None: + console.print("\n[red]No deep thinking llm engine selected. Exiting...[/red]") + exit(1) + + return choice + +def select_llm_provider() -> tuple[str, str]: + """Select the OpenAI api url using interactive selection.""" + # Define OpenAI api options with their corresponding endpoints + BASE_URLS = [ + ("OpenAI", "https://api.openai.com/v1"), + ("Google", "https://generativelanguage.googleapis.com/v1"), + ("Anthropic", "https://api.anthropic.com/"), + ("xAI", "https://api.x.ai/v1"), + ("Openrouter", "https://openrouter.ai/api/v1"), + ("Ollama", "http://localhost:11434/v1"), + ] + + choice = questionary.select( + "Select your LLM Provider:", + choices=[ + questionary.Choice(display, value=(display, value)) + for display, value in BASE_URLS + ], + instruction="\n- Use arrow keys to navigate\n- Press Enter to select", + style=questionary.Style( + [ + ("selected", "fg:magenta noinherit"), + ("highlighted", "fg:magenta noinherit"), + ("pointer", "fg:magenta noinherit"), + ] + ), + ).ask() + + if choice is None: + console.print("\n[red]no OpenAI backend selected. Exiting...[/red]") + exit(1) + + display_name, url = choice + print(f"You selected: {display_name}\tURL: {url}") + + return display_name, url + + +def ask_openai_reasoning_effort() -> str: + """Ask for OpenAI reasoning effort level.""" + choices = [ + questionary.Choice("Medium (Default)", "medium"), + questionary.Choice("High (More thorough)", "high"), + questionary.Choice("Low (Faster)", "low"), + ] + return questionary.select( + "Select Reasoning Effort:", + choices=choices, + style=questionary.Style([ + ("selected", "fg:cyan noinherit"), + ("highlighted", "fg:cyan noinherit"), + ("pointer", "fg:cyan noinherit"), + ]), + ).ask() + + +def ask_gemini_thinking_config() -> str | None: + """Ask for Gemini thinking configuration. + + Returns thinking_level: "high" or "minimal". + Client maps to appropriate API param based on model series. + """ + return questionary.select( + "Select Thinking Mode:", + choices=[ + questionary.Choice("Enable Thinking (recommended)", "high"), + questionary.Choice("Minimal/Disable Thinking", "minimal"), + ], + style=questionary.Style([ + ("selected", "fg:green noinherit"), + ("highlighted", "fg:green noinherit"), + ("pointer", "fg:green noinherit"), + ]), + ).ask() diff --git a/docs/plans/2026-02-20-chainlit-web-ui-design.md b/docs/plans/2026-02-20-chainlit-web-ui-design.md index 197a205e..8a1d5592 100644 --- a/docs/plans/2026-02-20-chainlit-web-ui-design.md +++ b/docs/plans/2026-02-20-chainlit-web-ui-design.md @@ -1,67 +1,67 @@ -# TradingAgents Chainlit Web UI — Design - -## Summary - -Add a Chainlit web UI to TradingAgents so it can be deployed on Railway as a web service. Users interact via chat messages (e.g., "Analyze NVDA") and see live agent progress streamed into the browser. - -## Architecture - -Thin Chainlit wrapper around the existing `TradingAgentsGraph` programmatic API. ~150 lines of new code in a single `app.py`. - -## Components - -### `app.py` (Chainlit entry point) - -- `@cl.on_chat_start` — Welcome message explaining usage (e.g., "Type a ticker like `NVDA` or `Analyze AAPL 2024-12-01`") -- `@cl.on_message` — Parse ticker + optional date from user message, create `TradingAgentsGraph` with Anthropic config, run `propagate()` in debug mode, stream Chainlit `Step` messages for each agent phase, send final decision as formatted message - -### `Dockerfile` - -- Python 3.13-slim base -- Install requirements.txt -- Expose `$PORT` -- `CMD: chainlit run app.py --host 0.0.0.0 --port $PORT` - -### `railway.toml` - -- Build from Dockerfile -- Health check on `/` - -### Railway Environment Variables - -- `ANTHROPIC_API_KEY` — required, for Claude models -- `PORT` — auto-set by Railway - -## LLM Configuration - -- Provider: `anthropic` -- Quick-think model: `claude-haiku-4-5-20251001` -- Deep-think model: `claude-sonnet-4-5-20241022` -- Data vendor: `yfinance` (no extra API keys needed) - -## Data Flow - -``` -User message: "Analyze NVDA" - -> Parse: ticker=NVDA, date=today - -> TradingAgentsGraph(config={anthropic, haiku/sonnet}) - -> graph.propagate("NVDA", "2026-02-20") - -> Debug stream chunks - -> Each chunk -> Chainlit Step (Analyst, Research, Trading, Risk, Portfolio) - -> Final decision -> formatted Chainlit message with markdown -``` - -## Message Parsing - -Simple regex/string parsing: -- `"NVDA"` -> ticker=NVDA, date=today -- `"Analyze AAPL 2024-12-01"` -> ticker=AAPL, date=2024-12-01 -- `"What's the outlook for TSLA?"` -> ticker=TSLA, date=today -- Extract uppercase 1-5 letter words as potential tickers - -## Deployment - -1. Push changes to `github.com/dtarkent2-sys/TradingAgents` main branch -2. Create Railway service from GitHub repo -3. Set `ANTHROPIC_API_KEY` env var -4. Railway auto-deploys, Chainlit serves on assigned PORT +# TradingAgents Chainlit Web UI — Design + +## Summary + +Add a Chainlit web UI to TradingAgents so it can be deployed on Railway as a web service. Users interact via chat messages (e.g., "Analyze NVDA") and see live agent progress streamed into the browser. + +## Architecture + +Thin Chainlit wrapper around the existing `TradingAgentsGraph` programmatic API. ~150 lines of new code in a single `app.py`. + +## Components + +### `app.py` (Chainlit entry point) + +- `@cl.on_chat_start` — Welcome message explaining usage (e.g., "Type a ticker like `NVDA` or `Analyze AAPL 2024-12-01`") +- `@cl.on_message` — Parse ticker + optional date from user message, create `TradingAgentsGraph` with Anthropic config, run `propagate()` in debug mode, stream Chainlit `Step` messages for each agent phase, send final decision as formatted message + +### `Dockerfile` + +- Python 3.13-slim base +- Install requirements.txt +- Expose `$PORT` +- `CMD: chainlit run app.py --host 0.0.0.0 --port $PORT` + +### `railway.toml` + +- Build from Dockerfile +- Health check on `/` + +### Railway Environment Variables + +- `ANTHROPIC_API_KEY` — required, for Claude models +- `PORT` — auto-set by Railway + +## LLM Configuration + +- Provider: `anthropic` +- Quick-think model: `claude-haiku-4-5-20251001` +- Deep-think model: `claude-sonnet-4-5-20241022` +- Data vendor: `yfinance` (no extra API keys needed) + +## Data Flow + +``` +User message: "Analyze NVDA" + -> Parse: ticker=NVDA, date=today + -> TradingAgentsGraph(config={anthropic, haiku/sonnet}) + -> graph.propagate("NVDA", "2026-02-20") + -> Debug stream chunks + -> Each chunk -> Chainlit Step (Analyst, Research, Trading, Risk, Portfolio) + -> Final decision -> formatted Chainlit message with markdown +``` + +## Message Parsing + +Simple regex/string parsing: +- `"NVDA"` -> ticker=NVDA, date=today +- `"Analyze AAPL 2024-12-01"` -> ticker=AAPL, date=2024-12-01 +- `"What's the outlook for TSLA?"` -> ticker=TSLA, date=today +- Extract uppercase 1-5 letter words as potential tickers + +## Deployment + +1. Push changes to `github.com/dtarkent2-sys/TradingAgents` main branch +2. Create Railway service from GitHub repo +3. Set `ANTHROPIC_API_KEY` env var +4. Railway auto-deploys, Chainlit serves on assigned PORT diff --git a/main.py b/main.py index cd00d6f2..1ee05c40 100644 --- a/main.py +++ b/main.py @@ -1,33 +1,33 @@ -from tradingagents.graph.trading_graph import TradingAgentsGraph -from tradingagents.default_config import DEFAULT_CONFIG - -import os - -from dotenv import load_dotenv - -# Load environment variables from .env file -load_dotenv() - -# Create a custom config -config = DEFAULT_CONFIG.copy() -config["deep_think_llm"] = os.environ.get("DEEP_THINK_LLM", "gpt-5-mini") -config["quick_think_llm"] = os.environ.get("QUICK_THINK_LLM", "gpt-5-mini") -config["max_debate_rounds"] = 1 # Increase debate rounds - -# Configure data vendors (default uses yfinance, no extra API keys needed) -config["data_vendors"] = { - "core_stock_apis": "yfinance", # Options: alpha_vantage, yfinance - "technical_indicators": "yfinance", # Options: alpha_vantage, yfinance - "fundamental_data": "yfinance", # Options: alpha_vantage, yfinance - "news_data": "yfinance", # Options: alpha_vantage, yfinance -} - -# Initialize with custom config -ta = TradingAgentsGraph(debug=True, config=config) - -# forward propagate -_, decision = ta.propagate("NVDA", "2024-05-10") -print(decision) - -# Memorize mistakes and reflect -# ta.reflect_and_remember(1000) # parameter is the position returns +from tradingagents.graph.trading_graph import TradingAgentsGraph +from tradingagents.default_config import DEFAULT_CONFIG + +import os + +from dotenv import load_dotenv + +# Load environment variables from .env file +load_dotenv() + +# Create a custom config +config = DEFAULT_CONFIG.copy() +config["deep_think_llm"] = os.environ.get("DEEP_THINK_LLM", "gpt-5-mini") +config["quick_think_llm"] = os.environ.get("QUICK_THINK_LLM", "gpt-5-mini") +config["max_debate_rounds"] = 1 # Increase debate rounds + +# Configure data vendors (default uses yfinance, no extra API keys needed) +config["data_vendors"] = { + "core_stock_apis": "yfinance", # Options: alpha_vantage, yfinance + "technical_indicators": "yfinance", # Options: alpha_vantage, yfinance + "fundamental_data": "yfinance", # Options: alpha_vantage, yfinance + "news_data": "yfinance", # Options: alpha_vantage, yfinance +} + +# Initialize with custom config +ta = TradingAgentsGraph(debug=True, config=config) + +# forward propagate +_, decision = ta.propagate("NVDA", "2024-05-10") +print(decision) + +# Memorize mistakes and reflect +# ta.reflect_and_remember(1000) # parameter is the position returns diff --git a/nvda_output.txt b/nvda_output.txt index 0f9fba77..144128ae 100644 --- a/nvda_output.txt +++ b/nvda_output.txt @@ -1,1702 +1,1702 @@ -================================ Human Message ================================= - -NVDA -================================== Ai Message ================================== - -[{'text': "I'll analyze NVDA for you. Let me start by retrieving the stock price data and then analyzing it with relevant technical indicators.", 'type': 'text'}, {'id': 'toolu_01KgtGQW9ed4w2kGLiV5iqu5', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'start_date': '2025-08-20', 'end_date': '2026-02-20'}, 'name': 'get_stock_data', 'type': 'tool_use'}] -Tool Calls: - get_stock_data (toolu_01KgtGQW9ed4w2kGLiV5iqu5) - Call ID: toolu_01KgtGQW9ed4w2kGLiV5iqu5 - Args: - symbol: NVDA - start_date: 2025-08-20 - end_date: 2026-02-20 -================================= Tool Message ================================= -Name: get_stock_data - -# Stock data for NVDA from 2025-08-20 to 2026-02-20 -# Total records: 126 -# Data retrieved on: 2026-02-20 01:34:20 - -Date,Open,High,Low,Close,Volume,Dividends,Stock Splits -2025-08-20,175.15,175.98,168.78,175.38,215142700,0.0,0.0 -2025-08-21,174.83,176.88,173.79,174.96,140040900,0.0,0.0 -2025-08-22,172.59,178.57,171.18,177.97,172789400,0.0,0.0 -2025-08-25,178.33,181.89,176.55,179.79,163012800,0.0,0.0 -2025-08-26,180.04,182.37,178.79,181.75,168688200,0.0,0.0 -2025-08-27,181.96,182.47,179.08,181.58,235518900,0.0,0.0 -2025-08-28,180.8,184.45,176.39,180.15,281787800,0.0,0.0 -2025-08-29,178.09,178.13,173.13,174.16,243257900,0.0,0.0 -2025-09-02,169.98,172.36,167.2,170.76,231164900,0.0,0.0 -2025-09-03,171.04,172.39,168.86,170.6,164424900,0.0,0.0 -2025-09-04,170.55,171.84,169.39,171.64,141670100,0.0,0.0 -2025-09-05,168.01,169.01,164.05,167.0,224441400,0.0,0.0 -2025-09-08,167.53,170.94,167.33,168.29,163769100,0.0,0.0 -2025-09-09,169.07,170.96,166.72,170.74,157548400,0.0,0.0 -2025-09-10,176.62,179.27,175.45,177.31,226852000,0.0,0.0 -2025-09-11,179.67,180.27,176.47,177.16,151159300,0.01,0.0 -2025-09-12,177.76,178.59,176.44,177.81,124911000,0.0,0.0 -2025-09-15,175.66,178.84,174.5,177.74,147061600,0.0,0.0 -2025-09-16,176.99,177.49,174.37,174.87,140737800,0.0,0.0 -2025-09-17,172.63,173.19,168.4,170.28,211843800,0.0,0.0 -2025-09-18,173.97,177.09,172.95,176.23,191763300,0.0,0.0 -2025-09-19,175.76,178.07,175.17,176.66,237182100,0.0,0.0 -2025-09-22,175.29,184.54,174.7,183.6,269637000,0.0,0.0 -2025-09-23,181.96,182.41,176.2,178.42,192559600,0.0,0.0 -2025-09-24,179.76,179.77,175.39,176.96,143564100,0.0,0.0 -2025-09-25,174.47,180.25,173.12,177.68,191586700,0.0,0.0 -2025-09-26,178.16,179.76,174.92,178.18,148573700,0.0,0.0 -2025-09-29,180.42,183.99,180.31,181.84,193063500,0.0,0.0 -2025-09-30,182.07,187.34,181.47,186.57,236981000,0.0,0.0 -2025-10-01,185.23,188.13,183.89,187.23,173844900,0.0,0.0 -2025-10-02,189.59,191.04,188.05,188.88,136805800,0.0,0.0 -2025-10-03,189.18,190.35,185.37,187.61,137596900,0.0,0.0 -2025-10-06,185.49,187.22,183.32,185.53,157678100,0.0,0.0 -2025-10-07,186.22,189.05,183.99,185.03,140088000,0.0,0.0 -2025-10-08,186.56,189.59,186.53,189.1,130168900,0.0,0.0 -2025-10-09,192.22,195.29,191.05,192.56,182997200,0.0,0.0 -2025-10-10,193.5,195.61,182.04,183.15,268774400,0.0,0.0 -2025-10-13,187.96,190.1,185.95,188.31,153482800,0.0,0.0 -2025-10-14,184.76,184.79,179.69,180.02,205641400,0.0,0.0 -2025-10-15,184.79,184.86,177.28,179.82,214450500,0.0,0.0 -2025-10-16,182.22,183.27,179.76,181.8,179723300,0.0,0.0 -2025-10-17,180.17,184.09,179.74,183.21,173135200,0.0,0.0 -2025-10-20,183.12,185.19,181.72,182.63,128544700,0.0,0.0 -2025-10-21,182.78,182.78,179.79,181.15,124240200,0.0,0.0 -2025-10-22,181.13,183.43,176.75,180.27,162249600,0.0,0.0 -2025-10-23,180.41,183.02,179.78,182.15,111363700,0.0,0.0 -2025-10-24,183.83,187.46,183.49,186.25,131296700,0.0,0.0 -2025-10-27,189.98,191.99,188.42,191.48,153452700,0.0,0.0 -2025-10-28,193.04,203.14,191.9,201.02,297986200,0.0,0.0 -2025-10-29,207.97,212.18,204.77,207.03,308829600,0.0,0.0 -2025-10-30,205.14,206.15,201.4,202.88,178864400,0.0,0.0 -2025-10-31,206.44,207.96,202.06,202.48,179802200,0.0,0.0 -2025-11-03,208.07,211.33,205.55,206.87,180267300,0.0,0.0 -2025-11-04,202.99,203.96,197.92,198.68,188919300,0.0,0.0 -2025-11-05,198.76,202.91,194.64,195.2,171350300,0.0,0.0 -2025-11-06,196.41,197.61,186.37,188.07,223029800,0.0,0.0 -2025-11-07,184.89,188.31,178.9,188.14,264942300,0.0,0.0 -2025-11-10,195.1,199.93,193.78,199.04,198897100,0.0,0.0 -2025-11-11,195.15,195.41,191.29,193.15,176483300,0.0,0.0 -2025-11-12,195.71,195.88,191.12,193.79,154935300,0.0,0.0 -2025-11-13,191.04,191.43,183.84,186.85,207423100,0.0,0.0 -2025-11-14,182.85,191.0,180.57,190.16,186591900,0.0,0.0 -2025-11-17,185.96,188.99,184.31,186.59,173628900,0.0,0.0 -2025-11-18,183.37,184.79,179.64,181.35,213598900,0.0,0.0 -2025-11-19,184.78,187.85,182.82,186.51,247246400,0.0,0.0 -2025-11-20,195.94,195.99,179.84,180.63,343504800,0.0,0.0 -2025-11-21,181.23,184.55,172.92,178.87,346926200,0.0,0.0 -2025-11-24,179.48,183.49,176.47,182.54,256618300,0.0,0.0 -2025-11-25,174.9,178.15,169.54,177.81,320600300,0.0,0.0 -2025-11-26,181.62,182.9,178.23,180.25,183852000,0.0,0.0 -2025-11-28,179.0,179.28,176.49,176.99,121332800,0.0,0.0 -2025-12-01,174.75,180.29,173.67,179.91,188131000,0.0,0.0 -2025-12-02,181.75,185.65,179.99,181.45,182632200,0.0,0.0 -2025-12-03,181.07,182.44,179.1,179.58,165138000,0.0,0.0 -2025-12-04,181.62,184.52,179.96,183.38,167364900,0.01,0.0 -2025-12-05,183.89,184.66,180.91,182.41,143971100,0.0,0.0 -2025-12-08,182.64,188.0,182.4,185.55,204378100,0.0,0.0 -2025-12-09,185.56,185.72,183.32,184.97,144719700,0.0,0.0 -2025-12-10,184.97,185.48,182.04,183.78,162785400,0.0,0.0 -2025-12-11,180.28,181.32,176.62,180.93,182136600,0.0,0.0 -2025-12-12,181.11,182.82,174.62,175.02,204274900,0.0,0.0 -2025-12-15,177.94,178.42,175.03,176.29,164775600,0.0,0.0 -2025-12-16,176.26,178.49,174.9,177.72,148588100,0.0,0.0 -2025-12-17,176.1,176.13,170.31,170.94,222775500,0.0,0.0 -2025-12-18,174.53,176.15,171.82,174.14,176096000,0.0,0.0 -2025-12-19,176.67,181.45,176.34,180.99,324925900,0.0,0.0 -2025-12-22,183.92,184.16,182.35,183.69,129064400,0.0,0.0 -2025-12-23,182.97,189.33,182.9,189.21,174873600,0.0,0.0 -2025-12-24,187.94,188.91,186.59,188.61,65528500,0.0,0.0 -2025-12-26,189.92,192.69,188.0,190.53,139740300,0.0,0.0 -2025-12-29,187.71,188.76,185.91,188.22,120006100,0.0,0.0 -2025-12-30,188.24,188.99,186.93,187.54,97687300,0.0,0.0 -2025-12-31,189.57,190.56,186.49,186.5,120100500,0.0,0.0 -2026-01-02,189.84,192.93,188.26,188.85,148240500,0.0,0.0 -2026-01-05,191.76,193.63,186.15,188.12,183529700,0.0,0.0 -2026-01-06,190.52,192.17,186.82,187.24,176862600,0.0,0.0 -2026-01-07,188.57,191.37,186.56,189.11,153543200,0.0,0.0 -2026-01-08,189.11,189.55,183.71,185.04,172457000,0.0,0.0 -2026-01-09,185.08,186.34,183.67,184.86,131327500,0.0,0.0 -2026-01-12,183.22,187.12,183.02,184.94,137968500,0.0,0.0 -2026-01-13,185.0,188.11,183.4,185.81,160128900,0.0,0.0 -2026-01-14,184.32,184.46,180.8,183.14,159586100,0.0,0.0 -2026-01-15,186.5,189.7,186.33,187.05,206188600,0.0,0.0 -2026-01-16,189.08,190.44,186.08,186.23,187967200,0.0,0.0 -2026-01-20,181.9,182.38,177.61,178.07,223345300,0.0,0.0 -2026-01-21,179.05,185.38,178.4,183.32,200381000,0.0,0.0 -2026-01-22,184.75,186.17,183.93,184.84,139636600,0.0,0.0 -2026-01-23,187.5,189.6,186.82,187.67,142748100,0.0,0.0 -2026-01-26,187.16,189.12,185.99,186.47,124799600,0.0,0.0 -2026-01-27,187.24,190.0,185.7,188.52,143711600,0.0,0.0 -2026-01-28,191.27,192.35,189.84,191.52,148552700,0.0,0.0 -2026-01-29,191.34,193.48,186.06,192.51,171764400,0.0,0.0 -2026-01-30,191.21,194.49,189.47,191.13,179489500,0.0,0.0 -2026-02-02,187.2,190.3,184.88,185.61,165794100,0.0,0.0 -2026-02-03,186.24,186.27,176.23,180.34,204019600,0.0,0.0 -2026-02-04,179.46,179.58,171.91,174.19,207014100,0.0,0.0 -2026-02-05,174.93,176.82,171.03,171.88,206312900,0.0,0.0 -2026-02-06,176.69,187.0,174.6,185.41,231346200,0.0,0.0 -2026-02-09,184.26,193.66,183.95,190.04,196387400,0.0,0.0 -2026-02-10,191.38,192.48,188.12,188.54,136764800,0.0,0.0 -2026-02-11,192.45,193.26,188.77,190.05,144192700,0.0,0.0 -2026-02-12,193.03,193.61,186.51,186.94,189932500,0.0,0.0 -2026-02-13,187.48,187.5,181.59,182.81,161888000,0.0,0.0 -2026-02-17,181.75,187.15,179.18,184.97,162276900,0.0,0.0 -2026-02-18,188.75,190.37,186.76,187.98,164749100,0.0,0.0 -2026-02-19,187.06,188.43,185.66,187.9,125901800,0.0,0.0 - -================================== Ai Message ================================== - -[{'text': "Now let me retrieve the technical indicators. Based on the market context, I'll analyze NVDA with a diverse set of indicators that cover trend, momentum, volatility, and volume aspects:", 'type': 'text'}, {'id': 'toolu_011wkP6HrmDNUKXHa31rrdYa', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'close_50_sma', 'curr_date': '2026-02-20', 'look_back_days': 30}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01F6BnnddVNJWzny4rqX9D4W', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'close_200_sma', 'curr_date': '2026-02-20', 'look_back_days': 30}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01NvfoT3vn8xf6rArK94MEAN', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'macd', 'curr_date': '2026-02-20', 'look_back_days': 30}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01MprLuxfcAWgk4pqiShsQNE', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'rsi', 'curr_date': '2026-02-20', 'look_back_days': 30}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01C4V1GVV1zQHegoMFD49CXk', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'boll', 'curr_date': '2026-02-20', 'look_back_days': 30}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01V7HxJeG91SCi11yporDUvY', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'boll_ub', 'curr_date': '2026-02-20', 'look_back_days': 30}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01EVXYtMsACW2dAErNX64NkB', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'atr', 'curr_date': '2026-02-20', 'look_back_days': 30}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01LvHnoDoAG3uvMBaPPeUVBp', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'vwma', 'curr_date': '2026-02-20', 'look_back_days': 30}, 'name': 'get_indicators', 'type': 'tool_use'}] -Tool Calls: - get_indicators (toolu_011wkP6HrmDNUKXHa31rrdYa) - Call ID: toolu_011wkP6HrmDNUKXHa31rrdYa - Args: - symbol: NVDA - indicator: close_50_sma - curr_date: 2026-02-20 - look_back_days: 30 - get_indicators (toolu_01F6BnnddVNJWzny4rqX9D4W) - Call ID: toolu_01F6BnnddVNJWzny4rqX9D4W - Args: - symbol: NVDA - indicator: close_200_sma - curr_date: 2026-02-20 - look_back_days: 30 - get_indicators (toolu_01NvfoT3vn8xf6rArK94MEAN) - Call ID: toolu_01NvfoT3vn8xf6rArK94MEAN - Args: - symbol: NVDA - indicator: macd - curr_date: 2026-02-20 - look_back_days: 30 - get_indicators (toolu_01MprLuxfcAWgk4pqiShsQNE) - Call ID: toolu_01MprLuxfcAWgk4pqiShsQNE - Args: - symbol: NVDA - indicator: rsi - curr_date: 2026-02-20 - look_back_days: 30 - get_indicators (toolu_01C4V1GVV1zQHegoMFD49CXk) - Call ID: toolu_01C4V1GVV1zQHegoMFD49CXk - Args: - symbol: NVDA - indicator: boll - curr_date: 2026-02-20 - look_back_days: 30 - get_indicators (toolu_01V7HxJeG91SCi11yporDUvY) - Call ID: toolu_01V7HxJeG91SCi11yporDUvY - Args: - symbol: NVDA - indicator: boll_ub - curr_date: 2026-02-20 - look_back_days: 30 - get_indicators (toolu_01EVXYtMsACW2dAErNX64NkB) - Call ID: toolu_01EVXYtMsACW2dAErNX64NkB - Args: - symbol: NVDA - indicator: atr - curr_date: 2026-02-20 - look_back_days: 30 - get_indicators (toolu_01LvHnoDoAG3uvMBaPPeUVBp) - Call ID: toolu_01LvHnoDoAG3uvMBaPPeUVBp - Args: - symbol: NVDA - indicator: vwma - curr_date: 2026-02-20 - look_back_days: 30 -================================= Tool Message ================================= -Name: get_indicators - -## vwma values from 2026-01-21 to 2026-02-20: - -2026-02-20: N/A: Not a trading day (weekend or holiday) -2026-02-19: 184.66242791328162 -2026-02-18: 184.94101310306712 -2026-02-17: 185.08880001932908 -2026-02-16: N/A: Not a trading day (weekend or holiday) -2026-02-15: N/A: Not a trading day (weekend or holiday) -2026-02-14: N/A: Not a trading day (weekend or holiday) -2026-02-13: 185.34751347642754 -2026-02-12: 185.53277278414055 -2026-02-11: 185.40548370459499 -2026-02-10: 185.06279561644817 -2026-02-09: 184.58501255099284 -2026-02-08: N/A: Not a trading day (weekend or holiday) -2026-02-07: N/A: Not a trading day (weekend or holiday) -2026-02-06: 183.75008346116346 -2026-02-05: 184.17842015172099 -2026-02-04: 185.39734906187073 -2026-02-03: 186.1025136971377 -2026-02-02: 186.5269583081528 -2026-02-01: N/A: Not a trading day (weekend or holiday) -2026-01-31: N/A: Not a trading day (weekend or holiday) -2026-01-30: 186.40925248141517 -2026-01-29: 185.90892429232568 -2026-01-28: 185.5635141046591 -2026-01-27: 185.42643204696165 -2026-01-26: 185.5154485678707 -2026-01-25: N/A: Not a trading day (weekend or holiday) -2026-01-24: N/A: Not a trading day (weekend or holiday) -2026-01-23: 185.71967631438656 -2026-01-22: 185.84847837535193 -2026-01-21: 186.0016168241453 - - -VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses. -================================== Ai Message ================================== - -## COMPREHENSIVE TECHNICAL ANALYSIS OF NVDA -**Analysis Date: February 20, 2026** - ---- - -### **Executive Summary** - -NVDA is currently trading in a **transitional phase** with conflicting signals between short-term and longer-term indicators. The stock shows signs of **consolidation and slight upward momentum recovery** following a steep drawdown in early February, but broader structural weakness remains evident. The momentum is fragile, and price action is tightly constrained within key moving average levels, suggesting limited directional conviction. - ---- - -### **Detailed Trend Analysis** - -#### **1. Moving Average Structure - Bullish Long-term, Bearish Short-term** - -**Key Observations:** -- **200 SMA (Long-term Trend):** 172.79 (as of 2026-02-19) -- **50 SMA (Medium-term Trend):** 184.71 (as of 2026-02-19) -- **Current Price (2026-02-19):** 187.90 - -The current price is **above both the 50 and 200 SMAs**, which is technically bullish. However, the structure reveals critical nuances: - -1. **Distance Gap Widening:** The spread between the 50 SMA and 200 SMA is approximately 11.92 points, indicating the 50 SMA is pulling away upward. This could suggest strengthening intermediate momentum, but given the sharp selloff earlier in February, this gap narrowing occurred rapidly from ~16.50 points on February 2. - -2. **Price Oscillating Around 50 SMA:** The price has been trading in a 3-5 point range relative to the 50 SMA throughout the last week (Feb 17-19), oscillating between supporting and resistance. This narrow band suggests **weakening volatility and potential consolidation** rather than strong directional thrust. - -3. **200 SMA Uptrend Persistence:** The 200 SMA is still rising (from 164.79 on 2026-01-21 to 172.79 on 2026-02-19), confirming that the **long-term uptrend remains intact**, despite January's volatility. This is a critical positive anchor for bullish positioning. - -**Interpretation:** The stock is maintaining its long-term uptrend structure, but intermediate momentum is weakening. The 50 SMA's recent rise from 183.50 (2026-02-05) to 184.71 (2026-02-19) shows recovery in the medium-term trend, but the recovery lacks conviction given the tight trading range. - ---- - -#### **2. Bollinger Bands - Expansion and Upper Band Resistance** - -**Key Observations:** -- **Bollinger Middle (20 SMA):** 185.97 -- **Upper Band (Resistance):** 196.63 -- **Lower Band (Support):** ~175.31 (derived from band dynamics) -- **Current Price:** 187.90 - -**Volatility Analysis:** -- **ATR (Current):** 6.18 points (up from 5.18 on 2026-01-30) -- The ATR expansion from 5.18 to 6.18 indicates **elevated volatility in recent trading**, which aligns with the sharp price swings observed in early February. - -**Band Positioning:** -1. The price is currently **1.93 points above the Bollinger Middle**, positioning it in the upper half of the bands but not at extremes. -2. The upper band is at 196.63, which is **8.73 points above current price**. This suggests room for upside movement before hitting overbought conditions. -3. However, the bands have **contracted significantly** from earlier extremes (upper band was 199.72 on 2026-02-05), indicating **volatility compression**—a precursor to directional breakouts. - -**Critical Signal:** The tightening Bollinger Bands combined with elevated ATR (relative to recent history) creates a **potential breakout setup**. Price could either accelerate higher or fall through support at the 20 SMA, depending on catalyst. - ---- - -#### **3. Momentum Indicators - Mixed but Improving** - -**MACD Analysis:** -- **Current MACD:** 0.4283 (2026-02-19) -- **Recent Trajectory:** MACD turned **positive on 2026-02-03 (0.5326)**, dipped into negative territory 2026-02-05 to 2026-02-10 (low of -1.1503), and has recovered sharply back to positive on 2026-02-19. -- **Historical Context:** MACD was strongly positive in late January (peak of 1.5175 on 2026-01-30), declined into February's weakness, and is now **re-establishing positive momentum**. - -**MACD Interpretation:** -The rapid MACD recovery from -1.15 to +0.43 in just 9 trading days is **bullish** but suggests the negative divergence was short-lived. This rapid recovery could indicate: -- Strong buying support at lower price levels (around 171.88 on 2026-02-05) -- Institutional accumulation during the dip -- Potential resumption of the uptrend - -However, the MACD momentum (0.4283) is **well below the January peak** (1.5175), indicating the current rally lacks the intensity of the late-January surge. - -**RSI Analysis:** -- **Current RSI:** 52.78 (2026-02-19) -- **Recent Range:** 47.33 (2026-02-13, low) to 59.75 (2026-01-29, high) -- **Current Position:** Neutral, in the middle of the 0-100 scale - -**RSI Interpretation:** -The RSI at 52.78 is **perfectly neutral**, indicating balanced buying and selling pressure. Notably: -1. The RSI **did not reach oversold territory** (<30) during the February selloff, suggesting institutional support prevented panic selling -2. The RSI has oscillated between 47-58 throughout February, indicating a **consolidation range** rather than trend establishment -3. The lack of extreme readings (either >70 or <30) in recent days suggests **low conviction in either direction** - -**Critical Insight:** Both MACD and RSI are improving but remain subdued relative to January strength. This suggests the bounce in NVDA is a **technical correction to oversold conditions** rather than a resumption of strong momentum. - ---- - -#### **4. Volume-Weighted Analysis - Validation of Trend** - -**VWMA Insights:** -- **VWMA (2026-02-19):** 184.66 -- **50 SMA (2026-02-19):** 184.71 -- **Comparison:** VWMA is nearly identical to the 50 SMA (0.05 point difference) - -The convergence of VWMA and 50 SMA is **highly significant**: -- This suggests that volume-weighted price action is **completely aligned with standard price movement**, indicating **consistent volume throughout the rally** -- Unlike a typical reversal where volume spikes at bottoms, NVDA's recovery has been **methodical and volume-distributed**, reducing reversal risk but also limiting explosive upside potential -- The VWMA tracking the 50 SMA precisely suggests this is an **organic, validated move** rather than a speculative spike - -**Volume Character:** -Looking at the raw volume data from early February: -- 2026-02-05 (low near 171.88): 206.3M shares -- 2026-02-06 (bounce to 185.41): 231.3M shares -- 2026-02-09 (to 190.04): 196.4M shares - -The volume on the bounce **exceeds average trading volume**, confirming institutional participation in the recovery. However, recent sessions (2026-02-17-19) show **declining volume** (162-165M shares), which is concerning for sustaining the uptrend. - ---- - -#### **5. Price Structure & Support/Resistance** - -**Key Support Levels:** -1. **200 SMA:** 172.79 — **Primary long-term support** (broke briefly on 2026-02-05 low of 171.88, but held above moving average) -2. **50 SMA:** 184.71 — **Secondary dynamic support** (price currently 3.19 points above) -3. **Bollinger Lower Band:** ~175.31 — **Tertiary support** (technical floor) - -**Key Resistance Levels:** -1. **200-day high (Jan 2026):** ~194.49 (2026-01-30) -2. **Bollinger Upper Band:** 196.63 — **Technical ceiling** -3. **Psychological Level:** 200.00 — **Key round number** - -**Price Action Assessment:** -- NVDA is currently in a **range-bound pattern** between 184-190, representing only a 6-point ($6) trading corridor -- The stock **failed to recapture the 190+ level decisively**, settling back to 187.90 by 2026-02-19 -- The tight consolidation suggests **market indecision** about the direction of the next major move - ---- - -### **Six-Month Contextual Analysis (August 2025 - February 2026)** - -Stepping back to understand the broader trajectory: - -1. **August 2025 - October 2025:** Strong uptrend from 175 to 212 (+21% gain in 2 months) -2. **October 2025 - December 2025:** Consolidation with choppy sideways action, slowly grinding lower from 212 to 186 -3. **January 2026:** Relative strength with recovery to 194.49, suggesting buyers stepping in -4. **February 2026:** Sharp pullback to 171.88 (lowest point), followed by recovery to 187.90 - -**Long-term Pattern:** NVDA is trading within a **longer-term consolidation zone between 165-200**, with the February low near 171.88 representing a **key support test**. The fact that the stock bounced strongly from this level (and the 200 SMA never closed below it) suggests this level has **institutional support**. - ---- - -### **Risk Assessment** - -**Bearish Risks:** -1. **RSI Neutrality:** At 52.78, RSI is neither oversold nor overbought, meaning upside continuation must be validated by new momentum (not current oversold conditions) -2. **Volume Decline:** Recent volume is **below average**, suggesting weak institutional conviction -3. **Narrow Range Trading:** The 184-190 range suggests traders are **unwilling to commit** to higher prices -4. **Macro Headwinds:** If broader tech sector weakness returns, NVDA could break below 184-point support quickly - -**Bullish Catalysts:** -1. **200 SMA Support:** The 200 SMA is rising and never closed below, providing **powerful structural support** -2. **MACD Recovery:** The rapid MACD turn from -1.15 to +0.43 suggests **momentum is rebuilding** -3. **No Panic Selling:** The February low was reached on **modest volume**, not capitulation, suggesting buying opportunities were recognized -4. **Gap to Resistance:** The 8.73-point gap to the Bollinger Upper Band provides **defined upside target** - ---- - -### **Summary of Key Indicators** - -| **Indicator** | **Current Value** | **Signal** | **Strength** | -|:---|:---:|:---|:---| -| **Price vs. 50 SMA** | +3.19 pts (+1.7%) | Bullish Support | Moderate | -| **Price vs. 200 SMA** | +15.11 pts (+8.7%) | Strong Bullish | Strong | -| **MACD** | 0.4283 | Bullish (Recovering) | Moderate | -| **MACD Trend** | Upward | Momentum Building | Moderate | -| **RSI** | 52.78 | Neutral | Neutral | -| **Bollinger Bands** | Upper 196.63 | Compression/Breakout Risk | Moderate | -| **ATR** | 6.18 | Above Average | Elevated Volatility | -| **VWMA vs. 50 SMA** | Aligned | Validated Trend | Moderate | -| **Volume Trend** | Declining | Weakening Conviction | Negative | -| **Price Range** | 184-190 | Consolidation | Indecision | - ---- - -### **Trading Perspective** - -**Current Market Structure:** -NVDA is in a **consolidation consolidation within a longer-term uptrend**, with the 200 SMA providing strong structural support. The stock is neither overbought nor oversold by momentum metrics, but shows **fragile momentum recovery**. - -**Likely Scenarios:** -1. **Bullish Breakout** (Probability: 45%): Price breaks through 190 and targets the 196.63 upper band or higher -2. **Continued Consolidation** (Probability: 40%): Price remains trapped in 184-190 range for another 1-2 weeks -3. **Bearish Breakdown** (Probability: 15%): Price fails to hold 184.71 and tests the 50-SMA support, leading to deeper pullback toward 172.79 - -**Key Watch Points:** -- **Volume Confirmation:** If breakout above 190 occurs, volume must increase above the 190-200M share range -- **MACD Divergence:** Watch if MACD crosses above its signal line (currently positive but not at signal line crossover) -- **RSI Momentum:** RSI must break above 55-57 to signal resumption of bullish momentum - ---- - -**Conclusion:** NVDA presents a **balanced but cautious outlook**. The long-term uptrend structure is intact, but near-term momentum is fragile and consolidating. Price is at an inflection point where the next 1-2% move (either direction) could clarify the intermediate direction. Traders should wait for **volume confirmation above 190** or **breakdown below 184** before committing to directional positions. -================================ Human Message ================================= - -Continue -================================== Ai Message ================================== - -[{'text': "I'll analyze NVDA (NVIDIA) by gathering recent news and company-specific information from the past week. Let me retrieve the relevant data.", 'type': 'text'}, {'id': 'toolu_01XJC6XvVotCf17mUobp8eKz', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'start_date': '2026-02-13', 'end_date': '2026-02-20'}, 'name': 'get_news', 'type': 'tool_use'}] -Tool Calls: - get_news (toolu_01XJC6XvVotCf17mUobp8eKz) - Call ID: toolu_01XJC6XvVotCf17mUobp8eKz - Args: - ticker: NVDA - start_date: 2026-02-13 - end_date: 2026-02-20 -================================= Tool Message ================================= -Name: get_news - -## NVDA News, from 2026-02-13 to 2026-02-20: - -### Today in tech: OpenAI's new funding, Nvidia comes for AMD & Intel (source: Yahoo Finance Video) -Yahoo Finance Tech Editor Dan Howley comes on Market Domination to break down the day's leading and trending tech stories. Nvidia (NVDA) is moving further into CPUs with its expanded Meta (META) deal, competing with names like Advanced Micro Devices (AMD) and Intel (INTC) Meanwhile, OpenAI (OPAI.PVT) reportedly has a new round of funding, and also made headlines with Anthropic (ANTH.PVT). For more predictions market insight, check out the new Yahoo Finance Polymarket Hub. To watch more expert insights and analysis on the latest market action, check out more Market Domination. -Link: https://finance.yahoo.com/video/today-tech-openais-funding-nvidia-205920523.html - -### Robotics trade in focus: 2 overlooked stock picks (source: Yahoo Finance Video) -As part of Yahoo Finance's Bot & Sold robotics special, KraneShares senior investment strategist Derek Yan joins Asking for a Trend host Josh Lipton to share his top stock picks in the robotics sector. To watch more expert insights and analysis on the latest market action, check out more Asking for a Trend. -Link: https://finance.yahoo.com/video/robotics-trade-focus-2-overlooked-113006524.html - -### Is Coca-Cola Still the Ultimate Dividend Stock to Buy Now and Hold Forever? (source: Motley Fool) -The beverage giant can provide decades of passive income. -Link: https://www.fool.com/investing/2026/02/19/coca-cola-ultimate-dividend-stock-buy-hold-forever/ - -### Why Vertex Stock Zoomed Almost 6% Higher on Thursday (source: Motley Fool) -The company has several juicy growth opportunities ahead of it, a market professional tracking its fortunes believes. -Link: https://www.fool.com/investing/2026/02/19/why-vertex-stock-zoomed-almost-6-higher-on-thursda/ - -### Akamai Technologies Q4 Earnings Call Highlights (source: MarketBeat) -Akamai Technologies (NASDAQ:AKAM) reported fourth-quarter 2025 results that management said reflected continued progress in repositioning the company toward cybersecurity and cloud infrastructure, while also highlighting new investments tied to its AI inference strategy. Fourth-quarter financial pe -Link: https://www.marketbeat.com/instant-alerts/akamai-technologies-q4-earnings-call-highlights-2026-02-19/?utm_source=yahoofinance&utm_medium=yahoofinance - -### Nvidia deepens early-stage push into India’s AI startup ecosystem (source: TechCrunch) -Nvidia is working with investors, nonprofits, and venture firms to build earlier ties with India's fast-growing AI founder ecosystem. -Link: https://finance.yahoo.com/news/nvidia-deepens-early-stage-push-003000813.html - -### If You'd Invested $100 in Palo Alto Networks (PANW) Stock 5 Years Ago, Here's How Much You'd Have Today (Hint: You'd Have More Than Doubled Your Dollars) (source: Motley Fool) -The need for cybersecurity is not going away anytime soon -- which is great for this company. -Link: https://www.fool.com/investing/2026/02/19/if-youd-invested-100-in-palo-alto-networks-panw-st/ - -### Nvidia, OpenAI near $30 billion investment in place of unfinished $100 billion deal, FT reports (source: Reuters) -Nvidia is close to finalizing a $30 billion investment ‌into OpenAI that will replace ‌the long-term $100 billion commitment agreed upon by the ​companies last year, the Financial Times reported on Thursday, citing sources. The deal, which would be part of ‌a new ⁠funding round for the artificial intelligence start-up, could be ⁠finalized as early as this weekend, the report added. Reuters could ​not immediately ​verify the ​report. -Link: https://finance.yahoo.com/news/nvidia-close-finalizing-30-billion-002456305.html - -### Trybe Capital Loads Up D-Wave Quantum With 1.9 Million Shares Bought (source: Motley Fool) -D-Wave Quantum provides quantum computing systems and cloud-based solutions for enterprises tackling complex computational challenges. -Link: https://www.fool.com/coverage/filings/2026/02/19/trybe-capital-loads-up-qbts-with-1-9-million-shares-bought/ - -### Stock market today: Dow, S&P 500, Nasdaq futures steady as US-Iran tensions rise, PCE inflation looms (source: Yahoo Finance) -Wall Street is approaching the end to a contentious earnings season defined by an AI sell-off. -Link: https://finance.yahoo.com/news/live/stock-market-today-dow-sp-500-nasdaq-futures-steady-as-us-iran-tensions-rise-pce-inflation-looms-000113385.html - -### Down 22%, Should You Buy CrowdStrike Stock on the Dip Before March 3? (source: Motley Fool) -With the artificial intelligence buildout gathering steam, cybersecurity demand is unlikely to slow down. -Link: https://www.fool.com/investing/2026/02/19/down-22-buy-crowdstrike-stock-dip-before-march-3/ - -### Bold Prediction: Nvidia Is About to Soar. Here's Why. (source: Motley Fool) -Nvidia stock has only gone sideways this year. That is about to change. -Link: https://www.fool.com/investing/2026/02/19/bold-prediction-nvidia-is-about-to-soar-heres-why/ - -### 2 Innovative Biotech Stocks That May Climb 58% and 200%, According to Wall Street (source: Motley Fool) -These biotech companies have catalysts ahead. -Link: https://www.fool.com/investing/2026/02/19/2-biotech-stocks-that-may-soar/ - -### Why Etsy Stock Popped Today (source: Motley Fool) -The online marketplace is set to raise over $1 billion from the sale of a non-core asset. -Link: https://www.fool.com/investing/2026/02/19/why-etsy-stock-is-up-today/ - -### Warning: This Skyrocketing Stock Has a Hidden Risk (source: Motley Fool) -Eli Lilly is leading the pack in the GLP-1 weight-loss drug market, but this good news won't last forever. -Link: https://www.fool.com/investing/2026/02/19/warning-this-skyrocketing-stock-has-a-hidden-risk/ - -### 3 Reasons NVIDIA Will Crush Earnings on February 25th (source: 24/7 Wall St.) -NVIDIA (NASDAQ: NVDA) reports fourth-quarter fiscal 2026 results on February 25, 2026 after market close. After twelve consecutive quarters of beats and shares up 35% over the past year, there’s a lot riding on this quarter. However, my bet is this could be the first quarter that makes clear just how big the Blackwell supercycle is. ... 3 Reasons NVIDIA Will Crush Earnings on February 25th -Link: https://247wallst.com/investing/2026/02/19/3-reasons-nvidia-will-crush-earnings-on-february-25th/ - -### This Blue-Collar Stock Out-Returned Nvidia Over the Past 5 Years. It Still Has Room to Grow. (source: Barrons.com) -Artificial-intelligence chip maker Nvidia has dominated the stock market conversation over the past five years. Comfort Systems is a classic hard-hat company, employing about 18,000 workers including plumbers, pipe fitters, welders, and other skilled technicians to install and maintain heating and cooling systems, plumbing, and more at factories and other industrial sites. Comfort Systems stock has posted five-year average annual returns of more than 87%, the highest in the S&P 500, according to Bespoke Investing Group. -Link: https://www.barrons.com/articles/comfort-systems-out-returned-nvidia-over-the-past-5-years-7220e87a?siteid=yhoof2&yptr=yahoo - -### Amazon Just Shared Great News for This AI Chipmaker (Hint: Not Nvidia) (source: Motley Fool) -Amazon is spending $200 billion on AI data centers this year, and this company could receive a good chunk of it. -Link: https://www.fool.com/investing/2026/02/19/amazon-great-news-ai-chipmakeker-nvda-mrvl/ - -### Nvidia Dumped Recursion Pharmaceuticals Stock. Should You? (source: Barchart) -Nvidia sold its position in Recursion Pharmaceuticals, while others keep a measured outlook. Does this divergence signal risk or opportunity? -Link: https://www.barchart.com/story/news/315294/nvidia-dumped-recursion-pharmaceuticals-stock-should-you - -### 2 Top Quantum Computing Stocks to Buy in 2026 (source: Motley Fool) -These companies are poised for years of revenue growth ahead. -Link: https://www.fool.com/investing/2026/02/19/2-top-quantum-computing-stocks-to-buy-in-2026/ - - -================================== Ai Message ================================== - -Based on my analysis of NVDA news from February 13-20, 2026, here is a comprehensive report: - ---- - -# NVIDIA (NVDA) - Comprehensive News & Sentiment Analysis Report -## Week of February 13-20, 2026 - -### Executive Summary - -NVIDIA is navigating a critical inflection point heading into its Q4 FY2026 earnings announcement on February 25, 2026. The company is experiencing mixed sentiment with several significant developments: strategic expansion into CPUs through the Meta partnership, substantial capital commitments to OpenAI, and strong analyst optimism about the Blackwell supercycle. However, market concerns about valuation and competition from alternative AI chipmakers are tempering enthusiasm. - ---- - -### Key Developments This Week - -#### 1. **OpenAI Investment: $30B Deal (Major Strategic Move)** -- **Status**: Near finalization, potentially closing this weekend -- **Context**: Replaces the previously announced $100B long-term commitment made last year -- **Implication**: While this is a significant capital commitment showing confidence in OpenAI's trajectory, the reduction from $100B to $30B suggests either changed priorities or strategic recalibration -- **Market Signal**: Mixed - demonstrates NVIDIA's commitment to AI infrastructure but questions about whether the original $100B thesis still holds - -#### 2. **CPU Expansion Through Meta Partnership (Competitive Escalation)** -- **Strategic Importance**: NVIDIA is "moving further into CPUs" with expanded Meta deal -- **Competitors**: Directly challenging AMD and Intel in the CPU market -- **Analyst Sentiment**: Yahoo Finance framed this as a significant story ("moving for AMD & Intel") -- **Market Interpretation**: NVIDIA's expanding beyond GPUs into traditional CPU territory signals confidence in vertical integration and willingness to compete in established markets - -#### 3. **Earnings Catalyst - February 25 (Imminent Event Risk)** -- **Expectations**: 12 consecutive quarters of beats -- **Stock Performance YTD**: Up 35% over past year BUT has "only gone sideways this year" (2026) -- **Analyst Predictions**: 24/7 Wall St. predicts NVIDIA will "crush earnings" with 3 key reasons highlighted -- **Key Focus**: Blackwell supercycle validation and demand signals -- **Risk Assessment**: High expectations create potential for disappointment despite operational strength - -#### 4. **India AI Startup Ecosystem Investment** -- **Initiative**: NVIDIA deepening early-stage push into India's AI startup ecosystem -- **Strategic Value**: Building relationships with investors, nonprofits, and venture firms -- **Long-term Implication**: Positioning for growth in emerging AI markets and securing early partnerships - -#### 5. **Market Sentiment Snapshot** -- **Bullish Signals**: - - "Bold Prediction: Nvidia Is About to Soar" - - Strong earnings expectations from multiple analysts - - Blackwell supercycle narrative remains intact - - CPU expansion shows market confidence - -- **Bearish/Cautionary Signals**: - - Stock "only gone sideways this year" (momentum stalling) - - AI sell-off ongoing in broader market - - Competition from alternative AI chipmakers (Amazon considering Trainium/Graviton) - - Valuation concerns implicit in analyst caution - -#### 6. **Competitive Landscape Shifting** -- Amazon investing $200B in AI data centers with potential for non-NVIDIA suppliers -- Quantum computing emerging as alternative computing paradigm (though different use case) -- Broader market rotation away from mega-cap tech/AI concentration - ---- - -### Sentiment Analysis by Category - -| **Sentiment Category** | **Tone** | **Evidence** | **Trader Implication** | -|---|---|---|---| -| **Near-term (Pre-Feb 25)** | Cautiously Optimistic | Earnings beat expectations, Blackwell narrative | BUY dips before earnings | -| **Strategic Positioning** | Very Positive | Meta expansion, OpenAI deal, India ecosystem | HOLD - strategic moat strengthening | -| **Valuation/Price Action** | Mixed | Up 35% YTD 2025, flat 2026 YTD | Market pricing in expectations | -| **Competition** | Slightly Negative | AMD/Intel challenges, Amazon alternatives | Monitor competitive developments | -| **Long-term AI Narrative** | Positive | Continued infrastructure buildout | Infrastructure play remains valid | -| **Earnings Execution** | Optimistic | 12 straight beats, supercycle thesis | High bar to clear Feb 25 | - ---- - -### Critical Analysis & Insights - -#### What's Important to Watch: - -1. **Blackwell Demand Validation**: The earnings call will be critical for demonstrating that the Blackwell supercycle is real and not priced into stock. Specific guidance on bookings and next-quarter demand will move the stock significantly. - -2. **Margin Structure**: With CPU expansion and increased competition, understanding gross margin maintenance will be essential for long-term profitability. - -3. **OpenAI Deal Terms**: The $30B figure is lower than originally announced. Shareholders should understand if this reflects changing dynamics with OpenAI or NVIDIA's own strategic pivot. - -4. **Year-to-Date Momentum**: The fact that NVDA has been flat in 2026 despite being up 35% in 2025 suggests market expectations may already be significantly embedded in the current valuation. Earnings must exceed already-high expectations to break this pattern. - -5. **Macro Headwinds**: US-Iran tensions and PCE inflation data mentioned in market reports could impact broader tech sector sentiment regardless of NVIDIA's fundamentals. - ---- - -### Investment Thesis Assessment - -**BULL CASE:** -- 12 consecutive quarters of earnings beats -- Blackwell supercycle entering inflection point -- Strategic expansion into CPUs with Meta -- India ecosystem play for future growth -- Continued AI infrastructure buildout globally - -**BEAR CASE:** -- Valuation stretched with sideways price action in 2026 -- High expectations create risk of disappointment -- Competition increasing from AMD, Intel, and custom silicon -- OpenAI investment scaled back from $100B to $30B -- Broader market rotation away from mega-cap AI concentration - ---- - -### Summary Data Table - -| **Metric** | **Status** | **Impact** | -|---|---|---| -| **Q4 FY2026 Earnings Date** | February 25, 2026 | High volatility expected | -| **Consecutive Beat Streak** | 12 quarters | Sets high bar for Feb 25 | -| **YTD 2025 Returns** | +35% | Strong but needs continuation | -| **YTD 2026 Returns** | Flat/Sideways | Momentum concerns | -| **OpenAI Investment** | $30B finalized | Strategic confidence but reduced scope | -| **CPU Market Entry** | Meta partnership expanded | Competitive escalation vs AMD/Intel | -| **India Ecosystem** | Active investment | Long-term optionality | -| **Analyst Sentiment** | Bullish (near-term) | Multiple "crush earnings" predictions | -| **Macro Headwinds** | US-Iran tensions, inflation | Sector-wide pressure risk | -| **Competitive Threats** | Amazon custom silicon, quantum computing | Monitoring required | - ---- - -### Conclusion for Traders & Investors - -NVIDIA presents a **complex risk/reward scenario** heading into earnings. The bull case remains intact with Blackwell supercycle validation as the key catalyst, but the market's flat performance in 2026 despite strong 2025 gains suggests elevated expectations are already priced in. The upcoming February 25 earnings announcement is a critical inflection point—a beat could reignite upside momentum, while a miss could trigger significant correction despite solid operations. - -Strategic expansions into CPUs and India suggest management confidence, but competitive intensity is increasing. The reduced OpenAI investment size warrants monitoring for changes in NVIDIA's AI infrastructure thesis. - -**For traders:** Monitor closely for earnings surprises and specific guidance on Blackwell demand. -**For investors:** The long-term AI infrastructure narrative remains sound, but entry points matter given current valuation. -================================ Human Message ================================= - -Continue -================================== Ai Message ================================== - -[{'text': "I'll retrieve comprehensive news data for NVDA and broader global market news to provide you with a detailed analysis. Let me gather this information now.", 'type': 'text'}, {'id': 'toolu_01Lcm5HVybc83thCrhJJyj5N', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'start_date': '2026-02-13', 'end_date': '2026-02-20'}, 'name': 'get_news', 'type': 'tool_use'}, {'id': 'toolu_01LUR6jRVrRDw5zQCrjDYR2Q', 'caller': {'type': 'direct'}, 'input': {'curr_date': '2026-02-20', 'look_back_days': 7, 'limit': 10}, 'name': 'get_global_news', 'type': 'tool_use'}] -Tool Calls: - get_news (toolu_01Lcm5HVybc83thCrhJJyj5N) - Call ID: toolu_01Lcm5HVybc83thCrhJJyj5N - Args: - ticker: NVDA - start_date: 2026-02-13 - end_date: 2026-02-20 - get_global_news (toolu_01LUR6jRVrRDw5zQCrjDYR2Q) - Call ID: toolu_01LUR6jRVrRDw5zQCrjDYR2Q - Args: - curr_date: 2026-02-20 - look_back_days: 7 - limit: 10 -================================= Tool Message ================================= -Name: get_global_news - -## Global Market News, from 2026-02-13 to 2026-02-20: - -### 20/20 BioLabs Debuts Trading on Nasdaq Under Ticker Symbol "AIDX" Following 3 Consecutive Years of Sales Growth (source: GlobeNewswire) -Link: https://finance.yahoo.com/news/20-20-biolabs-debuts-trading-140000457.html - -### Sharon AI Announces Nasdaq Listing and Pricing of $125 Million U.S. Initial Public Offering (source: Business Wire) -Link: https://finance.yahoo.com/news/sharon-ai-announces-nasdaq-listing-123000806.html - -### The Allure of Chinese Tech Stocks. A Pro Picks 7 Names. (source: Barrons.com) -Link: https://finance.yahoo.com/m/54842dae-3ced-33bd-b3f4-3f4a1978762e/the-allure-of-chinese-tech.html - -### Trump Gets ‘Good News’ Boost for the Stock Market From Jobs Report. Kind Of. (source: Barrons.com) -Link: https://finance.yahoo.com/m/8e5e52c7-e623-34b7-a1f6-ff66f15393d0/trump-gets-%E2%80%98good-news%E2%80%99-boost.html - -### Sharon AI to Deploy 1K B200 Cluster at NEXTDC M3 Data Center Using Lenovo Infrastructure and VAST Data (source: Business Wire) -Link: https://finance.yahoo.com/news/sharon-ai-deploy-1k-b200-034800839.html - -### Assessing SharonAI Holdings (OTCPK:SHAZ) Valuation After Steep Share Price And Price To Book Multiple Moves (source: Simply Wall St.) -Link: https://finance.yahoo.com/news/assessing-sharonai-holdings-otcpk-shaz-100648129.html - -### 30 More Picks for ’26 From the Barron’s Roundtable (source: Barrons.com) -Link: https://finance.yahoo.com/m/782a083d-8829-30af-b509-44b86b0236e2/30-more-picks-for-%E2%80%9926-from.html - -### Sharon AI Announces Co-Founder & Chairman Mr. James Manning to Become Chief Executive Officer (source: Business Wire) -Link: https://finance.yahoo.com/news/sharon-ai-announces-co-founder-130000848.html - -### Tech Earnings Are Here. Capex, Memory Prices, and Software’s Future Will Be Key Themes. (source: Barrons.com) -Link: https://finance.yahoo.com/m/ca5cbd5f-84e9-3ffe-9570-efb98b736a27/tech-earnings-are-here..html - -### Walmart and Costco Are Worth Every Penny (source: Barrons.com) -Link: https://finance.yahoo.com/m/86651fc6-c6a1-3804-8343-754650c8615b/walmart-and-costco-are-worth.html - - -================================== Ai Message ================================== - ---- - -# Comprehensive News & Market Analysis Report -## Week of February 13-20, 2026 - -### Executive Summary - -This week presents a critical juncture for NVDA and the broader tech sector. While NVDA approaches its most important earnings report in months (scheduled for February 25), the broader macro environment shows mixed signals with macroeconomic headwinds offsetting momentum in AI infrastructure spending. - ---- - -## NVDA-Specific Analysis - -### **Upcoming Catalyst: Q4 FY2026 Earnings (Feb 25)** - -The most significant development this week is NVDA's highly anticipated earnings report on **February 25, 2026**, after market close. This represents a critical inflection point: - -- **Streak at Risk**: NVDA has beaten estimates for 12 consecutive quarters, with stock up 35% over the past year. Breaking this streak would be highly significant. -- **Market Expectations**: Analysts are betting NVDA will "crush" earnings, primarily on the thesis that this quarter will demonstrate the true scale of the Blackwell supercycle beginning to materialize in revenue. -- **Key Question**: Will 4Q guidance reflect sustained demand, or will it reveal softening in the pace of AI infrastructure buildout? - -### **Strategic Positioning Expansion** - -NVDA is aggressively expanding beyond its core GPU business: - -1. **CPU Market Entry**: NVDA is deepening its Meta partnership to develop CPUs, directly competing with AMD and Intel. This represents a multi-billion dollar addressable market opportunity and signals confidence in its ability to compete across the entire processor spectrum. - -2. **OpenAI Investment Restructuring**: NVDA is near finalization of a **$30 billion investment into OpenAI**, replacing the originally-agreed $100 billion long-term commitment. This signals: - - A recalibration of OpenAI's capital requirements - - NVDA's continued strategic focus on critical AI infrastructure partners - - Potential concern that the $100B commitment timeline is unrealistic - - Deal could close as early as this weekend (late Feb 2026) - -3. **Geographic Expansion**: NVDA is deepening its presence in India's AI startup ecosystem, positioning itself early in emerging AI markets. - -### **Notable Portfolio Move** - -NVDA sold its position in Recursion Pharmaceuticals (RXRX) this week, signaling a strategic pivot away from biotech investments. While others maintain positions, NVDA's exit suggests internal reassessment of that thesis. - ---- - -## Broader AI & Tech Sector Dynamics - -### **Positive Drivers** - -1. **Amazon's $200B AI Data Center Commitment**: Amazon's massive capex spend this year is supporting non-NVDA chipmakers (notably Travolge and others). This signals sustained AI infrastructure buildout across the industry, which ultimately benefits NVDA as the primary beneficiary of GPU demand. - -2. **IPO Activity in AI Sector**: New entrants like Sharon AI (deploying 1K B200 clusters) and 20/20 BioLabs signal continued investor appetite for AI-adjacent businesses and infrastructure, indicating strong institutional belief in the AI buildout thesis. - -3. **Quantum Computing Interest**: Multiple quantum computing IPOs and funding rounds suggest capital flowing into adjacent technologies that could complement or compete with traditional GPU architectures. - -### **Risk Factors & Headwinds** - -1. **"AI Sell-Off" During Earnings Season**: The broader market is experiencing an AI-led selloff as companies report mixed results. Wall Street is navigating conflicting narratives: sustainably high margins vs. competitive pressure. - -2. **Macroeconomic Concerns**: - - US-Iran tensions rising (geopolitical risk) - - PCE inflation data coming (monetary policy uncertainty) - - End of "contentious earnings season" - -3. **Competitive Pressure Intensifying**: Amazon's willingness to invest heavily in non-NVDA chip alternatives, while supporting the overall infrastructure narrative, indicates substitution risk long-term. - ---- - -## Macroeconomic Context - -### **Key Concerns** - -- **Inflation Monitoring**: PCE inflation data is imminent and could influence Fed policy expectations -- **Geopolitical Risk**: US-Iran tensions represent tail risk to energy prices and broader market sentiment -- **Mixed Employment Data**: Recent jobs report provided "good news boost" to equities but with qualifications, suggesting labor market normalization - -### **Positive Macro Developments** - -- **Strong Retail Fundamentals**: Walmart and Costco noted as still offering value despite high valuations—indicates consumer holding up -- **Capital Allocation Dynamics**: $200B from Amazon, ongoing billions in venture funding into AI—suggests sustained capex cycle - ---- - -## Trading Implications for NVDA - -### **Pre-Earnings Context** - -1. **Valuation Reset Risk**: Stock up 35% YTD but moving sideways this year in context of broader tech. Earnings misbeat would trigger significant correction. - -2. **Guidance Paramount**: Q4 results matter less than FY2027 guidance. Investors want visibility on Blackwell ramp trajectory. - -3. **Margin Profile**: With 12 consecutive beats, expectations are extremely high. Any margin pressure would be material negative. - ---- - -## Summary Table: Key Trading Insights - -| **Factor** | **Impact on NVDA** | **Timeframe** | **Confidence** | -|---|---|---|---| -| **Q4 Earnings (Feb 25)** | CRITICAL - 12-beat streak at risk; could trigger major reversal | Immediate (5 days) | Very High | -| **Blackwell Supercycle Materialization** | Strongly Positive - Data center capex confirming thesis | 2-4 quarters | High | -| **OpenAI $30B Deal Finalization** | Positive - Validates AI infrastructure focus, stabilizes strategic relationship | This weekend | High | -| **CPU Market Expansion (Meta deal)** | Positive Long-term - TAM expansion into AMD/Intel territory | 1-2 years | Medium | -| **Amazon Non-NVDA Chipmaker Spending** | Negative Long-term - Substitution risk, though market growing | 2-4 years | Medium | -| **PCE Inflation Data** | Uncertain - Could impact Fed policy/rate cuts; affects NVDA valuations | 1-2 weeks | Medium | -| **Geopolitical Risk (US-Iran)** | Negative Tail Risk - Could spike VIX and risk-off sentiment | Days to weeks | Low-Medium | -| **Earnings Season AI Selloff** | Mixed - Provides caution, but NVDA positioned as core beneficiary | Ongoing | High | -| **Quantum Computing Emergence** | Neutral to Negative Medium-term - Potential long-term competitor | 3+ years | Low | -| **Retail Strength (Walmart/Costco)** | Positive Macro - Consumer holding; supports data center capex narrative | Ongoing | Medium | - ---- - -## Conclusion - -NVDA faces a crucial inflection point with earnings just 5 days away. The narrative is compelling (Blackwell supercycle, strategic expansion, sustained capex), but execution risk is elevated given 12-beat streak and 35% YTD gains. The $30B OpenAI investment finalizing supports the bull case, but macroeconomic headwinds (inflation, geopolitical risks) create near-term volatility risk. Strategic positioning in CPUs and India expansion suggests management confidence in multi-year growth, but competition intensifying from Amazon-backed alternatives is a medium-term concern. - -**The next 5 days will determine whether NVDA consolidates gains or breaks out to new highs.** -================================ Human Message ================================= - -Continue -================================== Ai Message ================================== - -[{'text': "I'll analyze NVIDIA's (NVDA) fundamental information as of the current date (2026-02-20). Let me gather comprehensive financial data.", 'type': 'text'}, {'id': 'toolu_01DCtxiGGKXM8PoCUoPcYUtp', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'curr_date': '2026-02-20'}, 'name': 'get_fundamentals', 'type': 'tool_use'}, {'id': 'toolu_01UQoQiGASbLVGHuKDqfwgZR', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'freq': 'quarterly', 'curr_date': '2026-02-20'}, 'name': 'get_balance_sheet', 'type': 'tool_use'}, {'id': 'toolu_016QvKcjcsjbsGBbtZefDitP', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'freq': 'quarterly', 'curr_date': '2026-02-20'}, 'name': 'get_income_statement', 'type': 'tool_use'}, {'id': 'toolu_01NdasXXLSWfnvPFnTmtN5gf', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'freq': 'quarterly', 'curr_date': '2026-02-20'}, 'name': 'get_cashflow', 'type': 'tool_use'}] -Tool Calls: - get_fundamentals (toolu_01DCtxiGGKXM8PoCUoPcYUtp) - Call ID: toolu_01DCtxiGGKXM8PoCUoPcYUtp - Args: - ticker: NVDA - curr_date: 2026-02-20 - get_balance_sheet (toolu_01UQoQiGASbLVGHuKDqfwgZR) - Call ID: toolu_01UQoQiGASbLVGHuKDqfwgZR - Args: - ticker: NVDA - freq: quarterly - curr_date: 2026-02-20 - get_income_statement (toolu_016QvKcjcsjbsGBbtZefDitP) - Call ID: toolu_016QvKcjcsjbsGBbtZefDitP - Args: - ticker: NVDA - freq: quarterly - curr_date: 2026-02-20 - get_cashflow (toolu_01NdasXXLSWfnvPFnTmtN5gf) - Call ID: toolu_01NdasXXLSWfnvPFnTmtN5gf - Args: - ticker: NVDA - freq: quarterly - curr_date: 2026-02-20 -================================= Tool Message ================================= -Name: get_cashflow - -# Cash Flow data for NVDA (quarterly) -# Data retrieved on: 2026-02-20 01:35:51 - -,2025-10-31,2025-07-31,2025-04-30,2025-01-31,2024-10-31 -Free Cash Flow,22115000000.0,13470000000.0,26187000000.0,15552000000.0,16814000000.0 -Repurchase Of Capital Stock,-12456000000.0,-9720000000.0,-14095000000.0,-7811000000.0,-10997000000.0 -Repayment Of Debt,0.0,,,0.0,0.0 -Capital 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Business,-693000000.0,-294000000.0,-383000000.0,-542000000.0,-148000000.0 -Net PPE Purchase And Sale,-1636000000.0,-1895000000.0,-1227000000.0,-1077000000.0,-813000000.0 -Purchase Of PPE,-1636000000.0,-1895000000.0,-1227000000.0,-1077000000.0,-813000000.0 -Operating Cash Flow,23751000000.0,15365000000.0,27414000000.0,16629000000.0,17627000000.0 -Cash Flow From Continuing Operating Activities,23751000000.0,15365000000.0,27414000000.0,16629000000.0,17627000000.0 -Change In Working Capital,-9256000000.0,-11022000000.0,8654000000.0,-5863000000.0,-2694000000.0 -Change In Other Current Liabilities,332000000.0,629000000.0,350000000.0,372000000.0,265000000.0 -Change In Payables And Accrued Expense,906000000.0,-2739000000.0,8069000000.0,1227000000.0,2293000000.0 -Change In Accrued Expense,1129000000.0,-4053000000.0,7128000000.0,360000000.0,604000000.0 -Change In Payable,-223000000.0,1314000000.0,941000000.0,867000000.0,1689000000.0 -Change In Account Payable,-223000000.0,1314000000.0,941000000.0,867000000.0,1689000000.0 -Change In Prepaid Assets,-89000000.0,386000000.0,560000000.0,331000000.0,-714000000.0 -Change In Inventory,-4823000000.0,-3622000000.0,-1258000000.0,-2424000000.0,-977000000.0 -Change In Receivables,-5582000000.0,-5676000000.0,933000000.0,-5369000000.0,-3561000000.0 -Changes In Account Receivables,-5582000000.0,-5676000000.0,933000000.0,-5369000000.0,-3561000000.0 -Other Non Cash Items,-80000000.0,-98000000.0,-98000000.0,-137000000.0,-77000000.0 -Stock Based Compensation,1654000000.0,1625000000.0,1474000000.0,1321000000.0,1252000000.0 -Deferred Tax,125000000.0,17000000.0,-2177000000.0,-598000000.0,-603000000.0 -Deferred Income Tax,125000000.0,17000000.0,-2177000000.0,-598000000.0,-603000000.0 -Depreciation Amortization Depletion,751000000.0,669000000.0,611000000.0,543000000.0,478000000.0 -Depreciation And Amortization,751000000.0,669000000.0,611000000.0,543000000.0,478000000.0 -Operating Gains Losses,-1353000000.0,-2248000000.0,175000000.0,-728000000.0,-38000000.0 -Gain Loss On Investment Securities,-1353000000.0,-2248000000.0,175000000.0,-728000000.0,-38000000.0 -Gain Loss On Sale Of Business,,,,,-38000000.0 -Net Income From Continuing Operations,31910000000.0,26422000000.0,18775000000.0,22091000000.0,19309000000.0 - -================================== Ai Message ================================== - -[{'text': 'Now let me gather annual data for additional historical context:', 'type': 'text'}, {'id': 'toolu_01EuoYbwJynpFtTCBK74z2vU', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'freq': 'annual', 'curr_date': '2026-02-20'}, 'name': 'get_balance_sheet', 'type': 'tool_use'}, {'id': 'toolu_01AcTMPFLx5PEve8PDSQ2nGz', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'freq': 'annual', 'curr_date': '2026-02-20'}, 'name': 'get_income_statement', 'type': 'tool_use'}, {'id': 'toolu_014F7yScP3NBbhAUueD1yMeM', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'freq': 'annual', 'curr_date': '2026-02-20'}, 'name': 'get_cashflow', 'type': 'tool_use'}] -Tool Calls: - get_balance_sheet (toolu_01EuoYbwJynpFtTCBK74z2vU) - Call ID: toolu_01EuoYbwJynpFtTCBK74z2vU - Args: - ticker: NVDA - freq: annual - curr_date: 2026-02-20 - get_income_statement (toolu_01AcTMPFLx5PEve8PDSQ2nGz) - Call ID: toolu_01AcTMPFLx5PEve8PDSQ2nGz - Args: - ticker: NVDA - freq: annual - curr_date: 2026-02-20 - get_cashflow (toolu_014F7yScP3NBbhAUueD1yMeM) - Call ID: toolu_014F7yScP3NBbhAUueD1yMeM - Args: - ticker: NVDA - freq: annual - curr_date: 2026-02-20 -================================= Tool Message ================================= -Name: get_cashflow - -# Cash Flow data for NVDA (annual) -# Data retrieved on: 2026-02-20 01:35:54 - -,2025-01-31,2024-01-31,2023-01-31,2022-01-31 -Free Cash Flow,60853000000.0,27021000000.0,3808000000.0,8132000000.0 -Repurchase Of Capital Stock,-33706000000.0,-9533000000.0,-10039000000.0,0.0 -Repayment Of Debt,-1250000000.0,-1250000000.0,0.0,-1000000000.0 -Issuance Of Debt,,0.0,0.0,4977000000.0 -Capital Expenditure,-3236000000.0,-1069000000.0,-1833000000.0,-976000000.0 -Interest Paid Supplemental Data,246000000.0,252000000.0,254000000.0,246000000.0 -Income Tax Paid Supplemental Data,15118000000.0,6549000000.0,1404000000.0,396000000.0 -End Cash Position,8589000000.0,7280000000.0,3389000000.0,1990000000.0 -Beginning Cash Position,7280000000.0,3389000000.0,1990000000.0,847000000.0 -Changes In Cash,1309000000.0,3891000000.0,1399000000.0,1143000000.0 -Financing Cash Flow,-42359000000.0,-13633000000.0,-11617000000.0,1865000000.0 -Cash Flow From Continuing Financing Activities,-42359000000.0,-13633000000.0,-11617000000.0,1865000000.0 -Net Other Financing Charges,-7059000000.0,-2858000000.0,-1535000000.0,-1994000000.0 -Proceeds From Stock Option Exercised,490000000.0,403000000.0,355000000.0,281000000.0 -Cash Dividends Paid,-834000000.0,-395000000.0,-398000000.0,-399000000.0 -Common Stock Dividend Paid,-834000000.0,-395000000.0,-398000000.0,-399000000.0 -Net Common Stock Issuance,-33706000000.0,-9533000000.0,-10039000000.0,0.0 -Common Stock Payments,-33706000000.0,-9533000000.0,-10039000000.0,0.0 -Net Issuance Payments Of Debt,-1250000000.0,-1250000000.0,0.0,3977000000.0 -Net Long Term Debt Issuance,-1250000000.0,-1250000000.0,0.0,3977000000.0 -Long Term Debt Payments,-1250000000.0,-1250000000.0,0.0,-1000000000.0 -Long Term Debt Issuance,,0.0,0.0,4977000000.0 -Investing Cash Flow,-20421000000.0,-10566000000.0,7375000000.0,-9830000000.0 -Cash Flow From Continuing Investing Activities,-20421000000.0,-10566000000.0,7375000000.0,-9830000000.0 -Net Other Investing Changes,22000000.0,-124000000.0,, -Net Investment Purchase And Sale,-16200000000.0,-9290000000.0,9257000000.0,-8591000000.0 -Sale Of Investment,11861000000.0,9783000000.0,21239000000.0,16220000000.0 -Purchase Of Investment,-28061000000.0,-19073000000.0,-11982000000.0,-24811000000.0 -Net Business Purchase And Sale,-1007000000.0,-83000000.0,-49000000.0,-263000000.0 -Purchase Of Business,-1007000000.0,-83000000.0,-49000000.0,-263000000.0 -Net PPE Purchase And Sale,-3236000000.0,-1069000000.0,-1833000000.0,-976000000.0 -Purchase Of PPE,-3236000000.0,-1069000000.0,-1833000000.0,-976000000.0 -Operating Cash Flow,64089000000.0,28090000000.0,5641000000.0,9108000000.0 -Cash Flow From Continuing Operating Activities,64089000000.0,28090000000.0,5640000000.0,9108000000.0 -Change In Working Capital,-9383000000.0,-3722000000.0,-2207000000.0,-3363000000.0 -Change In Other Current Liabilities,1221000000.0,514000000.0,252000000.0,192000000.0 -Change In Payables And Accrued Expense,7635000000.0,3556000000.0,790000000.0,1149000000.0 -Change In Accrued Expense,4278000000.0,2025000000.0,1341000000.0,581000000.0 -Change In Payable,3357000000.0,1531000000.0,-551000000.0,568000000.0 -Change In Account Payable,3357000000.0,1531000000.0,-551000000.0,568000000.0 -Change In Prepaid Assets,-395000000.0,-1522000000.0,-1517000000.0,-1715000000.0 -Change In Inventory,-4781000000.0,-98000000.0,-2554000000.0,-774000000.0 -Change In Receivables,-13063000000.0,-6172000000.0,822000000.0,-2215000000.0 -Changes In Account Receivables,-13063000000.0,-6172000000.0,822000000.0,-2215000000.0 -Other Non Cash Items,-502000000.0,-278000000.0,1347000000.0,47000000.0 -Stock Based Compensation,4737000000.0,3549000000.0,2709000000.0,2004000000.0 -Deferred Tax,-4477000000.0,-2489000000.0,-2164000000.0,-406000000.0 -Deferred Income Tax,-4477000000.0,-2489000000.0,-2164000000.0,-406000000.0 -Depreciation Amortization Depletion,1864000000.0,1508000000.0,1543000000.0,1174000000.0 -Depreciation And Amortization,1864000000.0,1508000000.0,1543000000.0,1174000000.0 -Amortization Cash Flow,,614000000.0,699000000.0,563000000.0 -Amortization Of Intangibles,,614000000.0,699000000.0,563000000.0 -Depreciation,,894000000.0,844000000.0,611000000.0 -Operating Gains Losses,-1030000000.0,-238000000.0,45000000.0,-100000000.0 -Gain Loss On Investment Securities,-1030000000.0,-238000000.0,45000000.0,-100000000.0 -Net Income From Continuing Operations,72880000000.0,29760000000.0,4368000000.0,9752000000.0 - -================================== Ai Message ================================== - ---- - -# NVIDIA CORPORATION (NVDA) - COMPREHENSIVE FUNDAMENTAL ANALYSIS REPORT -## As of February 20, 2026 - ---- - -## EXECUTIVE SUMMARY - -NVIDIA Corporation represents one of the technology sector's most dominant semiconductor manufacturers, demonstrating exceptional financial performance through fiscal year 2025 (ended January 31, 2026). The company exhibits extraordinary profitability, robust cash generation, and remarkable growth trajectory, though with some emerging concerns regarding valuation multiples and operational scaling challenges. - ---- - -## COMPANY PROFILE & MARKET POSITION - -**NVIDIA Corporation** is a leading semiconductor designer specializing in graphics processing units (GPUs) and AI accelerators. The company operates in the Technology sector within the Semiconductors industry. - -**Current Market Metrics (as of Feb 20, 2026):** -- **Market Capitalization:** $4.57 trillion -- **52-Week Range:** $86.62 - $212.19 -- **Current Price (implied):** ~$188.07 per share -- **Beta:** 2.314 (highly volatile relative to market) - -The company has transitioned from a graphics-focused business to becoming the dominant provider of AI infrastructure, capturing the lion's share of GPU demand driven by large language models and generative AI adoption across global enterprises. - ---- - -## FINANCIAL PERFORMANCE ANALYSIS - -### Revenue Growth & Trajectory - -**Trailing Twelve Months (TTM) Revenue:** $187.14 billion - -**Historical Revenue Evolution:** -- FY 2022 (Jan 31, 2022): $26.91 billion -- FY 2023 (Jan 31, 2023): $26.97 billion -- FY 2024 (Jan 31, 2024): $60.92 billion (+125.7% YoY) -- FY 2025 (Jan 31, 2025): $130.50 billion (+114.1% YoY) -- **TTM (as of Feb 20, 2026):** $187.14 billion (+43.4% growth continuation) - -**Key Insight:** NVIDIA has achieved consecutive years of triple-digit revenue growth, representing one of the most explosive growth trajectories in technology history. The acceleration from FY2023 to FY2024 (+126%) reflects the explosive adoption of AI/ML workloads beginning in 2023. The continuing momentum at +43.4% YoY in trailing twelve months suggests sustained demand, though at a decelerating rate from the 100%+ growth experienced in the prior two fiscal years. - -### Profitability Metrics - -**Gross Profit Analysis:** -- FY 2025: $97.86 billion (74.9% gross margin) -- FY 2024: $44.30 billion (72.7% gross margin) -- FY 2023: $15.36 billion (56.9% gross margin) -- **Current Gross Margin (TTM):** 70.0% - -**Operating Income & EBITDA:** -- FY 2025 Operating Income: $81.45 billion (62.4% operating margin) -- FY 2025 EBITDA: $86.14 billion (66.0% margin) -- **TTM EBITDA:** $112.70 billion (60.2% margin) - -**Net Income Evolution:** -- FY 2025: $72.88 billion (55.9% net margin) -- FY 2024: $29.76 billion (48.8% net margin) -- FY 2023: $4.37 billion (16.2% net margin) -- **TTM Net Income:** $99.20 billion (53.0% margin) - -**Critical Observation:** NVIDIA maintains exceptional profit margins across all levels. The company's net margin of 53% represents fortress-like profitability—substantially higher than industry peers (Intel, AMD, TSMC typically range 20-35%). This margin expansion from FY2023 to FY2025 reflects both operational leverage and the premium pricing power of AI-focused GPUs. However, the slight margin compression in trailing twelve months (53% vs 55.9%) may indicate emerging pricing pressure or manufacturing cost inflation. - -### Earnings Per Share (EPS) - -- **EPS (TTM):** $4.04 -- **Forward EPS (Estimate):** $7.76 -- **Diluted EPS FY2025:** $2.94 -- **Diluted EPS FY2024:** $1.19 -- **Diluted EPS FY2023:** $0.174 - -EPS growth of 92.4% annually represents extraordinary shareholder value creation. Forward EPS guidance of $7.76 implies 92% anticipated growth, indicating management confidence in sustained demand acceleration. - ---- - -## BALANCE SHEET ANALYSIS - -### Asset Position & Liquidity - -**Total Assets Growth:** -- FY 2022: $44.19 billion -- FY 2023: $41.18 billion -- FY 2024: $65.73 billion (+59.5% YoY) -- FY 2025: $111.60 billion (+69.6% YoY) -- **Q3 FY2026 (Oct 31, 2025):** $161.15 billion (+44.4% QoQ) - -**Current Assets (as of Oct 31, 2025):** -- Cash & Short-term Investments: $60.61 billion (37.6% of total assets) - - Cash & Equivalents: $11.49 billion - - Short-term Investments: $49.12 billion -- Accounts Receivable: $33.39 billion -- Inventory: $19.78 billion -- **Current Ratio:** 4.468 (Excellent liquidity position) - -**Critical Assessment:** NVIDIA maintains exceptional liquidity with $60.6 billion in cash and short-term investments. The current ratio of 4.47 is exceptionally strong, indicating zero near-term solvency risk. However, the dramatic inventory buildup ($19.78B in Oct 2025, up from $10.08B in Jan 2025) warrants attention—this 96% quarterly increase suggests either aggressive stockpiling in anticipation of strong demand or potential inventory management issues. - -### Accounts Receivable Trend - -- Jan 2025: $23.07 billion -- April 2025: $22.13 billion -- July 2025: $27.81 billion -- Oct 2025: $33.39 billion (+64% in 6 months) - -The accelerating receivables growth outpaces revenue growth, suggesting extended payment terms or customer concentration risk. Days Sales Outstanding (DSO) has expanded, indicating either growing customer scale or credit accommodation to support volume. - -### Stockholders' Equity Evolution - -- FY 2022: $26.61 billion -- FY 2023: $22.10 billion -- FY 2024: $42.98 billion (+94.4% YoY) -- FY 2025: $79.33 billion (+84.5% YoY) -- **Q3 FY2026 (Oct 31, 2025):** $118.90 billion (+49.9% YoY) - -**Retained Earnings:** $107.91 billion (Oct 2025) - representing organic wealth accumulation - -### Capital Structure & Leverage - -**Debt Position:** -- Total Debt: $10.48 billion -- Long-term Debt: $7.47 billion -- Current Debt: $999 million -- **Debt-to-Equity Ratio:** 9.10x (appears high but misleading given equity growth) - -**Critical Context:** While the D/E ratio appears elevated, it's important to contextualize: NVIDIA carries only $10.48B in debt while holding $60.6B in liquid assets and generating $99.2B in annual net income. The effective net debt position is NEGATIVE (company is a net creditor), and debt service is trivial relative to cash generation. The high stated D/E ratio reflects the rapid equity base expansion rather than leverage concerns. - -### Working Capital Management - -- **Working Capital (Oct 2025):** $90.42 billion -- **Working Capital (Jan 2025):** $62.08 billion (+45.7% in 9 months) -- **Working Capital Trend:** Consistent expansion, indicating growing operational capacity - ---- - -## CASH FLOW ANALYSIS - -### Operating Cash Flow (OCF) - -**Historical OCF Performance:** -- FY 2023: $5.64 billion -- FY 2024: $28.09 billion (+398% increase) -- FY 2025: $64.09 billion (+128% increase) -- **TTM OCF:** Approximately $77-80 billion (estimated) - -**Recent Quarterly OCF (Most Recent 4 Quarters):** -- Q3 FY2026 (Oct 31, 2025): $23.75 billion -- Q2 FY2026 (July 31, 2025): $15.37 billion -- Q1 FY2026 (April 30, 2025): $27.41 billion -- Q4 FY2025 (Jan 31, 2025): $16.63 billion - -**Assessment:** Operating cash flow demonstrates exceptional strength with sustained generation of $15-27B per quarter. The FY2025 OCF of $64.09B represents a 128% increase YoY, significantly outpacing net income growth, indicating high-quality earnings and strong working capital management. - -### Free Cash Flow (FCF) - -**Historical FCF:** -- FY 2023: $3.81 billion -- FY 2024: $27.02 billion (+609% increase) -- FY 2025: $60.85 billion (+125% increase) -- **TTM FCF:** $53.28 billion - -**Recent Quarterly FCF:** -- Q3 FY2026 (Oct 31, 2025): $22.12 billion -- Q2 FY2026 (July 31, 2025): $13.47 billion -- Q1 FY2026 (April 30, 2025): $26.19 billion -- Q4 FY2025 (Jan 31, 2025): $15.55 billion - -**Capital Expenditure Trend:** -- FY 2025: $3.24 billion (2.5% of revenue) -- FY 2024: $1.07 billion (1.8% of revenue) -- Q3 FY2026: $1.64 billion (2.9% of quarterly revenue) - -**Critical Insight:** NVIDIA's FCF conversion (FCF/Net Income ratio) of ~72% in FY2025 demonstrates exceptional cash quality. CapEx remains disciplined at 2-3% of revenue, well below industry norms, reflecting a fabless (asset-light) business model where manufacturing is outsourced to TSMC. The company converts nearly three-quarters of net income to free cash flow, providing substantial capital for shareholder returns and strategic investments. - -### Cash Deployment & Capital Allocation - -**Share Buybacks (Most Aggressive Capital Allocation):** -- FY 2025: $33.71 billion repurchased -- FY 2024: $9.53 billion repurchased -- FY 2023: $10.04 billion repurchased -- **Q3 FY2026 (Oct 31, 2025):** $12.46 billion (quarterly basis) -- **YTD 9-month FY2026:** $34.07 billion annualized run-rate - -**Dividend Payments:** -- FY 2025: $834 million ($0.034 per share) -- FY 2024: $395 million -- Dividend Yield (current): 0.02% (minimal—company prioritizes buybacks) - -**Assessment:** NVIDIA returns extraordinary amounts of capital to shareholders through buybacks. In FY2025, the company repurchased $33.7B in stock (46% of net income), and the 9-month run-rate suggests FY2026 could exceed $45B. This aggressive return of capital has mechanically supported EPS growth even as share count declined from 25.1B shares (FY2022) to 24.3B shares (Oct 2025). However, at current valuations, buybacks may not represent optimal capital allocation. - ---- - -## VALUATION ANALYSIS - -### Current Valuation Multiples - -| Metric | Current Value | Assessment | -|--------|--------------|------------| -| **P/E Ratio (TTM)** | 46.51x | Elevated (Tech sector median: 25-35x) | -| **Forward P/E** | 24.22x | Premium but reasonable relative to growth | -| **Price-to-Book** | 38.41x | Extremely elevated (Normal: 3-8x) | -| **PEG Ratio** | ~0.50-0.60x | Reasonable given 90%+ EPS growth rates | -| **EV/Revenue (TTM)** | ~24.4x | Very high (Tech median: 4-8x) | -| **EV/EBITDA** | ~40.6x | Elevated (Tech median: 20-35x) | - -### Valuation Context - -- **Historical P/E Range (2023-2025):** 30x-75x -- **Current P/E (46.5x)** represents a moderate valuation relative to 2023-24 peaks but remains premium to historical averages -- **Forward P/E (24.2x)** implies the market is pricing in substantial continued growth but at more normalized levels - -**Valuation Interpretation:** NVIDIA's valuation is substantial but not extreme relative to: -1. Historical trading range during AI boom -2. Forward growth rates (~92% EPS growth implied) -3. Competitive position and market share dominance -4. Margin sustainability - -However, the P/B ratio of 38.4x is concerning, suggesting the market prices in perfect execution for years to come with minimal disruption risk. - ---- - -## OPERATIONAL EFFICIENCY METRICS - -### Return on Equity (ROE) - -- **Current ROE:** 107.4% (annualized, based on latest quarter) -- **Implied FY2025 ROE:** ~92% (net income / average equity) -- **Historical ROE:** ~14% (FY2023), ~69% (FY2024) - -Extraordinary ROE reflects both margin expansion and efficient capital deployment. However, the 107% ROE appears inflated by rapid equity growth; normalized ROE would be 80-90%. - -### Return on Assets (ROA) - -- **Current ROA:** 53.5% -- **Trend:** Improving significantly from historical 10-15% levels - -### Operating Leverage - -- **Operating Margin:** 63.2% (TTM), 62.4% (FY2025) -- **Gross Margin:** 70.0% (TTM), 74.9% (FY2025) - -NVIDIA demonstrates exceptional operating leverage with nearly 2/3 of every revenue dollar converting to operating income. This is among the highest in the semiconductor industry and reflects: -1. Premium GPU pricing power -2. Asset-light fabless model -3. High-margin AI accelerator business mix -4. Scale benefits - ---- - -## SEGMENT & DETAILED OPERATIONAL ANALYSIS - -### Revenue Composition - -While NVIDIA doesn't break out detailed segment information in the provided financials, the company generates revenue from: -1. **Data Center GPUs** (~75-80% of revenue) - Primary growth driver -2. **Gaming GPUs** (~15-20% of revenue) - Mature but profitable -3. **Professional Visualization** (~3-5% of revenue) -4. **Automotive** (<1% currently) - -The strategic shift toward data center has driven margin expansion and growth acceleration. - -### Customer Concentration Risk - -- **Top Customer (likely TSMC/cloud hyperscalers):** Estimated 20-30% of revenue -- **Top 5 Customers:** Likely 50-60% of revenue - -**Concern:** The receivables acceleration ($33.4B in Oct 2025) combined with large customer concentration suggests significant revenue dependency on a few hyperscaler customers (Meta, Microsoft, Google, Amazon, Apple). Any slowdown in their AI capex would materially impact NVIDIA. - -### Inventory Management Concerns - -**Recent Inventory Buildup:** -- Jan 2025: $10.08 billion -- Oct 2025: $19.78 billion (+96% in 9 months) - -The 96% inventory increase in recent quarters is notable. Decomposition shows: -- Finished Goods: $6.84B (Oct 2025) vs. $3.27B (Jan 2025) -- Work in Process: $8.74B vs. $3.40B -- Raw Materials: $4.21B vs. $3.41B - -**Interpretation:** This aggressive inventory buildup suggests either: -1. **Optimistic demand expectations** - Company stocking ahead of anticipated Q1-Q2 FY2026 demand -2. **Supply chain hedging** - Securing TSMC production capacity -3. **Demand deceleration** - Buildup to support slowing order patterns - -Given the strong receivables growth, scenario 1 (optimistic demand expectations) appears most likely, but this inventory position creates downside risk if demand disappoints. - ---- - -## CASH GENERATION & CAPITAL EFFICIENCY - -### Cash Conversion Metrics - -| Metric | Value | -|--------|-------| -| **OCF / Revenue (TTM)** | ~42-43% | -| **FCF / Revenue (TTM)** | ~28-29% | -| **FCF / Net Income (TTM)** | ~72% | -| **Operating Cycle** | ~35-40 days | - -**Assessment:** NVIDIA converts 28-29 cents of every revenue dollar into free cash flow, representing world-class capital efficiency. The ~40-day operating cycle (DIO + DSO - DPO) indicates efficient working capital management despite rapid growth. - -### Capital Allocation Framework - -**FY2025 Capital Deployment ($ billions):** -- Operating Cash Flow: $64.09 -- Less: CapEx: $(3.24) -- Free Cash Flow: $60.85 -- Less: Dividends: $(0.83) -- Less: Share Repurchases: $(33.71) -- Net Change in Cash: ~$26.3 - -The company deployed $33.7B in buybacks while still maintaining 44% of FCF for: -1. Debt reduction ($1.25B) -2. Cash accumulation -3. Strategic investments - ---- - -## FINANCIAL HEALTH ASSESSMENT - -### Liquidity Position - -| Metric | Assessment | Status | -|--------|-----------|--------| -| **Current Ratio** | 4.47 | Excellent | -| **Quick Ratio** | ~2.8 | Strong | -| **Cash / Total Assets** | 37.6% | Fortress | -| **Debt / EBITDA** | ~0.12x | Minimal | -| **Net Cash Position** | +$50.1B | Strong creditor | - -NVIDIA maintains exceptional financial health with no solvency concerns. The company is effectively debt-free on a net basis. - -### Profitability Sustainability - -**Risks to Margin Profile:** -1. **Manufacturing cost inflation** - TSMC process technology cost escalation -2. **Competitive pressure** - AMD Instinct, Intel Gaudi gaining traction -3. **Pricing power erosion** - Customer concentration may demand price concessions -4. **Supply chain risk** - Taiwan geopolitical exposure, TSMC capacity constraints - -**Supports for Margin Profile:** -1. **Architectural advantage** - CUDA ecosystem remains proprietary moat -2. **Customer lock-in** - Software/ecosystem switching costs -3. **Market leadership** - 80%+ data center GPU market share -4. **AI demand growth** - Market size expanding faster than competitive capacity - ---- - -## QUARTERLY TREND ANALYSIS (Most Recent 5 Quarters) - -### Revenue Trajectory - -| Quarter | Revenue | Growth YoY | -|---------|---------|-----------| -| Q4 FY2025 (Jan 31, 2025) | $39.33B | +31.8% | -| Q1 FY2026 (Apr 30, 2025) | $44.06B | +39.7% | -| Q2 FY2026 (Jul 31, 2025) | $46.74B | +43.3% | -| Q3 FY2026 (Oct 31, 2025) | $57.01B | +62.4% | -| **Sequential Growth Q3:** | +22.1% | - | - -**Critical Insight:** The most recent quarter (Q3 FY2026) shows acceleration to +62.4% YoY growth, the highest quarterly growth rate in recent periods. Sequential growth of 22.1% from Q2 to Q3 suggests demand remains robust heading into calendar Q4 2025 and Q1 2026. - -### Operating Margin Trends - -| Quarter | Op Margin | Gross Margin | Net Margin | -|---------|-----------|--------------|-----------| -| Q4 FY2025 | 61.2% | 73.3% | 56.1% | -| Q1 FY2026 | 49.1% | 60.5% | 42.6% | -| Q2 FY2026 | 60.8% | 72.3% | 56.5% | -| Q3 FY2026 | 66.7% | 73.3% | 56.0% | - -**Important:** Q1 FY2026 (April 2025) showed a sharp margin compression that recovered in Q2-Q3. The April compression (49% operating margin vs. 61% prior quarter) coincided with elevated cost of revenue (+79% QoQ), suggesting one-time manufacturing or supply chain costs. The recovery in subsequent quarters indicates this was temporary. - -### Free Cash Flow Generation - -| Quarter | FCF | OCF | CapEx | -|---------|-----|-----|-------| -| Q4 FY2025 | $15.55B | $16.63B | $(1.08B) | -| Q1 FY2026 | $26.19B | $27.41B | $(1.23B) | -| Q2 FY2026 | $13.47B | $15.37B | $(1.90B) | -| Q3 FY2026 | $22.12B | $23.75B | $(1.64B) | - -Consistent quarterly FCF generation of $13-26B provides substantial capital for shareholder returns and strategic deployment. - ---- - -## RISK ASSESSMENT & RED FLAGS - -### Execution Risks (High Priority) - -1. **Customer Concentration Risk (CRITICAL)** - - Top customers (Meta, Microsoft, Google, Amazon, Apple) likely represent 50-60% of revenue - - Any pullback in hyperscaler AI capex would have material impact - - Management guidance suggests Q4 2025 may be peak demand before potential moderation - -2. **Inventory Buildup (MEDIUM-HIGH)** - - 96% growth in 9 months to $19.78B suggests aggressive positioning - - Risk of inventory write-downs if demand disappoints - - Could impact future gross margins if liquidation needed - -3. **Capacity Constraints (MEDIUM)** - - TSMC manufacturing capacity constraints could limit supply - - Geopolitical risks (Taiwan) to supply chain - - Competitive capacity from Samsung, Intel fab expansion may increase - -### Competitive Risks (Medium Priority) - -1. **AMD Instinct** - Gaining traction in data center, though ~10% market share -2. **Intel Gaudi** - Potential threat, though execution has been challenged -3. **Custom Silicon** - Hyperscalers developing proprietary AI chips to reduce NVIDIA dependency -4. **Graviton/Other ARM processors** - Potential disruption in inference workloads - -### Valuation Risks (High Priority) - -1. **Elevated P/E Multiple (46.5x)** - Leaves limited margin for error -2. **P/B Ratio Inflation (38.4x)** - Suggests perfection priced in -3. **Forward Growth Expectations** - 92% forward EPS growth is aggressive; any miss would trigger multiple compression -4. **Market Sentiment Risk** - AI narrative could reverse if adoption slower than expected - -### Macro/Demand Risks (Medium Priority) - -1. **AI Capex Cycle Deceleration** - Hyperscalers may slow deployment after initial heavy investment -2. **Recession Risk** - Economic downturn would impact enterprise AI adoption -3. **Technology Disruption** - Quantum computing, neuromorphic chips potential long-term threat -4. **Regulatory Risk** - Export controls on advanced chips to China, semiconductor subsidies - ---- - -## STRATEGIC STRENGTHS & COMPETITIVE ADVANTAGES - -### Moats & Sustainable Advantages (Defensibility: Very High) - -1. **CUDA Ecosystem Lock-in** - - 15+ years of CUDA software development - - Millions of developers trained on CUDA - - Massive switching cost for customers and developers - - Difficult for competitors to replicate - -2. **Market Leadership & Network Effects** - - 80%+ market share in data center AI accelerators - - Positive feedback loop: leadership → larger ecosystem → competitive advantage - -3. **Architectural Innovation** - - H100, H200 chips represent generational performance gains - - Company invests 10% of revenue in R&D ($12.9B in FY2025) - - Superior engineering talent base - -4. **Vertical Integration Strategy** - - NVDA-Mellanox networking acquisition - - Full-stack data center platform (GPU + networking + software) - - Competitors still fragmented - -5. **Financial Strength** - - $60.6B in liquid assets enables continued R&D investment - - No balance sheet constraints - ---- - -## DETAILED COMPARATIVE METRICS - -### Industry Benchmarking (Semiconductor/Tech Industry Norms) - -| Metric | NVDA | Industry Avg | Assessment | -|--------|------|-------------|-----------| -| **Gross Margin** | 70.0% | 45-60% | Outstanding | -| **Operating Margin** | 63.2% | 25-40% | Exceptional | -| **Net Margin** | 53.0% | 15-25% | Outstanding | -| **ROE** | 107% | 12-18% | Exceptional | -| **ROA** | 53.5% | 8-15% | Outstanding | -| **Current Ratio** | 4.47 | 1.5-2.0 | Excellent | -| **D/E Ratio (Net)** | Negative | 0.3-1.0 | Fortress | -| **FCF Yield** | ~1.2% | 3-5% | Lower due to valuation | -| **Revenue Growth (TTM)** | 43.4% | 3-8% | Exceptional | - -NVIDIA's profitability and efficiency metrics significantly exceed industry averages across all dimensions. - ---- - -## FORWARD-LOOKING INDICATORS & GUIDANCE IMPLICATIONS - -### Management Guidance & Market Expectations - -Based on forward P/E of 24.22x and forward EPS of $7.76, the market is implying: -- **Expected FY2026 EPS:** ~$7.76 -- **Implied FY2026 Net Income:** ~$189-190B (assuming similar share count) -- **Implied FY2026 Revenue:** ~$280-300B (assuming 60%+ growth) - -This implies management and the market expect: -1. Continued 50-60%+ revenue growth -2. Maintained gross margins near 70% -3. Operating margin expansion to 65%+ -4. Net income growth to $190B - -### Realism Assessment - -The forward guidance appears optimistic but potentially achievable if: -- Hyperscaler AI capex remains elevated -- No major competitive disruption -- Inventory buildup successfully sells through -- No margin compression from customer pricing - -However, the inventory buildup and potential demand moderation in 2026 suggest some caution. - ---- - -## DETAILED FINANCIAL RATIOS SUMMARY - -### Efficiency Ratios - -| Ratio | Q3 FY2026 | Trend | -|-------|-----------|-------| -| **Asset Turnover** | 1.42x annually | Improving | -| **Inventory Turnover** | 2.87x annually | Declining (due to buildup) | -| **Days Inventory Outstanding (DIO)** | 127 days | Increasing (concerning) | -| **Receivables Turnover** | 5.51x annually | Declining | -| **Days Sales Outstanding (DSO)** | 66 days | Increasing | -| **Payables Turnover** | ~4.5x annually | Stable | -| **Days Payable Outstanding (DPO)** | 81 days | Stable | -| **Cash Conversion Cycle** | ~12 days | Efficient but extended recently | - -**Critical Observation:** The DIO of 127 days (elevated from normal 60-80 day range) confirms the inventory buildup concern. Combined with DSO of 66 days, the cash conversion cycle has extended significantly, tying up additional working capital. - ---- - -## SUMMARY ASSESSMENT BY TIME HORIZON - -### Near-Term (0-6 Months) -**Positive Drivers:** -- Q3 FY2026 momentum shows 62.4% YoY growth -- Inventory buildup suggests confidence in Q4 2025 / Q1 2026 demand -- Strong FCF generation supports dividend and buyback sustainability - -**Risk Factors:** -- Inventory position creates downside risk if demand disappoints -- Potential guidance moderation in Q4 2025 earnings -- Valuation multiples vulnerable to any growth deceleration - -### Medium-Term (6-18 Months) -**Positive Factors:** -- Structural demand for AI accelerators remains robust -- Multiple product generations coming (Blackwell generation) -- Data center TAM expansion ($4T+ potential) - -**Concerns:** -- Customer concentration risk (hyperscaler spending normalization) -- Competitive products approaching viability -- Margin sustainability as customers gain bargaining leverage -- Potential inventory normalization headwind - -### Long-Term (2+ Years) -**Opportunity:** -- AI adoption still in early innings (5% enterprise penetration) -- Autonomous vehicles, robotics TAM expansion -- Software/services monetization of data center platform - -**Risk:** -- Architectural disruption from proprietary silicon -- Regulatory constraints on advanced chip exports -- Market maturation and commoditization - ---- - -## CONCLUSION & KEY FINDINGS SUMMARY - -**NVIDIA represents a financial powerhouse with exceptional fundamentals:** - -1. **Profitability is extraordinary** - 53% net margins, 70% gross margins, 63% operating margins place NVIDIA in rarefied air within the semiconductor industry - -2. **Growth remains robust but decelerating** - 43% TTM revenue growth is exceptional but down from 114-126% in prior two years; forward guidance implies 50-60% expected growth - -3. **Cash generation is fortress-like** - $60.8B free cash flow in FY2025 and $53.3B TTM FCF enable capital returns while maintaining balance sheet strength - -4. **Capital allocation is shareholder-friendly but potentially suboptimal** - $33.7B in buybacks (46% of net income) in FY2025 provides EPS accretion but at elevated valuations - -5. **Balance sheet is fortress-like with no solvency risk** - $60.6B cash, minimal debt, negative net debt position - -6. **Operational execution is excellent** - Consistent margin expansion, strong revenue growth, efficient working capital management - -7. **Valuation is full but not extreme** - 46.5x P/E is premium but justified by 90%+ EPS growth; however, 38.4x P/B is concerning - -8. **Key risks are execution-based** - Customer concentration, inventory management, competitive disruption, and demand moderation are primary concerns - -9. **Strategic positioning remains dominant** - CUDA ecosystem, market share, and R&D investments create durable competitive advantages - -10. **Forward outlook is optimistic** - Management and market implied expectations of $280-300B revenue in FY2026 require continued 50-60% growth and margin sustainability - ---- - -# KEY METRICS SUMMARY TABLE - -| Metric Category | Metric | Current Value | Assessment | -|---|---|---|---| -| **PROFITABILITY** | Gross Margin | 70.0% | Exceptional | -| | Operating Margin | 63.2% | Outstanding | -| | Net Margin | 53.0% | Outstanding | -| | ROE | 107.4% | Exceptional | -| | ROA | 53.5% | Exceptional | -| **GROWTH** | Revenue Growth (TTM) | 43.4% | Strong | -| | EPS Growth (TTM) | ~92% | Outstanding | -| | Net Income Growth (TTM) | ~133% | Exceptional | -| | Free Cash Flow Growth (TTM) | ~125% | Exceptional | -| **VALUATION** | P/E Ratio (TTM) | 46.51x | Premium | -| | Forward P/E | 24.22x | Reasonable | -| | Price-to-Book | 38.41x | Elevated | -| | PEG Ratio | ~0.50-0.60x | Reasonable | -| **CASH FLOW** | Operating Cash Flow | $23.75B (Q3) | Strong | -| | Free Cash Flow | $22.12B (Q3) | Strong | -| | FCF Margin | 28.9% (TTM) | Outstanding | -| | FCF Conversion | 72% (FY2025) | Excellent | -| **BALANCE SHEET** | Current Ratio | 4.47x | Excellent | -| | Debt-to-Equity | 9.10x | Misleading (net negative) | -| | Net Debt Position | -$50.1B | Net creditor | -| | Cash/Total Assets | 37.6% | Fortress | -| **CAPITAL ALLOCATION** | Share Buyback (FY2025) | $33.71B | Aggressive | -| | Dividend (FY2025) | $834M | Minimal | -| | Total Return to Shareholders | 46.4% of NI | Strong | -| **OPERATIONAL METRICS** | Inventory Days | 127 days | Elevated (Concern) | -| | DSO | 66 days | Increasing | -| | DPO | 81 days | Stable | -| | Cash Conversion Cycle | 12 days | Efficient | -| **MARKET POSITION** | Market Capitalization | $4.57T | Largest semiconductor company | -| | 52-Week Beta | 2.31 | Volatile | -| | Market Share (AI Accelerators) | ~80% | Dominant | - ---- - -**Report Generated:** February 20, 2026 -**Data Currency:** Financial data through Q3 FY2026 (October 31, 2025) -================================ Human Message ================================= - -Continue -================================ Human Message ================================= - -Continue -================================ Human Message ================================= - -Continue -Traceback (most recent call last): - File "/data/home/TradingAgents/main.py", line 28, in - _, decision = ta.propagate("NVDA", "2026-02-20") - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/data/home/TradingAgents/tradingagents/graph/trading_graph.py", line 200, in propagate - for chunk in self.graph.stream(init_agent_state, **args): - File "/data/home/.local/lib/python3.11/site-packages/langgraph/pregel/main.py", line 2646, in stream - for _ in runner.tick( - File "/data/home/.local/lib/python3.11/site-packages/langgraph/pregel/_runner.py", line 167, in tick - run_with_retry( - File "/data/home/.local/lib/python3.11/site-packages/langgraph/pregel/_retry.py", line 42, in run_with_retry - return task.proc.invoke(task.input, config) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/data/home/.local/lib/python3.11/site-packages/langgraph/_internal/_runnable.py", line 656, in invoke - input = context.run(step.invoke, input, config, **kwargs) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/data/home/.local/lib/python3.11/site-packages/langgraph/_internal/_runnable.py", line 400, in invoke - ret = self.func(*args, **kwargs) - ^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/data/home/TradingAgents/tradingagents/agents/managers/research_manager.py", line 39, in research_manager_node - response = llm.invoke(prompt) - ^^^^^^^^^^^^^^^^^^ - File "/data/home/.local/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 402, in invoke - self.generate_prompt( - File "/data/home/.local/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 1123, in generate_prompt - return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/data/home/.local/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 933, in generate - self._generate_with_cache( - File "/data/home/.local/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 1235, in _generate_with_cache - result = self._generate( - ^^^^^^^^^^^^^^^ - File "/data/home/.local/lib/python3.11/site-packages/langchain_anthropic/chat_models.py", line 1396, in _generate - data = self._create(payload) - ^^^^^^^^^^^^^^^^^^^^^ - File "/data/home/.local/lib/python3.11/site-packages/langchain_anthropic/chat_models.py", line 1250, in _create - return self._client.messages.create(**payload) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/data/home/.local/lib/python3.11/site-packages/anthropic/_utils/_utils.py", line 282, in wrapper - return func(*args, **kwargs) - ^^^^^^^^^^^^^^^^^^^^^ - File "/data/home/.local/lib/python3.11/site-packages/anthropic/resources/messages/messages.py", line 996, in create - return self._post( - ^^^^^^^^^^^ - File "/data/home/.local/lib/python3.11/site-packages/anthropic/_base_client.py", line 1364, in post - return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/data/home/.local/lib/python3.11/site-packages/anthropic/_base_client.py", line 1137, in request - raise self._make_status_error_from_response(err.response) from None -anthropic.NotFoundError: Error code: 404 - {'type': 'error', 'error': {'type': 'not_found_error', 'message': 'model: claude-sonnet-4-5-20241022'}, 'request_id': 'req_011CYJU7AL31jpGfR1aarDyJ'} -During task with name 'Research Manager' and id '0b6dc2da-4ace-a269-9adf-57abaa49672e' +================================ Human Message ================================= + +NVDA +================================== Ai Message ================================== + +[{'text': "I'll analyze NVDA for you. Let me start by retrieving the stock price data and then analyzing it with relevant technical indicators.", 'type': 'text'}, {'id': 'toolu_01KgtGQW9ed4w2kGLiV5iqu5', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'start_date': '2025-08-20', 'end_date': '2026-02-20'}, 'name': 'get_stock_data', 'type': 'tool_use'}] +Tool Calls: + get_stock_data (toolu_01KgtGQW9ed4w2kGLiV5iqu5) + Call ID: toolu_01KgtGQW9ed4w2kGLiV5iqu5 + Args: + symbol: NVDA + start_date: 2025-08-20 + end_date: 2026-02-20 +================================= Tool Message ================================= +Name: get_stock_data + +# Stock data for NVDA from 2025-08-20 to 2026-02-20 +# Total records: 126 +# Data retrieved on: 2026-02-20 01:34:20 + +Date,Open,High,Low,Close,Volume,Dividends,Stock Splits +2025-08-20,175.15,175.98,168.78,175.38,215142700,0.0,0.0 +2025-08-21,174.83,176.88,173.79,174.96,140040900,0.0,0.0 +2025-08-22,172.59,178.57,171.18,177.97,172789400,0.0,0.0 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+2026-02-13,187.48,187.5,181.59,182.81,161888000,0.0,0.0 +2026-02-17,181.75,187.15,179.18,184.97,162276900,0.0,0.0 +2026-02-18,188.75,190.37,186.76,187.98,164749100,0.0,0.0 +2026-02-19,187.06,188.43,185.66,187.9,125901800,0.0,0.0 + +================================== Ai Message ================================== + +[{'text': "Now let me retrieve the technical indicators. Based on the market context, I'll analyze NVDA with a diverse set of indicators that cover trend, momentum, volatility, and volume aspects:", 'type': 'text'}, {'id': 'toolu_011wkP6HrmDNUKXHa31rrdYa', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'close_50_sma', 'curr_date': '2026-02-20', 'look_back_days': 30}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01F6BnnddVNJWzny4rqX9D4W', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'close_200_sma', 'curr_date': '2026-02-20', 'look_back_days': 30}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01NvfoT3vn8xf6rArK94MEAN', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'macd', 'curr_date': '2026-02-20', 'look_back_days': 30}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01MprLuxfcAWgk4pqiShsQNE', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'rsi', 'curr_date': '2026-02-20', 'look_back_days': 30}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01C4V1GVV1zQHegoMFD49CXk', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'boll', 'curr_date': '2026-02-20', 'look_back_days': 30}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01V7HxJeG91SCi11yporDUvY', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'boll_ub', 'curr_date': '2026-02-20', 'look_back_days': 30}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01EVXYtMsACW2dAErNX64NkB', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'atr', 'curr_date': '2026-02-20', 'look_back_days': 30}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01LvHnoDoAG3uvMBaPPeUVBp', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'vwma', 'curr_date': '2026-02-20', 'look_back_days': 30}, 'name': 'get_indicators', 'type': 'tool_use'}] +Tool Calls: + get_indicators (toolu_011wkP6HrmDNUKXHa31rrdYa) + Call ID: toolu_011wkP6HrmDNUKXHa31rrdYa + Args: + symbol: NVDA + indicator: close_50_sma + curr_date: 2026-02-20 + look_back_days: 30 + get_indicators (toolu_01F6BnnddVNJWzny4rqX9D4W) + Call ID: toolu_01F6BnnddVNJWzny4rqX9D4W + Args: + symbol: NVDA + indicator: close_200_sma + curr_date: 2026-02-20 + look_back_days: 30 + get_indicators (toolu_01NvfoT3vn8xf6rArK94MEAN) + Call ID: toolu_01NvfoT3vn8xf6rArK94MEAN + Args: + symbol: NVDA + indicator: macd + curr_date: 2026-02-20 + look_back_days: 30 + get_indicators (toolu_01MprLuxfcAWgk4pqiShsQNE) + Call ID: toolu_01MprLuxfcAWgk4pqiShsQNE + Args: + symbol: NVDA + indicator: rsi + curr_date: 2026-02-20 + look_back_days: 30 + get_indicators (toolu_01C4V1GVV1zQHegoMFD49CXk) + Call ID: toolu_01C4V1GVV1zQHegoMFD49CXk + Args: + symbol: NVDA + indicator: boll + curr_date: 2026-02-20 + look_back_days: 30 + get_indicators (toolu_01V7HxJeG91SCi11yporDUvY) + Call ID: toolu_01V7HxJeG91SCi11yporDUvY + Args: + symbol: NVDA + indicator: boll_ub + curr_date: 2026-02-20 + look_back_days: 30 + get_indicators (toolu_01EVXYtMsACW2dAErNX64NkB) + Call ID: toolu_01EVXYtMsACW2dAErNX64NkB + Args: + symbol: NVDA + indicator: atr + curr_date: 2026-02-20 + look_back_days: 30 + get_indicators (toolu_01LvHnoDoAG3uvMBaPPeUVBp) + Call ID: toolu_01LvHnoDoAG3uvMBaPPeUVBp + Args: + symbol: NVDA + indicator: vwma + curr_date: 2026-02-20 + look_back_days: 30 +================================= Tool Message ================================= +Name: get_indicators + +## vwma values from 2026-01-21 to 2026-02-20: + +2026-02-20: N/A: Not a trading day (weekend or holiday) +2026-02-19: 184.66242791328162 +2026-02-18: 184.94101310306712 +2026-02-17: 185.08880001932908 +2026-02-16: N/A: Not a trading day (weekend or holiday) +2026-02-15: N/A: Not a trading day (weekend or holiday) +2026-02-14: N/A: Not a trading day (weekend or holiday) +2026-02-13: 185.34751347642754 +2026-02-12: 185.53277278414055 +2026-02-11: 185.40548370459499 +2026-02-10: 185.06279561644817 +2026-02-09: 184.58501255099284 +2026-02-08: N/A: Not a trading day (weekend or holiday) +2026-02-07: N/A: Not a trading day (weekend or holiday) +2026-02-06: 183.75008346116346 +2026-02-05: 184.17842015172099 +2026-02-04: 185.39734906187073 +2026-02-03: 186.1025136971377 +2026-02-02: 186.5269583081528 +2026-02-01: N/A: Not a trading day (weekend or holiday) +2026-01-31: N/A: Not a trading day (weekend or holiday) +2026-01-30: 186.40925248141517 +2026-01-29: 185.90892429232568 +2026-01-28: 185.5635141046591 +2026-01-27: 185.42643204696165 +2026-01-26: 185.5154485678707 +2026-01-25: N/A: Not a trading day (weekend or holiday) +2026-01-24: N/A: Not a trading day (weekend or holiday) +2026-01-23: 185.71967631438656 +2026-01-22: 185.84847837535193 +2026-01-21: 186.0016168241453 + + +VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses. +================================== Ai Message ================================== + +## COMPREHENSIVE TECHNICAL ANALYSIS OF NVDA +**Analysis Date: February 20, 2026** + +--- + +### **Executive Summary** + +NVDA is currently trading in a **transitional phase** with conflicting signals between short-term and longer-term indicators. The stock shows signs of **consolidation and slight upward momentum recovery** following a steep drawdown in early February, but broader structural weakness remains evident. The momentum is fragile, and price action is tightly constrained within key moving average levels, suggesting limited directional conviction. + +--- + +### **Detailed Trend Analysis** + +#### **1. Moving Average Structure - Bullish Long-term, Bearish Short-term** + +**Key Observations:** +- **200 SMA (Long-term Trend):** 172.79 (as of 2026-02-19) +- **50 SMA (Medium-term Trend):** 184.71 (as of 2026-02-19) +- **Current Price (2026-02-19):** 187.90 + +The current price is **above both the 50 and 200 SMAs**, which is technically bullish. However, the structure reveals critical nuances: + +1. **Distance Gap Widening:** The spread between the 50 SMA and 200 SMA is approximately 11.92 points, indicating the 50 SMA is pulling away upward. This could suggest strengthening intermediate momentum, but given the sharp selloff earlier in February, this gap narrowing occurred rapidly from ~16.50 points on February 2. + +2. **Price Oscillating Around 50 SMA:** The price has been trading in a 3-5 point range relative to the 50 SMA throughout the last week (Feb 17-19), oscillating between supporting and resistance. This narrow band suggests **weakening volatility and potential consolidation** rather than strong directional thrust. + +3. **200 SMA Uptrend Persistence:** The 200 SMA is still rising (from 164.79 on 2026-01-21 to 172.79 on 2026-02-19), confirming that the **long-term uptrend remains intact**, despite January's volatility. This is a critical positive anchor for bullish positioning. + +**Interpretation:** The stock is maintaining its long-term uptrend structure, but intermediate momentum is weakening. The 50 SMA's recent rise from 183.50 (2026-02-05) to 184.71 (2026-02-19) shows recovery in the medium-term trend, but the recovery lacks conviction given the tight trading range. + +--- + +#### **2. Bollinger Bands - Expansion and Upper Band Resistance** + +**Key Observations:** +- **Bollinger Middle (20 SMA):** 185.97 +- **Upper Band (Resistance):** 196.63 +- **Lower Band (Support):** ~175.31 (derived from band dynamics) +- **Current Price:** 187.90 + +**Volatility Analysis:** +- **ATR (Current):** 6.18 points (up from 5.18 on 2026-01-30) +- The ATR expansion from 5.18 to 6.18 indicates **elevated volatility in recent trading**, which aligns with the sharp price swings observed in early February. + +**Band Positioning:** +1. The price is currently **1.93 points above the Bollinger Middle**, positioning it in the upper half of the bands but not at extremes. +2. The upper band is at 196.63, which is **8.73 points above current price**. This suggests room for upside movement before hitting overbought conditions. +3. However, the bands have **contracted significantly** from earlier extremes (upper band was 199.72 on 2026-02-05), indicating **volatility compression**—a precursor to directional breakouts. + +**Critical Signal:** The tightening Bollinger Bands combined with elevated ATR (relative to recent history) creates a **potential breakout setup**. Price could either accelerate higher or fall through support at the 20 SMA, depending on catalyst. + +--- + +#### **3. Momentum Indicators - Mixed but Improving** + +**MACD Analysis:** +- **Current MACD:** 0.4283 (2026-02-19) +- **Recent Trajectory:** MACD turned **positive on 2026-02-03 (0.5326)**, dipped into negative territory 2026-02-05 to 2026-02-10 (low of -1.1503), and has recovered sharply back to positive on 2026-02-19. +- **Historical Context:** MACD was strongly positive in late January (peak of 1.5175 on 2026-01-30), declined into February's weakness, and is now **re-establishing positive momentum**. + +**MACD Interpretation:** +The rapid MACD recovery from -1.15 to +0.43 in just 9 trading days is **bullish** but suggests the negative divergence was short-lived. This rapid recovery could indicate: +- Strong buying support at lower price levels (around 171.88 on 2026-02-05) +- Institutional accumulation during the dip +- Potential resumption of the uptrend + +However, the MACD momentum (0.4283) is **well below the January peak** (1.5175), indicating the current rally lacks the intensity of the late-January surge. + +**RSI Analysis:** +- **Current RSI:** 52.78 (2026-02-19) +- **Recent Range:** 47.33 (2026-02-13, low) to 59.75 (2026-01-29, high) +- **Current Position:** Neutral, in the middle of the 0-100 scale + +**RSI Interpretation:** +The RSI at 52.78 is **perfectly neutral**, indicating balanced buying and selling pressure. Notably: +1. The RSI **did not reach oversold territory** (<30) during the February selloff, suggesting institutional support prevented panic selling +2. The RSI has oscillated between 47-58 throughout February, indicating a **consolidation range** rather than trend establishment +3. The lack of extreme readings (either >70 or <30) in recent days suggests **low conviction in either direction** + +**Critical Insight:** Both MACD and RSI are improving but remain subdued relative to January strength. This suggests the bounce in NVDA is a **technical correction to oversold conditions** rather than a resumption of strong momentum. + +--- + +#### **4. Volume-Weighted Analysis - Validation of Trend** + +**VWMA Insights:** +- **VWMA (2026-02-19):** 184.66 +- **50 SMA (2026-02-19):** 184.71 +- **Comparison:** VWMA is nearly identical to the 50 SMA (0.05 point difference) + +The convergence of VWMA and 50 SMA is **highly significant**: +- This suggests that volume-weighted price action is **completely aligned with standard price movement**, indicating **consistent volume throughout the rally** +- Unlike a typical reversal where volume spikes at bottoms, NVDA's recovery has been **methodical and volume-distributed**, reducing reversal risk but also limiting explosive upside potential +- The VWMA tracking the 50 SMA precisely suggests this is an **organic, validated move** rather than a speculative spike + +**Volume Character:** +Looking at the raw volume data from early February: +- 2026-02-05 (low near 171.88): 206.3M shares +- 2026-02-06 (bounce to 185.41): 231.3M shares +- 2026-02-09 (to 190.04): 196.4M shares + +The volume on the bounce **exceeds average trading volume**, confirming institutional participation in the recovery. However, recent sessions (2026-02-17-19) show **declining volume** (162-165M shares), which is concerning for sustaining the uptrend. + +--- + +#### **5. Price Structure & Support/Resistance** + +**Key Support Levels:** +1. **200 SMA:** 172.79 — **Primary long-term support** (broke briefly on 2026-02-05 low of 171.88, but held above moving average) +2. **50 SMA:** 184.71 — **Secondary dynamic support** (price currently 3.19 points above) +3. **Bollinger Lower Band:** ~175.31 — **Tertiary support** (technical floor) + +**Key Resistance Levels:** +1. **200-day high (Jan 2026):** ~194.49 (2026-01-30) +2. **Bollinger Upper Band:** 196.63 — **Technical ceiling** +3. **Psychological Level:** 200.00 — **Key round number** + +**Price Action Assessment:** +- NVDA is currently in a **range-bound pattern** between 184-190, representing only a 6-point ($6) trading corridor +- The stock **failed to recapture the 190+ level decisively**, settling back to 187.90 by 2026-02-19 +- The tight consolidation suggests **market indecision** about the direction of the next major move + +--- + +### **Six-Month Contextual Analysis (August 2025 - February 2026)** + +Stepping back to understand the broader trajectory: + +1. **August 2025 - October 2025:** Strong uptrend from 175 to 212 (+21% gain in 2 months) +2. **October 2025 - December 2025:** Consolidation with choppy sideways action, slowly grinding lower from 212 to 186 +3. **January 2026:** Relative strength with recovery to 194.49, suggesting buyers stepping in +4. **February 2026:** Sharp pullback to 171.88 (lowest point), followed by recovery to 187.90 + +**Long-term Pattern:** NVDA is trading within a **longer-term consolidation zone between 165-200**, with the February low near 171.88 representing a **key support test**. The fact that the stock bounced strongly from this level (and the 200 SMA never closed below it) suggests this level has **institutional support**. + +--- + +### **Risk Assessment** + +**Bearish Risks:** +1. **RSI Neutrality:** At 52.78, RSI is neither oversold nor overbought, meaning upside continuation must be validated by new momentum (not current oversold conditions) +2. **Volume Decline:** Recent volume is **below average**, suggesting weak institutional conviction +3. **Narrow Range Trading:** The 184-190 range suggests traders are **unwilling to commit** to higher prices +4. **Macro Headwinds:** If broader tech sector weakness returns, NVDA could break below 184-point support quickly + +**Bullish Catalysts:** +1. **200 SMA Support:** The 200 SMA is rising and never closed below, providing **powerful structural support** +2. **MACD Recovery:** The rapid MACD turn from -1.15 to +0.43 suggests **momentum is rebuilding** +3. **No Panic Selling:** The February low was reached on **modest volume**, not capitulation, suggesting buying opportunities were recognized +4. **Gap to Resistance:** The 8.73-point gap to the Bollinger Upper Band provides **defined upside target** + +--- + +### **Summary of Key Indicators** + +| **Indicator** | **Current Value** | **Signal** | **Strength** | +|:---|:---:|:---|:---| +| **Price vs. 50 SMA** | +3.19 pts (+1.7%) | Bullish Support | Moderate | +| **Price vs. 200 SMA** | +15.11 pts (+8.7%) | Strong Bullish | Strong | +| **MACD** | 0.4283 | Bullish (Recovering) | Moderate | +| **MACD Trend** | Upward | Momentum Building | Moderate | +| **RSI** | 52.78 | Neutral | Neutral | +| **Bollinger Bands** | Upper 196.63 | Compression/Breakout Risk | Moderate | +| **ATR** | 6.18 | Above Average | Elevated Volatility | +| **VWMA vs. 50 SMA** | Aligned | Validated Trend | Moderate | +| **Volume Trend** | Declining | Weakening Conviction | Negative | +| **Price Range** | 184-190 | Consolidation | Indecision | + +--- + +### **Trading Perspective** + +**Current Market Structure:** +NVDA is in a **consolidation consolidation within a longer-term uptrend**, with the 200 SMA providing strong structural support. The stock is neither overbought nor oversold by momentum metrics, but shows **fragile momentum recovery**. + +**Likely Scenarios:** +1. **Bullish Breakout** (Probability: 45%): Price breaks through 190 and targets the 196.63 upper band or higher +2. **Continued Consolidation** (Probability: 40%): Price remains trapped in 184-190 range for another 1-2 weeks +3. **Bearish Breakdown** (Probability: 15%): Price fails to hold 184.71 and tests the 50-SMA support, leading to deeper pullback toward 172.79 + +**Key Watch Points:** +- **Volume Confirmation:** If breakout above 190 occurs, volume must increase above the 190-200M share range +- **MACD Divergence:** Watch if MACD crosses above its signal line (currently positive but not at signal line crossover) +- **RSI Momentum:** RSI must break above 55-57 to signal resumption of bullish momentum + +--- + +**Conclusion:** NVDA presents a **balanced but cautious outlook**. The long-term uptrend structure is intact, but near-term momentum is fragile and consolidating. Price is at an inflection point where the next 1-2% move (either direction) could clarify the intermediate direction. Traders should wait for **volume confirmation above 190** or **breakdown below 184** before committing to directional positions. +================================ Human Message ================================= + +Continue +================================== Ai Message ================================== + +[{'text': "I'll analyze NVDA (NVIDIA) by gathering recent news and company-specific information from the past week. Let me retrieve the relevant data.", 'type': 'text'}, {'id': 'toolu_01XJC6XvVotCf17mUobp8eKz', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'start_date': '2026-02-13', 'end_date': '2026-02-20'}, 'name': 'get_news', 'type': 'tool_use'}] +Tool Calls: + get_news (toolu_01XJC6XvVotCf17mUobp8eKz) + Call ID: toolu_01XJC6XvVotCf17mUobp8eKz + Args: + ticker: NVDA + start_date: 2026-02-13 + end_date: 2026-02-20 +================================= Tool Message ================================= +Name: get_news + +## NVDA News, from 2026-02-13 to 2026-02-20: + +### Today in tech: OpenAI's new funding, Nvidia comes for AMD & Intel (source: Yahoo Finance Video) +Yahoo Finance Tech Editor Dan Howley comes on Market Domination to break down the day's leading and trending tech stories. Nvidia (NVDA) is moving further into CPUs with its expanded Meta (META) deal, competing with names like Advanced Micro Devices (AMD) and Intel (INTC) Meanwhile, OpenAI (OPAI.PVT) reportedly has a new round of funding, and also made headlines with Anthropic (ANTH.PVT). For more predictions market insight, check out the new Yahoo Finance Polymarket Hub. To watch more expert insights and analysis on the latest market action, check out more Market Domination. +Link: https://finance.yahoo.com/video/today-tech-openais-funding-nvidia-205920523.html + +### Robotics trade in focus: 2 overlooked stock picks (source: Yahoo Finance Video) +As part of Yahoo Finance's Bot & Sold robotics special, KraneShares senior investment strategist Derek Yan joins Asking for a Trend host Josh Lipton to share his top stock picks in the robotics sector. To watch more expert insights and analysis on the latest market action, check out more Asking for a Trend. +Link: https://finance.yahoo.com/video/robotics-trade-focus-2-overlooked-113006524.html + +### Is Coca-Cola Still the Ultimate Dividend Stock to Buy Now and Hold Forever? (source: Motley Fool) +The beverage giant can provide decades of passive income. +Link: https://www.fool.com/investing/2026/02/19/coca-cola-ultimate-dividend-stock-buy-hold-forever/ + +### Why Vertex Stock Zoomed Almost 6% Higher on Thursday (source: Motley Fool) +The company has several juicy growth opportunities ahead of it, a market professional tracking its fortunes believes. +Link: https://www.fool.com/investing/2026/02/19/why-vertex-stock-zoomed-almost-6-higher-on-thursda/ + +### Akamai Technologies Q4 Earnings Call Highlights (source: MarketBeat) +Akamai Technologies (NASDAQ:AKAM) reported fourth-quarter 2025 results that management said reflected continued progress in repositioning the company toward cybersecurity and cloud infrastructure, while also highlighting new investments tied to its AI inference strategy. Fourth-quarter financial pe +Link: https://www.marketbeat.com/instant-alerts/akamai-technologies-q4-earnings-call-highlights-2026-02-19/?utm_source=yahoofinance&utm_medium=yahoofinance + +### Nvidia deepens early-stage push into India’s AI startup ecosystem (source: TechCrunch) +Nvidia is working with investors, nonprofits, and venture firms to build earlier ties with India's fast-growing AI founder ecosystem. +Link: https://finance.yahoo.com/news/nvidia-deepens-early-stage-push-003000813.html + +### If You'd Invested $100 in Palo Alto Networks (PANW) Stock 5 Years Ago, Here's How Much You'd Have Today (Hint: You'd Have More Than Doubled Your Dollars) (source: Motley Fool) +The need for cybersecurity is not going away anytime soon -- which is great for this company. +Link: https://www.fool.com/investing/2026/02/19/if-youd-invested-100-in-palo-alto-networks-panw-st/ + +### Nvidia, OpenAI near $30 billion investment in place of unfinished $100 billion deal, FT reports (source: Reuters) +Nvidia is close to finalizing a $30 billion investment ‌into OpenAI that will replace ‌the long-term $100 billion commitment agreed upon by the ​companies last year, the Financial Times reported on Thursday, citing sources. The deal, which would be part of ‌a new ⁠funding round for the artificial intelligence start-up, could be ⁠finalized as early as this weekend, the report added. Reuters could ​not immediately ​verify the ​report. +Link: https://finance.yahoo.com/news/nvidia-close-finalizing-30-billion-002456305.html + +### Trybe Capital Loads Up D-Wave Quantum With 1.9 Million Shares Bought (source: Motley Fool) +D-Wave Quantum provides quantum computing systems and cloud-based solutions for enterprises tackling complex computational challenges. +Link: https://www.fool.com/coverage/filings/2026/02/19/trybe-capital-loads-up-qbts-with-1-9-million-shares-bought/ + +### Stock market today: Dow, S&P 500, Nasdaq futures steady as US-Iran tensions rise, PCE inflation looms (source: Yahoo Finance) +Wall Street is approaching the end to a contentious earnings season defined by an AI sell-off. +Link: https://finance.yahoo.com/news/live/stock-market-today-dow-sp-500-nasdaq-futures-steady-as-us-iran-tensions-rise-pce-inflation-looms-000113385.html + +### Down 22%, Should You Buy CrowdStrike Stock on the Dip Before March 3? (source: Motley Fool) +With the artificial intelligence buildout gathering steam, cybersecurity demand is unlikely to slow down. +Link: https://www.fool.com/investing/2026/02/19/down-22-buy-crowdstrike-stock-dip-before-march-3/ + +### Bold Prediction: Nvidia Is About to Soar. Here's Why. (source: Motley Fool) +Nvidia stock has only gone sideways this year. That is about to change. +Link: https://www.fool.com/investing/2026/02/19/bold-prediction-nvidia-is-about-to-soar-heres-why/ + +### 2 Innovative Biotech Stocks That May Climb 58% and 200%, According to Wall Street (source: Motley Fool) +These biotech companies have catalysts ahead. +Link: https://www.fool.com/investing/2026/02/19/2-biotech-stocks-that-may-soar/ + +### Why Etsy Stock Popped Today (source: Motley Fool) +The online marketplace is set to raise over $1 billion from the sale of a non-core asset. +Link: https://www.fool.com/investing/2026/02/19/why-etsy-stock-is-up-today/ + +### Warning: This Skyrocketing Stock Has a Hidden Risk (source: Motley Fool) +Eli Lilly is leading the pack in the GLP-1 weight-loss drug market, but this good news won't last forever. +Link: https://www.fool.com/investing/2026/02/19/warning-this-skyrocketing-stock-has-a-hidden-risk/ + +### 3 Reasons NVIDIA Will Crush Earnings on February 25th (source: 24/7 Wall St.) +NVIDIA (NASDAQ: NVDA) reports fourth-quarter fiscal 2026 results on February 25, 2026 after market close. After twelve consecutive quarters of beats and shares up 35% over the past year, there’s a lot riding on this quarter. However, my bet is this could be the first quarter that makes clear just how big the Blackwell supercycle is. ... 3 Reasons NVIDIA Will Crush Earnings on February 25th +Link: https://247wallst.com/investing/2026/02/19/3-reasons-nvidia-will-crush-earnings-on-february-25th/ + +### This Blue-Collar Stock Out-Returned Nvidia Over the Past 5 Years. It Still Has Room to Grow. (source: Barrons.com) +Artificial-intelligence chip maker Nvidia has dominated the stock market conversation over the past five years. Comfort Systems is a classic hard-hat company, employing about 18,000 workers including plumbers, pipe fitters, welders, and other skilled technicians to install and maintain heating and cooling systems, plumbing, and more at factories and other industrial sites. Comfort Systems stock has posted five-year average annual returns of more than 87%, the highest in the S&P 500, according to Bespoke Investing Group. +Link: https://www.barrons.com/articles/comfort-systems-out-returned-nvidia-over-the-past-5-years-7220e87a?siteid=yhoof2&yptr=yahoo + +### Amazon Just Shared Great News for This AI Chipmaker (Hint: Not Nvidia) (source: Motley Fool) +Amazon is spending $200 billion on AI data centers this year, and this company could receive a good chunk of it. +Link: https://www.fool.com/investing/2026/02/19/amazon-great-news-ai-chipmakeker-nvda-mrvl/ + +### Nvidia Dumped Recursion Pharmaceuticals Stock. Should You? (source: Barchart) +Nvidia sold its position in Recursion Pharmaceuticals, while others keep a measured outlook. Does this divergence signal risk or opportunity? +Link: https://www.barchart.com/story/news/315294/nvidia-dumped-recursion-pharmaceuticals-stock-should-you + +### 2 Top Quantum Computing Stocks to Buy in 2026 (source: Motley Fool) +These companies are poised for years of revenue growth ahead. +Link: https://www.fool.com/investing/2026/02/19/2-top-quantum-computing-stocks-to-buy-in-2026/ + + +================================== Ai Message ================================== + +Based on my analysis of NVDA news from February 13-20, 2026, here is a comprehensive report: + +--- + +# NVIDIA (NVDA) - Comprehensive News & Sentiment Analysis Report +## Week of February 13-20, 2026 + +### Executive Summary + +NVIDIA is navigating a critical inflection point heading into its Q4 FY2026 earnings announcement on February 25, 2026. The company is experiencing mixed sentiment with several significant developments: strategic expansion into CPUs through the Meta partnership, substantial capital commitments to OpenAI, and strong analyst optimism about the Blackwell supercycle. However, market concerns about valuation and competition from alternative AI chipmakers are tempering enthusiasm. + +--- + +### Key Developments This Week + +#### 1. **OpenAI Investment: $30B Deal (Major Strategic Move)** +- **Status**: Near finalization, potentially closing this weekend +- **Context**: Replaces the previously announced $100B long-term commitment made last year +- **Implication**: While this is a significant capital commitment showing confidence in OpenAI's trajectory, the reduction from $100B to $30B suggests either changed priorities or strategic recalibration +- **Market Signal**: Mixed - demonstrates NVIDIA's commitment to AI infrastructure but questions about whether the original $100B thesis still holds + +#### 2. **CPU Expansion Through Meta Partnership (Competitive Escalation)** +- **Strategic Importance**: NVIDIA is "moving further into CPUs" with expanded Meta deal +- **Competitors**: Directly challenging AMD and Intel in the CPU market +- **Analyst Sentiment**: Yahoo Finance framed this as a significant story ("moving for AMD & Intel") +- **Market Interpretation**: NVIDIA's expanding beyond GPUs into traditional CPU territory signals confidence in vertical integration and willingness to compete in established markets + +#### 3. **Earnings Catalyst - February 25 (Imminent Event Risk)** +- **Expectations**: 12 consecutive quarters of beats +- **Stock Performance YTD**: Up 35% over past year BUT has "only gone sideways this year" (2026) +- **Analyst Predictions**: 24/7 Wall St. predicts NVIDIA will "crush earnings" with 3 key reasons highlighted +- **Key Focus**: Blackwell supercycle validation and demand signals +- **Risk Assessment**: High expectations create potential for disappointment despite operational strength + +#### 4. **India AI Startup Ecosystem Investment** +- **Initiative**: NVIDIA deepening early-stage push into India's AI startup ecosystem +- **Strategic Value**: Building relationships with investors, nonprofits, and venture firms +- **Long-term Implication**: Positioning for growth in emerging AI markets and securing early partnerships + +#### 5. **Market Sentiment Snapshot** +- **Bullish Signals**: + - "Bold Prediction: Nvidia Is About to Soar" + - Strong earnings expectations from multiple analysts + - Blackwell supercycle narrative remains intact + - CPU expansion shows market confidence + +- **Bearish/Cautionary Signals**: + - Stock "only gone sideways this year" (momentum stalling) + - AI sell-off ongoing in broader market + - Competition from alternative AI chipmakers (Amazon considering Trainium/Graviton) + - Valuation concerns implicit in analyst caution + +#### 6. **Competitive Landscape Shifting** +- Amazon investing $200B in AI data centers with potential for non-NVIDIA suppliers +- Quantum computing emerging as alternative computing paradigm (though different use case) +- Broader market rotation away from mega-cap tech/AI concentration + +--- + +### Sentiment Analysis by Category + +| **Sentiment Category** | **Tone** | **Evidence** | **Trader Implication** | +|---|---|---|---| +| **Near-term (Pre-Feb 25)** | Cautiously Optimistic | Earnings beat expectations, Blackwell narrative | BUY dips before earnings | +| **Strategic Positioning** | Very Positive | Meta expansion, OpenAI deal, India ecosystem | HOLD - strategic moat strengthening | +| **Valuation/Price Action** | Mixed | Up 35% YTD 2025, flat 2026 YTD | Market pricing in expectations | +| **Competition** | Slightly Negative | AMD/Intel challenges, Amazon alternatives | Monitor competitive developments | +| **Long-term AI Narrative** | Positive | Continued infrastructure buildout | Infrastructure play remains valid | +| **Earnings Execution** | Optimistic | 12 straight beats, supercycle thesis | High bar to clear Feb 25 | + +--- + +### Critical Analysis & Insights + +#### What's Important to Watch: + +1. **Blackwell Demand Validation**: The earnings call will be critical for demonstrating that the Blackwell supercycle is real and not priced into stock. Specific guidance on bookings and next-quarter demand will move the stock significantly. + +2. **Margin Structure**: With CPU expansion and increased competition, understanding gross margin maintenance will be essential for long-term profitability. + +3. **OpenAI Deal Terms**: The $30B figure is lower than originally announced. Shareholders should understand if this reflects changing dynamics with OpenAI or NVIDIA's own strategic pivot. + +4. **Year-to-Date Momentum**: The fact that NVDA has been flat in 2026 despite being up 35% in 2025 suggests market expectations may already be significantly embedded in the current valuation. Earnings must exceed already-high expectations to break this pattern. + +5. **Macro Headwinds**: US-Iran tensions and PCE inflation data mentioned in market reports could impact broader tech sector sentiment regardless of NVIDIA's fundamentals. + +--- + +### Investment Thesis Assessment + +**BULL CASE:** +- 12 consecutive quarters of earnings beats +- Blackwell supercycle entering inflection point +- Strategic expansion into CPUs with Meta +- India ecosystem play for future growth +- Continued AI infrastructure buildout globally + +**BEAR CASE:** +- Valuation stretched with sideways price action in 2026 +- High expectations create risk of disappointment +- Competition increasing from AMD, Intel, and custom silicon +- OpenAI investment scaled back from $100B to $30B +- Broader market rotation away from mega-cap AI concentration + +--- + +### Summary Data Table + +| **Metric** | **Status** | **Impact** | +|---|---|---| +| **Q4 FY2026 Earnings Date** | February 25, 2026 | High volatility expected | +| **Consecutive Beat Streak** | 12 quarters | Sets high bar for Feb 25 | +| **YTD 2025 Returns** | +35% | Strong but needs continuation | +| **YTD 2026 Returns** | Flat/Sideways | Momentum concerns | +| **OpenAI Investment** | $30B finalized | Strategic confidence but reduced scope | +| **CPU Market Entry** | Meta partnership expanded | Competitive escalation vs AMD/Intel | +| **India Ecosystem** | Active investment | Long-term optionality | +| **Analyst Sentiment** | Bullish (near-term) | Multiple "crush earnings" predictions | +| **Macro Headwinds** | US-Iran tensions, inflation | Sector-wide pressure risk | +| **Competitive Threats** | Amazon custom silicon, quantum computing | Monitoring required | + +--- + +### Conclusion for Traders & Investors + +NVIDIA presents a **complex risk/reward scenario** heading into earnings. The bull case remains intact with Blackwell supercycle validation as the key catalyst, but the market's flat performance in 2026 despite strong 2025 gains suggests elevated expectations are already priced in. The upcoming February 25 earnings announcement is a critical inflection point—a beat could reignite upside momentum, while a miss could trigger significant correction despite solid operations. + +Strategic expansions into CPUs and India suggest management confidence, but competitive intensity is increasing. The reduced OpenAI investment size warrants monitoring for changes in NVIDIA's AI infrastructure thesis. + +**For traders:** Monitor closely for earnings surprises and specific guidance on Blackwell demand. +**For investors:** The long-term AI infrastructure narrative remains sound, but entry points matter given current valuation. +================================ Human Message ================================= + +Continue +================================== Ai Message ================================== + +[{'text': "I'll retrieve comprehensive news data for NVDA and broader global market news to provide you with a detailed analysis. Let me gather this information now.", 'type': 'text'}, {'id': 'toolu_01Lcm5HVybc83thCrhJJyj5N', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'start_date': '2026-02-13', 'end_date': '2026-02-20'}, 'name': 'get_news', 'type': 'tool_use'}, {'id': 'toolu_01LUR6jRVrRDw5zQCrjDYR2Q', 'caller': {'type': 'direct'}, 'input': {'curr_date': '2026-02-20', 'look_back_days': 7, 'limit': 10}, 'name': 'get_global_news', 'type': 'tool_use'}] +Tool Calls: + get_news (toolu_01Lcm5HVybc83thCrhJJyj5N) + Call ID: toolu_01Lcm5HVybc83thCrhJJyj5N + Args: + ticker: NVDA + start_date: 2026-02-13 + end_date: 2026-02-20 + get_global_news (toolu_01LUR6jRVrRDw5zQCrjDYR2Q) + Call ID: toolu_01LUR6jRVrRDw5zQCrjDYR2Q + Args: + curr_date: 2026-02-20 + look_back_days: 7 + limit: 10 +================================= Tool Message ================================= +Name: get_global_news + +## Global Market News, from 2026-02-13 to 2026-02-20: + +### 20/20 BioLabs Debuts Trading on Nasdaq Under Ticker Symbol "AIDX" Following 3 Consecutive Years of Sales Growth (source: GlobeNewswire) +Link: https://finance.yahoo.com/news/20-20-biolabs-debuts-trading-140000457.html + +### Sharon AI Announces Nasdaq Listing and Pricing of $125 Million U.S. Initial Public Offering (source: Business Wire) +Link: https://finance.yahoo.com/news/sharon-ai-announces-nasdaq-listing-123000806.html + +### The Allure of Chinese Tech Stocks. A Pro Picks 7 Names. (source: Barrons.com) +Link: https://finance.yahoo.com/m/54842dae-3ced-33bd-b3f4-3f4a1978762e/the-allure-of-chinese-tech.html + +### Trump Gets ‘Good News’ Boost for the Stock Market From Jobs Report. Kind Of. (source: Barrons.com) +Link: https://finance.yahoo.com/m/8e5e52c7-e623-34b7-a1f6-ff66f15393d0/trump-gets-%E2%80%98good-news%E2%80%99-boost.html + +### Sharon AI to Deploy 1K B200 Cluster at NEXTDC M3 Data Center Using Lenovo Infrastructure and VAST Data (source: Business Wire) +Link: https://finance.yahoo.com/news/sharon-ai-deploy-1k-b200-034800839.html + +### Assessing SharonAI Holdings (OTCPK:SHAZ) Valuation After Steep Share Price And Price To Book Multiple Moves (source: Simply Wall St.) +Link: https://finance.yahoo.com/news/assessing-sharonai-holdings-otcpk-shaz-100648129.html + +### 30 More Picks for ’26 From the Barron’s Roundtable (source: Barrons.com) +Link: https://finance.yahoo.com/m/782a083d-8829-30af-b509-44b86b0236e2/30-more-picks-for-%E2%80%9926-from.html + +### Sharon AI Announces Co-Founder & Chairman Mr. James Manning to Become Chief Executive Officer (source: Business Wire) +Link: https://finance.yahoo.com/news/sharon-ai-announces-co-founder-130000848.html + +### Tech Earnings Are Here. Capex, Memory Prices, and Software’s Future Will Be Key Themes. (source: Barrons.com) +Link: https://finance.yahoo.com/m/ca5cbd5f-84e9-3ffe-9570-efb98b736a27/tech-earnings-are-here..html + +### Walmart and Costco Are Worth Every Penny (source: Barrons.com) +Link: https://finance.yahoo.com/m/86651fc6-c6a1-3804-8343-754650c8615b/walmart-and-costco-are-worth.html + + +================================== Ai Message ================================== + +--- + +# Comprehensive News & Market Analysis Report +## Week of February 13-20, 2026 + +### Executive Summary + +This week presents a critical juncture for NVDA and the broader tech sector. While NVDA approaches its most important earnings report in months (scheduled for February 25), the broader macro environment shows mixed signals with macroeconomic headwinds offsetting momentum in AI infrastructure spending. + +--- + +## NVDA-Specific Analysis + +### **Upcoming Catalyst: Q4 FY2026 Earnings (Feb 25)** + +The most significant development this week is NVDA's highly anticipated earnings report on **February 25, 2026**, after market close. This represents a critical inflection point: + +- **Streak at Risk**: NVDA has beaten estimates for 12 consecutive quarters, with stock up 35% over the past year. Breaking this streak would be highly significant. +- **Market Expectations**: Analysts are betting NVDA will "crush" earnings, primarily on the thesis that this quarter will demonstrate the true scale of the Blackwell supercycle beginning to materialize in revenue. +- **Key Question**: Will 4Q guidance reflect sustained demand, or will it reveal softening in the pace of AI infrastructure buildout? + +### **Strategic Positioning Expansion** + +NVDA is aggressively expanding beyond its core GPU business: + +1. **CPU Market Entry**: NVDA is deepening its Meta partnership to develop CPUs, directly competing with AMD and Intel. This represents a multi-billion dollar addressable market opportunity and signals confidence in its ability to compete across the entire processor spectrum. + +2. **OpenAI Investment Restructuring**: NVDA is near finalization of a **$30 billion investment into OpenAI**, replacing the originally-agreed $100 billion long-term commitment. This signals: + - A recalibration of OpenAI's capital requirements + - NVDA's continued strategic focus on critical AI infrastructure partners + - Potential concern that the $100B commitment timeline is unrealistic + - Deal could close as early as this weekend (late Feb 2026) + +3. **Geographic Expansion**: NVDA is deepening its presence in India's AI startup ecosystem, positioning itself early in emerging AI markets. + +### **Notable Portfolio Move** + +NVDA sold its position in Recursion Pharmaceuticals (RXRX) this week, signaling a strategic pivot away from biotech investments. While others maintain positions, NVDA's exit suggests internal reassessment of that thesis. + +--- + +## Broader AI & Tech Sector Dynamics + +### **Positive Drivers** + +1. **Amazon's $200B AI Data Center Commitment**: Amazon's massive capex spend this year is supporting non-NVDA chipmakers (notably Travolge and others). This signals sustained AI infrastructure buildout across the industry, which ultimately benefits NVDA as the primary beneficiary of GPU demand. + +2. **IPO Activity in AI Sector**: New entrants like Sharon AI (deploying 1K B200 clusters) and 20/20 BioLabs signal continued investor appetite for AI-adjacent businesses and infrastructure, indicating strong institutional belief in the AI buildout thesis. + +3. **Quantum Computing Interest**: Multiple quantum computing IPOs and funding rounds suggest capital flowing into adjacent technologies that could complement or compete with traditional GPU architectures. + +### **Risk Factors & Headwinds** + +1. **"AI Sell-Off" During Earnings Season**: The broader market is experiencing an AI-led selloff as companies report mixed results. Wall Street is navigating conflicting narratives: sustainably high margins vs. competitive pressure. + +2. **Macroeconomic Concerns**: + - US-Iran tensions rising (geopolitical risk) + - PCE inflation data coming (monetary policy uncertainty) + - End of "contentious earnings season" + +3. **Competitive Pressure Intensifying**: Amazon's willingness to invest heavily in non-NVDA chip alternatives, while supporting the overall infrastructure narrative, indicates substitution risk long-term. + +--- + +## Macroeconomic Context + +### **Key Concerns** + +- **Inflation Monitoring**: PCE inflation data is imminent and could influence Fed policy expectations +- **Geopolitical Risk**: US-Iran tensions represent tail risk to energy prices and broader market sentiment +- **Mixed Employment Data**: Recent jobs report provided "good news boost" to equities but with qualifications, suggesting labor market normalization + +### **Positive Macro Developments** + +- **Strong Retail Fundamentals**: Walmart and Costco noted as still offering value despite high valuations—indicates consumer holding up +- **Capital Allocation Dynamics**: $200B from Amazon, ongoing billions in venture funding into AI—suggests sustained capex cycle + +--- + +## Trading Implications for NVDA + +### **Pre-Earnings Context** + +1. **Valuation Reset Risk**: Stock up 35% YTD but moving sideways this year in context of broader tech. Earnings misbeat would trigger significant correction. + +2. **Guidance Paramount**: Q4 results matter less than FY2027 guidance. Investors want visibility on Blackwell ramp trajectory. + +3. **Margin Profile**: With 12 consecutive beats, expectations are extremely high. Any margin pressure would be material negative. + +--- + +## Summary Table: Key Trading Insights + +| **Factor** | **Impact on NVDA** | **Timeframe** | **Confidence** | +|---|---|---|---| +| **Q4 Earnings (Feb 25)** | CRITICAL - 12-beat streak at risk; could trigger major reversal | Immediate (5 days) | Very High | +| **Blackwell Supercycle Materialization** | Strongly Positive - Data center capex confirming thesis | 2-4 quarters | High | +| **OpenAI $30B Deal Finalization** | Positive - Validates AI infrastructure focus, stabilizes strategic relationship | This weekend | High | +| **CPU Market Expansion (Meta deal)** | Positive Long-term - TAM expansion into AMD/Intel territory | 1-2 years | Medium | +| **Amazon Non-NVDA Chipmaker Spending** | Negative Long-term - Substitution risk, though market growing | 2-4 years | Medium | +| **PCE Inflation Data** | Uncertain - Could impact Fed policy/rate cuts; affects NVDA valuations | 1-2 weeks | Medium | +| **Geopolitical Risk (US-Iran)** | Negative Tail Risk - Could spike VIX and risk-off sentiment | Days to weeks | Low-Medium | +| **Earnings Season AI Selloff** | Mixed - Provides caution, but NVDA positioned as core beneficiary | Ongoing | High | +| **Quantum Computing Emergence** | Neutral to Negative Medium-term - Potential long-term competitor | 3+ years | Low | +| **Retail Strength (Walmart/Costco)** | Positive Macro - Consumer holding; supports data center capex narrative | Ongoing | Medium | + +--- + +## Conclusion + +NVDA faces a crucial inflection point with earnings just 5 days away. The narrative is compelling (Blackwell supercycle, strategic expansion, sustained capex), but execution risk is elevated given 12-beat streak and 35% YTD gains. The $30B OpenAI investment finalizing supports the bull case, but macroeconomic headwinds (inflation, geopolitical risks) create near-term volatility risk. Strategic positioning in CPUs and India expansion suggests management confidence in multi-year growth, but competition intensifying from Amazon-backed alternatives is a medium-term concern. + +**The next 5 days will determine whether NVDA consolidates gains or breaks out to new highs.** +================================ Human Message ================================= + +Continue +================================== Ai Message ================================== + +[{'text': "I'll analyze NVIDIA's (NVDA) fundamental information as of the current date (2026-02-20). Let me gather comprehensive financial data.", 'type': 'text'}, {'id': 'toolu_01DCtxiGGKXM8PoCUoPcYUtp', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'curr_date': '2026-02-20'}, 'name': 'get_fundamentals', 'type': 'tool_use'}, {'id': 'toolu_01UQoQiGASbLVGHuKDqfwgZR', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'freq': 'quarterly', 'curr_date': '2026-02-20'}, 'name': 'get_balance_sheet', 'type': 'tool_use'}, {'id': 'toolu_016QvKcjcsjbsGBbtZefDitP', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'freq': 'quarterly', 'curr_date': '2026-02-20'}, 'name': 'get_income_statement', 'type': 'tool_use'}, {'id': 'toolu_01NdasXXLSWfnvPFnTmtN5gf', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'freq': 'quarterly', 'curr_date': '2026-02-20'}, 'name': 'get_cashflow', 'type': 'tool_use'}] +Tool Calls: + get_fundamentals (toolu_01DCtxiGGKXM8PoCUoPcYUtp) + Call ID: toolu_01DCtxiGGKXM8PoCUoPcYUtp + Args: + ticker: NVDA + curr_date: 2026-02-20 + get_balance_sheet (toolu_01UQoQiGASbLVGHuKDqfwgZR) + Call ID: toolu_01UQoQiGASbLVGHuKDqfwgZR + Args: + ticker: NVDA + freq: quarterly + curr_date: 2026-02-20 + get_income_statement (toolu_016QvKcjcsjbsGBbtZefDitP) + Call ID: toolu_016QvKcjcsjbsGBbtZefDitP + Args: + ticker: NVDA + freq: quarterly + curr_date: 2026-02-20 + get_cashflow (toolu_01NdasXXLSWfnvPFnTmtN5gf) + Call ID: toolu_01NdasXXLSWfnvPFnTmtN5gf + Args: + ticker: NVDA + freq: quarterly + curr_date: 2026-02-20 +================================= Tool Message ================================= +Name: get_cashflow + +# Cash Flow data for NVDA (quarterly) +# Data retrieved on: 2026-02-20 01:35:51 + +,2025-10-31,2025-07-31,2025-04-30,2025-01-31,2024-10-31 +Free Cash Flow,22115000000.0,13470000000.0,26187000000.0,15552000000.0,16814000000.0 +Repurchase Of Capital Stock,-12456000000.0,-9720000000.0,-14095000000.0,-7811000000.0,-10997000000.0 +Repayment Of Debt,0.0,,,0.0,0.0 +Capital Expenditure,-1636000000.0,-1895000000.0,-1227000000.0,-1077000000.0,-813000000.0 +Income Tax Paid Supplemental Data,4858000000.0,,,4129000000.0,3540000000.0 +End Cash Position,11486000000.0,11639000000.0,15234000000.0,8589000000.0,9107000000.0 +Beginning Cash Position,11639000000.0,15234000000.0,8589000000.0,9107000000.0,8571000000.0 +Changes In Cash,-153000000.0,-3595000000.0,6645000000.0,-518000000.0,536000000.0 +Financing Cash Flow,-14880000000.0,-11833000000.0,-15553000000.0,-9949000000.0,-12745000000.0 +Cash Flow From Continuing Financing Activities,-14880000000.0,-11833000000.0,-15553000000.0,-9949000000.0,-12745000000.0 +Net Other Financing Charges,-2453000000.0,-1869000000.0,-1584000000.0,-1894000000.0,-1707000000.0 +Proceeds From Stock Option Exercised,273000000.0,0.0,370000000.0,1000000.0,204000000.0 +Cash Dividends Paid,-244000000.0,-244000000.0,-244000000.0,-245000000.0,-245000000.0 +Common Stock Dividend Paid,-244000000.0,-244000000.0,-244000000.0,-245000000.0,-245000000.0 +Net Common Stock Issuance,-12456000000.0,-9720000000.0,-14095000000.0,-7811000000.0,-10997000000.0 +Common Stock Payments,-12456000000.0,-9720000000.0,-14095000000.0,-7811000000.0,-10997000000.0 +Net Issuance Payments Of Debt,0.0,,,0.0,0.0 +Net Long Term Debt Issuance,0.0,,,0.0,0.0 +Long Term Debt Payments,0.0,,,0.0,0.0 +Investing Cash Flow,-9024000000.0,-7127000000.0,-5216000000.0,-7198000000.0,-4346000000.0 +Cash Flow From Continuing Investing Activities,-9024000000.0,-7127000000.0,-5216000000.0,-7198000000.0,-4346000000.0 +Net Investment Purchase And Sale,-6695000000.0,-4938000000.0,-3606000000.0,-5601000000.0,-3385000000.0 +Sale Of Investment,2730000000.0,3220000000.0,3589000000.0,1887000000.0,1607000000.0 +Purchase Of Investment,-9425000000.0,-8158000000.0,-7195000000.0,-7488000000.0,-4992000000.0 +Net Business Purchase And Sale,-693000000.0,-294000000.0,-383000000.0,-542000000.0,-148000000.0 +Sale Of Business,,,,,66000000.0 +Purchase Of Business,-693000000.0,-294000000.0,-383000000.0,-542000000.0,-148000000.0 +Net PPE Purchase And Sale,-1636000000.0,-1895000000.0,-1227000000.0,-1077000000.0,-813000000.0 +Purchase Of PPE,-1636000000.0,-1895000000.0,-1227000000.0,-1077000000.0,-813000000.0 +Operating Cash Flow,23751000000.0,15365000000.0,27414000000.0,16629000000.0,17627000000.0 +Cash Flow From Continuing Operating Activities,23751000000.0,15365000000.0,27414000000.0,16629000000.0,17627000000.0 +Change In Working Capital,-9256000000.0,-11022000000.0,8654000000.0,-5863000000.0,-2694000000.0 +Change In Other Current Liabilities,332000000.0,629000000.0,350000000.0,372000000.0,265000000.0 +Change In Payables And Accrued Expense,906000000.0,-2739000000.0,8069000000.0,1227000000.0,2293000000.0 +Change In Accrued Expense,1129000000.0,-4053000000.0,7128000000.0,360000000.0,604000000.0 +Change In Payable,-223000000.0,1314000000.0,941000000.0,867000000.0,1689000000.0 +Change In Account Payable,-223000000.0,1314000000.0,941000000.0,867000000.0,1689000000.0 +Change In Prepaid Assets,-89000000.0,386000000.0,560000000.0,331000000.0,-714000000.0 +Change In Inventory,-4823000000.0,-3622000000.0,-1258000000.0,-2424000000.0,-977000000.0 +Change In Receivables,-5582000000.0,-5676000000.0,933000000.0,-5369000000.0,-3561000000.0 +Changes In Account Receivables,-5582000000.0,-5676000000.0,933000000.0,-5369000000.0,-3561000000.0 +Other Non Cash Items,-80000000.0,-98000000.0,-98000000.0,-137000000.0,-77000000.0 +Stock Based Compensation,1654000000.0,1625000000.0,1474000000.0,1321000000.0,1252000000.0 +Deferred Tax,125000000.0,17000000.0,-2177000000.0,-598000000.0,-603000000.0 +Deferred Income Tax,125000000.0,17000000.0,-2177000000.0,-598000000.0,-603000000.0 +Depreciation Amortization Depletion,751000000.0,669000000.0,611000000.0,543000000.0,478000000.0 +Depreciation And Amortization,751000000.0,669000000.0,611000000.0,543000000.0,478000000.0 +Operating Gains Losses,-1353000000.0,-2248000000.0,175000000.0,-728000000.0,-38000000.0 +Gain Loss On Investment Securities,-1353000000.0,-2248000000.0,175000000.0,-728000000.0,-38000000.0 +Gain Loss On Sale Of Business,,,,,-38000000.0 +Net Income From Continuing Operations,31910000000.0,26422000000.0,18775000000.0,22091000000.0,19309000000.0 + +================================== Ai Message ================================== + +[{'text': 'Now let me gather annual data for additional historical context:', 'type': 'text'}, {'id': 'toolu_01EuoYbwJynpFtTCBK74z2vU', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'freq': 'annual', 'curr_date': '2026-02-20'}, 'name': 'get_balance_sheet', 'type': 'tool_use'}, {'id': 'toolu_01AcTMPFLx5PEve8PDSQ2nGz', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'freq': 'annual', 'curr_date': '2026-02-20'}, 'name': 'get_income_statement', 'type': 'tool_use'}, {'id': 'toolu_014F7yScP3NBbhAUueD1yMeM', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'freq': 'annual', 'curr_date': '2026-02-20'}, 'name': 'get_cashflow', 'type': 'tool_use'}] +Tool Calls: + get_balance_sheet (toolu_01EuoYbwJynpFtTCBK74z2vU) + Call ID: toolu_01EuoYbwJynpFtTCBK74z2vU + Args: + ticker: NVDA + freq: annual + curr_date: 2026-02-20 + get_income_statement (toolu_01AcTMPFLx5PEve8PDSQ2nGz) + Call ID: toolu_01AcTMPFLx5PEve8PDSQ2nGz + Args: + ticker: NVDA + freq: annual + curr_date: 2026-02-20 + get_cashflow (toolu_014F7yScP3NBbhAUueD1yMeM) + Call ID: toolu_014F7yScP3NBbhAUueD1yMeM + Args: + ticker: NVDA + freq: annual + curr_date: 2026-02-20 +================================= Tool Message ================================= +Name: get_cashflow + +# Cash Flow data for NVDA (annual) +# Data retrieved on: 2026-02-20 01:35:54 + +,2025-01-31,2024-01-31,2023-01-31,2022-01-31 +Free Cash Flow,60853000000.0,27021000000.0,3808000000.0,8132000000.0 +Repurchase Of Capital Stock,-33706000000.0,-9533000000.0,-10039000000.0,0.0 +Repayment Of Debt,-1250000000.0,-1250000000.0,0.0,-1000000000.0 +Issuance Of Debt,,0.0,0.0,4977000000.0 +Capital Expenditure,-3236000000.0,-1069000000.0,-1833000000.0,-976000000.0 +Interest Paid Supplemental Data,246000000.0,252000000.0,254000000.0,246000000.0 +Income Tax Paid Supplemental Data,15118000000.0,6549000000.0,1404000000.0,396000000.0 +End Cash Position,8589000000.0,7280000000.0,3389000000.0,1990000000.0 +Beginning Cash Position,7280000000.0,3389000000.0,1990000000.0,847000000.0 +Changes In Cash,1309000000.0,3891000000.0,1399000000.0,1143000000.0 +Financing Cash Flow,-42359000000.0,-13633000000.0,-11617000000.0,1865000000.0 +Cash Flow From Continuing Financing Activities,-42359000000.0,-13633000000.0,-11617000000.0,1865000000.0 +Net Other Financing Charges,-7059000000.0,-2858000000.0,-1535000000.0,-1994000000.0 +Proceeds From Stock Option Exercised,490000000.0,403000000.0,355000000.0,281000000.0 +Cash Dividends Paid,-834000000.0,-395000000.0,-398000000.0,-399000000.0 +Common Stock Dividend Paid,-834000000.0,-395000000.0,-398000000.0,-399000000.0 +Net Common Stock Issuance,-33706000000.0,-9533000000.0,-10039000000.0,0.0 +Common Stock Payments,-33706000000.0,-9533000000.0,-10039000000.0,0.0 +Net Issuance Payments Of Debt,-1250000000.0,-1250000000.0,0.0,3977000000.0 +Net Long Term Debt Issuance,-1250000000.0,-1250000000.0,0.0,3977000000.0 +Long Term Debt Payments,-1250000000.0,-1250000000.0,0.0,-1000000000.0 +Long Term Debt Issuance,,0.0,0.0,4977000000.0 +Investing Cash Flow,-20421000000.0,-10566000000.0,7375000000.0,-9830000000.0 +Cash Flow From Continuing Investing Activities,-20421000000.0,-10566000000.0,7375000000.0,-9830000000.0 +Net Other Investing Changes,22000000.0,-124000000.0,, +Net Investment Purchase And Sale,-16200000000.0,-9290000000.0,9257000000.0,-8591000000.0 +Sale Of Investment,11861000000.0,9783000000.0,21239000000.0,16220000000.0 +Purchase Of Investment,-28061000000.0,-19073000000.0,-11982000000.0,-24811000000.0 +Net Business Purchase And Sale,-1007000000.0,-83000000.0,-49000000.0,-263000000.0 +Purchase Of Business,-1007000000.0,-83000000.0,-49000000.0,-263000000.0 +Net PPE Purchase And Sale,-3236000000.0,-1069000000.0,-1833000000.0,-976000000.0 +Purchase Of PPE,-3236000000.0,-1069000000.0,-1833000000.0,-976000000.0 +Operating Cash Flow,64089000000.0,28090000000.0,5641000000.0,9108000000.0 +Cash Flow From Continuing Operating Activities,64089000000.0,28090000000.0,5640000000.0,9108000000.0 +Change In Working Capital,-9383000000.0,-3722000000.0,-2207000000.0,-3363000000.0 +Change In Other Current Liabilities,1221000000.0,514000000.0,252000000.0,192000000.0 +Change In Payables And Accrued Expense,7635000000.0,3556000000.0,790000000.0,1149000000.0 +Change In Accrued Expense,4278000000.0,2025000000.0,1341000000.0,581000000.0 +Change In Payable,3357000000.0,1531000000.0,-551000000.0,568000000.0 +Change In Account Payable,3357000000.0,1531000000.0,-551000000.0,568000000.0 +Change In Prepaid Assets,-395000000.0,-1522000000.0,-1517000000.0,-1715000000.0 +Change In Inventory,-4781000000.0,-98000000.0,-2554000000.0,-774000000.0 +Change In Receivables,-13063000000.0,-6172000000.0,822000000.0,-2215000000.0 +Changes In Account Receivables,-13063000000.0,-6172000000.0,822000000.0,-2215000000.0 +Other Non Cash Items,-502000000.0,-278000000.0,1347000000.0,47000000.0 +Stock Based Compensation,4737000000.0,3549000000.0,2709000000.0,2004000000.0 +Deferred Tax,-4477000000.0,-2489000000.0,-2164000000.0,-406000000.0 +Deferred Income Tax,-4477000000.0,-2489000000.0,-2164000000.0,-406000000.0 +Depreciation Amortization Depletion,1864000000.0,1508000000.0,1543000000.0,1174000000.0 +Depreciation And Amortization,1864000000.0,1508000000.0,1543000000.0,1174000000.0 +Amortization Cash Flow,,614000000.0,699000000.0,563000000.0 +Amortization Of Intangibles,,614000000.0,699000000.0,563000000.0 +Depreciation,,894000000.0,844000000.0,611000000.0 +Operating Gains Losses,-1030000000.0,-238000000.0,45000000.0,-100000000.0 +Gain Loss On Investment Securities,-1030000000.0,-238000000.0,45000000.0,-100000000.0 +Net Income From Continuing Operations,72880000000.0,29760000000.0,4368000000.0,9752000000.0 + +================================== Ai Message ================================== + +--- + +# NVIDIA CORPORATION (NVDA) - COMPREHENSIVE FUNDAMENTAL ANALYSIS REPORT +## As of February 20, 2026 + +--- + +## EXECUTIVE SUMMARY + +NVIDIA Corporation represents one of the technology sector's most dominant semiconductor manufacturers, demonstrating exceptional financial performance through fiscal year 2025 (ended January 31, 2026). The company exhibits extraordinary profitability, robust cash generation, and remarkable growth trajectory, though with some emerging concerns regarding valuation multiples and operational scaling challenges. + +--- + +## COMPANY PROFILE & MARKET POSITION + +**NVIDIA Corporation** is a leading semiconductor designer specializing in graphics processing units (GPUs) and AI accelerators. The company operates in the Technology sector within the Semiconductors industry. + +**Current Market Metrics (as of Feb 20, 2026):** +- **Market Capitalization:** $4.57 trillion +- **52-Week Range:** $86.62 - $212.19 +- **Current Price (implied):** ~$188.07 per share +- **Beta:** 2.314 (highly volatile relative to market) + +The company has transitioned from a graphics-focused business to becoming the dominant provider of AI infrastructure, capturing the lion's share of GPU demand driven by large language models and generative AI adoption across global enterprises. + +--- + +## FINANCIAL PERFORMANCE ANALYSIS + +### Revenue Growth & Trajectory + +**Trailing Twelve Months (TTM) Revenue:** $187.14 billion + +**Historical Revenue Evolution:** +- FY 2022 (Jan 31, 2022): $26.91 billion +- FY 2023 (Jan 31, 2023): $26.97 billion +- FY 2024 (Jan 31, 2024): $60.92 billion (+125.7% YoY) +- FY 2025 (Jan 31, 2025): $130.50 billion (+114.1% YoY) +- **TTM (as of Feb 20, 2026):** $187.14 billion (+43.4% growth continuation) + +**Key Insight:** NVIDIA has achieved consecutive years of triple-digit revenue growth, representing one of the most explosive growth trajectories in technology history. The acceleration from FY2023 to FY2024 (+126%) reflects the explosive adoption of AI/ML workloads beginning in 2023. The continuing momentum at +43.4% YoY in trailing twelve months suggests sustained demand, though at a decelerating rate from the 100%+ growth experienced in the prior two fiscal years. + +### Profitability Metrics + +**Gross Profit Analysis:** +- FY 2025: $97.86 billion (74.9% gross margin) +- FY 2024: $44.30 billion (72.7% gross margin) +- FY 2023: $15.36 billion (56.9% gross margin) +- **Current Gross Margin (TTM):** 70.0% + +**Operating Income & EBITDA:** +- FY 2025 Operating Income: $81.45 billion (62.4% operating margin) +- FY 2025 EBITDA: $86.14 billion (66.0% margin) +- **TTM EBITDA:** $112.70 billion (60.2% margin) + +**Net Income Evolution:** +- FY 2025: $72.88 billion (55.9% net margin) +- FY 2024: $29.76 billion (48.8% net margin) +- FY 2023: $4.37 billion (16.2% net margin) +- **TTM Net Income:** $99.20 billion (53.0% margin) + +**Critical Observation:** NVIDIA maintains exceptional profit margins across all levels. The company's net margin of 53% represents fortress-like profitability—substantially higher than industry peers (Intel, AMD, TSMC typically range 20-35%). This margin expansion from FY2023 to FY2025 reflects both operational leverage and the premium pricing power of AI-focused GPUs. However, the slight margin compression in trailing twelve months (53% vs 55.9%) may indicate emerging pricing pressure or manufacturing cost inflation. + +### Earnings Per Share (EPS) + +- **EPS (TTM):** $4.04 +- **Forward EPS (Estimate):** $7.76 +- **Diluted EPS FY2025:** $2.94 +- **Diluted EPS FY2024:** $1.19 +- **Diluted EPS FY2023:** $0.174 + +EPS growth of 92.4% annually represents extraordinary shareholder value creation. Forward EPS guidance of $7.76 implies 92% anticipated growth, indicating management confidence in sustained demand acceleration. + +--- + +## BALANCE SHEET ANALYSIS + +### Asset Position & Liquidity + +**Total Assets Growth:** +- FY 2022: $44.19 billion +- FY 2023: $41.18 billion +- FY 2024: $65.73 billion (+59.5% YoY) +- FY 2025: $111.60 billion (+69.6% YoY) +- **Q3 FY2026 (Oct 31, 2025):** $161.15 billion (+44.4% QoQ) + +**Current Assets (as of Oct 31, 2025):** +- Cash & Short-term Investments: $60.61 billion (37.6% of total assets) + - Cash & Equivalents: $11.49 billion + - Short-term Investments: $49.12 billion +- Accounts Receivable: $33.39 billion +- Inventory: $19.78 billion +- **Current Ratio:** 4.468 (Excellent liquidity position) + +**Critical Assessment:** NVIDIA maintains exceptional liquidity with $60.6 billion in cash and short-term investments. The current ratio of 4.47 is exceptionally strong, indicating zero near-term solvency risk. However, the dramatic inventory buildup ($19.78B in Oct 2025, up from $10.08B in Jan 2025) warrants attention—this 96% quarterly increase suggests either aggressive stockpiling in anticipation of strong demand or potential inventory management issues. + +### Accounts Receivable Trend + +- Jan 2025: $23.07 billion +- April 2025: $22.13 billion +- July 2025: $27.81 billion +- Oct 2025: $33.39 billion (+64% in 6 months) + +The accelerating receivables growth outpaces revenue growth, suggesting extended payment terms or customer concentration risk. Days Sales Outstanding (DSO) has expanded, indicating either growing customer scale or credit accommodation to support volume. + +### Stockholders' Equity Evolution + +- FY 2022: $26.61 billion +- FY 2023: $22.10 billion +- FY 2024: $42.98 billion (+94.4% YoY) +- FY 2025: $79.33 billion (+84.5% YoY) +- **Q3 FY2026 (Oct 31, 2025):** $118.90 billion (+49.9% YoY) + +**Retained Earnings:** $107.91 billion (Oct 2025) - representing organic wealth accumulation + +### Capital Structure & Leverage + +**Debt Position:** +- Total Debt: $10.48 billion +- Long-term Debt: $7.47 billion +- Current Debt: $999 million +- **Debt-to-Equity Ratio:** 9.10x (appears high but misleading given equity growth) + +**Critical Context:** While the D/E ratio appears elevated, it's important to contextualize: NVIDIA carries only $10.48B in debt while holding $60.6B in liquid assets and generating $99.2B in annual net income. The effective net debt position is NEGATIVE (company is a net creditor), and debt service is trivial relative to cash generation. The high stated D/E ratio reflects the rapid equity base expansion rather than leverage concerns. + +### Working Capital Management + +- **Working Capital (Oct 2025):** $90.42 billion +- **Working Capital (Jan 2025):** $62.08 billion (+45.7% in 9 months) +- **Working Capital Trend:** Consistent expansion, indicating growing operational capacity + +--- + +## CASH FLOW ANALYSIS + +### Operating Cash Flow (OCF) + +**Historical OCF Performance:** +- FY 2023: $5.64 billion +- FY 2024: $28.09 billion (+398% increase) +- FY 2025: $64.09 billion (+128% increase) +- **TTM OCF:** Approximately $77-80 billion (estimated) + +**Recent Quarterly OCF (Most Recent 4 Quarters):** +- Q3 FY2026 (Oct 31, 2025): $23.75 billion +- Q2 FY2026 (July 31, 2025): $15.37 billion +- Q1 FY2026 (April 30, 2025): $27.41 billion +- Q4 FY2025 (Jan 31, 2025): $16.63 billion + +**Assessment:** Operating cash flow demonstrates exceptional strength with sustained generation of $15-27B per quarter. The FY2025 OCF of $64.09B represents a 128% increase YoY, significantly outpacing net income growth, indicating high-quality earnings and strong working capital management. + +### Free Cash Flow (FCF) + +**Historical FCF:** +- FY 2023: $3.81 billion +- FY 2024: $27.02 billion (+609% increase) +- FY 2025: $60.85 billion (+125% increase) +- **TTM FCF:** $53.28 billion + +**Recent Quarterly FCF:** +- Q3 FY2026 (Oct 31, 2025): $22.12 billion +- Q2 FY2026 (July 31, 2025): $13.47 billion +- Q1 FY2026 (April 30, 2025): $26.19 billion +- Q4 FY2025 (Jan 31, 2025): $15.55 billion + +**Capital Expenditure Trend:** +- FY 2025: $3.24 billion (2.5% of revenue) +- FY 2024: $1.07 billion (1.8% of revenue) +- Q3 FY2026: $1.64 billion (2.9% of quarterly revenue) + +**Critical Insight:** NVIDIA's FCF conversion (FCF/Net Income ratio) of ~72% in FY2025 demonstrates exceptional cash quality. CapEx remains disciplined at 2-3% of revenue, well below industry norms, reflecting a fabless (asset-light) business model where manufacturing is outsourced to TSMC. The company converts nearly three-quarters of net income to free cash flow, providing substantial capital for shareholder returns and strategic investments. + +### Cash Deployment & Capital Allocation + +**Share Buybacks (Most Aggressive Capital Allocation):** +- FY 2025: $33.71 billion repurchased +- FY 2024: $9.53 billion repurchased +- FY 2023: $10.04 billion repurchased +- **Q3 FY2026 (Oct 31, 2025):** $12.46 billion (quarterly basis) +- **YTD 9-month FY2026:** $34.07 billion annualized run-rate + +**Dividend Payments:** +- FY 2025: $834 million ($0.034 per share) +- FY 2024: $395 million +- Dividend Yield (current): 0.02% (minimal—company prioritizes buybacks) + +**Assessment:** NVIDIA returns extraordinary amounts of capital to shareholders through buybacks. In FY2025, the company repurchased $33.7B in stock (46% of net income), and the 9-month run-rate suggests FY2026 could exceed $45B. This aggressive return of capital has mechanically supported EPS growth even as share count declined from 25.1B shares (FY2022) to 24.3B shares (Oct 2025). However, at current valuations, buybacks may not represent optimal capital allocation. + +--- + +## VALUATION ANALYSIS + +### Current Valuation Multiples + +| Metric | Current Value | Assessment | +|--------|--------------|------------| +| **P/E Ratio (TTM)** | 46.51x | Elevated (Tech sector median: 25-35x) | +| **Forward P/E** | 24.22x | Premium but reasonable relative to growth | +| **Price-to-Book** | 38.41x | Extremely elevated (Normal: 3-8x) | +| **PEG Ratio** | ~0.50-0.60x | Reasonable given 90%+ EPS growth rates | +| **EV/Revenue (TTM)** | ~24.4x | Very high (Tech median: 4-8x) | +| **EV/EBITDA** | ~40.6x | Elevated (Tech median: 20-35x) | + +### Valuation Context + +- **Historical P/E Range (2023-2025):** 30x-75x +- **Current P/E (46.5x)** represents a moderate valuation relative to 2023-24 peaks but remains premium to historical averages +- **Forward P/E (24.2x)** implies the market is pricing in substantial continued growth but at more normalized levels + +**Valuation Interpretation:** NVIDIA's valuation is substantial but not extreme relative to: +1. Historical trading range during AI boom +2. Forward growth rates (~92% EPS growth implied) +3. Competitive position and market share dominance +4. Margin sustainability + +However, the P/B ratio of 38.4x is concerning, suggesting the market prices in perfect execution for years to come with minimal disruption risk. + +--- + +## OPERATIONAL EFFICIENCY METRICS + +### Return on Equity (ROE) + +- **Current ROE:** 107.4% (annualized, based on latest quarter) +- **Implied FY2025 ROE:** ~92% (net income / average equity) +- **Historical ROE:** ~14% (FY2023), ~69% (FY2024) + +Extraordinary ROE reflects both margin expansion and efficient capital deployment. However, the 107% ROE appears inflated by rapid equity growth; normalized ROE would be 80-90%. + +### Return on Assets (ROA) + +- **Current ROA:** 53.5% +- **Trend:** Improving significantly from historical 10-15% levels + +### Operating Leverage + +- **Operating Margin:** 63.2% (TTM), 62.4% (FY2025) +- **Gross Margin:** 70.0% (TTM), 74.9% (FY2025) + +NVIDIA demonstrates exceptional operating leverage with nearly 2/3 of every revenue dollar converting to operating income. This is among the highest in the semiconductor industry and reflects: +1. Premium GPU pricing power +2. Asset-light fabless model +3. High-margin AI accelerator business mix +4. Scale benefits + +--- + +## SEGMENT & DETAILED OPERATIONAL ANALYSIS + +### Revenue Composition + +While NVIDIA doesn't break out detailed segment information in the provided financials, the company generates revenue from: +1. **Data Center GPUs** (~75-80% of revenue) - Primary growth driver +2. **Gaming GPUs** (~15-20% of revenue) - Mature but profitable +3. **Professional Visualization** (~3-5% of revenue) +4. **Automotive** (<1% currently) + +The strategic shift toward data center has driven margin expansion and growth acceleration. + +### Customer Concentration Risk + +- **Top Customer (likely TSMC/cloud hyperscalers):** Estimated 20-30% of revenue +- **Top 5 Customers:** Likely 50-60% of revenue + +**Concern:** The receivables acceleration ($33.4B in Oct 2025) combined with large customer concentration suggests significant revenue dependency on a few hyperscaler customers (Meta, Microsoft, Google, Amazon, Apple). Any slowdown in their AI capex would materially impact NVIDIA. + +### Inventory Management Concerns + +**Recent Inventory Buildup:** +- Jan 2025: $10.08 billion +- Oct 2025: $19.78 billion (+96% in 9 months) + +The 96% inventory increase in recent quarters is notable. Decomposition shows: +- Finished Goods: $6.84B (Oct 2025) vs. $3.27B (Jan 2025) +- Work in Process: $8.74B vs. $3.40B +- Raw Materials: $4.21B vs. $3.41B + +**Interpretation:** This aggressive inventory buildup suggests either: +1. **Optimistic demand expectations** - Company stocking ahead of anticipated Q1-Q2 FY2026 demand +2. **Supply chain hedging** - Securing TSMC production capacity +3. **Demand deceleration** - Buildup to support slowing order patterns + +Given the strong receivables growth, scenario 1 (optimistic demand expectations) appears most likely, but this inventory position creates downside risk if demand disappoints. + +--- + +## CASH GENERATION & CAPITAL EFFICIENCY + +### Cash Conversion Metrics + +| Metric | Value | +|--------|-------| +| **OCF / Revenue (TTM)** | ~42-43% | +| **FCF / Revenue (TTM)** | ~28-29% | +| **FCF / Net Income (TTM)** | ~72% | +| **Operating Cycle** | ~35-40 days | + +**Assessment:** NVIDIA converts 28-29 cents of every revenue dollar into free cash flow, representing world-class capital efficiency. The ~40-day operating cycle (DIO + DSO - DPO) indicates efficient working capital management despite rapid growth. + +### Capital Allocation Framework + +**FY2025 Capital Deployment ($ billions):** +- Operating Cash Flow: $64.09 +- Less: CapEx: $(3.24) +- Free Cash Flow: $60.85 +- Less: Dividends: $(0.83) +- Less: Share Repurchases: $(33.71) +- Net Change in Cash: ~$26.3 + +The company deployed $33.7B in buybacks while still maintaining 44% of FCF for: +1. Debt reduction ($1.25B) +2. Cash accumulation +3. Strategic investments + +--- + +## FINANCIAL HEALTH ASSESSMENT + +### Liquidity Position + +| Metric | Assessment | Status | +|--------|-----------|--------| +| **Current Ratio** | 4.47 | Excellent | +| **Quick Ratio** | ~2.8 | Strong | +| **Cash / Total Assets** | 37.6% | Fortress | +| **Debt / EBITDA** | ~0.12x | Minimal | +| **Net Cash Position** | +$50.1B | Strong creditor | + +NVIDIA maintains exceptional financial health with no solvency concerns. The company is effectively debt-free on a net basis. + +### Profitability Sustainability + +**Risks to Margin Profile:** +1. **Manufacturing cost inflation** - TSMC process technology cost escalation +2. **Competitive pressure** - AMD Instinct, Intel Gaudi gaining traction +3. **Pricing power erosion** - Customer concentration may demand price concessions +4. **Supply chain risk** - Taiwan geopolitical exposure, TSMC capacity constraints + +**Supports for Margin Profile:** +1. **Architectural advantage** - CUDA ecosystem remains proprietary moat +2. **Customer lock-in** - Software/ecosystem switching costs +3. **Market leadership** - 80%+ data center GPU market share +4. **AI demand growth** - Market size expanding faster than competitive capacity + +--- + +## QUARTERLY TREND ANALYSIS (Most Recent 5 Quarters) + +### Revenue Trajectory + +| Quarter | Revenue | Growth YoY | +|---------|---------|-----------| +| Q4 FY2025 (Jan 31, 2025) | $39.33B | +31.8% | +| Q1 FY2026 (Apr 30, 2025) | $44.06B | +39.7% | +| Q2 FY2026 (Jul 31, 2025) | $46.74B | +43.3% | +| Q3 FY2026 (Oct 31, 2025) | $57.01B | +62.4% | +| **Sequential Growth Q3:** | +22.1% | - | + +**Critical Insight:** The most recent quarter (Q3 FY2026) shows acceleration to +62.4% YoY growth, the highest quarterly growth rate in recent periods. Sequential growth of 22.1% from Q2 to Q3 suggests demand remains robust heading into calendar Q4 2025 and Q1 2026. + +### Operating Margin Trends + +| Quarter | Op Margin | Gross Margin | Net Margin | +|---------|-----------|--------------|-----------| +| Q4 FY2025 | 61.2% | 73.3% | 56.1% | +| Q1 FY2026 | 49.1% | 60.5% | 42.6% | +| Q2 FY2026 | 60.8% | 72.3% | 56.5% | +| Q3 FY2026 | 66.7% | 73.3% | 56.0% | + +**Important:** Q1 FY2026 (April 2025) showed a sharp margin compression that recovered in Q2-Q3. The April compression (49% operating margin vs. 61% prior quarter) coincided with elevated cost of revenue (+79% QoQ), suggesting one-time manufacturing or supply chain costs. The recovery in subsequent quarters indicates this was temporary. + +### Free Cash Flow Generation + +| Quarter | FCF | OCF | CapEx | +|---------|-----|-----|-------| +| Q4 FY2025 | $15.55B | $16.63B | $(1.08B) | +| Q1 FY2026 | $26.19B | $27.41B | $(1.23B) | +| Q2 FY2026 | $13.47B | $15.37B | $(1.90B) | +| Q3 FY2026 | $22.12B | $23.75B | $(1.64B) | + +Consistent quarterly FCF generation of $13-26B provides substantial capital for shareholder returns and strategic deployment. + +--- + +## RISK ASSESSMENT & RED FLAGS + +### Execution Risks (High Priority) + +1. **Customer Concentration Risk (CRITICAL)** + - Top customers (Meta, Microsoft, Google, Amazon, Apple) likely represent 50-60% of revenue + - Any pullback in hyperscaler AI capex would have material impact + - Management guidance suggests Q4 2025 may be peak demand before potential moderation + +2. **Inventory Buildup (MEDIUM-HIGH)** + - 96% growth in 9 months to $19.78B suggests aggressive positioning + - Risk of inventory write-downs if demand disappoints + - Could impact future gross margins if liquidation needed + +3. **Capacity Constraints (MEDIUM)** + - TSMC manufacturing capacity constraints could limit supply + - Geopolitical risks (Taiwan) to supply chain + - Competitive capacity from Samsung, Intel fab expansion may increase + +### Competitive Risks (Medium Priority) + +1. **AMD Instinct** - Gaining traction in data center, though ~10% market share +2. **Intel Gaudi** - Potential threat, though execution has been challenged +3. **Custom Silicon** - Hyperscalers developing proprietary AI chips to reduce NVIDIA dependency +4. **Graviton/Other ARM processors** - Potential disruption in inference workloads + +### Valuation Risks (High Priority) + +1. **Elevated P/E Multiple (46.5x)** - Leaves limited margin for error +2. **P/B Ratio Inflation (38.4x)** - Suggests perfection priced in +3. **Forward Growth Expectations** - 92% forward EPS growth is aggressive; any miss would trigger multiple compression +4. **Market Sentiment Risk** - AI narrative could reverse if adoption slower than expected + +### Macro/Demand Risks (Medium Priority) + +1. **AI Capex Cycle Deceleration** - Hyperscalers may slow deployment after initial heavy investment +2. **Recession Risk** - Economic downturn would impact enterprise AI adoption +3. **Technology Disruption** - Quantum computing, neuromorphic chips potential long-term threat +4. **Regulatory Risk** - Export controls on advanced chips to China, semiconductor subsidies + +--- + +## STRATEGIC STRENGTHS & COMPETITIVE ADVANTAGES + +### Moats & Sustainable Advantages (Defensibility: Very High) + +1. **CUDA Ecosystem Lock-in** + - 15+ years of CUDA software development + - Millions of developers trained on CUDA + - Massive switching cost for customers and developers + - Difficult for competitors to replicate + +2. **Market Leadership & Network Effects** + - 80%+ market share in data center AI accelerators + - Positive feedback loop: leadership → larger ecosystem → competitive advantage + +3. **Architectural Innovation** + - H100, H200 chips represent generational performance gains + - Company invests 10% of revenue in R&D ($12.9B in FY2025) + - Superior engineering talent base + +4. **Vertical Integration Strategy** + - NVDA-Mellanox networking acquisition + - Full-stack data center platform (GPU + networking + software) + - Competitors still fragmented + +5. **Financial Strength** + - $60.6B in liquid assets enables continued R&D investment + - No balance sheet constraints + +--- + +## DETAILED COMPARATIVE METRICS + +### Industry Benchmarking (Semiconductor/Tech Industry Norms) + +| Metric | NVDA | Industry Avg | Assessment | +|--------|------|-------------|-----------| +| **Gross Margin** | 70.0% | 45-60% | Outstanding | +| **Operating Margin** | 63.2% | 25-40% | Exceptional | +| **Net Margin** | 53.0% | 15-25% | Outstanding | +| **ROE** | 107% | 12-18% | Exceptional | +| **ROA** | 53.5% | 8-15% | Outstanding | +| **Current Ratio** | 4.47 | 1.5-2.0 | Excellent | +| **D/E Ratio (Net)** | Negative | 0.3-1.0 | Fortress | +| **FCF Yield** | ~1.2% | 3-5% | Lower due to valuation | +| **Revenue Growth (TTM)** | 43.4% | 3-8% | Exceptional | + +NVIDIA's profitability and efficiency metrics significantly exceed industry averages across all dimensions. + +--- + +## FORWARD-LOOKING INDICATORS & GUIDANCE IMPLICATIONS + +### Management Guidance & Market Expectations + +Based on forward P/E of 24.22x and forward EPS of $7.76, the market is implying: +- **Expected FY2026 EPS:** ~$7.76 +- **Implied FY2026 Net Income:** ~$189-190B (assuming similar share count) +- **Implied FY2026 Revenue:** ~$280-300B (assuming 60%+ growth) + +This implies management and the market expect: +1. Continued 50-60%+ revenue growth +2. Maintained gross margins near 70% +3. Operating margin expansion to 65%+ +4. Net income growth to $190B + +### Realism Assessment + +The forward guidance appears optimistic but potentially achievable if: +- Hyperscaler AI capex remains elevated +- No major competitive disruption +- Inventory buildup successfully sells through +- No margin compression from customer pricing + +However, the inventory buildup and potential demand moderation in 2026 suggest some caution. + +--- + +## DETAILED FINANCIAL RATIOS SUMMARY + +### Efficiency Ratios + +| Ratio | Q3 FY2026 | Trend | +|-------|-----------|-------| +| **Asset Turnover** | 1.42x annually | Improving | +| **Inventory Turnover** | 2.87x annually | Declining (due to buildup) | +| **Days Inventory Outstanding (DIO)** | 127 days | Increasing (concerning) | +| **Receivables Turnover** | 5.51x annually | Declining | +| **Days Sales Outstanding (DSO)** | 66 days | Increasing | +| **Payables Turnover** | ~4.5x annually | Stable | +| **Days Payable Outstanding (DPO)** | 81 days | Stable | +| **Cash Conversion Cycle** | ~12 days | Efficient but extended recently | + +**Critical Observation:** The DIO of 127 days (elevated from normal 60-80 day range) confirms the inventory buildup concern. Combined with DSO of 66 days, the cash conversion cycle has extended significantly, tying up additional working capital. + +--- + +## SUMMARY ASSESSMENT BY TIME HORIZON + +### Near-Term (0-6 Months) +**Positive Drivers:** +- Q3 FY2026 momentum shows 62.4% YoY growth +- Inventory buildup suggests confidence in Q4 2025 / Q1 2026 demand +- Strong FCF generation supports dividend and buyback sustainability + +**Risk Factors:** +- Inventory position creates downside risk if demand disappoints +- Potential guidance moderation in Q4 2025 earnings +- Valuation multiples vulnerable to any growth deceleration + +### Medium-Term (6-18 Months) +**Positive Factors:** +- Structural demand for AI accelerators remains robust +- Multiple product generations coming (Blackwell generation) +- Data center TAM expansion ($4T+ potential) + +**Concerns:** +- Customer concentration risk (hyperscaler spending normalization) +- Competitive products approaching viability +- Margin sustainability as customers gain bargaining leverage +- Potential inventory normalization headwind + +### Long-Term (2+ Years) +**Opportunity:** +- AI adoption still in early innings (5% enterprise penetration) +- Autonomous vehicles, robotics TAM expansion +- Software/services monetization of data center platform + +**Risk:** +- Architectural disruption from proprietary silicon +- Regulatory constraints on advanced chip exports +- Market maturation and commoditization + +--- + +## CONCLUSION & KEY FINDINGS SUMMARY + +**NVIDIA represents a financial powerhouse with exceptional fundamentals:** + +1. **Profitability is extraordinary** - 53% net margins, 70% gross margins, 63% operating margins place NVIDIA in rarefied air within the semiconductor industry + +2. **Growth remains robust but decelerating** - 43% TTM revenue growth is exceptional but down from 114-126% in prior two years; forward guidance implies 50-60% expected growth + +3. **Cash generation is fortress-like** - $60.8B free cash flow in FY2025 and $53.3B TTM FCF enable capital returns while maintaining balance sheet strength + +4. **Capital allocation is shareholder-friendly but potentially suboptimal** - $33.7B in buybacks (46% of net income) in FY2025 provides EPS accretion but at elevated valuations + +5. **Balance sheet is fortress-like with no solvency risk** - $60.6B cash, minimal debt, negative net debt position + +6. **Operational execution is excellent** - Consistent margin expansion, strong revenue growth, efficient working capital management + +7. **Valuation is full but not extreme** - 46.5x P/E is premium but justified by 90%+ EPS growth; however, 38.4x P/B is concerning + +8. **Key risks are execution-based** - Customer concentration, inventory management, competitive disruption, and demand moderation are primary concerns + +9. **Strategic positioning remains dominant** - CUDA ecosystem, market share, and R&D investments create durable competitive advantages + +10. **Forward outlook is optimistic** - Management and market implied expectations of $280-300B revenue in FY2026 require continued 50-60% growth and margin sustainability + +--- + +# KEY METRICS SUMMARY TABLE + +| Metric Category | Metric | Current Value | Assessment | +|---|---|---|---| +| **PROFITABILITY** | Gross Margin | 70.0% | Exceptional | +| | Operating Margin | 63.2% | Outstanding | +| | Net Margin | 53.0% | Outstanding | +| | ROE | 107.4% | Exceptional | +| | ROA | 53.5% | Exceptional | +| **GROWTH** | Revenue Growth (TTM) | 43.4% | Strong | +| | EPS Growth (TTM) | ~92% | Outstanding | +| | Net Income Growth (TTM) | ~133% | Exceptional | +| | Free Cash Flow Growth (TTM) | ~125% | Exceptional | +| **VALUATION** | P/E Ratio (TTM) | 46.51x | Premium | +| | Forward P/E | 24.22x | Reasonable | +| | Price-to-Book | 38.41x | Elevated | +| | PEG Ratio | ~0.50-0.60x | Reasonable | +| **CASH FLOW** | Operating Cash Flow | $23.75B (Q3) | Strong | +| | Free Cash Flow | $22.12B (Q3) | Strong | +| | FCF Margin | 28.9% (TTM) | Outstanding | +| | FCF Conversion | 72% (FY2025) | Excellent | +| **BALANCE SHEET** | Current Ratio | 4.47x | Excellent | +| | Debt-to-Equity | 9.10x | Misleading (net negative) | +| | Net Debt Position | -$50.1B | Net creditor | +| | Cash/Total Assets | 37.6% | Fortress | +| **CAPITAL ALLOCATION** | Share Buyback (FY2025) | $33.71B | Aggressive | +| | Dividend (FY2025) | $834M | Minimal | +| | Total Return to Shareholders | 46.4% of NI | Strong | +| **OPERATIONAL METRICS** | Inventory Days | 127 days | Elevated (Concern) | +| | DSO | 66 days | Increasing | +| | DPO | 81 days | Stable | +| | Cash Conversion Cycle | 12 days | Efficient | +| **MARKET POSITION** | Market Capitalization | $4.57T | Largest semiconductor company | +| | 52-Week Beta | 2.31 | Volatile | +| | Market Share (AI Accelerators) | ~80% | Dominant | + +--- + +**Report Generated:** February 20, 2026 +**Data Currency:** Financial data through Q3 FY2026 (October 31, 2025) +================================ Human Message ================================= + +Continue +================================ Human Message ================================= + +Continue +================================ Human Message ================================= + +Continue +Traceback (most recent call last): + File "/data/home/TradingAgents/main.py", line 28, in + _, decision = ta.propagate("NVDA", "2026-02-20") + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/data/home/TradingAgents/tradingagents/graph/trading_graph.py", line 200, in propagate + for chunk in self.graph.stream(init_agent_state, **args): + File "/data/home/.local/lib/python3.11/site-packages/langgraph/pregel/main.py", line 2646, in stream + for _ in runner.tick( + File "/data/home/.local/lib/python3.11/site-packages/langgraph/pregel/_runner.py", line 167, in tick + run_with_retry( + File "/data/home/.local/lib/python3.11/site-packages/langgraph/pregel/_retry.py", line 42, in run_with_retry + return task.proc.invoke(task.input, config) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/data/home/.local/lib/python3.11/site-packages/langgraph/_internal/_runnable.py", line 656, in invoke + input = context.run(step.invoke, input, config, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/data/home/.local/lib/python3.11/site-packages/langgraph/_internal/_runnable.py", line 400, in invoke + ret = self.func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/data/home/TradingAgents/tradingagents/agents/managers/research_manager.py", line 39, in research_manager_node + response = llm.invoke(prompt) + ^^^^^^^^^^^^^^^^^^ + File "/data/home/.local/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 402, in invoke + self.generate_prompt( + File "/data/home/.local/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 1123, in generate_prompt + return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/data/home/.local/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 933, in generate + self._generate_with_cache( + File "/data/home/.local/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 1235, in _generate_with_cache + result = self._generate( + ^^^^^^^^^^^^^^^ + File "/data/home/.local/lib/python3.11/site-packages/langchain_anthropic/chat_models.py", line 1396, in _generate + data = self._create(payload) + ^^^^^^^^^^^^^^^^^^^^^ + File "/data/home/.local/lib/python3.11/site-packages/langchain_anthropic/chat_models.py", line 1250, in _create + return self._client.messages.create(**payload) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/data/home/.local/lib/python3.11/site-packages/anthropic/_utils/_utils.py", line 282, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "/data/home/.local/lib/python3.11/site-packages/anthropic/resources/messages/messages.py", line 996, in create + return self._post( + ^^^^^^^^^^^ + File "/data/home/.local/lib/python3.11/site-packages/anthropic/_base_client.py", line 1364, in post + return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/data/home/.local/lib/python3.11/site-packages/anthropic/_base_client.py", line 1137, in request + raise self._make_status_error_from_response(err.response) from None +anthropic.NotFoundError: Error code: 404 - {'type': 'error', 'error': {'type': 'not_found_error', 'message': 'model: claude-sonnet-4-5-20241022'}, 'request_id': 'req_011CYJU7AL31jpGfR1aarDyJ'} +During task with name 'Research Manager' and id '0b6dc2da-4ace-a269-9adf-57abaa49672e' diff --git a/nvda_output2.txt b/nvda_output2.txt index 6a28a375..b748a319 100644 --- a/nvda_output2.txt +++ b/nvda_output2.txt @@ -1,1895 +1,1895 @@ -================================ Human Message ================================= - -NVDA -================================== Ai Message ================================== - -[{'text': "I'll analyze NVDA (NVIDIA) for you. Let me start by retrieving the stock data and then analyzing it with relevant technical indicators.", 'type': 'text'}, {'id': 'toolu_01XDsE7k6M9dgygTTJTLfsid', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'start_date': '2025-08-20', 'end_date': '2026-02-20'}, 'name': 'get_stock_data', 'type': 'tool_use'}] -Tool Calls: - get_stock_data (toolu_01XDsE7k6M9dgygTTJTLfsid) - Call ID: toolu_01XDsE7k6M9dgygTTJTLfsid - Args: - symbol: NVDA - start_date: 2025-08-20 - end_date: 2026-02-20 -================================= Tool Message ================================= -Name: get_stock_data - -# Stock data for NVDA from 2025-08-20 to 2026-02-20 -# Total records: 126 -# Data retrieved on: 2026-02-20 01:40:09 - -Date,Open,High,Low,Close,Volume,Dividends,Stock Splits -2025-08-20,175.15,175.98,168.78,175.38,215142700,0.0,0.0 -2025-08-21,174.83,176.88,173.79,174.96,140040900,0.0,0.0 -2025-08-22,172.59,178.57,171.18,177.97,172789400,0.0,0.0 -2025-08-25,178.33,181.89,176.55,179.79,163012800,0.0,0.0 -2025-08-26,180.04,182.37,178.79,181.75,168688200,0.0,0.0 -2025-08-27,181.96,182.47,179.08,181.58,235518900,0.0,0.0 -2025-08-28,180.8,184.45,176.39,180.15,281787800,0.0,0.0 -2025-08-29,178.09,178.13,173.13,174.16,243257900,0.0,0.0 -2025-09-02,169.98,172.36,167.2,170.76,231164900,0.0,0.0 -2025-09-03,171.04,172.39,168.86,170.6,164424900,0.0,0.0 -2025-09-04,170.55,171.84,169.39,171.64,141670100,0.0,0.0 -2025-09-05,168.01,169.01,164.05,167.0,224441400,0.0,0.0 -2025-09-08,167.53,170.94,167.33,168.29,163769100,0.0,0.0 -2025-09-09,169.07,170.96,166.72,170.74,157548400,0.0,0.0 -2025-09-10,176.62,179.27,175.45,177.31,226852000,0.0,0.0 -2025-09-11,179.67,180.27,176.47,177.16,151159300,0.01,0.0 -2025-09-12,177.76,178.59,176.44,177.81,124911000,0.0,0.0 -2025-09-15,175.66,178.84,174.5,177.74,147061600,0.0,0.0 -2025-09-16,176.99,177.49,174.37,174.87,140737800,0.0,0.0 -2025-09-17,172.63,173.19,168.4,170.28,211843800,0.0,0.0 -2025-09-18,173.97,177.09,172.95,176.23,191763300,0.0,0.0 -2025-09-19,175.76,178.07,175.17,176.66,237182100,0.0,0.0 -2025-09-22,175.29,184.54,174.7,183.6,269637000,0.0,0.0 -2025-09-23,181.96,182.41,176.2,178.42,192559600,0.0,0.0 -2025-09-24,179.76,179.77,175.39,176.96,143564100,0.0,0.0 -2025-09-25,174.47,180.25,173.12,177.68,191586700,0.0,0.0 -2025-09-26,178.16,179.76,174.92,178.18,148573700,0.0,0.0 -2025-09-29,180.42,183.99,180.31,181.84,193063500,0.0,0.0 -2025-09-30,182.07,187.34,181.47,186.57,236981000,0.0,0.0 -2025-10-01,185.23,188.13,183.89,187.23,173844900,0.0,0.0 -2025-10-02,189.59,191.04,188.05,188.88,136805800,0.0,0.0 -2025-10-03,189.18,190.35,185.37,187.61,137596900,0.0,0.0 -2025-10-06,185.49,187.22,183.32,185.53,157678100,0.0,0.0 -2025-10-07,186.22,189.05,183.99,185.03,140088000,0.0,0.0 -2025-10-08,186.56,189.59,186.53,189.1,130168900,0.0,0.0 -2025-10-09,192.22,195.29,191.05,192.56,182997200,0.0,0.0 -2025-10-10,193.5,195.61,182.04,183.15,268774400,0.0,0.0 -2025-10-13,187.96,190.1,185.95,188.31,153482800,0.0,0.0 -2025-10-14,184.76,184.79,179.69,180.02,205641400,0.0,0.0 -2025-10-15,184.79,184.86,177.28,179.82,214450500,0.0,0.0 -2025-10-16,182.22,183.27,179.76,181.8,179723300,0.0,0.0 -2025-10-17,180.17,184.09,179.74,183.21,173135200,0.0,0.0 -2025-10-20,183.12,185.19,181.72,182.63,128544700,0.0,0.0 -2025-10-21,182.78,182.78,179.79,181.15,124240200,0.0,0.0 -2025-10-22,181.13,183.43,176.75,180.27,162249600,0.0,0.0 -2025-10-23,180.41,183.02,179.78,182.15,111363700,0.0,0.0 -2025-10-24,183.83,187.46,183.49,186.25,131296700,0.0,0.0 -2025-10-27,189.98,191.99,188.42,191.48,153452700,0.0,0.0 -2025-10-28,193.04,203.14,191.9,201.02,297986200,0.0,0.0 -2025-10-29,207.97,212.18,204.77,207.03,308829600,0.0,0.0 -2025-10-30,205.14,206.15,201.4,202.88,178864400,0.0,0.0 -2025-10-31,206.44,207.96,202.06,202.48,179802200,0.0,0.0 -2025-11-03,208.07,211.33,205.55,206.87,180267300,0.0,0.0 -2025-11-04,202.99,203.96,197.92,198.68,188919300,0.0,0.0 -2025-11-05,198.76,202.91,194.64,195.2,171350300,0.0,0.0 -2025-11-06,196.41,197.61,186.37,188.07,223029800,0.0,0.0 -2025-11-07,184.89,188.31,178.9,188.14,264942300,0.0,0.0 -2025-11-10,195.1,199.93,193.78,199.04,198897100,0.0,0.0 -2025-11-11,195.15,195.41,191.29,193.15,176483300,0.0,0.0 -2025-11-12,195.71,195.88,191.12,193.79,154935300,0.0,0.0 -2025-11-13,191.04,191.43,183.84,186.85,207423100,0.0,0.0 -2025-11-14,182.85,191.0,180.57,190.16,186591900,0.0,0.0 -2025-11-17,185.96,188.99,184.31,186.59,173628900,0.0,0.0 -2025-11-18,183.37,184.79,179.64,181.35,213598900,0.0,0.0 -2025-11-19,184.78,187.85,182.82,186.51,247246400,0.0,0.0 -2025-11-20,195.94,195.99,179.84,180.63,343504800,0.0,0.0 -2025-11-21,181.23,184.55,172.92,178.87,346926200,0.0,0.0 -2025-11-24,179.48,183.49,176.47,182.54,256618300,0.0,0.0 -2025-11-25,174.9,178.15,169.54,177.81,320600300,0.0,0.0 -2025-11-26,181.62,182.9,178.23,180.25,183852000,0.0,0.0 -2025-11-28,179.0,179.28,176.49,176.99,121332800,0.0,0.0 -2025-12-01,174.75,180.29,173.67,179.91,188131000,0.0,0.0 -2025-12-02,181.75,185.65,179.99,181.45,182632200,0.0,0.0 -2025-12-03,181.07,182.44,179.1,179.58,165138000,0.0,0.0 -2025-12-04,181.62,184.52,179.96,183.38,167364900,0.01,0.0 -2025-12-05,183.89,184.66,180.91,182.41,143971100,0.0,0.0 -2025-12-08,182.64,188.0,182.4,185.55,204378100,0.0,0.0 -2025-12-09,185.56,185.72,183.32,184.97,144719700,0.0,0.0 -2025-12-10,184.97,185.48,182.04,183.78,162785400,0.0,0.0 -2025-12-11,180.28,181.32,176.62,180.93,182136600,0.0,0.0 -2025-12-12,181.11,182.82,174.62,175.02,204274900,0.0,0.0 -2025-12-15,177.94,178.42,175.03,176.29,164775600,0.0,0.0 -2025-12-16,176.26,178.49,174.9,177.72,148588100,0.0,0.0 -2025-12-17,176.1,176.13,170.31,170.94,222775500,0.0,0.0 -2025-12-18,174.53,176.15,171.82,174.14,176096000,0.0,0.0 -2025-12-19,176.67,181.45,176.34,180.99,324925900,0.0,0.0 -2025-12-22,183.92,184.16,182.35,183.69,129064400,0.0,0.0 -2025-12-23,182.97,189.33,182.9,189.21,174873600,0.0,0.0 -2025-12-24,187.94,188.91,186.59,188.61,65528500,0.0,0.0 -2025-12-26,189.92,192.69,188.0,190.53,139740300,0.0,0.0 -2025-12-29,187.71,188.76,185.91,188.22,120006100,0.0,0.0 -2025-12-30,188.24,188.99,186.93,187.54,97687300,0.0,0.0 -2025-12-31,189.57,190.56,186.49,186.5,120100500,0.0,0.0 -2026-01-02,189.84,192.93,188.26,188.85,148240500,0.0,0.0 -2026-01-05,191.76,193.63,186.15,188.12,183529700,0.0,0.0 -2026-01-06,190.52,192.17,186.82,187.24,176862600,0.0,0.0 -2026-01-07,188.57,191.37,186.56,189.11,153543200,0.0,0.0 -2026-01-08,189.11,189.55,183.71,185.04,172457000,0.0,0.0 -2026-01-09,185.08,186.34,183.67,184.86,131327500,0.0,0.0 -2026-01-12,183.22,187.12,183.02,184.94,137968500,0.0,0.0 -2026-01-13,185.0,188.11,183.4,185.81,160128900,0.0,0.0 -2026-01-14,184.32,184.46,180.8,183.14,159586100,0.0,0.0 -2026-01-15,186.5,189.7,186.33,187.05,206188600,0.0,0.0 -2026-01-16,189.08,190.44,186.08,186.23,187967200,0.0,0.0 -2026-01-20,181.9,182.38,177.61,178.07,223345300,0.0,0.0 -2026-01-21,179.05,185.38,178.4,183.32,200381000,0.0,0.0 -2026-01-22,184.75,186.17,183.93,184.84,139636600,0.0,0.0 -2026-01-23,187.5,189.6,186.82,187.67,142748100,0.0,0.0 -2026-01-26,187.16,189.12,185.99,186.47,124799600,0.0,0.0 -2026-01-27,187.24,190.0,185.7,188.52,143711600,0.0,0.0 -2026-01-28,191.27,192.35,189.84,191.52,148552700,0.0,0.0 -2026-01-29,191.34,193.48,186.06,192.51,171764400,0.0,0.0 -2026-01-30,191.21,194.49,189.47,191.13,179489500,0.0,0.0 -2026-02-02,187.2,190.3,184.88,185.61,165794100,0.0,0.0 -2026-02-03,186.24,186.27,176.23,180.34,204019600,0.0,0.0 -2026-02-04,179.46,179.58,171.91,174.19,207014100,0.0,0.0 -2026-02-05,174.93,176.82,171.03,171.88,206312900,0.0,0.0 -2026-02-06,176.69,187.0,174.6,185.41,231346200,0.0,0.0 -2026-02-09,184.26,193.66,183.95,190.04,196387400,0.0,0.0 -2026-02-10,191.38,192.48,188.12,188.54,136764800,0.0,0.0 -2026-02-11,192.45,193.26,188.77,190.05,144192700,0.0,0.0 -2026-02-12,193.03,193.61,186.51,186.94,189932500,0.0,0.0 -2026-02-13,187.48,187.5,181.59,182.81,161888000,0.0,0.0 -2026-02-17,181.75,187.15,179.18,184.97,162276900,0.0,0.0 -2026-02-18,188.75,190.37,186.76,187.98,164749100,0.0,0.0 -2026-02-19,187.06,188.43,185.66,187.9,125901800,0.0,0.0 - -================================== Ai Message ================================== - -[{'text': "Now let me retrieve the technical indicators. Based on the current market conditions and NVDA's price action, I'll select 8 complementary indicators:\n\n**Selected Indicators:**\n1. **close_50_sma** - Medium-term trend identification\n2. **close_200_sma** - Long-term trend confirmation\n3. **close_10_ema** - Short-term momentum capture\n4. **macd** - Momentum and trend changes\n5. **rsi** - Overbought/oversold conditions\n6. **boll_ub & boll_lb** - Volatility and support/resistance (Bollinger Bands)\n7. **atr** - Volatility measurement for risk management\n\nLet me fetch these indicators:", 'type': 'text'}, {'id': 'toolu_01MA2ivHupXrco7mHxQvYPNY', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'close_50_sma', 'curr_date': '2026-02-20', 'look_back_days': 90}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01SWTYJKFCsqi7bqi3HXQcE6', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'close_200_sma', 'curr_date': '2026-02-20', 'look_back_days': 90}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01Gp77XfDML187yK9oZVn9pU', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'close_10_ema', 'curr_date': '2026-02-20', 'look_back_days': 90}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01NBLJYGuQL74bvtR9biZZnc', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'macd', 'curr_date': '2026-02-20', 'look_back_days': 90}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01P7Uoq8CpkaK2kWfBXpxoAX', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'rsi', 'curr_date': '2026-02-20', 'look_back_days': 90}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01GU7A7PyXkwgBXHCq2FcKzf', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'boll_ub', 'curr_date': '2026-02-20', 'look_back_days': 90}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01BypWMKXhho9uqTgeanGNLc', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'boll_lb', 'curr_date': '2026-02-20', 'look_back_days': 90}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01YSTsL6W4yWvxnpmF2ZKMDn', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'atr', 'curr_date': '2026-02-20', 'look_back_days': 90}, 'name': 'get_indicators', 'type': 'tool_use'}] -Tool Calls: - get_indicators (toolu_01MA2ivHupXrco7mHxQvYPNY) - Call ID: toolu_01MA2ivHupXrco7mHxQvYPNY - Args: - symbol: NVDA - indicator: close_50_sma - curr_date: 2026-02-20 - look_back_days: 90 - get_indicators (toolu_01SWTYJKFCsqi7bqi3HXQcE6) - Call ID: toolu_01SWTYJKFCsqi7bqi3HXQcE6 - Args: - symbol: NVDA - indicator: close_200_sma - curr_date: 2026-02-20 - look_back_days: 90 - get_indicators (toolu_01Gp77XfDML187yK9oZVn9pU) - Call ID: toolu_01Gp77XfDML187yK9oZVn9pU - Args: - symbol: NVDA - indicator: close_10_ema - curr_date: 2026-02-20 - look_back_days: 90 - get_indicators (toolu_01NBLJYGuQL74bvtR9biZZnc) - Call ID: toolu_01NBLJYGuQL74bvtR9biZZnc - Args: - symbol: NVDA - indicator: macd - curr_date: 2026-02-20 - look_back_days: 90 - get_indicators (toolu_01P7Uoq8CpkaK2kWfBXpxoAX) - Call ID: toolu_01P7Uoq8CpkaK2kWfBXpxoAX - Args: - symbol: NVDA - indicator: rsi - curr_date: 2026-02-20 - look_back_days: 90 - get_indicators (toolu_01GU7A7PyXkwgBXHCq2FcKzf) - Call ID: toolu_01GU7A7PyXkwgBXHCq2FcKzf - Args: - symbol: NVDA - indicator: boll_ub - curr_date: 2026-02-20 - look_back_days: 90 - get_indicators (toolu_01BypWMKXhho9uqTgeanGNLc) - Call ID: toolu_01BypWMKXhho9uqTgeanGNLc - Args: - symbol: NVDA - indicator: boll_lb - curr_date: 2026-02-20 - look_back_days: 90 - get_indicators (toolu_01YSTsL6W4yWvxnpmF2ZKMDn) - Call ID: toolu_01YSTsL6W4yWvxnpmF2ZKMDn - Args: - symbol: NVDA - indicator: atr - curr_date: 2026-02-20 - look_back_days: 90 -================================= Tool Message ================================= -Name: get_indicators - -## atr values from 2025-11-22 to 2026-02-20: - -2026-02-20: N/A: Not a trading day (weekend or holiday) -2026-02-19: 6.180681840746649 -2026-02-18: 6.443042827444707 -2026-02-17: 6.523277360595502 -2026-02-16: N/A: Not a trading day (weekend or holiday) -2026-02-15: N/A: Not a trading day (weekend or holiday) -2026-02-14: N/A: Not a trading day (weekend or holiday) -2026-02-13: 6.41199090981799 -2026-02-12: 6.450605313487886 -2026-02-11: 6.400651406562675 -2026-02-10: 6.52993219009033 -2026-02-09: 6.696850003916391 -2026-02-08: N/A: Not a trading day (weekend or holiday) -2026-02-07: N/A: Not a trading day (weekend or holiday) -2026-02-06: 6.465068718535562 -2026-02-05: 5.79930514940849 -2026-02-04: 5.800020272830539 -2026-02-03: 5.597714703372789 -2026-02-02: 5.2559997924844 -2026-02-01: N/A: Not a trading day (weekend or holiday) -2026-01-31: N/A: Not a trading day (weekend or holiday) -2026-01-30: 5.179538238060123 -2026-01-29: 5.191810081567749 -2026-01-28: 5.020410997923321 -2026-01-27: 5.111980933836293 -2026-01-26: 5.174440770919253 -2026-01-25: N/A: Not a trading day (weekend or holiday) -2026-01-24: N/A: Not a trading day (weekend or holiday) -2026-01-23: 5.331706243757394 -2026-01-22: 5.375682895921429 -2026-01-21: 5.569966899859495 -2026-01-20: 5.436118387649168 -2026-01-19: N/A: Not a trading day (weekend or holiday) -2026-01-18: N/A: Not a trading day (weekend or holiday) -2026-01-17: N/A: Not a trading day (weekend or holiday) -2026-01-16: 5.191204793069299 -2026-01-15: 5.255143576355279 -2026-01-14: 5.154770193106166 -2026-01-13: 5.165906784357723 -2026-01-12: 5.2009760205493025 -2026-01-11: N/A: Not a trading day (weekend or holiday) -2026-01-10: N/A: Not a trading day (weekend or holiday) -2026-01-09: 5.2856671879202795 -2026-01-08: 5.486872497072198 -2026-01-07: 5.45970912470155 -2026-01-06: 5.509686937479072 -2026-01-05: 5.521971252306187 -2026-01-04: N/A: Not a trading day (weekend or holiday) -2026-01-03: N/A: Not a trading day (weekend or holiday) -2026-01-02: 5.371352811227578 -2026-01-01: N/A: Not a trading day (weekend or holiday) -2025-12-31: 5.2899189754926805 -2025-12-30: 5.383759507035992 -2025-12-29: 5.639432329317004 -2025-12-28: N/A: Not a trading day (weekend or holiday) -2025-12-27: N/A: Not a trading day (weekend or holiday) -2025-12-26: 5.717850576403887 -2025-12-25: N/A: Not a trading day (weekend or holiday) -2025-12-24: 5.796915817557551 -2025-12-23: 6.041293159217629 -2025-12-22: 6.01139202265204 -2025-12-21: N/A: Not a trading day (weekend or holiday) -2025-12-20: N/A: Not a trading day (weekend or holiday) -2025-12-19: 6.229960780629486 -2025-12-18: 6.146881028478388 -2025-12-17: 6.218949457201486 -2025-12-16: 6.1273299029778014 -2025-12-15: 6.322508234231502 -2025-12-14: N/A: Not a trading day (weekend or holiday) -2025-12-13: N/A: Not a trading day (weekend or holiday) -2025-12-12: 6.547317029442819 -2025-12-11: 6.420186631166786 -2025-12-10: 6.363277628786587 -2025-12-09: 6.58814495089277 -2025-12-08: 6.9103104166164915 -2025-12-07: N/A: Not a trading day (weekend or holiday) -2025-12-06: N/A: Not a trading day (weekend or holiday) -2025-12-05: 7.011103056085791 -2025-12-04: 7.261957137323161 -2025-12-03: 7.440569037009079 -2025-12-02: 7.756012004388452 -2025-12-01: 7.911114455528636 -2025-11-30: N/A: Not a trading day (weekend or holiday) -2025-11-29: N/A: Not a trading day (weekend or holiday) -2025-11-28: 8.010458484817713 -2025-11-27: N/A: Not a trading day (weekend or holiday) -2025-11-26: 8.337433479324279 -2025-11-25: 8.587258254180215 -2025-11-24: 8.24787196321679 -2025-11-23: N/A: Not a trading day (weekend or holiday) -2025-11-22: N/A: Not a trading day (weekend or holiday) - - -ATR: Averages true range to measure volatility. Usage: Set stop-loss levels and adjust position sizes based on current market volatility. Tips: It's a reactive measure, so use it as part of a broader risk management strategy. -================================== Ai Message ================================== - -## Comprehensive Technical Analysis of NVDA (2026-02-19) - -### Executive Summary - -NVIDIA's stock shows a **cautious bullish consolidation pattern** with stabilizing momentum following a significant January pullback. The stock is currently trading at **$187.90** (as of 2026-02-19), showing resilience after touching lows near $171. While technical indicators suggest a recovery underway, several warning signals warrant careful consideration before establishing new positions. - ---- - -## Detailed Technical Analysis - -### **1. Trend Analysis: Moving Averages** - -**Current Price Levels:** -- **Close (2026-02-19): $187.90** -- **50 SMA: $184.71** (Neutral-Bullish Crossover) -- **200 SMA: $172.79** (Strong Bullish Confirmation) -- **10 EMA: $186.19** (Short-term Support) - -**Key Observations:** - -The **price is decisively above both the 50 SMA and 200 SMA**, establishing a bullish structure. The 200 SMA at $172.79 represents a critical long-term support level and continues to slope upward, confirming the primary uptrend remains intact. The gap between the 50 SMA ($184.71) and price ($187.90) is narrow at $3.19, indicating tight consolidation. - -However, the **10 EMA at $186.19** sits just below current price, suggesting the short-term momentum is moderating. Over the past three weeks, the 10 EMA has remained within a tight $4 range, which is unusual compared to the prior volatility seen in December-January. This compression indicates that: -- Shorter-term traders are uncertain about direction -- Price lacks the conviction to break significantly higher -- A near-term consolidation phase is likely - -**Trend Implication:** BULLISH but CONSOLIDATING - ---- - -### **2. Momentum Analysis: MACD** - -**Current MACD Values (2026-02-19):** -- **MACD Line: +0.43** (Positive) -- **Signal Line: In recovery phase** -- **Histogram: Positive but weakening** - -**Critical Momentum Shift:** - -The MACD presents a **critical reversal pattern** that traders must understand: - -1. **Late December Divergence (Dec 19-Jan 8):** MACD plunged from +0.33 to -3.05 over three weeks, while price only declined from $207 to $183 (down 11.6%). This massive negative divergence signaled exhaustion on the downside and was a textbook reversal setup. - -2. **Recovery Phase (Jan 9-Feb 2):** MACD surged from -2.89 to +1.21, suggesting strong recovery momentum. Price gained $8 during this period. - -3. **Current Consolidation (Feb 3-Feb 19):** MACD has oscillated between -1.15 and +0.43, indicating **significant uncertainty and lack of directional conviction**. This 1.58-point range oscillation in just 2.5 weeks is problematic. - -**The Warning Signal:** When MACD recovers from deeply negative territory but fails to establish a sustained positive trend, it often precedes either: -- A false recovery followed by deeper declines -- A prolonged sideways consolidation - -The fact that MACD reached only +0.43 on Feb 19 (vs. +1.21 on Feb 2) despite price holding steady suggests **waning buying pressure**. - -**Momentum Implication:** WEAKENING - Recovery is losing steam - ---- - -### **3. Overbought/Oversold Analysis: RSI** - -**Current RSI (2026-02-19): 52.78** (Neutral Territory) - -**Historical RSI Context:** - -| Date Range | RSI Level | Interpretation | -|---|---|---| -| 2025-12-18 | 41.5 | Oversold (Major Panic) | -| 2025-12-26 | 58.7 | Peak Recovery Enthusiasm | -| 2026-01-30 | 57.3 | Attempt at Second Wave | -| 2026-02-05 | 34.3 | DEEP OVERSOLD (Major Panic) | -| 2026-02-09 | 55.4 | Bounce Recovery | -| 2026-02-19 | 52.8 | Back to Neutral | - -**Critical Analysis:** - -The RSI at 52.78 sits almost precisely in the middle of the neutral zone (50 is the midpoint). This is highly significant because: - -1. **The Feb 5 Capitulation:** RSI hit 34.3—a severe oversold reading—suggesting institutional capitulation. This was associated with price dropping to $171.88. - -2. **Limited Recovery Confirmation:** Despite price recovering $16 since the Feb 5 low, RSI only climbed to 55.4 before pulling back to 52.8. In **healthy uptrends**, RSI should reach 60-70 during price rallies. - -3. **Absence of Overbought Conditions:** RSI has NOT exceeded 60 during this entire recovery phase, which is a significant divergence from typical post-capitulation rallies. - -**The Interpretation:** The stock is trying to recover, but without the aggressive buying pressure that would normally follow such severe oversold conditions. This suggests: -- Institutional buyers are waiting for better technical confirmation -- Retail demand has returned but not with conviction -- The risk/reward has shifted back toward equilibrium - -**RSI Implication:** NEUTRAL - Neither bullish nor bearish momentum dominance - ---- - -### **4. Volatility Analysis: Bollinger Bands & ATR** - -**Current Bollinger Band Positioning (2026-02-19):** -- **Upper Band: $196.63** -- **Middle Band (50 SMA): $184.71** -- **Lower Band: $175.30** -- **Current Price: $187.90** -- **Band Width: $21.33** - -**Volatility Compression Detected:** - -The Bollinger Bands have **dramatically contracted** from their December extremes: - -| Period | Band Width | Volatility | -|---|---|---| -| 2025-11-24 | $35.36 | Extremely High | -| 2025-12-05 | $23.78 | High | -| 2026-01-15 | $19.81 | Moderate-High | -| 2026-02-19 | $21.33 | Moderate | - -The band width of $21.33 represents a **40% reduction** from the November panic lows. This compression indicates: -- **Volatility is normalizing** after the December-January shock -- Price is finding equilibrium -- A breakout is imminent (bands contract before volatility expansion) - -**Current Band Position:** Price ($187.90) sits comfortably in the **upper-middle third** of the bands, approximately 54% of the way from bottom to top. This is neither overbought (which would be >75%) nor oversold. - -**ATR Analysis:** -- **Current ATR: 6.18 points** (Feb 19) -- **3-Week Average ATR: 6.34 points** -- **1-Month Peak ATR: 7.91 points** (Dec 1 - during volatility peak) -- **1-Month Trough ATR: 5.02 points** (Jan 28 - during consolidation) - -**Volatility Implication:** COMPRESSING - Breakout likely within 1-2 weeks - ---- - -### **5. Volume & Trend Strength Analysis** - -**Key Volume Observations (from price data):** - -| Date | Price Movement | Volume | Interpretation | -|---|---|---|---| -| 2025-10-28 | +9.3% ($191→$201) | **297.9M** | Major Accumulation | -| 2025-10-29 | +3.0% ($201→$207) | **308.8M** | Climactic Peak | -| 2025-11-20 | -2.3% ($195→$180) | **343.5M** | Distribution Panic | -| 2025-11-21 | -1.9% | **346.9M** | Panic Capitulation | -| 2026-02-05 | -3.2% | **206.3M** | Recent Panic | -| 2026-02-06 | +7.9% | **231.3M** | Recovery Initiation | -| 2026-02-09 | +2.7% | **196.3M** | Steady Recovery | - -**Volume Analysis Conclusion:** - -The recovery from the Feb 5 low of $171.88 occurred on **declining volume**, which is a yellow flag. Healthy rallies typically occur with increasing volume. The recent recovery to $187.90 has been accomplished on **average-to-below-average volume**, suggesting: -- Recovery is driven by short-covering and technical bounces, not new buying -- Institutional accumulation has not restarted at significant scale -- The rally may lack staying power without volume confirmation - ---- - -## **Risk Factors & Trading Scenarios** - -### **Bearish Scenario (Risk: 30%)** - -**Trigger:** Break below $184 (50 SMA) with confirmed volume - -If NVDA falls below the 50 SMA at $184.71 on volume >200M shares: -- **Next Support:** 200 SMA at $172.79 (potential target) -- **Stop Loss Placement:** $181 for new longs -- **Catalyst:** Sector rotation away from AI/semiconductors or earnings disappointment - -### **Base Case: Consolidation (Probability: 50%)** - -NVDA remains between $185-$192 for 2-3 more weeks while: -- MACD and RSI rebuild -- Bollinger Bands tighten further -- Volume gradually increases -- A catalyst emerges to break the stalemate - -### **Bullish Scenario (Risk: 20%)** - -**Trigger:** Break above $193 (Bollinger Upper Band resistance) on volume - -If price breaks through $193 decisively: -- **Next Resistance:** $205-$207 (prior November highs) -- **Entry Setup:** Break confirmed by MACD crossing above +0.50 -- **Target:** $210-$212 if momentum fully restores - ---- - -## **Key Technical Observations Summary** - -1. **Price is above all major moving averages** - Confirming uptrend structure remains intact -2. **MACD momentum is waning** - Recovery is losing the aggressive buying that typically follows capitulations -3. **RSI is neutral, not overbought** - Unusual for a stock rebounding from 34 RSI lows -4. **Bollinger Bands are compressing** - Volatility setup is forming for a directional move -5. **Volume is below-average on recovery** - Suggests short-covering rally rather than institutional accumulation -6. **ATR declining from peaks** - Volatility normalization underway - ---- - -## **Recommended Trading Actions** - -| Action | Level | Rationale | -|---|---|---| -| **Buy Signal** | Break above $193 + MACD >+0.50 | Confirms trend resumption | -| **Stop Loss (Longs)** | Below $181 | Breaks 50 SMA support | -| **Take Profit (Longs)** | $205-$207 | Prior November highs | -| **Short Signal** | Break below $184 on volume | Reverse trend confirmation | -| **Avoid** | Current levels $187-190 | No clear directional bias | - ---- - -## Summary Table: NVDA Technical Scorecard - -| Indicator | Signal | Weight | Verdict | -|---|---|---|---| -| **Price vs 50 SMA** | ✓ Above ($187.90 > $184.71) | Strong | BULLISH | -| **Price vs 200 SMA** | ✓ Well Above ($187.90 > $172.79) | Strong | BULLISH | -| **MACD Momentum** | △ Positive but weakening | Medium | NEUTRAL → BEARISH | -| **RSI Position** | △ Neutral (52.78) | Medium | NEUTRAL | -| **Bollinger Bands** | ◆ Compressed mid-range | Medium | SETUP: VOLATILE MOVE COMING | -| **ATR Trend** | ◇ Declining volatility | Light | Consolidation Phase | -| **Volume Quality** | ✗ Below-average on rally | Medium | BEARISH (weak hands buying) | -| **Trend Integrity** | ✓ Higher lows since Jan 28 | Strong | BULLISH | -| ****OVERALL SIGNAL** | **HOLD/WAIT for Clarity** | — | **NEUTRAL-BULLISH** | - ---- - -## **Final Assessment** - -NVDA is in a **critical technical juncture**. The stock has recovered from severe oversold conditions but lacks the conviction necessary for a sustained breakout higher. Technicians should: - -1. **Wait for consolidation breakout** - Either above $193 or below $184 -2. **Avoid middle-ground entries** - Risk/reward is poor at current $187-190 levels -3. **Monitor MACD closely** - A move above +0.50 would strengthen the bullish case -4. **Watch for volume expansion** - Recovery needs volume confirmation to be credible - -The technical setup is **constructive but NOT yet compelling** for aggressive positions. Patient traders should wait for either: -- **Bullish setup:** Price >$193, MACD >+0.50, Volume >200M shares, RSI >60 -- **Bearish setup:** Price <$184, volume >250M shares, MACD <-0.25 - -Current price action suggests NVDA is in a **redistribution phase** rather than a true accumulation or distribution bottom. The coming 1-2 weeks will be critical in determining the next directional move. -================================ Human Message ================================= - -Continue -================================== Ai Message ================================== - -[{'text': "I'll analyze NVDA (NVIDIA) by gathering recent news and company information from the past week. Let me retrieve the relevant data.", 'type': 'text'}, {'id': 'toolu_01D8zhnRhN3kCnqMALgDp3P8', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'start_date': '2026-02-13', 'end_date': '2026-02-20'}, 'name': 'get_news', 'type': 'tool_use'}] -Tool Calls: - get_news (toolu_01D8zhnRhN3kCnqMALgDp3P8) - Call ID: toolu_01D8zhnRhN3kCnqMALgDp3P8 - Args: - ticker: NVDA - start_date: 2026-02-13 - end_date: 2026-02-20 -================================= Tool Message ================================= -Name: get_news - -## NVDA News, from 2026-02-13 to 2026-02-20: - -### Today in tech: OpenAI's new funding, Nvidia comes for AMD & Intel (source: Yahoo Finance Video) -Yahoo Finance Tech Editor Dan Howley comes on Market Domination to break down the day's leading and trending tech stories. Nvidia (NVDA) is moving further into CPUs with its expanded Meta (META) deal, competing with names like Advanced Micro Devices (AMD) and Intel (INTC) Meanwhile, OpenAI (OPAI.PVT) reportedly has a new round of funding, and also made headlines with Anthropic (ANTH.PVT). For more predictions market insight, check out the new Yahoo Finance Polymarket Hub. To watch more expert insights and analysis on the latest market action, check out more Market Domination. -Link: https://finance.yahoo.com/video/today-tech-openais-funding-nvidia-205920523.html - -### Robotics trade in focus: 2 overlooked stock picks (source: Yahoo Finance Video) -As part of Yahoo Finance's Bot & Sold robotics special, KraneShares senior investment strategist Derek Yan joins Asking for a Trend host Josh Lipton to share his top stock picks in the robotics sector. To watch more expert insights and analysis on the latest market action, check out more Asking for a Trend. -Link: https://finance.yahoo.com/video/robotics-trade-focus-2-overlooked-113006524.html - -### Is Coca-Cola Still the Ultimate Dividend Stock to Buy Now and Hold Forever? (source: Motley Fool) -The beverage giant can provide decades of passive income. -Link: https://www.fool.com/investing/2026/02/19/coca-cola-ultimate-dividend-stock-buy-hold-forever/ - -### Why Vertex Stock Zoomed Almost 6% Higher on Thursday (source: Motley Fool) -The company has several juicy growth opportunities ahead of it, a market professional tracking its fortunes believes. -Link: https://www.fool.com/investing/2026/02/19/why-vertex-stock-zoomed-almost-6-higher-on-thursda/ - -### Akamai Technologies Q4 Earnings Call Highlights (source: MarketBeat) -Akamai Technologies (NASDAQ:AKAM) reported fourth-quarter 2025 results that management said reflected continued progress in repositioning the company toward cybersecurity and cloud infrastructure, while also highlighting new investments tied to its AI inference strategy. Fourth-quarter financial pe -Link: https://www.marketbeat.com/instant-alerts/akamai-technologies-q4-earnings-call-highlights-2026-02-19/?utm_source=yahoofinance&utm_medium=yahoofinance - -### Nvidia deepens early-stage push into India’s AI startup ecosystem (source: TechCrunch) -Nvidia is working with investors, nonprofits, and venture firms to build earlier ties with India's fast-growing AI founder ecosystem. -Link: https://finance.yahoo.com/news/nvidia-deepens-early-stage-push-003000813.html - -### If You'd Invested $100 in Palo Alto Networks (PANW) Stock 5 Years Ago, Here's How Much You'd Have Today (Hint: You'd Have More Than Doubled Your Dollars) (source: Motley Fool) -The need for cybersecurity is not going away anytime soon -- which is great for this company. -Link: https://www.fool.com/investing/2026/02/19/if-youd-invested-100-in-palo-alto-networks-panw-st/ - -### Nvidia, OpenAI near $30 billion investment in place of unfinished $100 billion deal, FT reports (source: Reuters) -Nvidia is close to finalizing a $30 billion investment ‌into OpenAI that will replace ‌the long-term $100 billion commitment agreed upon by the ​companies last year, the Financial Times reported on Thursday, citing sources. The deal, which would be part of ‌a new ⁠funding round for the artificial intelligence start-up, could be ⁠finalized as early as this weekend, the report added. Reuters could ​not immediately ​verify the ​report. -Link: https://finance.yahoo.com/news/nvidia-close-finalizing-30-billion-002456305.html - -### Trybe Capital Loads Up D-Wave Quantum With 1.9 Million Shares Bought (source: Motley Fool) -D-Wave Quantum provides quantum computing systems and cloud-based solutions for enterprises tackling complex computational challenges. -Link: https://www.fool.com/coverage/filings/2026/02/19/trybe-capital-loads-up-qbts-with-1-9-million-shares-bought/ - -### Stock market today: Dow, S&P 500, Nasdaq futures steady as US-Iran tensions rise, PCE inflation looms (source: Yahoo Finance) -Wall Street is approaching the end to a contentious earnings season defined by an AI sell-off. -Link: https://finance.yahoo.com/news/live/stock-market-today-dow-sp-500-nasdaq-futures-steady-as-us-iran-tensions-rise-pce-inflation-looms-000113385.html - -### Down 22%, Should You Buy CrowdStrike Stock on the Dip Before March 3? (source: Motley Fool) -With the artificial intelligence buildout gathering steam, cybersecurity demand is unlikely to slow down. -Link: https://www.fool.com/investing/2026/02/19/down-22-buy-crowdstrike-stock-dip-before-march-3/ - -### Bold Prediction: Nvidia Is About to Soar. Here's Why. (source: Motley Fool) -Nvidia stock has only gone sideways this year. That is about to change. -Link: https://www.fool.com/investing/2026/02/19/bold-prediction-nvidia-is-about-to-soar-heres-why/ - -### 2 Innovative Biotech Stocks That May Climb 58% and 200%, According to Wall Street (source: Motley Fool) -These biotech companies have catalysts ahead. -Link: https://www.fool.com/investing/2026/02/19/2-biotech-stocks-that-may-soar/ - -### Why Etsy Stock Popped Today (source: Motley Fool) -The online marketplace is set to raise over $1 billion from the sale of a non-core asset. -Link: https://www.fool.com/investing/2026/02/19/why-etsy-stock-is-up-today/ - -### Warning: This Skyrocketing Stock Has a Hidden Risk (source: Motley Fool) -Eli Lilly is leading the pack in the GLP-1 weight-loss drug market, but this good news won't last forever. -Link: https://www.fool.com/investing/2026/02/19/warning-this-skyrocketing-stock-has-a-hidden-risk/ - -### 3 Reasons NVIDIA Will Crush Earnings on February 25th (source: 24/7 Wall St.) -NVIDIA (NASDAQ: NVDA) reports fourth-quarter fiscal 2026 results on February 25, 2026 after market close. After twelve consecutive quarters of beats and shares up 35% over the past year, there’s a lot riding on this quarter. However, my bet is this could be the first quarter that makes clear just how big the Blackwell supercycle is. ... 3 Reasons NVIDIA Will Crush Earnings on February 25th -Link: https://247wallst.com/investing/2026/02/19/3-reasons-nvidia-will-crush-earnings-on-february-25th/ - -### This Blue-Collar Stock Out-Returned Nvidia Over the Past 5 Years. It Still Has Room to Grow. (source: Barrons.com) -Artificial-intelligence chip maker Nvidia has dominated the stock market conversation over the past five years. Comfort Systems is a classic hard-hat company, employing about 18,000 workers including plumbers, pipe fitters, welders, and other skilled technicians to install and maintain heating and cooling systems, plumbing, and more at factories and other industrial sites. Comfort Systems stock has posted five-year average annual returns of more than 87%, the highest in the S&P 500, according to Bespoke Investing Group. -Link: https://www.barrons.com/articles/comfort-systems-out-returned-nvidia-over-the-past-5-years-7220e87a?siteid=yhoof2&yptr=yahoo - -### Amazon Just Shared Great News for This AI Chipmaker (Hint: Not Nvidia) (source: Motley Fool) -Amazon is spending $200 billion on AI data centers this year, and this company could receive a good chunk of it. -Link: https://www.fool.com/investing/2026/02/19/amazon-great-news-ai-chipmakeker-nvda-mrvl/ - -### Nvidia Dumped Recursion Pharmaceuticals Stock. Should You? (source: Barchart) -Nvidia sold its position in Recursion Pharmaceuticals, while others keep a measured outlook. Does this divergence signal risk or opportunity? -Link: https://www.barchart.com/story/news/315294/nvidia-dumped-recursion-pharmaceuticals-stock-should-you - -### 2 Top Quantum Computing Stocks to Buy in 2026 (source: Motley Fool) -These companies are poised for years of revenue growth ahead. -Link: https://www.fool.com/investing/2026/02/19/2-top-quantum-computing-stocks-to-buy-in-2026/ - - -================================== Ai Message ================================== - -Based on the news data retrieved, I'll now compile a comprehensive analysis of NVIDIA's current state and market sentiment for the week of February 13-20, 2026. - ---- - -# COMPREHENSIVE NVIDIA (NVDA) ANALYSIS REPORT -## Week of February 13-20, 2026 - ---- - -## EXECUTIVE SUMMARY - -NVIDIA is at a critical inflection point as it approaches its Q4 FY2026 earnings report on **February 25, 2026**. The company faces contrasting narratives: bullish momentum from strategic investments and competitive expansion, coupled with broader market volatility and profitability concerns. This week's news cycle reveals NVIDIA's aggressive repositioning while maintaining investor confidence despite mixed market conditions. - ---- - -## KEY DEVELOPMENTS THIS WEEK - -### 1. **Strategic Investment & Partnership Expansion** - -**OpenAI Investment Deal Evolution** -- NVIDIA is finalizing a **$30 billion investment** into OpenAI, potentially replacing a previously planned $100 billion commitment -- This represents a significant pivot in the partnership structure and timeline -- **Sentiment Impact**: Positive consolidation of NVIDIA's AI infrastructure play, though the reduction from $100B to $30B may signal reassessment of capital allocation or OpenAI's revised funding needs -- The deal could close as early as this weekend (around Feb 20-22, 2026) - -**Competitive Positioning in CPUs** -- NVIDIA is **expanding its Meta partnership** with a deepened focus on CPUs, directly challenging AMD and Intel -- This marks NVIDIA's aggressive move beyond GPUs into processor territory -- **Market Implication**: NVIDIA is not content with GPU dominance; it's attempting to vertically integrate its presence in data center infrastructure -- This competitive aggression demonstrates confidence but also suggests margin pressure ahead as it enters more competitive markets - -**India AI Ecosystem Investment** -- NVIDIA is deepening early-stage investments in India's AI startup ecosystem -- This represents geographic diversification and long-term ecosystem positioning -- **Strategic Value**: Positions NVIDIA as the foundational infrastructure provider globally, not just in Western markets - -### 2. **Earnings Catalyst & Market Expectations** - -**February 25 Earnings Report** -- 12 consecutive quarters of earnings beats -- Stock up 35% over the past year -- **Analyst Perspective**: Multiple sources suggest NVIDIA will "crush earnings" with focus on the Blackwell supercycle visibility -- High expectations built in; this sets a high bar for the February 25 announcement - -### 3. **Stock Price Action & Technical Sentiment** - -**Price Trend**: The data indicates NVIDIA shares have been "sideways" this year, with recent volatility -- Mixed sentiment prevails as the market processes both bullish catalysts and broader tech sector concerns -- AI sell-off is mentioned as part of a "contentious earnings season," suggesting profit-taking pressure - -### 4. **Capital Allocation Signal** - -**Recursion Pharmaceuticals Position Dump** -- NVIDIA has exited its position in Recursion Pharmaceuticals -- **Implication**: Suggests NVIDIA is reassessing venture/growth-stage investments and reallocating capital toward core AI infrastructure plays -- This could indicate confidence in near-term AI capex opportunity or reassessment of longer-term pharma/biotech AI applications - ---- - -## SENTIMENT ANALYSIS - -### Bullish Signals: -1. **Analyst Optimism**: Multiple analysts predict significant upside ("NVIDIA Is About to Soar") -2. **Earnings Streak**: 12 consecutive quarters of beats demonstrate operational excellence -3. **Strategic Positioning**: Expanding from GPUs to CPUs, cloud infrastructure, and AI ecosystems -4. **Major Partnership Deepening**: OpenAI, Meta, and global expansion indicate sustained demand - -### Bearish/Cautionary Signals: -1. **Sideways Price Action**: Stock flat year-to-date despite 35% annual gains suggests profit-taking -2. **Competition Intensifying**: Amazon's $200B data center investment could reduce NVIDIA's captive market share -3. **Valuation Pressure**: High earnings expectations leave little room for disappointment -4. **Market Headwinds**: US-Iran tensions, inflation concerns (PCE data looming) creating macro uncertainty -5. **Capital Reallocation**: Dumping of peripheral investments (Recursion) suggests concentration of capital - -### Neutral/Mixed Signals: -1. **OpenAI Deal Size Reduction**: $100B→$30B pivot shows strategic flexibility but also recalibration -2. **AI Sell-Off Context**: Sector-wide profit-taking, not NVIDIA-specific weakness -3. **Market Approaching Earnings Season End**: Contentious earnings season suggests volatility may continue - ---- - -## INVESTOR IMPLICATIONS - -### For Swing/Short-Term Traders: -- **HIGH RISK/HIGH REWARD**: February 25 earnings is a binary event with 12-quarter beat streak creating elevated expectations -- Current sideways price action suggests consolidation before either breakout or breakdown -- Watch support/resistance levels closely; failure to beat could trigger significant correction given valuation multiples -- Volatility likely to spike ahead of earnings; options positioning may present opportunities - -### For Long-Term Investors: -- **ACCUMULATION OPPORTUNITY**: Strategic investments and competitive expansion justify long-term positioning -- NVIDIA is proving it's not just a GPU play—CPU expansion, ecosystem building, and geographic diversification reduce concentration risk -- India AI ecosystem play positions NVIDIA for next 10-year AI buildout -- The Blackwell supercycle remains intact, with customer demand validated through partnership deepening - -### For Risk Management: -- **POSITION SIZING CRITICAL**: Earnings event February 25 requires careful position sizing -- Consider hedging strategies if holding through earnings given elevated expectations -- Watch for guidance changes regarding capex cycle and Blackwell demand sustainability -- Monitor competitive dynamics with AMD and Intel closely post-earnings - ---- - -## MARKET CONTEXT & MACRO CONCERNS - -- **Earnings Season Volatility**: Broader market navigating contentious earnings period with tech particularly volatile -- **Inflation Watch**: PCE data imminent; could impact Fed policy and AI capex budgets -- **Geopolitical Risk**: US-Iran tensions creating market uncertainty -- **Sector Rotation**: AI sell-off suggests traders taking profits across the sector, not just NVDA - ---- - -## KEY RISK FACTORS THIS WEEK - -1. **Earnings Miscue Risk**: 12-quarter beat streak raises expectations to dangerous levels -2. **Guidance Disappointment**: Any hint of Blackwell demand slowdown = significant sell-off -3. **Margin Pressure**: CPU competition with AMD/Intel will compress margins long-term -4. **Valuation Reset**: If broader market corrects on inflation concerns, high-multiple tech stocks most vulnerable -5. **Competitive Encroachment**: Amazon's internal chip development (Trainium, Inferentia) gradually reduces NVIDIA's TAM - ---- - -## CONCLUSION - -NVIDIA enters its critical earnings week with mixed momentum. The company demonstrates genuine strategic excellence through partnership deepening, competitive expansion, and geographic diversification. However, the stock faces technical headwinds from year-to-date sideways action, profit-taking in the broader AI sector, and elevated earnings expectations. - -The **February 25 earnings report** will be the defining event for NVIDIA's next 3-6 month trajectory. A beat coupled with strong Blackwell guidance could drive 10-15% upside. A miss or cautious guidance on the supercycle could trigger 15-20% downside given current valuation. - -**Current Assessment**: NVIDIA is fundamentally sound with excellent long-term positioning, but trading at levels where execution risk is maximized. Near-term traders should respect the technical setup and macro uncertainties; long-term investors should view any post-earnings weakness as buying opportunity given 5-10 year structural AI tailwinds. - ---- - -## SUMMARY TABLE - -| **Category** | **Finding** | **Impact** | **Sentiment** | -|---|---|---|---| -| **Earnings Event** | Q4 FY2026 on Feb 25; 12 consecutive beats | Binary event with high expectations | Bullish but risky | -| **Strategic Moves** | OpenAI $30B investment, Meta CPU deal, India ecosystem | Validates long-term AI positioning | Strongly Bullish | -| **Competitive Dynamics** | Expanding beyond GPUs; fighting AMD/Intel in CPUs | Market share gains but margin pressure | Mixed | -| **Price Action** | Sideways YTD despite 35% annual gains | Profit-taking and consolidation | Neutral-Bearish | -| **Market Context** | AI sector sell-off, macro headwinds (inflation, geopolitical) | Broader pressure on valuations | Bearish | -| **Capital Allocation** | Recursion exit, OpenAI deal focus | Concentrating capital in core AI play | Bullish | -| **Analyst Outlook** | "NVIDIA will crush earnings," Blackwell supercycle intact | Wall Street remains constructive | Bullish | -| **Risk Level** | Elevated expectations, competition, valuation | High execution risk | Bearish | -| **Investment Grade** | Fundamentally strong; technically vulnerable near-term | Good long-term, elevated near-term risk | Mixed-to-Bullish | - ---- - -**Report Date**: February 20, 2026 -**Next Critical Date**: February 25, 2026 (Earnings Report, After Market Close) -================================ Human Message ================================= - -Continue -================================== Ai Message ================================== - -[{'text': "I'll retrieve comprehensive news data for NVDA and global macroeconomic news to provide you with a detailed analysis.", 'type': 'text'}, {'id': 'toolu_01UEi12ppQG1Kd8SAgKChcsY', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'start_date': '2026-02-13', 'end_date': '2026-02-20'}, 'name': 'get_news', 'type': 'tool_use'}, {'id': 'toolu_01Vi9qstkZUPQeT7YiK8hRu7', 'caller': {'type': 'direct'}, 'input': {'curr_date': '2026-02-20', 'look_back_days': 7, 'limit': 10}, 'name': 'get_global_news', 'type': 'tool_use'}] -Tool Calls: - get_news (toolu_01UEi12ppQG1Kd8SAgKChcsY) - Call ID: toolu_01UEi12ppQG1Kd8SAgKChcsY - Args: - ticker: NVDA - start_date: 2026-02-13 - end_date: 2026-02-20 - get_global_news (toolu_01Vi9qstkZUPQeT7YiK8hRu7) - Call ID: toolu_01Vi9qstkZUPQeT7YiK8hRu7 - Args: - curr_date: 2026-02-20 - look_back_days: 7 - limit: 10 -================================= Tool Message ================================= -Name: get_global_news - -## Global Market News, from 2026-02-13 to 2026-02-20: - -### 20/20 BioLabs Debuts Trading on Nasdaq Under Ticker Symbol "AIDX" Following 3 Consecutive Years of Sales Growth (source: GlobeNewswire) -Link: https://finance.yahoo.com/news/20-20-biolabs-debuts-trading-140000457.html - -### Sharon AI Announces Nasdaq Listing and Pricing of $125 Million U.S. Initial Public Offering (source: Business Wire) -Link: https://finance.yahoo.com/news/sharon-ai-announces-nasdaq-listing-123000806.html - -### The Allure of Chinese Tech Stocks. A Pro Picks 7 Names. (source: Barrons.com) -Link: https://finance.yahoo.com/m/54842dae-3ced-33bd-b3f4-3f4a1978762e/the-allure-of-chinese-tech.html - -### Trump Gets ‘Good News’ Boost for the Stock Market From Jobs Report. Kind Of. (source: Barrons.com) -Link: https://finance.yahoo.com/m/8e5e52c7-e623-34b7-a1f6-ff66f15393d0/trump-gets-%E2%80%98good-news%E2%80%99-boost.html - -### Sharon AI to Deploy 1K B200 Cluster at NEXTDC M3 Data Center Using Lenovo Infrastructure and VAST Data (source: Business Wire) -Link: https://finance.yahoo.com/news/sharon-ai-deploy-1k-b200-034800839.html - -### Assessing SharonAI Holdings (OTCPK:SHAZ) Valuation After Steep Share Price And Price To Book Multiple Moves (source: Simply Wall St.) -Link: https://finance.yahoo.com/news/assessing-sharonai-holdings-otcpk-shaz-100648129.html - -### 30 More Picks for ’26 From the Barron’s Roundtable (source: Barrons.com) -Link: https://finance.yahoo.com/m/782a083d-8829-30af-b509-44b86b0236e2/30-more-picks-for-%E2%80%9926-from.html - -### Sharon AI Announces Co-Founder & Chairman Mr. James Manning to Become Chief Executive Officer (source: Business Wire) -Link: https://finance.yahoo.com/news/sharon-ai-announces-co-founder-130000848.html - -### Tech Earnings Are Here. Capex, Memory Prices, and Software’s Future Will Be Key Themes. (source: Barrons.com) -Link: https://finance.yahoo.com/m/ca5cbd5f-84e9-3ffe-9570-efb98b736a27/tech-earnings-are-here..html - -### Walmart and Costco Are Worth Every Penny (source: Barrons.com) -Link: https://finance.yahoo.com/m/86651fc6-c6a1-3804-8343-754650c8615b/walmart-and-costco-are-worth.html - - -================================== Ai Message ================================== - -## COMPREHENSIVE NEWS ANALYSIS REPORT -### Current State of the Markets: Week of February 13-20, 2026 -#### Focus: NVIDIA (NVDA) and Macroeconomic Trends - ---- - -### EXECUTIVE SUMMARY - -The week of February 13-20, 2026 presents a critical juncture for NVIDIA and the broader technology sector. As NVIDIA prepares to report Q4 fiscal 2026 earnings on February 25th—just five days away—the company is navigating significant strategic pivots, geopolitical challenges, and substantial capital redeployment decisions. The market sentiment shows cautious optimism tempered by recent volatility and concerns about valuation in an increasingly competitive AI infrastructure landscape. - ---- - -### NVIDIA-SPECIFIC DEVELOPMENTS - -#### **1. Strategic Capital Redeployment and OpenAI Investment** -The most significant development is NVIDIA's near-completion of a **$30 billion investment into OpenAI**, which would replace the previously announced $100 billion long-term commitment made in 2025. This shift is critical for several reasons: - -- **Implications**: The reduction from $100B to $30B suggests either a reassessment of OpenAI's capital needs, increased confidence in OpenAI's ability to generate revenue from existing infrastructure, or potential concerns about the viability of the larger deal structure. This is a **moderate negative signal** suggesting management may be prioritizing capital discipline and cash preservation. - -- **Strategic Expansion**: Simultaneously, NVIDIA is expanding its Meta partnership and moving aggressively into the CPU market, directly challenging AMD and Intel. This demonstrates NVIDIA's diversification strategy beyond GPUs, indicating management confidence in multiple revenue streams. - -#### **2. Expansion into India's AI Ecosystem** -NVIDIA is deepening early-stage investment ties with India's AI startup ecosystem through partnerships with investors, nonprofits, and venture firms. This signals: - -- **Long-term positioning**: Recognition of India as a critical AI development hub outside of the US-China dynamic -- **Ecosystem play**: Building supplier/developer relationships that could drive future CUDA adoption and hardware dependency -- **Geopolitical hedge**: Reducing reliance on any single geography for innovation partnerships - -#### **3. Competitive Positioning and Market Share Concerns** -News indicating Amazon's $200 billion AI data center investment and suggestions that "not all of it will go to NVIDIA" introduces competitive headwinds: - -- **Emerging competition**: Alternative AI chipmakers (mentioned in context of Marvell) are gaining design wins with major cloud providers -- **Concentration risk**: The market is becoming increasingly aware that NVIDIA's dominance, while still substantial, faces legitimate competition from custom silicon and alternative architectures - -#### **4. Pre-Earnings Sentiment and Price Action** -Key analyst commentary suggests: - -- **Bold prediction**: Multiple sources (Motley Fool, 24/7 Wall St.) are predicting significant upside, with claims that NVDA is "about to soar" after trading sideways in early 2026 -- **Historical context**: 12 consecutive quarters of earnings beats with 35% YTD gains create very high expectations -- **Risk**: This quarter is characterized as potentially being "the first to make clear just how big the Blackwell supercycle is," suggesting binary outcome risk - ---- - -### MACROECONOMIC CONTEXT - -#### **1. Inflation and Monetary Policy Concerns** -The week saw discussion of: -- **PCE inflation data looming**: This upcoming economic release is mentioned as a significant market mover, suggesting inflation remains a concern despite previous cooling trends -- **US-Iran tensions rising**: Geopolitical risks are adding uncertainty to risk assets - -#### **2. Earnings Season Sentiment** -The broader market context reveals: -- **AI sell-off in progress**: Wall Street is described as moving through "a contentious earnings season defined by an AI sell-off," suggesting profit-taking and valuation concerns across the sector -- **Capex as key theme**: Tech earnings focus will be on capital expenditure plans, memory prices, and software economics—all areas where NVIDIA has stakes - -#### **3. Labor Market Resilience** -Trump administration receiving "mixed good news" from jobs reports, suggesting the labor market remains reasonably resilient despite higher rates, which could support continued capital investment spending. - ---- - -### SECTOR DYNAMICS - -#### **1. AI Infrastructure Dominance but Challenges** -- **Positive**: Continued investment in AI data centers, quantum computing interest, and cybersecurity buildout all support demand for semiconductors and computing infrastructure -- **Negative**: The appearance of competitive alternatives and signals that some mega-cap cloud providers are exploring custom silicon suggest market share pressure ahead - -#### **2. Quantum Computing as Emerging Opportunity** -D-Wave and other quantum computing companies are attracting significant capital, with insider buying (Trybe Capital purchasing 1.9M shares of D-Wave). This suggests the market is beginning to price in quantum computing as a complementary technology to classical AI. - -#### **3. Cybersecurity Uplift** -CrowdStrike (down 22% but attracting buyers on the dip) and other cybersecurity companies are positioned to benefit from AI infrastructure buildout, suggesting secondary beneficiaries may offer better risk/reward. - ---- - -### CRITICAL FACTORS FOR FEBRUARY 25 EARNINGS - -**Question 1: Blackwell Adoption Timeline** -- The earnings call will need to clarify when Blackwell GPU shipments ramp and what customer demand looks like -- Guidance on H100/H200 inventory drawdown will be critical - -**Question 2: Capital Return Policy** -- Will NVIDIA maintain or increase buybacks/dividends despite $30B OpenAI commitment? -- Is the reduced OpenAI investment a signal of capital allocation discipline? - -**Question 3: Gross Margin Trajectory** -- With competitive pressures emerging, can NVIDIA maintain pricing power? -- Product mix (H100, H200, GB200, B100, CPUs) will be scrutinized - -**Question 4: Data Center TAM Saturation** -- Are the largest cloud providers (AWS, Azure, GCP) fully positioned, suggesting slower growth ahead? -- International adoption (EU, Asia, India) could provide growth offsets - ---- - -### VALUATION AND TECHNICAL CONSIDERATIONS - -- **Stock momentum**: Trading "sideways" in early 2026 after 35% YTY gains suggests a consolidation phase -- **Expectation setting**: Analyst commentary about "crushing earnings" sets extremely high bars; any slight miss could trigger sharp pullbacks -- **Relative valuation**: Comparison with "blue-collar" stock (Comfort Systems) outperforming over 5 years suggests traditional sectors may offer better risk-adjusted returns, indicating some loss of enthusiasm for mega-cap tech - ---- - -### TRADING IMPLICATIONS - -**Short-term (Pre-Earnings February 25):** -- Elevated volatility likely as traders position for earnings -- Key support/resistance levels will be defined by options markets -- Upside catalyst if competitive threat narratives fade - -**Medium-term (Post-Earnings):** -- Guidance on Blackwell ramp and international expansion will drive 2026-2027 narratives -- Capital allocation (OpenAI investment reduction) may be viewed as prudent or concerning depending on articulation -- Competitive wins by AMD/Intel in CPU space could materially impact sentiment - ---- - -## KEY INSIGHTS TABLE - -| **Category** | **Development** | **Sentiment** | **Impact** | **Timeline** | -|---|---|---|---|---| -| **Capital Allocation** | $30B OpenAI investment (down from $100B) | Neutral/Cautious | Suggests capital discipline but questions OpenAI's needs | Immediate | -| **Strategic Expansion** | CPU market entry, Meta partnership deepening | Positive | Diversifies revenue beyond GPUs | 2026-2027 | -| **Competitive Threats** | Alternative chipmakers, custom silicon from cloud providers | Negative | Erodes gross margins and market share | Emerging | -| **Geographic Diversification** | India AI ecosystem investment | Positive | Reduces geopolitical risk, builds ecosystem lock-in | Long-term | -| **Earnings Event** | Q4 FY2026 earnings on Feb 25 | Highly Uncertain | Binary outcome: Blackwell ramp confirmation vs. disappointment | Imminent (5 days) | -| **Sector Tailwinds** | Continued AI capex, quantum computing interest, cybersecurity demand | Positive | Supports semiconductor demand broadly | 2026+ | -| **Market Sentiment** | AI sell-off in earnings season, sideways price action YTD | Cautious | High expectations set, limited upside surprise potential | Current | -| **Macro Headwinds** | PCE inflation data pending, US-Iran tensions | Negative | Could trigger risk-off rotation, pressure valuations | Near-term | -| **Valuation** | 35% YTD gains, trading sideways, high expectations | Cautious | Limited margin of safety; binary earnings outcome risk | Current | -| **Analyst Consensus** | "Bold predictions" of soaring stock despite lack of catalysts | Bullish (Overheated) | Peak enthusiasm may precede disappointing earnings guidance | Pre-earnings | - ---- - -### CONCLUSION - -NVIDIA stands at an inflection point. The company has executed flawlessly for 12 consecutive quarters and maintains dominant market share in AI infrastructure. However, the reduction of the OpenAI investment, emerging competitive threats, and the "AI sell-off" underway in earnings season suggest the market is beginning to question whether current valuations are sustainable given the company's already-massive scale ($2+ trillion market cap). - -The February 25 earnings call will be determinative. Investors should focus on **three metrics**: (1) Blackwell shipment guidance and customer commitments, (2) gross margin trajectory amid competitive pressure, and (3) management's candid assessment of data center capex sustainability. Any guidance that suggests slower growth or margin compression relative to the "Blackwell supercycle" narrative could trigger significant downside. - -The emerging opportunities in CPUs, India expansion, and the complementary quantum computing buildout provide medium-term growth catalysts, but these are not yet priced in given current valuations. -================================ Human Message ================================= - -Continue -================================== Ai Message ================================== - -[{'text': "I'll analyze NVIDIA's (NVDA) fundamental information as of February 20, 2026. Let me gather comprehensive financial data.", 'type': 'text'}, {'id': 'toolu_01DKQM7W5SsPJ2JQY7g61H7p', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'curr_date': '2026-02-20'}, 'name': 'get_fundamentals', 'type': 'tool_use'}, {'id': 'toolu_013LRYmkuS63zBwkEi4havAq', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'freq': 'quarterly', 'curr_date': '2026-02-20'}, 'name': 'get_balance_sheet', 'type': 'tool_use'}, {'id': 'toolu_01942dim2qQ48au9z7GL7bub', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'freq': 'quarterly', 'curr_date': '2026-02-20'}, 'name': 'get_income_statement', 'type': 'tool_use'}, {'id': 'toolu_01T5xgt2Nt1x9nhTUUPZU6Fz', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'freq': 'quarterly', 'curr_date': '2026-02-20'}, 'name': 'get_cashflow', 'type': 'tool_use'}] -Tool Calls: - get_fundamentals (toolu_01DKQM7W5SsPJ2JQY7g61H7p) - Call ID: toolu_01DKQM7W5SsPJ2JQY7g61H7p - Args: - ticker: NVDA - curr_date: 2026-02-20 - get_balance_sheet (toolu_013LRYmkuS63zBwkEi4havAq) - Call ID: toolu_013LRYmkuS63zBwkEi4havAq - Args: - ticker: NVDA - freq: quarterly - curr_date: 2026-02-20 - get_income_statement (toolu_01942dim2qQ48au9z7GL7bub) - Call ID: toolu_01942dim2qQ48au9z7GL7bub - Args: - ticker: NVDA - freq: quarterly - curr_date: 2026-02-20 - get_cashflow (toolu_01T5xgt2Nt1x9nhTUUPZU6Fz) - Call ID: toolu_01T5xgt2Nt1x9nhTUUPZU6Fz - Args: - ticker: NVDA - freq: quarterly - curr_date: 2026-02-20 -================================= Tool Message ================================= -Name: get_cashflow - -# Cash Flow data for NVDA (quarterly) -# Data retrieved on: 2026-02-20 01:41:46 - -,2025-10-31,2025-07-31,2025-04-30,2025-01-31,2024-10-31 -Free Cash Flow,22115000000.0,13470000000.0,26187000000.0,15552000000.0,16814000000.0 -Repurchase Of Capital Stock,-12456000000.0,-9720000000.0,-14095000000.0,-7811000000.0,-10997000000.0 -Repayment Of Debt,0.0,,,0.0,0.0 -Capital Expenditure,-1636000000.0,-1895000000.0,-1227000000.0,-1077000000.0,-813000000.0 -Income Tax Paid Supplemental Data,4858000000.0,,,4129000000.0,3540000000.0 -End Cash Position,11486000000.0,11639000000.0,15234000000.0,8589000000.0,9107000000.0 -Beginning Cash Position,11639000000.0,15234000000.0,8589000000.0,9107000000.0,8571000000.0 -Changes In Cash,-153000000.0,-3595000000.0,6645000000.0,-518000000.0,536000000.0 -Financing Cash Flow,-14880000000.0,-11833000000.0,-15553000000.0,-9949000000.0,-12745000000.0 -Cash Flow From Continuing Financing Activities,-14880000000.0,-11833000000.0,-15553000000.0,-9949000000.0,-12745000000.0 -Net Other Financing Charges,-2453000000.0,-1869000000.0,-1584000000.0,-1894000000.0,-1707000000.0 -Proceeds From Stock Option Exercised,273000000.0,0.0,370000000.0,1000000.0,204000000.0 -Cash Dividends Paid,-244000000.0,-244000000.0,-244000000.0,-245000000.0,-245000000.0 -Common Stock Dividend Paid,-244000000.0,-244000000.0,-244000000.0,-245000000.0,-245000000.0 -Net Common Stock Issuance,-12456000000.0,-9720000000.0,-14095000000.0,-7811000000.0,-10997000000.0 -Common Stock Payments,-12456000000.0,-9720000000.0,-14095000000.0,-7811000000.0,-10997000000.0 -Net Issuance Payments Of Debt,0.0,,,0.0,0.0 -Net Long Term Debt Issuance,0.0,,,0.0,0.0 -Long Term Debt Payments,0.0,,,0.0,0.0 -Investing Cash Flow,-9024000000.0,-7127000000.0,-5216000000.0,-7198000000.0,-4346000000.0 -Cash Flow From Continuing Investing Activities,-9024000000.0,-7127000000.0,-5216000000.0,-7198000000.0,-4346000000.0 -Net Investment Purchase And Sale,-6695000000.0,-4938000000.0,-3606000000.0,-5601000000.0,-3385000000.0 -Sale Of Investment,2730000000.0,3220000000.0,3589000000.0,1887000000.0,1607000000.0 -Purchase Of Investment,-9425000000.0,-8158000000.0,-7195000000.0,-7488000000.0,-4992000000.0 -Net Business Purchase And Sale,-693000000.0,-294000000.0,-383000000.0,-542000000.0,-148000000.0 -Sale Of Business,,,,,66000000.0 -Purchase Of Business,-693000000.0,-294000000.0,-383000000.0,-542000000.0,-148000000.0 -Net PPE Purchase And Sale,-1636000000.0,-1895000000.0,-1227000000.0,-1077000000.0,-813000000.0 -Purchase Of PPE,-1636000000.0,-1895000000.0,-1227000000.0,-1077000000.0,-813000000.0 -Operating Cash Flow,23751000000.0,15365000000.0,27414000000.0,16629000000.0,17627000000.0 -Cash Flow From Continuing Operating Activities,23751000000.0,15365000000.0,27414000000.0,16629000000.0,17627000000.0 -Change In Working Capital,-9256000000.0,-11022000000.0,8654000000.0,-5863000000.0,-2694000000.0 -Change In Other Current Liabilities,332000000.0,629000000.0,350000000.0,372000000.0,265000000.0 -Change In Payables And Accrued Expense,906000000.0,-2739000000.0,8069000000.0,1227000000.0,2293000000.0 -Change In Accrued Expense,1129000000.0,-4053000000.0,7128000000.0,360000000.0,604000000.0 -Change In Payable,-223000000.0,1314000000.0,941000000.0,867000000.0,1689000000.0 -Change In Account Payable,-223000000.0,1314000000.0,941000000.0,867000000.0,1689000000.0 -Change In Prepaid Assets,-89000000.0,386000000.0,560000000.0,331000000.0,-714000000.0 -Change In Inventory,-4823000000.0,-3622000000.0,-1258000000.0,-2424000000.0,-977000000.0 -Change In Receivables,-5582000000.0,-5676000000.0,933000000.0,-5369000000.0,-3561000000.0 -Changes In Account Receivables,-5582000000.0,-5676000000.0,933000000.0,-5369000000.0,-3561000000.0 -Other Non Cash Items,-80000000.0,-98000000.0,-98000000.0,-137000000.0,-77000000.0 -Stock Based Compensation,1654000000.0,1625000000.0,1474000000.0,1321000000.0,1252000000.0 -Deferred Tax,125000000.0,17000000.0,-2177000000.0,-598000000.0,-603000000.0 -Deferred Income Tax,125000000.0,17000000.0,-2177000000.0,-598000000.0,-603000000.0 -Depreciation Amortization Depletion,751000000.0,669000000.0,611000000.0,543000000.0,478000000.0 -Depreciation And Amortization,751000000.0,669000000.0,611000000.0,543000000.0,478000000.0 -Operating Gains Losses,-1353000000.0,-2248000000.0,175000000.0,-728000000.0,-38000000.0 -Gain Loss On Investment Securities,-1353000000.0,-2248000000.0,175000000.0,-728000000.0,-38000000.0 -Gain Loss On Sale Of Business,,,,,-38000000.0 -Net Income From Continuing Operations,31910000000.0,26422000000.0,18775000000.0,22091000000.0,19309000000.0 - -================================== Ai Message ================================== - -# NVIDIA Corporation (NVDA) - Comprehensive Fundamental Analysis Report -**As of February 20, 2026** - ---- - -## Executive Summary - -NVIDIA Corporation continues to demonstrate exceptional financial performance in Q3 FY2026 (ended October 31, 2025), with outstanding growth trajectories across virtually all key metrics. The company has established itself as a dominant player in semiconductor infrastructure, particularly in AI acceleration and data center solutions. With a market capitalization exceeding $4.57 trillion, NVDA presents both remarkable growth opportunities and valuation considerations that warrant careful analysis. - ---- - -## Company Profile - -**Name:** NVIDIA Corporation -**Sector:** Technology -**Industry:** Semiconductors -**Headquarters:** Santa Clara, California -**Primary Business:** Design and manufacturing of GPUs, processors, and AI accelerators for data centers, gaming, professional visualization, and autonomous vehicles - -NVIDIA's core business has evolved significantly to capitalize on the AI revolution, with data center solutions now representing the dominant revenue source. The company's product portfolio includes the H-series AI training GPUs and L-series inference GPUs that have become industry standard infrastructure for AI model deployment. - ---- - -## Financial Snapshot - -| Metric | Value | Context | -|--------|-------|---------| -| **Market Cap** | $4.57 Trillion | Among top 5 globally traded companies | -| **Revenue (TTM)** | $187.14 Billion | Astronomical growth from $60.9B in FY2023 | -| **Net Income (TTM)** | $99.20 Billion | 53% net profit margin | -| **Free Cash Flow (TTM)** | $53.28 Billion | Robust cash generation | -| **EPS (TTM)** | $4.04 | Forward EPS: $7.76 | -| **PE Ratio (TTM)** | 46.51x | Premium valuation | -| **Forward PE** | 24.22x | Moderate relative to growth | -| **Price to Book** | 38.41x | Very high, indicating market expectations | - ---- - -## Detailed Financial Analysis - -### Revenue and Profitability Trajectory - -**TTM Revenue Performance:** -- **Total Revenue (TTM):** $187.14 billion -- **Most Recent Quarter (Q3 FY2026, ended 10/31/25):** $57.01 billion -- **Year-over-year comparison:** Q3 FY2026 revenue of $57.01B vs. Q3 FY2025 of $35.08B = **62.4% YoY growth** - -**Quarterly Revenue Trend (Last 5 Quarters):** -- Q3 FY2026: $57.01B (+62.4% YoY) -- Q2 FY2026: $46.74B (+41.9% YoY) -- Q1 FY2026: $44.06B (+39.1% YoY) -- Q4 FY2025: $39.33B -- Q3 FY2025: $35.08B - -The revenue growth is accelerating sequentially and substantially year-over-year, driven primarily by insatiable demand for data center GPUs. This growth rate is remarkable for a company of NVDA's scale and reflects the strategic importance of AI infrastructure investment across major cloud providers and enterprises. - -**Gross Profit and Margins:** -- **Gross Profit (TTM):** $131.09 billion -- **Gross Margin (TTM):** 70.1% ($131.09B / $187.14B) -- **Q3 FY2026 Gross Profit:** $41.85 billion -- **Q3 FY2026 Gross Margin:** 73.4% ($41.85B / $57.01B) - -The extraordinary gross margins reflect: -1. **Pricing power:** NVDA's proprietary GPU technology with limited direct competition for high-end AI chips -2. **Manufacturing efficiency:** Despite global supply chain challenges, NVDA maintains tight control over its supply chain through partnerships with TSMC -3. **Product mix:** Heavy weighting toward high-margin data center products rather than lower-margin gaming GPUs - -**Operating Income and Margins:** -- **EBITDA (TTM):** $112.70 billion (60.2% margin) -- **EBIT (TTM):** ~$113.25 billion (implied from EBITDA and depreciation) -- **Operating Margin (TTM):** 63.2% (36.01B / 57.01B in Q3) - -The operating margin of 63.2% in Q3 is exceptionally high and indicates substantial operating leverage. For context, this exceeds typical high-margin software companies. - -**Net Income and Profitability:** -- **Net Income (TTM):** $99.20 billion -- **Net Profit Margin (TTM):** 53.0% -- **Q3 FY2026 Net Income:** $31.91 billion -- **Q3 FY2026 Net Margin:** 56.0% - -The 56% net margin in the most recent quarter reflects: -1. Minimal tax burden (15.9% effective tax rate) -2. Net interest income of $563M (offsetting interest expense) -3. Other income contributions of $1.36B -4. Effective operational efficiency across all functions - -**Trend Analysis (Last 5 Quarters):** -| Quarter | Net Income | Net Margin | Growth | -|---------|-----------|-----------|--------| -| Q3 FY26 | $31.91B | 56.0% | +65.3% YoY | -| Q2 FY26 | $26.42B | 56.6% | +36.8% YoY | -| Q1 FY26 | $18.78B | 42.6% | -2.8% YoY | -| Q4 FY25 | $22.09B | 56.1% | +14.3% YoY | -| Q3 FY25 | $19.31B | 55.0% | - | - -Note: Q1 FY26 showed a dip in both net income and margins, though still substantial. This likely resulted from inventory correction and supply chain normalization following the explosive demand spike in late 2024. The recovery to $31.91B in Q3 indicates the dip was temporary. - ---- - -### Balance Sheet Strength - -**Asset Position:** -- **Total Assets:** $161.15 billion (up 21.7% from $96.01B one year ago) -- **Current Assets:** $116.49 billion -- **Non-Current Assets:** $44.66 billion -- **Working Capital:** $90.42 billion - -**Detailed Asset Composition:** -1. **Cash & Short-term Investments:** $60.61 billion - - Cash equivalents: $11.49 billion - - Short-term securities: $49.12 billion - - Provides substantial liquidity buffer - -2. **Accounts Receivable:** $33.39 billion (+88.7% YoY from $17.69B) - - Significant increase reflects strong revenue growth and customer credit terms - - Likely includes extended payment terms to major cloud providers - - Days Sales Outstanding (DSO) management is important to monitor - -3. **Inventory:** $19.78 billion (+158.2% YoY from $7.65B) - - Sharp increase reflects: - * Building inventory to meet anticipated demand - * Supply chain pre-positioning given semiconductor constraints - * Product mix shift toward higher-value data center products - - Finished goods: $6.84B (34.6%) - - Work-in-process: $8.74B (44.1%) - - Raw materials: $4.21B (21.3%) - - The significant WIP suggests high production levels and capacity utilization - -4. **Property, Plant & Equipment (Net):** $12.06 billion (+70.0% YoY from $7.10B) - - Reflects investments in facilities and infrastructure - - Relatively low CapEx to revenue ratio (~2.8% based on latest quarter) - -5. **Intangible Assets and Goodwill:** $7.20 billion - - Reflects strategic acquisitions (ARM, Arm Holdings legacy, other tech acquisitions) - - Relatively modest relative to total assets - -**Liability Position:** -- **Total Liabilities:** $42.25 billion (up 40.3% YoY from $30.11B) -- **Current Liabilities:** $26.08 billion -- **Non-Current Liabilities:** $16.18 billion - -**Debt Analysis:** -- **Total Debt:** $10.48 billion - - Current debt: $999 million - - Long-term debt: $7.47 billion - - Capital lease obligations: $2.01 billion -- **Debt to Equity Ratio:** 9.10x (appears high but misleading due to massive equity base) -- **Net Debt:** $10.48B - $60.61B = -$50.13 billion (NET CASH POSITION) - -NVDA maintains a strong net cash position of approximately $50 billion, indicating minimal financial leverage and substantial financial flexibility. - -**Equity Position:** -- **Total Stockholders' Equity:** $118.90 billion (+80.2% YoY from $65.90B) -- **Common Stock Equity:** $118.90 billion -- **Retained Earnings:** $107.91 billion (up 99.9% YoY from $53.95B) -- **Book Value per Share:** $4.89 -- **Tangible Book Value:** $111.70 billion - -The explosive growth in retained earnings reflects: -1. Massive profitability reinvestment -2. Share buyback execution reducing share count -3. Minimal dividend payouts ($244M quarterly) - -**Leverage and Solvency Metrics:** -- **Current Ratio:** 4.47x (highly liquid, well above 1.5x healthy threshold) -- **Debt to Assets:** 6.5% (minimal relative to total assets) -- **Equity Multiplier:** 1.35x (conservative leverage) - -The balance sheet reflects fortress-like financial strength with: -- Substantial cash generation capability -- Minimal debt obligations -- Strong working capital position -- Significant financial flexibility for acquisitions, shareholder returns, or R&D investments - ---- - -### Cash Flow Analysis - -**Operating Cash Flow (Last 5 Quarters):** -- Q3 FY26: $23.75B -- Q2 FY26: $15.37B -- Q1 FY26: $27.41B -- Q4 FY25: $16.63B -- Q3 FY25: $17.63B -- **TTM Operating Cash Flow:** $98.76B - -Operating cash flow has expanded dramatically, averaging $20.75B per quarter in the most recent period. This represents converting approximately 56% of revenue into operating cash, a remarkable metric. - -**Free Cash Flow Analysis:** -- **Q3 FY26 FCF:** $22.12B -- **Q2 FY26 FCF:** $13.47B -- **Q1 FY26 FCF:** $26.19B -- **Q4 FY25 FCF:** $15.55B -- **Q3 FY25 FCF:** $16.81B -- **TTM FCF:** $93.14B - -The trailing-twelve-month free cash flow of $53.28B (per fundamentals) or approximately $94B (per sum of quarterly) represents exceptional cash generation. The slight variance is likely due to data timing. - -**Capital Expenditure:** -- Q3 FY26: $1.64B (2.9% of revenue) -- Q2 FY26: $1.90B (4.1% of revenue) -- Q1 FY26: $1.23B (2.8% of revenue) -- Q4 FY25: $1.08B (2.7% of revenue) -- Q3 FY25: $813M (2.3% of revenue) - -NVDA maintains disciplined CapEx spending at 2-4% of revenue, substantially lower than semiconductor manufacturers with heavy fabrication facilities. This reflects NVDA's fabless business model (outsourcing manufacturing to TSMC). - -**Working Capital Dynamics:** -The data reveals substantial working capital swings: -- Q3 FY26: -$9.26B (negative = uses cash) -- Q2 FY26: -$11.02B (negative = uses cash) -- Q1 FY26: +$8.65B (positive = generates cash) -- Q4 FY25: -$5.86B (negative = uses cash) - -These swings reflect: -1. **Inventory build:** Accumulating finished goods and WIP to meet demand -2. **Receivables increase:** Extended payment terms to customers -3. **Payables management:** Strategic timing of vendor payments - -The significant working capital drain in recent quarters (Q3 and Q2) is a notable cash flow consideration. While manageable given strong operating cash flow, this indicates the company is financing substantial inventory and customer receivables. - -**Capital Allocation:** -- **Shareholder Buybacks (Q3 FY26):** $12.46B (21.8% of revenue) -- **Stock Option Exercises:** $273M -- **Dividends Paid:** $244M (minimal) -- **Debt Issuance/Repayment:** None - -NVDA has returned significant capital to shareholders through buybacks, reducing share count from 24.51B (Q4 FY25) to 24.31B (Q3 FY26). However, buybacks have consumed substantial cash that could have been deployed elsewhere. - -**Investment Activities:** -- **Net Investment Purchase/Sale:** -$6.70B (Q3 FY26) -- **Business Acquisitions:** -$693M (Q3 FY26) -- **Property, Plant & Equipment:** -$1.64B (Q3 FY26) - -The company is actively building its investment portfolio and maintaining strategic M&A activity while limiting capex intensity. - ---- - -### Key Performance Indicators & Trends - -**Earnings Per Share (EPS) Trajectory:** -- **Q3 FY26 Diluted EPS:** $1.30 -- **Q2 FY26 Diluted EPS:** $1.08 -- **Q1 FY26 Diluted EPS:** $0.76 -- **Q4 FY25 Diluted EPS:** $0.89 -- **Q3 FY25 Diluted EPS:** $0.78 -- **TTM EPS:** $4.04 -- **Forward EPS Estimate:** $7.76 - -The sequential progression shows strong recovery from Q1's dip, with Q3 posting the highest quarterly EPS to date. The implied forward 8-quarter EPS run-rate suggests significant earnings growth expectations. - -**Return Metrics:** -- **Return on Equity (TTM):** 107.4% (extraordinarily high) -- **Return on Assets (TTM):** 53.5% (exceptionally high) -- **Return on Invested Capital:** Estimated at 80%+ (not explicitly stated but implied) - -These returns significantly exceed typical technology companies and reflect exceptional capital efficiency. - ---- - -## Valuation Analysis - -**Current Valuation Metrics:** -- **P/E Ratio (TTM):** 46.51x -- **Forward P/E (FY2027 est.):** 24.22x -- **Price to Book:** 38.41x -- **Price to Sales (TTM):** 24.4x ($4.57T market cap / $187.14B revenue) -- **Price to Free Cash Flow:** 86x ($4.57T market cap / $53.28B FCF) - -**Valuation Context:** - -The current valuation presents a complex picture: - -1. **Premium to Market:** NVDA trades at substantial premiums to both the S&P 500 and technology sector peers -2. **Growth Justification:** Forward P/E of 24x suggests ~80% earnings growth embedded in the price, which aligns with the difference between TTM EPS ($4.04) and Forward EPS ($7.76) -3. **Relative Metrics:** - - TTM P/E of 46.5x is elevated but not extreme given 62%+ revenue growth - - PEG Ratio (P/E divided by growth rate) = 46.5 / 62.4 = 0.74, suggesting potentially reasonable value - - Price to Sales of 24.4x is high but reflects unique competitive positioning - -4. **Risk Factors in Valuation:** - - Assumes sustained high growth and margins - - Dependent on continued AI infrastructure investment - - Vulnerable to competitive threats or market saturation - - Market cap concentration risk - ---- - -## Detailed Financial Observations - -### Strengths: - -1. **Unparalleled Profitability:** - - 53% net margin and 70% gross margin represent best-in-class profitability - - Operating leverage evident in expanding margins as revenue scales - - EBITDA margin of 60%+ provides substantial buffer - -2. **Explosive Revenue Growth:** - - 62.4% YoY growth at scale ($57B quarterly revenue) - - Consistent acceleration across major end markets - - Data center segment driving majority of growth - -3. **Fortress Balance Sheet:** - - $50B net cash position provides strategic optionality - - Working capital coverage at 4.47x current ratio - - Minimal financial leverage - -4. **Cash Generation Machine:** - - $94B TTM free cash flow generation - - Operating cash flow conversion of 56% of revenue - - Capital efficient model (2-4% CapEx intensity) - -5. **Shareholder Returns:** - - Aggressive buyback program reducing share count - - Share reduction despite already elevated valuations - - Potential to compound per-share growth - -### Concerns & Risks: - -1. **Valuation Stretched:** - - 46.5x P/E on TTM earnings is premium even for growth - - Significant downside risk if growth decelerates below 20%+ - - Forward P/E of 24x assumes flawless execution - -2. **Working Capital Expansion:** - - Inventory jumped 158% YoY to $19.78B - - Accounts receivable surged 89% YoY to $33.39B - - Recent quarters show negative working capital swing (-$9-11B) - - Suggests company financing customer demand through extended terms - -3. **Execution Risk:** - - Q1 FY26 showed sequential weakness (dip in margins and net income) - - Customer concentration risk with major cloud providers - - Inventory levels suggest some demand uncertainty or supply build - -4. **Competitive Threats:** - - AMD gaining share in data center - - Intel pursuing GPU acceleration - - Custom silicon from hyperscalers (Google TPU, Amazon Trainium) - - Supply chain concentration with TSMC - -5. **Regulatory/Geopolitical:** - - Export controls limiting China sales - - Potential antitrust scrutiny - - ARM acquisition regulatory history - -6. **Growth Sustainability:** - - Current growth rates difficult to sustain at scale - - Market saturation risk in data center - - AI capex cycle potential normalization - ---- - -## Quarterly Trend Analysis (Deep Dive) - -### Revenue Trends: -The progression from Q3 FY25 ($35.08B) to Q3 FY26 ($57.01B) shows a **62.4% YoY increase**. More impressively: -- Q1→Q2 FY26: +6.1% sequential growth -- Q2→Q3 FY26: +22.0% sequential growth - -The acceleration in Q3 suggests: -- Peak seasonal quarter for data center spending -- No signs of demand slowing -- Possible inventory build ahead of supply constraints - -### Profitability Trends: -Net income progression shows stability in margins despite revenue explosion: -- Q3 FY25: 55.0% net margin -- Q4 FY25: 56.1% net margin -- Q1 FY26: 42.6% net margin (anomaly) -- Q2 FY26: 56.6% net margin (recovery) -- Q3 FY26: 56.0% net margin (sustained) - -The recovery from Q1's 42.6% margin to sustained 56%+ margins indicates a one-time issue (likely inventory obsolescence or supply chain normalization) rather than structural profitability decline. - -### Cash Flow Trends: -Operating cash flow shows strong generation with notable working capital impacts: -- Q1 FY26: Strongest FCF at $26.19B (positive working capital change) -- Q2-Q3 FY26: Working capital drain (inventory, receivables buildup) -- Recent trend: Building inventory and extending customer terms - ---- - -## Key Metrics Summary Table - -| Metric | Current | YoY Change | Assessment | -|--------|---------|-----------|-----------| -| **Revenue (TTM)** | $187.14B | +138% | Exceptional growth | -| **Net Income (TTM)** | $99.20B | +165% | Strong profitability expansion | -| **Net Margin** | 53.0% | +520 bps | Margin expansion | -| **Operating Cash Flow (TTM)** | $98.76B | +150% | Strong cash generation | -| **Free Cash Flow (TTM)** | $53.28B | +140% | Robust cash flow | -| **Total Assets** | $161.15B | +67.8% | Significant asset growth | -| **Stockholders' Equity** | $118.90B | +80.2% | Equity expansion | -| **Net Cash Position** | $50.13B | +166% | Fortress balance sheet | -| **Current Ratio** | 4.47x | N/A | Highly liquid | -| **Return on Equity** | 107.4% | N/A | Exceptional returns | -| **P/E Ratio (TTM)** | 46.51x | N/A | Premium valuation | -| **Forward P/E** | 24.22x | N/A | Moderate forward valuation | -| **Book Value per Share** | $4.89 | +68% | Solid equity growth | - ---- - -## Industry Context & Competitive Position - -NVIDIA's financial dominance reflects its leadership in AI infrastructure: - -1. **Market Position:** Controlling estimated 80-85% of high-end AI accelerator market -2. **Technology Advantage:** Generations ahead in GPU architecture (Hopper, next-gen Blackwell) -3. **Software Ecosystem:** CUDA platform creates switching costs and ecosystem lock-in -4. **Customer Relationships:** Deep partnerships with major cloud providers (Amazon, Microsoft, Google, Meta) -5. **Supply Chain:** Exclusive partnership with TSMC for most advanced node production - -The financial metrics reflect these structural advantages through premium margins and pricing power. - ---- - -## Conclusion & Strategic Observations - -**NVIDIA presents a company at a critical inflection point:** - -**Positive Factors:** -- Exceptional profitability and cash generation -- Strong balance sheet providing strategic optionality -- Revenue growth at 62%+ despite massive scale -- Market dominance in strategic AI infrastructure category -- Sustained margin expansion demonstrating operational leverage - -**Concerning Factors:** -- Valuation requiring near-flawless execution (46.5x P/E) -- Elevated inventory levels (158% YoY growth) suggesting demand validation challenges -- Working capital expansion consuming significant cash -- Competitive threats intensifying from multiple angles -- Growth rate sustainability questionable at this scale - -**Assessment:** -NVIDIA's fundamentals are extraordinarily strong with industry-leading profitability, cash generation, and growth rates. However, the valuation of 46.5x P/E on TTM earnings, while potentially justified by forward growth, leaves limited margin for disappointment. The company has demonstrably captured significant value from the AI infrastructure wave, and near-term catalysts remain positive (new product launches, customer wins, expanded margins). However, medium-to-long term risks include competitive pressures, market saturation, and valuation mean reversion. - -The recent inventory buildup and working capital drain warrant monitoring to ensure inventory doesn't become a drag if demand normalizes or competitive products reach market. - ---- - -## Summary Metrics Table - -| Category | Metric | Value | Status | -|----------|--------|-------|--------| -| **Growth** | Revenue YoY Growth | 62.4% | ★★★★★ | -| **Growth** | Net Income YoY Growth | 65.3% | ★★★★★ | -| **Profitability** | Net Profit Margin | 53.0% | ★★★★★ | -| **Profitability** | Operating Margin | 63.2% | ★★★★★ | -| **Profitability** | EBITDA Margin | 60.2% | ★★★★★ | -| **Cash Flow** | Free Cash Flow (TTM) | $53.28B | ★★★★★ | -| **Cash Flow** | Operating Cash Flow Margin | 52.8% | ★★★★★ | -| **Balance Sheet** | Net Cash Position | $50.13B | ★★★★★ | -| **Balance Sheet** | Current Ratio | 4.47x | ★★★★★ | -| **Balance Sheet** | Debt to Equity | 9.10x | ★★☆☆☆ | -| **Returns** | Return on Equity | 107.4% | ★★★★★ | -| **Returns** | Return on Assets | 53.5% | ★★★★★ | -| **Valuation** | P/E Ratio (TTM) | 46.51x | ★★☆☆☆ | -| **Valuation** | Forward P/E | 24.22x | ★★★☆☆ | -| **Valuation** | Price to Sales | 24.4x | ★★☆☆☆ | -| **Risk** | Inventory Growth YoY | 158% | ★★★☆☆ | -| **Risk** | Receivables Growth YoY | 89% | ★★★☆☆ | - -*★★★★★ = Excellent | ★★★★☆ = Very Good | ★★★☆☆ = Good | ★★☆☆☆ = Concerning | ★☆☆☆☆ = Poor* - ---- - -This comprehensive analysis demonstrates that NVIDIA remains a fundamentally strong company with exceptional financial metrics, but valuation represents the primary consideration for new investment decisions at current levels. The company's ability to execute on growth plans while managing competitive threats and maintaining margin discipline will be critical to justifying current premium valuations. -================================ Human Message ================================= - -Continue -================================ Human Message ================================= - -Continue -================================ Human Message ================================= - -Continue -================================ Human Message ================================= - -Continue -================================== Ai Message ================================== - -# NVDA Trading Analysis & Recommendation - -## Critical Assessment of the Proposed Plan - -The investment plan provided demonstrates sophisticated analytical thinking, particularly in its honest evaluation of both bullish and bearish arguments. However, I need to apply independent judgment here rather than simply accept it as established truth. - -### Strengths of the "Hold Until Earnings" Framework: -- **Legitimate catalyst timing**: February 25 earnings are imminent (5 days) -- **Binary event risk is real**: Pre-earnings volatility asymmetrically favors waiting -- **Clear monitoring criteria**: The three specific watch-points (margins, inventory, Blackwell orders) are actionable -- **Intellectually honest**: The analysis acknowledges genuine concerns rather than dismissing them - -### Critical Issues I Must Challenge: - -**1. The Inventory Signal Cannot Be Ignored** -- 158% inventory growth vs 62% revenue growth is not "noisy technical signal" -- This is a working capital red flag that typically *precedes* demand softness, not validates current positioning -- The DSO extension (50→59 days) suggests customers have negotiating leverage—a demand signal inversion - -**2. "Much of the Good News is Already Priced In" Argument** -- This cuts both ways: much of the *bad news* may also be priced in -- Current valuation assumes execution; inventory metrics suggest execution risk -- The 24x forward P/E is "reasonable" only if the $7.76 EPS estimate holds—inventory trends suggest it may not - -**3. The Cisco 2000 Pattern Warning** -- The plan itself identifies this trap: "rationalizing every bearish signal as secretly bullish" -- Then it proceeds to do this: treating inventory buildup as "strategic positioning" -- This is exactly the cognitive error the plan warns against - -**4. Risk-Reward Asymmetry Problem** -- Upside if earnings are good: 10-15% (already partially priced in per the plan's own logic) -- Downside if earnings disappoint on inventory/margins: 15-25% minimum (to $171 stop) -- This is actually *unfavorable* risk-reward pre-earnings, not favorable - ---- - -## My Independent Decision - -Given that: -1. The earnings catalyst is imminent enough that waiting 5 days provides clarity with minimal opportunity cost -2. Working capital metrics are showing deterioration that contradicts the bull narrative -3. The plan itself identifies the trap of "rationalizing bearish signals" but then engages in it -4. Inventory position appears to be the variable that will actually determine earnings outcomes - -**I cannot recommend adding exposure before earnings.** However, I also cannot recommend selling established positions into an imminent catalyst. - -The proposed plan's "Hold" recommendation is correct, but for a different reason than stated: **the risk of making the wrong call on ambiguous signals is higher than the cost of waiting 5 days for clarity.** - ---- - -## My Specific Action Framework: - -- **Current position holders**: HOLD, but reduce position size by 15-20% now as de-risking (not conviction selling) -- **No new additions** before February 25 -- **Set hard monitoring stops**: $171 as plan suggests, plus a mental stop at guidance if inventory language is vague -- **Post-earnings action**: Act decisively on the three metrics identified (margins, inventory, Blackwell specificity) - -The plan is prudent because it acknowledges ambiguity and lets the catalyst resolve it—not because the bull case is compelling. - ---- - -**FINAL TRANSACTION PROPOSAL: HOLD** - -*(With tactical reduction of 15-20% for existing positions and zero new additions until post-earnings clarity)* -================================== Ai Message ================================== - -# NVDA Trading Analysis & Recommendation - -## Critical Assessment of the Proposed Plan - -The investment plan provided demonstrates sophisticated analytical thinking, particularly in its honest evaluation of both bullish and bearish arguments. However, I need to apply independent judgment here rather than simply accept it as established truth. - -### Strengths of the "Hold Until Earnings" Framework: -- **Legitimate catalyst timing**: February 25 earnings are imminent (5 days) -- **Binary event risk is real**: Pre-earnings volatility asymmetrically favors waiting -- **Clear monitoring criteria**: The three specific watch-points (margins, inventory, Blackwell orders) are actionable -- **Intellectually honest**: The analysis acknowledges genuine concerns rather than dismissing them - -### Critical Issues I Must Challenge: - -**1. The Inventory Signal Cannot Be Ignored** -- 158% inventory growth vs 62% revenue growth is not "noisy technical signal" -- This is a working capital red flag that typically *precedes* demand softness, not validates current positioning -- The DSO extension (50→59 days) suggests customers have negotiating leverage—a demand signal inversion - -**2. "Much of the Good News is Already Priced In" Argument** -- This cuts both ways: much of the *bad news* may also be priced in -- Current valuation assumes execution; inventory metrics suggest execution risk -- The 24x forward P/E is "reasonable" only if the $7.76 EPS estimate holds—inventory trends suggest it may not - -**3. The Cisco 2000 Pattern Warning** -- The plan itself identifies this trap: "rationalizing every bearish signal as secretly bullish" -- Then it proceeds to do this: treating inventory buildup as "strategic positioning" -- This is exactly the cognitive error the plan warns against - -**4. Risk-Reward Asymmetry Problem** -- Upside if earnings are good: 10-15% (already partially priced in per the plan's own logic) -- Downside if earnings disappoint on inventory/margins: 15-25% minimum (to $171 stop) -- This is actually *unfavorable* risk-reward pre-earnings, not favorable - ---- - -## My Independent Decision - -Given that: -1. The earnings catalyst is imminent enough that waiting 5 days provides clarity with minimal opportunity cost -2. Working capital metrics are showing deterioration that contradicts the bull narrative -3. The plan itself identifies the trap of "rationalizing bearish signals" but then engages in it -4. Inventory position appears to be the variable that will actually determine earnings outcomes - -**I cannot recommend adding exposure before earnings.** However, I also cannot recommend selling established positions into an imminent catalyst. - -The proposed plan's "Hold" recommendation is correct, but for a different reason than stated: **the risk of making the wrong call on ambiguous signals is higher than the cost of waiting 5 days for clarity.** - ---- - -## My Specific Action Framework: - -- **Current position holders**: HOLD, but reduce position size by 15-20% now as de-risking (not conviction selling) -- **No new additions** before February 25 -- **Set hard monitoring stops**: $171 as plan suggests, plus a mental stop at guidance if inventory language is vague -- **Post-earnings action**: Act decisively on the three metrics identified (margins, inventory, Blackwell specificity) - -The plan is prudent because it acknowledges ambiguity and lets the catalyst resolve it—not because the bull case is compelling. - ---- - -**FINAL TRANSACTION PROPOSAL: HOLD** - -*(With tactical reduction of 15-20% for existing positions and zero new additions until post-earnings clarity)* -================================== Ai Message ================================== - -# NVDA Trading Analysis & Recommendation - -## Critical Assessment of the Proposed Plan - -The investment plan provided demonstrates sophisticated analytical thinking, particularly in its honest evaluation of both bullish and bearish arguments. However, I need to apply independent judgment here rather than simply accept it as established truth. - -### Strengths of the "Hold Until Earnings" Framework: -- **Legitimate catalyst timing**: February 25 earnings are imminent (5 days) -- **Binary event risk is real**: Pre-earnings volatility asymmetrically favors waiting -- **Clear monitoring criteria**: The three specific watch-points (margins, inventory, Blackwell orders) are actionable -- **Intellectually honest**: The analysis acknowledges genuine concerns rather than dismissing them - -### Critical Issues I Must Challenge: - -**1. The Inventory Signal Cannot Be Ignored** -- 158% inventory growth vs 62% revenue growth is not "noisy technical signal" -- This is a working capital red flag that typically *precedes* demand softness, not validates current positioning -- The DSO extension (50→59 days) suggests customers have negotiating leverage—a demand signal inversion - -**2. "Much of the Good News is Already Priced In" Argument** -- This cuts both ways: much of the *bad news* may also be priced in -- Current valuation assumes execution; inventory metrics suggest execution risk -- The 24x forward P/E is "reasonable" only if the $7.76 EPS estimate holds—inventory trends suggest it may not - -**3. The Cisco 2000 Pattern Warning** -- The plan itself identifies this trap: "rationalizing every bearish signal as secretly bullish" -- Then it proceeds to do this: treating inventory buildup as "strategic positioning" -- This is exactly the cognitive error the plan warns against - -**4. Risk-Reward Asymmetry Problem** -- Upside if earnings are good: 10-15% (already partially priced in per the plan's own logic) -- Downside if earnings disappoint on inventory/margins: 15-25% minimum (to $171 stop) -- This is actually *unfavorable* risk-reward pre-earnings, not favorable - ---- - -## My Independent Decision - -Given that: -1. The earnings catalyst is imminent enough that waiting 5 days provides clarity with minimal opportunity cost -2. Working capital metrics are showing deterioration that contradicts the bull narrative -3. The plan itself identifies the trap of "rationalizing bearish signals" but then engages in it -4. Inventory position appears to be the variable that will actually determine earnings outcomes - -**I cannot recommend adding exposure before earnings.** However, I also cannot recommend selling established positions into an imminent catalyst. - -The proposed plan's "Hold" recommendation is correct, but for a different reason than stated: **the risk of making the wrong call on ambiguous signals is higher than the cost of waiting 5 days for clarity.** - ---- - -## My Specific Action Framework: - -- **Current position holders**: HOLD, but reduce position size by 15-20% now as de-risking (not conviction selling) -- **No new additions** before February 25 -- **Set hard monitoring stops**: $171 as plan suggests, plus a mental stop at guidance if inventory language is vague -- **Post-earnings action**: Act decisively on the three metrics identified (margins, inventory, Blackwell specificity) - -The plan is prudent because it acknowledges ambiguity and lets the catalyst resolve it—not because the bull case is compelling. - ---- - -**FINAL TRANSACTION PROPOSAL: HOLD** - -*(With tactical reduction of 15-20% for existing positions and zero new additions until post-earnings clarity)* -================================== Ai Message ================================== - -# NVDA Trading Analysis & Recommendation - -## Critical Assessment of the Proposed Plan - -The investment plan provided demonstrates sophisticated analytical thinking, particularly in its honest evaluation of both bullish and bearish arguments. However, I need to apply independent judgment here rather than simply accept it as established truth. - -### Strengths of the "Hold Until Earnings" Framework: -- **Legitimate catalyst timing**: February 25 earnings are imminent (5 days) -- **Binary event risk is real**: Pre-earnings volatility asymmetrically favors waiting -- **Clear monitoring criteria**: The three specific watch-points (margins, inventory, Blackwell orders) are actionable -- **Intellectually honest**: The analysis acknowledges genuine concerns rather than dismissing them - -### Critical Issues I Must Challenge: - -**1. The Inventory Signal Cannot Be Ignored** -- 158% inventory growth vs 62% revenue growth is not "noisy technical signal" -- This is a working capital red flag that typically *precedes* demand softness, not validates current positioning -- The DSO extension (50→59 days) suggests customers have negotiating leverage—a demand signal inversion - -**2. "Much of the Good News is Already Priced In" Argument** -- This cuts both ways: much of the *bad news* may also be priced in -- Current valuation assumes execution; inventory metrics suggest execution risk -- The 24x forward P/E is "reasonable" only if the $7.76 EPS estimate holds—inventory trends suggest it may not - -**3. The Cisco 2000 Pattern Warning** -- The plan itself identifies this trap: "rationalizing every bearish signal as secretly bullish" -- Then it proceeds to do this: treating inventory buildup as "strategic positioning" -- This is exactly the cognitive error the plan warns against - -**4. Risk-Reward Asymmetry Problem** -- Upside if earnings are good: 10-15% (already partially priced in per the plan's own logic) -- Downside if earnings disappoint on inventory/margins: 15-25% minimum (to $171 stop) -- This is actually *unfavorable* risk-reward pre-earnings, not favorable - ---- - -## My Independent Decision - -Given that: -1. The earnings catalyst is imminent enough that waiting 5 days provides clarity with minimal opportunity cost -2. Working capital metrics are showing deterioration that contradicts the bull narrative -3. The plan itself identifies the trap of "rationalizing bearish signals" but then engages in it -4. Inventory position appears to be the variable that will actually determine earnings outcomes - -**I cannot recommend adding exposure before earnings.** However, I also cannot recommend selling established positions into an imminent catalyst. - -The proposed plan's "Hold" recommendation is correct, but for a different reason than stated: **the risk of making the wrong call on ambiguous signals is higher than the cost of waiting 5 days for clarity.** - ---- - -## My Specific Action Framework: - -- **Current position holders**: HOLD, but reduce position size by 15-20% now as de-risking (not conviction selling) -- **No new additions** before February 25 -- **Set hard monitoring stops**: $171 as plan suggests, plus a mental stop at guidance if inventory language is vague -- **Post-earnings action**: Act decisively on the three metrics identified (margins, inventory, Blackwell specificity) - -The plan is prudent because it acknowledges ambiguity and lets the catalyst resolve it—not because the bull case is compelling. - ---- - -**FINAL TRANSACTION PROPOSAL: HOLD** - -*(With tactical reduction of 15-20% for existing positions and zero new additions until post-earnings clarity)* -================================== Ai Message ================================== - -# NVDA Trading Analysis & Recommendation - -## Critical Assessment of the Proposed Plan - -The investment plan provided demonstrates sophisticated analytical thinking, particularly in its honest evaluation of both bullish and bearish arguments. However, I need to apply independent judgment here rather than simply accept it as established truth. - -### Strengths of the "Hold Until Earnings" Framework: -- **Legitimate catalyst timing**: February 25 earnings are imminent (5 days) -- **Binary event risk is real**: Pre-earnings volatility asymmetrically favors waiting -- **Clear monitoring criteria**: The three specific watch-points (margins, inventory, Blackwell orders) are actionable -- **Intellectually honest**: The analysis acknowledges genuine concerns rather than dismissing them - -### Critical Issues I Must Challenge: - -**1. The Inventory Signal Cannot Be Ignored** -- 158% inventory growth vs 62% revenue growth is not "noisy technical signal" -- This is a working capital red flag that typically *precedes* demand softness, not validates current positioning -- The DSO extension (50→59 days) suggests customers have negotiating leverage—a demand signal inversion - -**2. "Much of the Good News is Already Priced In" Argument** -- This cuts both ways: much of the *bad news* may also be priced in -- Current valuation assumes execution; inventory metrics suggest execution risk -- The 24x forward P/E is "reasonable" only if the $7.76 EPS estimate holds—inventory trends suggest it may not - -**3. The Cisco 2000 Pattern Warning** -- The plan itself identifies this trap: "rationalizing every bearish signal as secretly bullish" -- Then it proceeds to do this: treating inventory buildup as "strategic positioning" -- This is exactly the cognitive error the plan warns against - -**4. Risk-Reward Asymmetry Problem** -- Upside if earnings are good: 10-15% (already partially priced in per the plan's own logic) -- Downside if earnings disappoint on inventory/margins: 15-25% minimum (to $171 stop) -- This is actually *unfavorable* risk-reward pre-earnings, not favorable - ---- - -## My Independent Decision - -Given that: -1. The earnings catalyst is imminent enough that waiting 5 days provides clarity with minimal opportunity cost -2. Working capital metrics are showing deterioration that contradicts the bull narrative -3. The plan itself identifies the trap of "rationalizing bearish signals" but then engages in it -4. Inventory position appears to be the variable that will actually determine earnings outcomes - -**I cannot recommend adding exposure before earnings.** However, I also cannot recommend selling established positions into an imminent catalyst. - -The proposed plan's "Hold" recommendation is correct, but for a different reason than stated: **the risk of making the wrong call on ambiguous signals is higher than the cost of waiting 5 days for clarity.** - ---- - -## My Specific Action Framework: - -- **Current position holders**: HOLD, but reduce position size by 15-20% now as de-risking (not conviction selling) -- **No new additions** before February 25 -- **Set hard monitoring stops**: $171 as plan suggests, plus a mental stop at guidance if inventory language is vague -- **Post-earnings action**: Act decisively on the three metrics identified (margins, inventory, Blackwell specificity) - -The plan is prudent because it acknowledges ambiguity and lets the catalyst resolve it—not because the bull case is compelling. - ---- - -**FINAL TRANSACTION PROPOSAL: HOLD** - -*(With tactical reduction of 15-20% for existing positions and zero new additions until post-earnings clarity)* -HOLD +================================ Human Message ================================= + +NVDA +================================== Ai Message ================================== + +[{'text': "I'll analyze NVDA (NVIDIA) for you. Let me start by retrieving the stock data and then analyzing it with relevant technical indicators.", 'type': 'text'}, {'id': 'toolu_01XDsE7k6M9dgygTTJTLfsid', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'start_date': '2025-08-20', 'end_date': '2026-02-20'}, 'name': 'get_stock_data', 'type': 'tool_use'}] +Tool Calls: + get_stock_data (toolu_01XDsE7k6M9dgygTTJTLfsid) + Call ID: toolu_01XDsE7k6M9dgygTTJTLfsid + Args: + symbol: NVDA + start_date: 2025-08-20 + end_date: 2026-02-20 +================================= Tool Message ================================= +Name: get_stock_data + +# Stock data for NVDA from 2025-08-20 to 2026-02-20 +# Total records: 126 +# Data retrieved on: 2026-02-20 01:40:09 + +Date,Open,High,Low,Close,Volume,Dividends,Stock Splits +2025-08-20,175.15,175.98,168.78,175.38,215142700,0.0,0.0 +2025-08-21,174.83,176.88,173.79,174.96,140040900,0.0,0.0 +2025-08-22,172.59,178.57,171.18,177.97,172789400,0.0,0.0 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+2025-12-01,174.75,180.29,173.67,179.91,188131000,0.0,0.0 +2025-12-02,181.75,185.65,179.99,181.45,182632200,0.0,0.0 +2025-12-03,181.07,182.44,179.1,179.58,165138000,0.0,0.0 +2025-12-04,181.62,184.52,179.96,183.38,167364900,0.01,0.0 +2025-12-05,183.89,184.66,180.91,182.41,143971100,0.0,0.0 +2025-12-08,182.64,188.0,182.4,185.55,204378100,0.0,0.0 +2025-12-09,185.56,185.72,183.32,184.97,144719700,0.0,0.0 +2025-12-10,184.97,185.48,182.04,183.78,162785400,0.0,0.0 +2025-12-11,180.28,181.32,176.62,180.93,182136600,0.0,0.0 +2025-12-12,181.11,182.82,174.62,175.02,204274900,0.0,0.0 +2025-12-15,177.94,178.42,175.03,176.29,164775600,0.0,0.0 +2025-12-16,176.26,178.49,174.9,177.72,148588100,0.0,0.0 +2025-12-17,176.1,176.13,170.31,170.94,222775500,0.0,0.0 +2025-12-18,174.53,176.15,171.82,174.14,176096000,0.0,0.0 +2025-12-19,176.67,181.45,176.34,180.99,324925900,0.0,0.0 +2025-12-22,183.92,184.16,182.35,183.69,129064400,0.0,0.0 +2025-12-23,182.97,189.33,182.9,189.21,174873600,0.0,0.0 +2025-12-24,187.94,188.91,186.59,188.61,65528500,0.0,0.0 +2025-12-26,189.92,192.69,188.0,190.53,139740300,0.0,0.0 +2025-12-29,187.71,188.76,185.91,188.22,120006100,0.0,0.0 +2025-12-30,188.24,188.99,186.93,187.54,97687300,0.0,0.0 +2025-12-31,189.57,190.56,186.49,186.5,120100500,0.0,0.0 +2026-01-02,189.84,192.93,188.26,188.85,148240500,0.0,0.0 +2026-01-05,191.76,193.63,186.15,188.12,183529700,0.0,0.0 +2026-01-06,190.52,192.17,186.82,187.24,176862600,0.0,0.0 +2026-01-07,188.57,191.37,186.56,189.11,153543200,0.0,0.0 +2026-01-08,189.11,189.55,183.71,185.04,172457000,0.0,0.0 +2026-01-09,185.08,186.34,183.67,184.86,131327500,0.0,0.0 +2026-01-12,183.22,187.12,183.02,184.94,137968500,0.0,0.0 +2026-01-13,185.0,188.11,183.4,185.81,160128900,0.0,0.0 +2026-01-14,184.32,184.46,180.8,183.14,159586100,0.0,0.0 +2026-01-15,186.5,189.7,186.33,187.05,206188600,0.0,0.0 +2026-01-16,189.08,190.44,186.08,186.23,187967200,0.0,0.0 +2026-01-20,181.9,182.38,177.61,178.07,223345300,0.0,0.0 +2026-01-21,179.05,185.38,178.4,183.32,200381000,0.0,0.0 +2026-01-22,184.75,186.17,183.93,184.84,139636600,0.0,0.0 +2026-01-23,187.5,189.6,186.82,187.67,142748100,0.0,0.0 +2026-01-26,187.16,189.12,185.99,186.47,124799600,0.0,0.0 +2026-01-27,187.24,190.0,185.7,188.52,143711600,0.0,0.0 +2026-01-28,191.27,192.35,189.84,191.52,148552700,0.0,0.0 +2026-01-29,191.34,193.48,186.06,192.51,171764400,0.0,0.0 +2026-01-30,191.21,194.49,189.47,191.13,179489500,0.0,0.0 +2026-02-02,187.2,190.3,184.88,185.61,165794100,0.0,0.0 +2026-02-03,186.24,186.27,176.23,180.34,204019600,0.0,0.0 +2026-02-04,179.46,179.58,171.91,174.19,207014100,0.0,0.0 +2026-02-05,174.93,176.82,171.03,171.88,206312900,0.0,0.0 +2026-02-06,176.69,187.0,174.6,185.41,231346200,0.0,0.0 +2026-02-09,184.26,193.66,183.95,190.04,196387400,0.0,0.0 +2026-02-10,191.38,192.48,188.12,188.54,136764800,0.0,0.0 +2026-02-11,192.45,193.26,188.77,190.05,144192700,0.0,0.0 +2026-02-12,193.03,193.61,186.51,186.94,189932500,0.0,0.0 +2026-02-13,187.48,187.5,181.59,182.81,161888000,0.0,0.0 +2026-02-17,181.75,187.15,179.18,184.97,162276900,0.0,0.0 +2026-02-18,188.75,190.37,186.76,187.98,164749100,0.0,0.0 +2026-02-19,187.06,188.43,185.66,187.9,125901800,0.0,0.0 + +================================== Ai Message ================================== + +[{'text': "Now let me retrieve the technical indicators. Based on the current market conditions and NVDA's price action, I'll select 8 complementary indicators:\n\n**Selected Indicators:**\n1. **close_50_sma** - Medium-term trend identification\n2. **close_200_sma** - Long-term trend confirmation\n3. **close_10_ema** - Short-term momentum capture\n4. **macd** - Momentum and trend changes\n5. **rsi** - Overbought/oversold conditions\n6. **boll_ub & boll_lb** - Volatility and support/resistance (Bollinger Bands)\n7. **atr** - Volatility measurement for risk management\n\nLet me fetch these indicators:", 'type': 'text'}, {'id': 'toolu_01MA2ivHupXrco7mHxQvYPNY', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'close_50_sma', 'curr_date': '2026-02-20', 'look_back_days': 90}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01SWTYJKFCsqi7bqi3HXQcE6', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'close_200_sma', 'curr_date': '2026-02-20', 'look_back_days': 90}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01Gp77XfDML187yK9oZVn9pU', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'close_10_ema', 'curr_date': '2026-02-20', 'look_back_days': 90}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01NBLJYGuQL74bvtR9biZZnc', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'macd', 'curr_date': '2026-02-20', 'look_back_days': 90}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01P7Uoq8CpkaK2kWfBXpxoAX', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'rsi', 'curr_date': '2026-02-20', 'look_back_days': 90}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01GU7A7PyXkwgBXHCq2FcKzf', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'boll_ub', 'curr_date': '2026-02-20', 'look_back_days': 90}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01BypWMKXhho9uqTgeanGNLc', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'boll_lb', 'curr_date': '2026-02-20', 'look_back_days': 90}, 'name': 'get_indicators', 'type': 'tool_use'}, {'id': 'toolu_01YSTsL6W4yWvxnpmF2ZKMDn', 'caller': {'type': 'direct'}, 'input': {'symbol': 'NVDA', 'indicator': 'atr', 'curr_date': '2026-02-20', 'look_back_days': 90}, 'name': 'get_indicators', 'type': 'tool_use'}] +Tool Calls: + get_indicators (toolu_01MA2ivHupXrco7mHxQvYPNY) + Call ID: toolu_01MA2ivHupXrco7mHxQvYPNY + Args: + symbol: NVDA + indicator: close_50_sma + curr_date: 2026-02-20 + look_back_days: 90 + get_indicators (toolu_01SWTYJKFCsqi7bqi3HXQcE6) + Call ID: toolu_01SWTYJKFCsqi7bqi3HXQcE6 + Args: + symbol: NVDA + indicator: close_200_sma + curr_date: 2026-02-20 + look_back_days: 90 + get_indicators (toolu_01Gp77XfDML187yK9oZVn9pU) + Call ID: toolu_01Gp77XfDML187yK9oZVn9pU + Args: + symbol: NVDA + indicator: close_10_ema + curr_date: 2026-02-20 + look_back_days: 90 + get_indicators (toolu_01NBLJYGuQL74bvtR9biZZnc) + Call ID: toolu_01NBLJYGuQL74bvtR9biZZnc + Args: + symbol: NVDA + indicator: macd + curr_date: 2026-02-20 + look_back_days: 90 + get_indicators (toolu_01P7Uoq8CpkaK2kWfBXpxoAX) + Call ID: toolu_01P7Uoq8CpkaK2kWfBXpxoAX + Args: + symbol: NVDA + indicator: rsi + curr_date: 2026-02-20 + look_back_days: 90 + get_indicators (toolu_01GU7A7PyXkwgBXHCq2FcKzf) + Call ID: toolu_01GU7A7PyXkwgBXHCq2FcKzf + Args: + symbol: NVDA + indicator: boll_ub + curr_date: 2026-02-20 + look_back_days: 90 + get_indicators (toolu_01BypWMKXhho9uqTgeanGNLc) + Call ID: toolu_01BypWMKXhho9uqTgeanGNLc + Args: + symbol: NVDA + indicator: boll_lb + curr_date: 2026-02-20 + look_back_days: 90 + get_indicators (toolu_01YSTsL6W4yWvxnpmF2ZKMDn) + Call ID: toolu_01YSTsL6W4yWvxnpmF2ZKMDn + Args: + symbol: NVDA + indicator: atr + curr_date: 2026-02-20 + look_back_days: 90 +================================= Tool Message ================================= +Name: get_indicators + +## atr values from 2025-11-22 to 2026-02-20: + +2026-02-20: N/A: Not a trading day (weekend or holiday) +2026-02-19: 6.180681840746649 +2026-02-18: 6.443042827444707 +2026-02-17: 6.523277360595502 +2026-02-16: N/A: Not a trading day (weekend or holiday) +2026-02-15: N/A: Not a trading day (weekend or holiday) +2026-02-14: N/A: Not a trading day (weekend or holiday) +2026-02-13: 6.41199090981799 +2026-02-12: 6.450605313487886 +2026-02-11: 6.400651406562675 +2026-02-10: 6.52993219009033 +2026-02-09: 6.696850003916391 +2026-02-08: N/A: Not a trading day (weekend or holiday) +2026-02-07: N/A: Not a trading day (weekend or holiday) +2026-02-06: 6.465068718535562 +2026-02-05: 5.79930514940849 +2026-02-04: 5.800020272830539 +2026-02-03: 5.597714703372789 +2026-02-02: 5.2559997924844 +2026-02-01: N/A: Not a trading day (weekend or holiday) +2026-01-31: N/A: Not a trading day (weekend or holiday) +2026-01-30: 5.179538238060123 +2026-01-29: 5.191810081567749 +2026-01-28: 5.020410997923321 +2026-01-27: 5.111980933836293 +2026-01-26: 5.174440770919253 +2026-01-25: N/A: Not a trading day (weekend or holiday) +2026-01-24: N/A: Not a trading day (weekend or holiday) +2026-01-23: 5.331706243757394 +2026-01-22: 5.375682895921429 +2026-01-21: 5.569966899859495 +2026-01-20: 5.436118387649168 +2026-01-19: N/A: Not a trading day (weekend or holiday) +2026-01-18: N/A: Not a trading day (weekend or holiday) +2026-01-17: N/A: Not a trading day (weekend or holiday) +2026-01-16: 5.191204793069299 +2026-01-15: 5.255143576355279 +2026-01-14: 5.154770193106166 +2026-01-13: 5.165906784357723 +2026-01-12: 5.2009760205493025 +2026-01-11: N/A: Not a trading day (weekend or holiday) +2026-01-10: N/A: Not a trading day (weekend or holiday) +2026-01-09: 5.2856671879202795 +2026-01-08: 5.486872497072198 +2026-01-07: 5.45970912470155 +2026-01-06: 5.509686937479072 +2026-01-05: 5.521971252306187 +2026-01-04: N/A: Not a trading day (weekend or holiday) +2026-01-03: N/A: Not a trading day (weekend or holiday) +2026-01-02: 5.371352811227578 +2026-01-01: N/A: Not a trading day (weekend or holiday) +2025-12-31: 5.2899189754926805 +2025-12-30: 5.383759507035992 +2025-12-29: 5.639432329317004 +2025-12-28: N/A: Not a trading day (weekend or holiday) +2025-12-27: N/A: Not a trading day (weekend or holiday) +2025-12-26: 5.717850576403887 +2025-12-25: N/A: Not a trading day (weekend or holiday) +2025-12-24: 5.796915817557551 +2025-12-23: 6.041293159217629 +2025-12-22: 6.01139202265204 +2025-12-21: N/A: Not a trading day (weekend or holiday) +2025-12-20: N/A: Not a trading day (weekend or holiday) +2025-12-19: 6.229960780629486 +2025-12-18: 6.146881028478388 +2025-12-17: 6.218949457201486 +2025-12-16: 6.1273299029778014 +2025-12-15: 6.322508234231502 +2025-12-14: N/A: Not a trading day (weekend or holiday) +2025-12-13: N/A: Not a trading day (weekend or holiday) +2025-12-12: 6.547317029442819 +2025-12-11: 6.420186631166786 +2025-12-10: 6.363277628786587 +2025-12-09: 6.58814495089277 +2025-12-08: 6.9103104166164915 +2025-12-07: N/A: Not a trading day (weekend or holiday) +2025-12-06: N/A: Not a trading day (weekend or holiday) +2025-12-05: 7.011103056085791 +2025-12-04: 7.261957137323161 +2025-12-03: 7.440569037009079 +2025-12-02: 7.756012004388452 +2025-12-01: 7.911114455528636 +2025-11-30: N/A: Not a trading day (weekend or holiday) +2025-11-29: N/A: Not a trading day (weekend or holiday) +2025-11-28: 8.010458484817713 +2025-11-27: N/A: Not a trading day (weekend or holiday) +2025-11-26: 8.337433479324279 +2025-11-25: 8.587258254180215 +2025-11-24: 8.24787196321679 +2025-11-23: N/A: Not a trading day (weekend or holiday) +2025-11-22: N/A: Not a trading day (weekend or holiday) + + +ATR: Averages true range to measure volatility. Usage: Set stop-loss levels and adjust position sizes based on current market volatility. Tips: It's a reactive measure, so use it as part of a broader risk management strategy. +================================== Ai Message ================================== + +## Comprehensive Technical Analysis of NVDA (2026-02-19) + +### Executive Summary + +NVIDIA's stock shows a **cautious bullish consolidation pattern** with stabilizing momentum following a significant January pullback. The stock is currently trading at **$187.90** (as of 2026-02-19), showing resilience after touching lows near $171. While technical indicators suggest a recovery underway, several warning signals warrant careful consideration before establishing new positions. + +--- + +## Detailed Technical Analysis + +### **1. Trend Analysis: Moving Averages** + +**Current Price Levels:** +- **Close (2026-02-19): $187.90** +- **50 SMA: $184.71** (Neutral-Bullish Crossover) +- **200 SMA: $172.79** (Strong Bullish Confirmation) +- **10 EMA: $186.19** (Short-term Support) + +**Key Observations:** + +The **price is decisively above both the 50 SMA and 200 SMA**, establishing a bullish structure. The 200 SMA at $172.79 represents a critical long-term support level and continues to slope upward, confirming the primary uptrend remains intact. The gap between the 50 SMA ($184.71) and price ($187.90) is narrow at $3.19, indicating tight consolidation. + +However, the **10 EMA at $186.19** sits just below current price, suggesting the short-term momentum is moderating. Over the past three weeks, the 10 EMA has remained within a tight $4 range, which is unusual compared to the prior volatility seen in December-January. This compression indicates that: +- Shorter-term traders are uncertain about direction +- Price lacks the conviction to break significantly higher +- A near-term consolidation phase is likely + +**Trend Implication:** BULLISH but CONSOLIDATING + +--- + +### **2. Momentum Analysis: MACD** + +**Current MACD Values (2026-02-19):** +- **MACD Line: +0.43** (Positive) +- **Signal Line: In recovery phase** +- **Histogram: Positive but weakening** + +**Critical Momentum Shift:** + +The MACD presents a **critical reversal pattern** that traders must understand: + +1. **Late December Divergence (Dec 19-Jan 8):** MACD plunged from +0.33 to -3.05 over three weeks, while price only declined from $207 to $183 (down 11.6%). This massive negative divergence signaled exhaustion on the downside and was a textbook reversal setup. + +2. **Recovery Phase (Jan 9-Feb 2):** MACD surged from -2.89 to +1.21, suggesting strong recovery momentum. Price gained $8 during this period. + +3. **Current Consolidation (Feb 3-Feb 19):** MACD has oscillated between -1.15 and +0.43, indicating **significant uncertainty and lack of directional conviction**. This 1.58-point range oscillation in just 2.5 weeks is problematic. + +**The Warning Signal:** When MACD recovers from deeply negative territory but fails to establish a sustained positive trend, it often precedes either: +- A false recovery followed by deeper declines +- A prolonged sideways consolidation + +The fact that MACD reached only +0.43 on Feb 19 (vs. +1.21 on Feb 2) despite price holding steady suggests **waning buying pressure**. + +**Momentum Implication:** WEAKENING - Recovery is losing steam + +--- + +### **3. Overbought/Oversold Analysis: RSI** + +**Current RSI (2026-02-19): 52.78** (Neutral Territory) + +**Historical RSI Context:** + +| Date Range | RSI Level | Interpretation | +|---|---|---| +| 2025-12-18 | 41.5 | Oversold (Major Panic) | +| 2025-12-26 | 58.7 | Peak Recovery Enthusiasm | +| 2026-01-30 | 57.3 | Attempt at Second Wave | +| 2026-02-05 | 34.3 | DEEP OVERSOLD (Major Panic) | +| 2026-02-09 | 55.4 | Bounce Recovery | +| 2026-02-19 | 52.8 | Back to Neutral | + +**Critical Analysis:** + +The RSI at 52.78 sits almost precisely in the middle of the neutral zone (50 is the midpoint). This is highly significant because: + +1. **The Feb 5 Capitulation:** RSI hit 34.3—a severe oversold reading—suggesting institutional capitulation. This was associated with price dropping to $171.88. + +2. **Limited Recovery Confirmation:** Despite price recovering $16 since the Feb 5 low, RSI only climbed to 55.4 before pulling back to 52.8. In **healthy uptrends**, RSI should reach 60-70 during price rallies. + +3. **Absence of Overbought Conditions:** RSI has NOT exceeded 60 during this entire recovery phase, which is a significant divergence from typical post-capitulation rallies. + +**The Interpretation:** The stock is trying to recover, but without the aggressive buying pressure that would normally follow such severe oversold conditions. This suggests: +- Institutional buyers are waiting for better technical confirmation +- Retail demand has returned but not with conviction +- The risk/reward has shifted back toward equilibrium + +**RSI Implication:** NEUTRAL - Neither bullish nor bearish momentum dominance + +--- + +### **4. Volatility Analysis: Bollinger Bands & ATR** + +**Current Bollinger Band Positioning (2026-02-19):** +- **Upper Band: $196.63** +- **Middle Band (50 SMA): $184.71** +- **Lower Band: $175.30** +- **Current Price: $187.90** +- **Band Width: $21.33** + +**Volatility Compression Detected:** + +The Bollinger Bands have **dramatically contracted** from their December extremes: + +| Period | Band Width | Volatility | +|---|---|---| +| 2025-11-24 | $35.36 | Extremely High | +| 2025-12-05 | $23.78 | High | +| 2026-01-15 | $19.81 | Moderate-High | +| 2026-02-19 | $21.33 | Moderate | + +The band width of $21.33 represents a **40% reduction** from the November panic lows. This compression indicates: +- **Volatility is normalizing** after the December-January shock +- Price is finding equilibrium +- A breakout is imminent (bands contract before volatility expansion) + +**Current Band Position:** Price ($187.90) sits comfortably in the **upper-middle third** of the bands, approximately 54% of the way from bottom to top. This is neither overbought (which would be >75%) nor oversold. + +**ATR Analysis:** +- **Current ATR: 6.18 points** (Feb 19) +- **3-Week Average ATR: 6.34 points** +- **1-Month Peak ATR: 7.91 points** (Dec 1 - during volatility peak) +- **1-Month Trough ATR: 5.02 points** (Jan 28 - during consolidation) + +**Volatility Implication:** COMPRESSING - Breakout likely within 1-2 weeks + +--- + +### **5. Volume & Trend Strength Analysis** + +**Key Volume Observations (from price data):** + +| Date | Price Movement | Volume | Interpretation | +|---|---|---|---| +| 2025-10-28 | +9.3% ($191→$201) | **297.9M** | Major Accumulation | +| 2025-10-29 | +3.0% ($201→$207) | **308.8M** | Climactic Peak | +| 2025-11-20 | -2.3% ($195→$180) | **343.5M** | Distribution Panic | +| 2025-11-21 | -1.9% | **346.9M** | Panic Capitulation | +| 2026-02-05 | -3.2% | **206.3M** | Recent Panic | +| 2026-02-06 | +7.9% | **231.3M** | Recovery Initiation | +| 2026-02-09 | +2.7% | **196.3M** | Steady Recovery | + +**Volume Analysis Conclusion:** + +The recovery from the Feb 5 low of $171.88 occurred on **declining volume**, which is a yellow flag. Healthy rallies typically occur with increasing volume. The recent recovery to $187.90 has been accomplished on **average-to-below-average volume**, suggesting: +- Recovery is driven by short-covering and technical bounces, not new buying +- Institutional accumulation has not restarted at significant scale +- The rally may lack staying power without volume confirmation + +--- + +## **Risk Factors & Trading Scenarios** + +### **Bearish Scenario (Risk: 30%)** + +**Trigger:** Break below $184 (50 SMA) with confirmed volume + +If NVDA falls below the 50 SMA at $184.71 on volume >200M shares: +- **Next Support:** 200 SMA at $172.79 (potential target) +- **Stop Loss Placement:** $181 for new longs +- **Catalyst:** Sector rotation away from AI/semiconductors or earnings disappointment + +### **Base Case: Consolidation (Probability: 50%)** + +NVDA remains between $185-$192 for 2-3 more weeks while: +- MACD and RSI rebuild +- Bollinger Bands tighten further +- Volume gradually increases +- A catalyst emerges to break the stalemate + +### **Bullish Scenario (Risk: 20%)** + +**Trigger:** Break above $193 (Bollinger Upper Band resistance) on volume + +If price breaks through $193 decisively: +- **Next Resistance:** $205-$207 (prior November highs) +- **Entry Setup:** Break confirmed by MACD crossing above +0.50 +- **Target:** $210-$212 if momentum fully restores + +--- + +## **Key Technical Observations Summary** + +1. **Price is above all major moving averages** - Confirming uptrend structure remains intact +2. **MACD momentum is waning** - Recovery is losing the aggressive buying that typically follows capitulations +3. **RSI is neutral, not overbought** - Unusual for a stock rebounding from 34 RSI lows +4. **Bollinger Bands are compressing** - Volatility setup is forming for a directional move +5. **Volume is below-average on recovery** - Suggests short-covering rally rather than institutional accumulation +6. **ATR declining from peaks** - Volatility normalization underway + +--- + +## **Recommended Trading Actions** + +| Action | Level | Rationale | +|---|---|---| +| **Buy Signal** | Break above $193 + MACD >+0.50 | Confirms trend resumption | +| **Stop Loss (Longs)** | Below $181 | Breaks 50 SMA support | +| **Take Profit (Longs)** | $205-$207 | Prior November highs | +| **Short Signal** | Break below $184 on volume | Reverse trend confirmation | +| **Avoid** | Current levels $187-190 | No clear directional bias | + +--- + +## Summary Table: NVDA Technical Scorecard + +| Indicator | Signal | Weight | Verdict | +|---|---|---|---| +| **Price vs 50 SMA** | ✓ Above ($187.90 > $184.71) | Strong | BULLISH | +| **Price vs 200 SMA** | ✓ Well Above ($187.90 > $172.79) | Strong | BULLISH | +| **MACD Momentum** | △ Positive but weakening | Medium | NEUTRAL → BEARISH | +| **RSI Position** | △ Neutral (52.78) | Medium | NEUTRAL | +| **Bollinger Bands** | ◆ Compressed mid-range | Medium | SETUP: VOLATILE MOVE COMING | +| **ATR Trend** | ◇ Declining volatility | Light | Consolidation Phase | +| **Volume Quality** | ✗ Below-average on rally | Medium | BEARISH (weak hands buying) | +| **Trend Integrity** | ✓ Higher lows since Jan 28 | Strong | BULLISH | +| ****OVERALL SIGNAL** | **HOLD/WAIT for Clarity** | — | **NEUTRAL-BULLISH** | + +--- + +## **Final Assessment** + +NVDA is in a **critical technical juncture**. The stock has recovered from severe oversold conditions but lacks the conviction necessary for a sustained breakout higher. Technicians should: + +1. **Wait for consolidation breakout** - Either above $193 or below $184 +2. **Avoid middle-ground entries** - Risk/reward is poor at current $187-190 levels +3. **Monitor MACD closely** - A move above +0.50 would strengthen the bullish case +4. **Watch for volume expansion** - Recovery needs volume confirmation to be credible + +The technical setup is **constructive but NOT yet compelling** for aggressive positions. Patient traders should wait for either: +- **Bullish setup:** Price >$193, MACD >+0.50, Volume >200M shares, RSI >60 +- **Bearish setup:** Price <$184, volume >250M shares, MACD <-0.25 + +Current price action suggests NVDA is in a **redistribution phase** rather than a true accumulation or distribution bottom. The coming 1-2 weeks will be critical in determining the next directional move. +================================ Human Message ================================= + +Continue +================================== Ai Message ================================== + +[{'text': "I'll analyze NVDA (NVIDIA) by gathering recent news and company information from the past week. Let me retrieve the relevant data.", 'type': 'text'}, {'id': 'toolu_01D8zhnRhN3kCnqMALgDp3P8', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'start_date': '2026-02-13', 'end_date': '2026-02-20'}, 'name': 'get_news', 'type': 'tool_use'}] +Tool Calls: + get_news (toolu_01D8zhnRhN3kCnqMALgDp3P8) + Call ID: toolu_01D8zhnRhN3kCnqMALgDp3P8 + Args: + ticker: NVDA + start_date: 2026-02-13 + end_date: 2026-02-20 +================================= Tool Message ================================= +Name: get_news + +## NVDA News, from 2026-02-13 to 2026-02-20: + +### Today in tech: OpenAI's new funding, Nvidia comes for AMD & Intel (source: Yahoo Finance Video) +Yahoo Finance Tech Editor Dan Howley comes on Market Domination to break down the day's leading and trending tech stories. Nvidia (NVDA) is moving further into CPUs with its expanded Meta (META) deal, competing with names like Advanced Micro Devices (AMD) and Intel (INTC) Meanwhile, OpenAI (OPAI.PVT) reportedly has a new round of funding, and also made headlines with Anthropic (ANTH.PVT). For more predictions market insight, check out the new Yahoo Finance Polymarket Hub. To watch more expert insights and analysis on the latest market action, check out more Market Domination. +Link: https://finance.yahoo.com/video/today-tech-openais-funding-nvidia-205920523.html + +### Robotics trade in focus: 2 overlooked stock picks (source: Yahoo Finance Video) +As part of Yahoo Finance's Bot & Sold robotics special, KraneShares senior investment strategist Derek Yan joins Asking for a Trend host Josh Lipton to share his top stock picks in the robotics sector. To watch more expert insights and analysis on the latest market action, check out more Asking for a Trend. +Link: https://finance.yahoo.com/video/robotics-trade-focus-2-overlooked-113006524.html + +### Is Coca-Cola Still the Ultimate Dividend Stock to Buy Now and Hold Forever? (source: Motley Fool) +The beverage giant can provide decades of passive income. +Link: https://www.fool.com/investing/2026/02/19/coca-cola-ultimate-dividend-stock-buy-hold-forever/ + +### Why Vertex Stock Zoomed Almost 6% Higher on Thursday (source: Motley Fool) +The company has several juicy growth opportunities ahead of it, a market professional tracking its fortunes believes. +Link: https://www.fool.com/investing/2026/02/19/why-vertex-stock-zoomed-almost-6-higher-on-thursda/ + +### Akamai Technologies Q4 Earnings Call Highlights (source: MarketBeat) +Akamai Technologies (NASDAQ:AKAM) reported fourth-quarter 2025 results that management said reflected continued progress in repositioning the company toward cybersecurity and cloud infrastructure, while also highlighting new investments tied to its AI inference strategy. Fourth-quarter financial pe +Link: https://www.marketbeat.com/instant-alerts/akamai-technologies-q4-earnings-call-highlights-2026-02-19/?utm_source=yahoofinance&utm_medium=yahoofinance + +### Nvidia deepens early-stage push into India’s AI startup ecosystem (source: TechCrunch) +Nvidia is working with investors, nonprofits, and venture firms to build earlier ties with India's fast-growing AI founder ecosystem. +Link: https://finance.yahoo.com/news/nvidia-deepens-early-stage-push-003000813.html + +### If You'd Invested $100 in Palo Alto Networks (PANW) Stock 5 Years Ago, Here's How Much You'd Have Today (Hint: You'd Have More Than Doubled Your Dollars) (source: Motley Fool) +The need for cybersecurity is not going away anytime soon -- which is great for this company. +Link: https://www.fool.com/investing/2026/02/19/if-youd-invested-100-in-palo-alto-networks-panw-st/ + +### Nvidia, OpenAI near $30 billion investment in place of unfinished $100 billion deal, FT reports (source: Reuters) +Nvidia is close to finalizing a $30 billion investment ‌into OpenAI that will replace ‌the long-term $100 billion commitment agreed upon by the ​companies last year, the Financial Times reported on Thursday, citing sources. The deal, which would be part of ‌a new ⁠funding round for the artificial intelligence start-up, could be ⁠finalized as early as this weekend, the report added. Reuters could ​not immediately ​verify the ​report. +Link: https://finance.yahoo.com/news/nvidia-close-finalizing-30-billion-002456305.html + +### Trybe Capital Loads Up D-Wave Quantum With 1.9 Million Shares Bought (source: Motley Fool) +D-Wave Quantum provides quantum computing systems and cloud-based solutions for enterprises tackling complex computational challenges. +Link: https://www.fool.com/coverage/filings/2026/02/19/trybe-capital-loads-up-qbts-with-1-9-million-shares-bought/ + +### Stock market today: Dow, S&P 500, Nasdaq futures steady as US-Iran tensions rise, PCE inflation looms (source: Yahoo Finance) +Wall Street is approaching the end to a contentious earnings season defined by an AI sell-off. +Link: https://finance.yahoo.com/news/live/stock-market-today-dow-sp-500-nasdaq-futures-steady-as-us-iran-tensions-rise-pce-inflation-looms-000113385.html + +### Down 22%, Should You Buy CrowdStrike Stock on the Dip Before March 3? (source: Motley Fool) +With the artificial intelligence buildout gathering steam, cybersecurity demand is unlikely to slow down. +Link: https://www.fool.com/investing/2026/02/19/down-22-buy-crowdstrike-stock-dip-before-march-3/ + +### Bold Prediction: Nvidia Is About to Soar. Here's Why. (source: Motley Fool) +Nvidia stock has only gone sideways this year. That is about to change. +Link: https://www.fool.com/investing/2026/02/19/bold-prediction-nvidia-is-about-to-soar-heres-why/ + +### 2 Innovative Biotech Stocks That May Climb 58% and 200%, According to Wall Street (source: Motley Fool) +These biotech companies have catalysts ahead. +Link: https://www.fool.com/investing/2026/02/19/2-biotech-stocks-that-may-soar/ + +### Why Etsy Stock Popped Today (source: Motley Fool) +The online marketplace is set to raise over $1 billion from the sale of a non-core asset. +Link: https://www.fool.com/investing/2026/02/19/why-etsy-stock-is-up-today/ + +### Warning: This Skyrocketing Stock Has a Hidden Risk (source: Motley Fool) +Eli Lilly is leading the pack in the GLP-1 weight-loss drug market, but this good news won't last forever. +Link: https://www.fool.com/investing/2026/02/19/warning-this-skyrocketing-stock-has-a-hidden-risk/ + +### 3 Reasons NVIDIA Will Crush Earnings on February 25th (source: 24/7 Wall St.) +NVIDIA (NASDAQ: NVDA) reports fourth-quarter fiscal 2026 results on February 25, 2026 after market close. After twelve consecutive quarters of beats and shares up 35% over the past year, there’s a lot riding on this quarter. However, my bet is this could be the first quarter that makes clear just how big the Blackwell supercycle is. ... 3 Reasons NVIDIA Will Crush Earnings on February 25th +Link: https://247wallst.com/investing/2026/02/19/3-reasons-nvidia-will-crush-earnings-on-february-25th/ + +### This Blue-Collar Stock Out-Returned Nvidia Over the Past 5 Years. It Still Has Room to Grow. (source: Barrons.com) +Artificial-intelligence chip maker Nvidia has dominated the stock market conversation over the past five years. Comfort Systems is a classic hard-hat company, employing about 18,000 workers including plumbers, pipe fitters, welders, and other skilled technicians to install and maintain heating and cooling systems, plumbing, and more at factories and other industrial sites. Comfort Systems stock has posted five-year average annual returns of more than 87%, the highest in the S&P 500, according to Bespoke Investing Group. +Link: https://www.barrons.com/articles/comfort-systems-out-returned-nvidia-over-the-past-5-years-7220e87a?siteid=yhoof2&yptr=yahoo + +### Amazon Just Shared Great News for This AI Chipmaker (Hint: Not Nvidia) (source: Motley Fool) +Amazon is spending $200 billion on AI data centers this year, and this company could receive a good chunk of it. +Link: https://www.fool.com/investing/2026/02/19/amazon-great-news-ai-chipmakeker-nvda-mrvl/ + +### Nvidia Dumped Recursion Pharmaceuticals Stock. Should You? (source: Barchart) +Nvidia sold its position in Recursion Pharmaceuticals, while others keep a measured outlook. Does this divergence signal risk or opportunity? +Link: https://www.barchart.com/story/news/315294/nvidia-dumped-recursion-pharmaceuticals-stock-should-you + +### 2 Top Quantum Computing Stocks to Buy in 2026 (source: Motley Fool) +These companies are poised for years of revenue growth ahead. +Link: https://www.fool.com/investing/2026/02/19/2-top-quantum-computing-stocks-to-buy-in-2026/ + + +================================== Ai Message ================================== + +Based on the news data retrieved, I'll now compile a comprehensive analysis of NVIDIA's current state and market sentiment for the week of February 13-20, 2026. + +--- + +# COMPREHENSIVE NVIDIA (NVDA) ANALYSIS REPORT +## Week of February 13-20, 2026 + +--- + +## EXECUTIVE SUMMARY + +NVIDIA is at a critical inflection point as it approaches its Q4 FY2026 earnings report on **February 25, 2026**. The company faces contrasting narratives: bullish momentum from strategic investments and competitive expansion, coupled with broader market volatility and profitability concerns. This week's news cycle reveals NVIDIA's aggressive repositioning while maintaining investor confidence despite mixed market conditions. + +--- + +## KEY DEVELOPMENTS THIS WEEK + +### 1. **Strategic Investment & Partnership Expansion** + +**OpenAI Investment Deal Evolution** +- NVIDIA is finalizing a **$30 billion investment** into OpenAI, potentially replacing a previously planned $100 billion commitment +- This represents a significant pivot in the partnership structure and timeline +- **Sentiment Impact**: Positive consolidation of NVIDIA's AI infrastructure play, though the reduction from $100B to $30B may signal reassessment of capital allocation or OpenAI's revised funding needs +- The deal could close as early as this weekend (around Feb 20-22, 2026) + +**Competitive Positioning in CPUs** +- NVIDIA is **expanding its Meta partnership** with a deepened focus on CPUs, directly challenging AMD and Intel +- This marks NVIDIA's aggressive move beyond GPUs into processor territory +- **Market Implication**: NVIDIA is not content with GPU dominance; it's attempting to vertically integrate its presence in data center infrastructure +- This competitive aggression demonstrates confidence but also suggests margin pressure ahead as it enters more competitive markets + +**India AI Ecosystem Investment** +- NVIDIA is deepening early-stage investments in India's AI startup ecosystem +- This represents geographic diversification and long-term ecosystem positioning +- **Strategic Value**: Positions NVIDIA as the foundational infrastructure provider globally, not just in Western markets + +### 2. **Earnings Catalyst & Market Expectations** + +**February 25 Earnings Report** +- 12 consecutive quarters of earnings beats +- Stock up 35% over the past year +- **Analyst Perspective**: Multiple sources suggest NVIDIA will "crush earnings" with focus on the Blackwell supercycle visibility +- High expectations built in; this sets a high bar for the February 25 announcement + +### 3. **Stock Price Action & Technical Sentiment** + +**Price Trend**: The data indicates NVIDIA shares have been "sideways" this year, with recent volatility +- Mixed sentiment prevails as the market processes both bullish catalysts and broader tech sector concerns +- AI sell-off is mentioned as part of a "contentious earnings season," suggesting profit-taking pressure + +### 4. **Capital Allocation Signal** + +**Recursion Pharmaceuticals Position Dump** +- NVIDIA has exited its position in Recursion Pharmaceuticals +- **Implication**: Suggests NVIDIA is reassessing venture/growth-stage investments and reallocating capital toward core AI infrastructure plays +- This could indicate confidence in near-term AI capex opportunity or reassessment of longer-term pharma/biotech AI applications + +--- + +## SENTIMENT ANALYSIS + +### Bullish Signals: +1. **Analyst Optimism**: Multiple analysts predict significant upside ("NVIDIA Is About to Soar") +2. **Earnings Streak**: 12 consecutive quarters of beats demonstrate operational excellence +3. **Strategic Positioning**: Expanding from GPUs to CPUs, cloud infrastructure, and AI ecosystems +4. **Major Partnership Deepening**: OpenAI, Meta, and global expansion indicate sustained demand + +### Bearish/Cautionary Signals: +1. **Sideways Price Action**: Stock flat year-to-date despite 35% annual gains suggests profit-taking +2. **Competition Intensifying**: Amazon's $200B data center investment could reduce NVIDIA's captive market share +3. **Valuation Pressure**: High earnings expectations leave little room for disappointment +4. **Market Headwinds**: US-Iran tensions, inflation concerns (PCE data looming) creating macro uncertainty +5. **Capital Reallocation**: Dumping of peripheral investments (Recursion) suggests concentration of capital + +### Neutral/Mixed Signals: +1. **OpenAI Deal Size Reduction**: $100B→$30B pivot shows strategic flexibility but also recalibration +2. **AI Sell-Off Context**: Sector-wide profit-taking, not NVIDIA-specific weakness +3. **Market Approaching Earnings Season End**: Contentious earnings season suggests volatility may continue + +--- + +## INVESTOR IMPLICATIONS + +### For Swing/Short-Term Traders: +- **HIGH RISK/HIGH REWARD**: February 25 earnings is a binary event with 12-quarter beat streak creating elevated expectations +- Current sideways price action suggests consolidation before either breakout or breakdown +- Watch support/resistance levels closely; failure to beat could trigger significant correction given valuation multiples +- Volatility likely to spike ahead of earnings; options positioning may present opportunities + +### For Long-Term Investors: +- **ACCUMULATION OPPORTUNITY**: Strategic investments and competitive expansion justify long-term positioning +- NVIDIA is proving it's not just a GPU play—CPU expansion, ecosystem building, and geographic diversification reduce concentration risk +- India AI ecosystem play positions NVIDIA for next 10-year AI buildout +- The Blackwell supercycle remains intact, with customer demand validated through partnership deepening + +### For Risk Management: +- **POSITION SIZING CRITICAL**: Earnings event February 25 requires careful position sizing +- Consider hedging strategies if holding through earnings given elevated expectations +- Watch for guidance changes regarding capex cycle and Blackwell demand sustainability +- Monitor competitive dynamics with AMD and Intel closely post-earnings + +--- + +## MARKET CONTEXT & MACRO CONCERNS + +- **Earnings Season Volatility**: Broader market navigating contentious earnings period with tech particularly volatile +- **Inflation Watch**: PCE data imminent; could impact Fed policy and AI capex budgets +- **Geopolitical Risk**: US-Iran tensions creating market uncertainty +- **Sector Rotation**: AI sell-off suggests traders taking profits across the sector, not just NVDA + +--- + +## KEY RISK FACTORS THIS WEEK + +1. **Earnings Miscue Risk**: 12-quarter beat streak raises expectations to dangerous levels +2. **Guidance Disappointment**: Any hint of Blackwell demand slowdown = significant sell-off +3. **Margin Pressure**: CPU competition with AMD/Intel will compress margins long-term +4. **Valuation Reset**: If broader market corrects on inflation concerns, high-multiple tech stocks most vulnerable +5. **Competitive Encroachment**: Amazon's internal chip development (Trainium, Inferentia) gradually reduces NVIDIA's TAM + +--- + +## CONCLUSION + +NVIDIA enters its critical earnings week with mixed momentum. The company demonstrates genuine strategic excellence through partnership deepening, competitive expansion, and geographic diversification. However, the stock faces technical headwinds from year-to-date sideways action, profit-taking in the broader AI sector, and elevated earnings expectations. + +The **February 25 earnings report** will be the defining event for NVIDIA's next 3-6 month trajectory. A beat coupled with strong Blackwell guidance could drive 10-15% upside. A miss or cautious guidance on the supercycle could trigger 15-20% downside given current valuation. + +**Current Assessment**: NVIDIA is fundamentally sound with excellent long-term positioning, but trading at levels where execution risk is maximized. Near-term traders should respect the technical setup and macro uncertainties; long-term investors should view any post-earnings weakness as buying opportunity given 5-10 year structural AI tailwinds. + +--- + +## SUMMARY TABLE + +| **Category** | **Finding** | **Impact** | **Sentiment** | +|---|---|---|---| +| **Earnings Event** | Q4 FY2026 on Feb 25; 12 consecutive beats | Binary event with high expectations | Bullish but risky | +| **Strategic Moves** | OpenAI $30B investment, Meta CPU deal, India ecosystem | Validates long-term AI positioning | Strongly Bullish | +| **Competitive Dynamics** | Expanding beyond GPUs; fighting AMD/Intel in CPUs | Market share gains but margin pressure | Mixed | +| **Price Action** | Sideways YTD despite 35% annual gains | Profit-taking and consolidation | Neutral-Bearish | +| **Market Context** | AI sector sell-off, macro headwinds (inflation, geopolitical) | Broader pressure on valuations | Bearish | +| **Capital Allocation** | Recursion exit, OpenAI deal focus | Concentrating capital in core AI play | Bullish | +| **Analyst Outlook** | "NVIDIA will crush earnings," Blackwell supercycle intact | Wall Street remains constructive | Bullish | +| **Risk Level** | Elevated expectations, competition, valuation | High execution risk | Bearish | +| **Investment Grade** | Fundamentally strong; technically vulnerable near-term | Good long-term, elevated near-term risk | Mixed-to-Bullish | + +--- + +**Report Date**: February 20, 2026 +**Next Critical Date**: February 25, 2026 (Earnings Report, After Market Close) +================================ Human Message ================================= + +Continue +================================== Ai Message ================================== + +[{'text': "I'll retrieve comprehensive news data for NVDA and global macroeconomic news to provide you with a detailed analysis.", 'type': 'text'}, {'id': 'toolu_01UEi12ppQG1Kd8SAgKChcsY', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'start_date': '2026-02-13', 'end_date': '2026-02-20'}, 'name': 'get_news', 'type': 'tool_use'}, {'id': 'toolu_01Vi9qstkZUPQeT7YiK8hRu7', 'caller': {'type': 'direct'}, 'input': {'curr_date': '2026-02-20', 'look_back_days': 7, 'limit': 10}, 'name': 'get_global_news', 'type': 'tool_use'}] +Tool Calls: + get_news (toolu_01UEi12ppQG1Kd8SAgKChcsY) + Call ID: toolu_01UEi12ppQG1Kd8SAgKChcsY + Args: + ticker: NVDA + start_date: 2026-02-13 + end_date: 2026-02-20 + get_global_news (toolu_01Vi9qstkZUPQeT7YiK8hRu7) + Call ID: toolu_01Vi9qstkZUPQeT7YiK8hRu7 + Args: + curr_date: 2026-02-20 + look_back_days: 7 + limit: 10 +================================= Tool Message ================================= +Name: get_global_news + +## Global Market News, from 2026-02-13 to 2026-02-20: + +### 20/20 BioLabs Debuts Trading on Nasdaq Under Ticker Symbol "AIDX" Following 3 Consecutive Years of Sales Growth (source: GlobeNewswire) +Link: https://finance.yahoo.com/news/20-20-biolabs-debuts-trading-140000457.html + +### Sharon AI Announces Nasdaq Listing and Pricing of $125 Million U.S. Initial Public Offering (source: Business Wire) +Link: https://finance.yahoo.com/news/sharon-ai-announces-nasdaq-listing-123000806.html + +### The Allure of Chinese Tech Stocks. A Pro Picks 7 Names. (source: Barrons.com) +Link: https://finance.yahoo.com/m/54842dae-3ced-33bd-b3f4-3f4a1978762e/the-allure-of-chinese-tech.html + +### Trump Gets ‘Good News’ Boost for the Stock Market From Jobs Report. Kind Of. (source: Barrons.com) +Link: https://finance.yahoo.com/m/8e5e52c7-e623-34b7-a1f6-ff66f15393d0/trump-gets-%E2%80%98good-news%E2%80%99-boost.html + +### Sharon AI to Deploy 1K B200 Cluster at NEXTDC M3 Data Center Using Lenovo Infrastructure and VAST Data (source: Business Wire) +Link: https://finance.yahoo.com/news/sharon-ai-deploy-1k-b200-034800839.html + +### Assessing SharonAI Holdings (OTCPK:SHAZ) Valuation After Steep Share Price And Price To Book Multiple Moves (source: Simply Wall St.) +Link: https://finance.yahoo.com/news/assessing-sharonai-holdings-otcpk-shaz-100648129.html + +### 30 More Picks for ’26 From the Barron’s Roundtable (source: Barrons.com) +Link: https://finance.yahoo.com/m/782a083d-8829-30af-b509-44b86b0236e2/30-more-picks-for-%E2%80%9926-from.html + +### Sharon AI Announces Co-Founder & Chairman Mr. James Manning to Become Chief Executive Officer (source: Business Wire) +Link: https://finance.yahoo.com/news/sharon-ai-announces-co-founder-130000848.html + +### Tech Earnings Are Here. Capex, Memory Prices, and Software’s Future Will Be Key Themes. (source: Barrons.com) +Link: https://finance.yahoo.com/m/ca5cbd5f-84e9-3ffe-9570-efb98b736a27/tech-earnings-are-here..html + +### Walmart and Costco Are Worth Every Penny (source: Barrons.com) +Link: https://finance.yahoo.com/m/86651fc6-c6a1-3804-8343-754650c8615b/walmart-and-costco-are-worth.html + + +================================== Ai Message ================================== + +## COMPREHENSIVE NEWS ANALYSIS REPORT +### Current State of the Markets: Week of February 13-20, 2026 +#### Focus: NVIDIA (NVDA) and Macroeconomic Trends + +--- + +### EXECUTIVE SUMMARY + +The week of February 13-20, 2026 presents a critical juncture for NVIDIA and the broader technology sector. As NVIDIA prepares to report Q4 fiscal 2026 earnings on February 25th—just five days away—the company is navigating significant strategic pivots, geopolitical challenges, and substantial capital redeployment decisions. The market sentiment shows cautious optimism tempered by recent volatility and concerns about valuation in an increasingly competitive AI infrastructure landscape. + +--- + +### NVIDIA-SPECIFIC DEVELOPMENTS + +#### **1. Strategic Capital Redeployment and OpenAI Investment** +The most significant development is NVIDIA's near-completion of a **$30 billion investment into OpenAI**, which would replace the previously announced $100 billion long-term commitment made in 2025. This shift is critical for several reasons: + +- **Implications**: The reduction from $100B to $30B suggests either a reassessment of OpenAI's capital needs, increased confidence in OpenAI's ability to generate revenue from existing infrastructure, or potential concerns about the viability of the larger deal structure. This is a **moderate negative signal** suggesting management may be prioritizing capital discipline and cash preservation. + +- **Strategic Expansion**: Simultaneously, NVIDIA is expanding its Meta partnership and moving aggressively into the CPU market, directly challenging AMD and Intel. This demonstrates NVIDIA's diversification strategy beyond GPUs, indicating management confidence in multiple revenue streams. + +#### **2. Expansion into India's AI Ecosystem** +NVIDIA is deepening early-stage investment ties with India's AI startup ecosystem through partnerships with investors, nonprofits, and venture firms. This signals: + +- **Long-term positioning**: Recognition of India as a critical AI development hub outside of the US-China dynamic +- **Ecosystem play**: Building supplier/developer relationships that could drive future CUDA adoption and hardware dependency +- **Geopolitical hedge**: Reducing reliance on any single geography for innovation partnerships + +#### **3. Competitive Positioning and Market Share Concerns** +News indicating Amazon's $200 billion AI data center investment and suggestions that "not all of it will go to NVIDIA" introduces competitive headwinds: + +- **Emerging competition**: Alternative AI chipmakers (mentioned in context of Marvell) are gaining design wins with major cloud providers +- **Concentration risk**: The market is becoming increasingly aware that NVIDIA's dominance, while still substantial, faces legitimate competition from custom silicon and alternative architectures + +#### **4. Pre-Earnings Sentiment and Price Action** +Key analyst commentary suggests: + +- **Bold prediction**: Multiple sources (Motley Fool, 24/7 Wall St.) are predicting significant upside, with claims that NVDA is "about to soar" after trading sideways in early 2026 +- **Historical context**: 12 consecutive quarters of earnings beats with 35% YTD gains create very high expectations +- **Risk**: This quarter is characterized as potentially being "the first to make clear just how big the Blackwell supercycle is," suggesting binary outcome risk + +--- + +### MACROECONOMIC CONTEXT + +#### **1. Inflation and Monetary Policy Concerns** +The week saw discussion of: +- **PCE inflation data looming**: This upcoming economic release is mentioned as a significant market mover, suggesting inflation remains a concern despite previous cooling trends +- **US-Iran tensions rising**: Geopolitical risks are adding uncertainty to risk assets + +#### **2. Earnings Season Sentiment** +The broader market context reveals: +- **AI sell-off in progress**: Wall Street is described as moving through "a contentious earnings season defined by an AI sell-off," suggesting profit-taking and valuation concerns across the sector +- **Capex as key theme**: Tech earnings focus will be on capital expenditure plans, memory prices, and software economics—all areas where NVIDIA has stakes + +#### **3. Labor Market Resilience** +Trump administration receiving "mixed good news" from jobs reports, suggesting the labor market remains reasonably resilient despite higher rates, which could support continued capital investment spending. + +--- + +### SECTOR DYNAMICS + +#### **1. AI Infrastructure Dominance but Challenges** +- **Positive**: Continued investment in AI data centers, quantum computing interest, and cybersecurity buildout all support demand for semiconductors and computing infrastructure +- **Negative**: The appearance of competitive alternatives and signals that some mega-cap cloud providers are exploring custom silicon suggest market share pressure ahead + +#### **2. Quantum Computing as Emerging Opportunity** +D-Wave and other quantum computing companies are attracting significant capital, with insider buying (Trybe Capital purchasing 1.9M shares of D-Wave). This suggests the market is beginning to price in quantum computing as a complementary technology to classical AI. + +#### **3. Cybersecurity Uplift** +CrowdStrike (down 22% but attracting buyers on the dip) and other cybersecurity companies are positioned to benefit from AI infrastructure buildout, suggesting secondary beneficiaries may offer better risk/reward. + +--- + +### CRITICAL FACTORS FOR FEBRUARY 25 EARNINGS + +**Question 1: Blackwell Adoption Timeline** +- The earnings call will need to clarify when Blackwell GPU shipments ramp and what customer demand looks like +- Guidance on H100/H200 inventory drawdown will be critical + +**Question 2: Capital Return Policy** +- Will NVIDIA maintain or increase buybacks/dividends despite $30B OpenAI commitment? +- Is the reduced OpenAI investment a signal of capital allocation discipline? + +**Question 3: Gross Margin Trajectory** +- With competitive pressures emerging, can NVIDIA maintain pricing power? +- Product mix (H100, H200, GB200, B100, CPUs) will be scrutinized + +**Question 4: Data Center TAM Saturation** +- Are the largest cloud providers (AWS, Azure, GCP) fully positioned, suggesting slower growth ahead? +- International adoption (EU, Asia, India) could provide growth offsets + +--- + +### VALUATION AND TECHNICAL CONSIDERATIONS + +- **Stock momentum**: Trading "sideways" in early 2026 after 35% YTY gains suggests a consolidation phase +- **Expectation setting**: Analyst commentary about "crushing earnings" sets extremely high bars; any slight miss could trigger sharp pullbacks +- **Relative valuation**: Comparison with "blue-collar" stock (Comfort Systems) outperforming over 5 years suggests traditional sectors may offer better risk-adjusted returns, indicating some loss of enthusiasm for mega-cap tech + +--- + +### TRADING IMPLICATIONS + +**Short-term (Pre-Earnings February 25):** +- Elevated volatility likely as traders position for earnings +- Key support/resistance levels will be defined by options markets +- Upside catalyst if competitive threat narratives fade + +**Medium-term (Post-Earnings):** +- Guidance on Blackwell ramp and international expansion will drive 2026-2027 narratives +- Capital allocation (OpenAI investment reduction) may be viewed as prudent or concerning depending on articulation +- Competitive wins by AMD/Intel in CPU space could materially impact sentiment + +--- + +## KEY INSIGHTS TABLE + +| **Category** | **Development** | **Sentiment** | **Impact** | **Timeline** | +|---|---|---|---|---| +| **Capital Allocation** | $30B OpenAI investment (down from $100B) | Neutral/Cautious | Suggests capital discipline but questions OpenAI's needs | Immediate | +| **Strategic Expansion** | CPU market entry, Meta partnership deepening | Positive | Diversifies revenue beyond GPUs | 2026-2027 | +| **Competitive Threats** | Alternative chipmakers, custom silicon from cloud providers | Negative | Erodes gross margins and market share | Emerging | +| **Geographic Diversification** | India AI ecosystem investment | Positive | Reduces geopolitical risk, builds ecosystem lock-in | Long-term | +| **Earnings Event** | Q4 FY2026 earnings on Feb 25 | Highly Uncertain | Binary outcome: Blackwell ramp confirmation vs. disappointment | Imminent (5 days) | +| **Sector Tailwinds** | Continued AI capex, quantum computing interest, cybersecurity demand | Positive | Supports semiconductor demand broadly | 2026+ | +| **Market Sentiment** | AI sell-off in earnings season, sideways price action YTD | Cautious | High expectations set, limited upside surprise potential | Current | +| **Macro Headwinds** | PCE inflation data pending, US-Iran tensions | Negative | Could trigger risk-off rotation, pressure valuations | Near-term | +| **Valuation** | 35% YTD gains, trading sideways, high expectations | Cautious | Limited margin of safety; binary earnings outcome risk | Current | +| **Analyst Consensus** | "Bold predictions" of soaring stock despite lack of catalysts | Bullish (Overheated) | Peak enthusiasm may precede disappointing earnings guidance | Pre-earnings | + +--- + +### CONCLUSION + +NVIDIA stands at an inflection point. The company has executed flawlessly for 12 consecutive quarters and maintains dominant market share in AI infrastructure. However, the reduction of the OpenAI investment, emerging competitive threats, and the "AI sell-off" underway in earnings season suggest the market is beginning to question whether current valuations are sustainable given the company's already-massive scale ($2+ trillion market cap). + +The February 25 earnings call will be determinative. Investors should focus on **three metrics**: (1) Blackwell shipment guidance and customer commitments, (2) gross margin trajectory amid competitive pressure, and (3) management's candid assessment of data center capex sustainability. Any guidance that suggests slower growth or margin compression relative to the "Blackwell supercycle" narrative could trigger significant downside. + +The emerging opportunities in CPUs, India expansion, and the complementary quantum computing buildout provide medium-term growth catalysts, but these are not yet priced in given current valuations. +================================ Human Message ================================= + +Continue +================================== Ai Message ================================== + +[{'text': "I'll analyze NVIDIA's (NVDA) fundamental information as of February 20, 2026. Let me gather comprehensive financial data.", 'type': 'text'}, {'id': 'toolu_01DKQM7W5SsPJ2JQY7g61H7p', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'curr_date': '2026-02-20'}, 'name': 'get_fundamentals', 'type': 'tool_use'}, {'id': 'toolu_013LRYmkuS63zBwkEi4havAq', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'freq': 'quarterly', 'curr_date': '2026-02-20'}, 'name': 'get_balance_sheet', 'type': 'tool_use'}, {'id': 'toolu_01942dim2qQ48au9z7GL7bub', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'freq': 'quarterly', 'curr_date': '2026-02-20'}, 'name': 'get_income_statement', 'type': 'tool_use'}, {'id': 'toolu_01T5xgt2Nt1x9nhTUUPZU6Fz', 'caller': {'type': 'direct'}, 'input': {'ticker': 'NVDA', 'freq': 'quarterly', 'curr_date': '2026-02-20'}, 'name': 'get_cashflow', 'type': 'tool_use'}] +Tool Calls: + get_fundamentals (toolu_01DKQM7W5SsPJ2JQY7g61H7p) + Call ID: toolu_01DKQM7W5SsPJ2JQY7g61H7p + Args: + ticker: NVDA + curr_date: 2026-02-20 + get_balance_sheet (toolu_013LRYmkuS63zBwkEi4havAq) + Call ID: toolu_013LRYmkuS63zBwkEi4havAq + Args: + ticker: NVDA + freq: quarterly + curr_date: 2026-02-20 + get_income_statement (toolu_01942dim2qQ48au9z7GL7bub) + Call ID: toolu_01942dim2qQ48au9z7GL7bub + Args: + ticker: NVDA + freq: quarterly + curr_date: 2026-02-20 + get_cashflow (toolu_01T5xgt2Nt1x9nhTUUPZU6Fz) + Call ID: toolu_01T5xgt2Nt1x9nhTUUPZU6Fz + Args: + ticker: NVDA + freq: quarterly + curr_date: 2026-02-20 +================================= Tool Message ================================= +Name: get_cashflow + +# Cash Flow data for NVDA (quarterly) +# Data retrieved on: 2026-02-20 01:41:46 + +,2025-10-31,2025-07-31,2025-04-30,2025-01-31,2024-10-31 +Free Cash Flow,22115000000.0,13470000000.0,26187000000.0,15552000000.0,16814000000.0 +Repurchase Of Capital Stock,-12456000000.0,-9720000000.0,-14095000000.0,-7811000000.0,-10997000000.0 +Repayment Of Debt,0.0,,,0.0,0.0 +Capital Expenditure,-1636000000.0,-1895000000.0,-1227000000.0,-1077000000.0,-813000000.0 +Income Tax Paid Supplemental Data,4858000000.0,,,4129000000.0,3540000000.0 +End Cash Position,11486000000.0,11639000000.0,15234000000.0,8589000000.0,9107000000.0 +Beginning Cash Position,11639000000.0,15234000000.0,8589000000.0,9107000000.0,8571000000.0 +Changes In Cash,-153000000.0,-3595000000.0,6645000000.0,-518000000.0,536000000.0 +Financing Cash Flow,-14880000000.0,-11833000000.0,-15553000000.0,-9949000000.0,-12745000000.0 +Cash Flow From Continuing Financing Activities,-14880000000.0,-11833000000.0,-15553000000.0,-9949000000.0,-12745000000.0 +Net Other Financing Charges,-2453000000.0,-1869000000.0,-1584000000.0,-1894000000.0,-1707000000.0 +Proceeds From Stock Option Exercised,273000000.0,0.0,370000000.0,1000000.0,204000000.0 +Cash Dividends Paid,-244000000.0,-244000000.0,-244000000.0,-245000000.0,-245000000.0 +Common Stock Dividend Paid,-244000000.0,-244000000.0,-244000000.0,-245000000.0,-245000000.0 +Net Common Stock Issuance,-12456000000.0,-9720000000.0,-14095000000.0,-7811000000.0,-10997000000.0 +Common Stock Payments,-12456000000.0,-9720000000.0,-14095000000.0,-7811000000.0,-10997000000.0 +Net Issuance Payments Of Debt,0.0,,,0.0,0.0 +Net Long Term Debt Issuance,0.0,,,0.0,0.0 +Long Term Debt Payments,0.0,,,0.0,0.0 +Investing Cash Flow,-9024000000.0,-7127000000.0,-5216000000.0,-7198000000.0,-4346000000.0 +Cash Flow From Continuing Investing Activities,-9024000000.0,-7127000000.0,-5216000000.0,-7198000000.0,-4346000000.0 +Net Investment Purchase And Sale,-6695000000.0,-4938000000.0,-3606000000.0,-5601000000.0,-3385000000.0 +Sale Of Investment,2730000000.0,3220000000.0,3589000000.0,1887000000.0,1607000000.0 +Purchase Of Investment,-9425000000.0,-8158000000.0,-7195000000.0,-7488000000.0,-4992000000.0 +Net Business Purchase And Sale,-693000000.0,-294000000.0,-383000000.0,-542000000.0,-148000000.0 +Sale Of Business,,,,,66000000.0 +Purchase Of Business,-693000000.0,-294000000.0,-383000000.0,-542000000.0,-148000000.0 +Net PPE Purchase And Sale,-1636000000.0,-1895000000.0,-1227000000.0,-1077000000.0,-813000000.0 +Purchase Of PPE,-1636000000.0,-1895000000.0,-1227000000.0,-1077000000.0,-813000000.0 +Operating Cash Flow,23751000000.0,15365000000.0,27414000000.0,16629000000.0,17627000000.0 +Cash Flow From Continuing Operating Activities,23751000000.0,15365000000.0,27414000000.0,16629000000.0,17627000000.0 +Change In Working Capital,-9256000000.0,-11022000000.0,8654000000.0,-5863000000.0,-2694000000.0 +Change In Other Current Liabilities,332000000.0,629000000.0,350000000.0,372000000.0,265000000.0 +Change In Payables And Accrued Expense,906000000.0,-2739000000.0,8069000000.0,1227000000.0,2293000000.0 +Change In Accrued Expense,1129000000.0,-4053000000.0,7128000000.0,360000000.0,604000000.0 +Change In Payable,-223000000.0,1314000000.0,941000000.0,867000000.0,1689000000.0 +Change In Account Payable,-223000000.0,1314000000.0,941000000.0,867000000.0,1689000000.0 +Change In Prepaid Assets,-89000000.0,386000000.0,560000000.0,331000000.0,-714000000.0 +Change In Inventory,-4823000000.0,-3622000000.0,-1258000000.0,-2424000000.0,-977000000.0 +Change In Receivables,-5582000000.0,-5676000000.0,933000000.0,-5369000000.0,-3561000000.0 +Changes In Account Receivables,-5582000000.0,-5676000000.0,933000000.0,-5369000000.0,-3561000000.0 +Other Non Cash Items,-80000000.0,-98000000.0,-98000000.0,-137000000.0,-77000000.0 +Stock Based Compensation,1654000000.0,1625000000.0,1474000000.0,1321000000.0,1252000000.0 +Deferred Tax,125000000.0,17000000.0,-2177000000.0,-598000000.0,-603000000.0 +Deferred Income Tax,125000000.0,17000000.0,-2177000000.0,-598000000.0,-603000000.0 +Depreciation Amortization Depletion,751000000.0,669000000.0,611000000.0,543000000.0,478000000.0 +Depreciation And Amortization,751000000.0,669000000.0,611000000.0,543000000.0,478000000.0 +Operating Gains Losses,-1353000000.0,-2248000000.0,175000000.0,-728000000.0,-38000000.0 +Gain Loss On Investment Securities,-1353000000.0,-2248000000.0,175000000.0,-728000000.0,-38000000.0 +Gain Loss On Sale Of Business,,,,,-38000000.0 +Net Income From Continuing Operations,31910000000.0,26422000000.0,18775000000.0,22091000000.0,19309000000.0 + +================================== Ai Message ================================== + +# NVIDIA Corporation (NVDA) - Comprehensive Fundamental Analysis Report +**As of February 20, 2026** + +--- + +## Executive Summary + +NVIDIA Corporation continues to demonstrate exceptional financial performance in Q3 FY2026 (ended October 31, 2025), with outstanding growth trajectories across virtually all key metrics. The company has established itself as a dominant player in semiconductor infrastructure, particularly in AI acceleration and data center solutions. With a market capitalization exceeding $4.57 trillion, NVDA presents both remarkable growth opportunities and valuation considerations that warrant careful analysis. + +--- + +## Company Profile + +**Name:** NVIDIA Corporation +**Sector:** Technology +**Industry:** Semiconductors +**Headquarters:** Santa Clara, California +**Primary Business:** Design and manufacturing of GPUs, processors, and AI accelerators for data centers, gaming, professional visualization, and autonomous vehicles + +NVIDIA's core business has evolved significantly to capitalize on the AI revolution, with data center solutions now representing the dominant revenue source. The company's product portfolio includes the H-series AI training GPUs and L-series inference GPUs that have become industry standard infrastructure for AI model deployment. + +--- + +## Financial Snapshot + +| Metric | Value | Context | +|--------|-------|---------| +| **Market Cap** | $4.57 Trillion | Among top 5 globally traded companies | +| **Revenue (TTM)** | $187.14 Billion | Astronomical growth from $60.9B in FY2023 | +| **Net Income (TTM)** | $99.20 Billion | 53% net profit margin | +| **Free Cash Flow (TTM)** | $53.28 Billion | Robust cash generation | +| **EPS (TTM)** | $4.04 | Forward EPS: $7.76 | +| **PE Ratio (TTM)** | 46.51x | Premium valuation | +| **Forward PE** | 24.22x | Moderate relative to growth | +| **Price to Book** | 38.41x | Very high, indicating market expectations | + +--- + +## Detailed Financial Analysis + +### Revenue and Profitability Trajectory + +**TTM Revenue Performance:** +- **Total Revenue (TTM):** $187.14 billion +- **Most Recent Quarter (Q3 FY2026, ended 10/31/25):** $57.01 billion +- **Year-over-year comparison:** Q3 FY2026 revenue of $57.01B vs. Q3 FY2025 of $35.08B = **62.4% YoY growth** + +**Quarterly Revenue Trend (Last 5 Quarters):** +- Q3 FY2026: $57.01B (+62.4% YoY) +- Q2 FY2026: $46.74B (+41.9% YoY) +- Q1 FY2026: $44.06B (+39.1% YoY) +- Q4 FY2025: $39.33B +- Q3 FY2025: $35.08B + +The revenue growth is accelerating sequentially and substantially year-over-year, driven primarily by insatiable demand for data center GPUs. This growth rate is remarkable for a company of NVDA's scale and reflects the strategic importance of AI infrastructure investment across major cloud providers and enterprises. + +**Gross Profit and Margins:** +- **Gross Profit (TTM):** $131.09 billion +- **Gross Margin (TTM):** 70.1% ($131.09B / $187.14B) +- **Q3 FY2026 Gross Profit:** $41.85 billion +- **Q3 FY2026 Gross Margin:** 73.4% ($41.85B / $57.01B) + +The extraordinary gross margins reflect: +1. **Pricing power:** NVDA's proprietary GPU technology with limited direct competition for high-end AI chips +2. **Manufacturing efficiency:** Despite global supply chain challenges, NVDA maintains tight control over its supply chain through partnerships with TSMC +3. **Product mix:** Heavy weighting toward high-margin data center products rather than lower-margin gaming GPUs + +**Operating Income and Margins:** +- **EBITDA (TTM):** $112.70 billion (60.2% margin) +- **EBIT (TTM):** ~$113.25 billion (implied from EBITDA and depreciation) +- **Operating Margin (TTM):** 63.2% (36.01B / 57.01B in Q3) + +The operating margin of 63.2% in Q3 is exceptionally high and indicates substantial operating leverage. For context, this exceeds typical high-margin software companies. + +**Net Income and Profitability:** +- **Net Income (TTM):** $99.20 billion +- **Net Profit Margin (TTM):** 53.0% +- **Q3 FY2026 Net Income:** $31.91 billion +- **Q3 FY2026 Net Margin:** 56.0% + +The 56% net margin in the most recent quarter reflects: +1. Minimal tax burden (15.9% effective tax rate) +2. Net interest income of $563M (offsetting interest expense) +3. Other income contributions of $1.36B +4. Effective operational efficiency across all functions + +**Trend Analysis (Last 5 Quarters):** +| Quarter | Net Income | Net Margin | Growth | +|---------|-----------|-----------|--------| +| Q3 FY26 | $31.91B | 56.0% | +65.3% YoY | +| Q2 FY26 | $26.42B | 56.6% | +36.8% YoY | +| Q1 FY26 | $18.78B | 42.6% | -2.8% YoY | +| Q4 FY25 | $22.09B | 56.1% | +14.3% YoY | +| Q3 FY25 | $19.31B | 55.0% | - | + +Note: Q1 FY26 showed a dip in both net income and margins, though still substantial. This likely resulted from inventory correction and supply chain normalization following the explosive demand spike in late 2024. The recovery to $31.91B in Q3 indicates the dip was temporary. + +--- + +### Balance Sheet Strength + +**Asset Position:** +- **Total Assets:** $161.15 billion (up 21.7% from $96.01B one year ago) +- **Current Assets:** $116.49 billion +- **Non-Current Assets:** $44.66 billion +- **Working Capital:** $90.42 billion + +**Detailed Asset Composition:** +1. **Cash & Short-term Investments:** $60.61 billion + - Cash equivalents: $11.49 billion + - Short-term securities: $49.12 billion + - Provides substantial liquidity buffer + +2. **Accounts Receivable:** $33.39 billion (+88.7% YoY from $17.69B) + - Significant increase reflects strong revenue growth and customer credit terms + - Likely includes extended payment terms to major cloud providers + - Days Sales Outstanding (DSO) management is important to monitor + +3. **Inventory:** $19.78 billion (+158.2% YoY from $7.65B) + - Sharp increase reflects: + * Building inventory to meet anticipated demand + * Supply chain pre-positioning given semiconductor constraints + * Product mix shift toward higher-value data center products + - Finished goods: $6.84B (34.6%) + - Work-in-process: $8.74B (44.1%) + - Raw materials: $4.21B (21.3%) + - The significant WIP suggests high production levels and capacity utilization + +4. **Property, Plant & Equipment (Net):** $12.06 billion (+70.0% YoY from $7.10B) + - Reflects investments in facilities and infrastructure + - Relatively low CapEx to revenue ratio (~2.8% based on latest quarter) + +5. **Intangible Assets and Goodwill:** $7.20 billion + - Reflects strategic acquisitions (ARM, Arm Holdings legacy, other tech acquisitions) + - Relatively modest relative to total assets + +**Liability Position:** +- **Total Liabilities:** $42.25 billion (up 40.3% YoY from $30.11B) +- **Current Liabilities:** $26.08 billion +- **Non-Current Liabilities:** $16.18 billion + +**Debt Analysis:** +- **Total Debt:** $10.48 billion + - Current debt: $999 million + - Long-term debt: $7.47 billion + - Capital lease obligations: $2.01 billion +- **Debt to Equity Ratio:** 9.10x (appears high but misleading due to massive equity base) +- **Net Debt:** $10.48B - $60.61B = -$50.13 billion (NET CASH POSITION) + +NVDA maintains a strong net cash position of approximately $50 billion, indicating minimal financial leverage and substantial financial flexibility. + +**Equity Position:** +- **Total Stockholders' Equity:** $118.90 billion (+80.2% YoY from $65.90B) +- **Common Stock Equity:** $118.90 billion +- **Retained Earnings:** $107.91 billion (up 99.9% YoY from $53.95B) +- **Book Value per Share:** $4.89 +- **Tangible Book Value:** $111.70 billion + +The explosive growth in retained earnings reflects: +1. Massive profitability reinvestment +2. Share buyback execution reducing share count +3. Minimal dividend payouts ($244M quarterly) + +**Leverage and Solvency Metrics:** +- **Current Ratio:** 4.47x (highly liquid, well above 1.5x healthy threshold) +- **Debt to Assets:** 6.5% (minimal relative to total assets) +- **Equity Multiplier:** 1.35x (conservative leverage) + +The balance sheet reflects fortress-like financial strength with: +- Substantial cash generation capability +- Minimal debt obligations +- Strong working capital position +- Significant financial flexibility for acquisitions, shareholder returns, or R&D investments + +--- + +### Cash Flow Analysis + +**Operating Cash Flow (Last 5 Quarters):** +- Q3 FY26: $23.75B +- Q2 FY26: $15.37B +- Q1 FY26: $27.41B +- Q4 FY25: $16.63B +- Q3 FY25: $17.63B +- **TTM Operating Cash Flow:** $98.76B + +Operating cash flow has expanded dramatically, averaging $20.75B per quarter in the most recent period. This represents converting approximately 56% of revenue into operating cash, a remarkable metric. + +**Free Cash Flow Analysis:** +- **Q3 FY26 FCF:** $22.12B +- **Q2 FY26 FCF:** $13.47B +- **Q1 FY26 FCF:** $26.19B +- **Q4 FY25 FCF:** $15.55B +- **Q3 FY25 FCF:** $16.81B +- **TTM FCF:** $93.14B + +The trailing-twelve-month free cash flow of $53.28B (per fundamentals) or approximately $94B (per sum of quarterly) represents exceptional cash generation. The slight variance is likely due to data timing. + +**Capital Expenditure:** +- Q3 FY26: $1.64B (2.9% of revenue) +- Q2 FY26: $1.90B (4.1% of revenue) +- Q1 FY26: $1.23B (2.8% of revenue) +- Q4 FY25: $1.08B (2.7% of revenue) +- Q3 FY25: $813M (2.3% of revenue) + +NVDA maintains disciplined CapEx spending at 2-4% of revenue, substantially lower than semiconductor manufacturers with heavy fabrication facilities. This reflects NVDA's fabless business model (outsourcing manufacturing to TSMC). + +**Working Capital Dynamics:** +The data reveals substantial working capital swings: +- Q3 FY26: -$9.26B (negative = uses cash) +- Q2 FY26: -$11.02B (negative = uses cash) +- Q1 FY26: +$8.65B (positive = generates cash) +- Q4 FY25: -$5.86B (negative = uses cash) + +These swings reflect: +1. **Inventory build:** Accumulating finished goods and WIP to meet demand +2. **Receivables increase:** Extended payment terms to customers +3. **Payables management:** Strategic timing of vendor payments + +The significant working capital drain in recent quarters (Q3 and Q2) is a notable cash flow consideration. While manageable given strong operating cash flow, this indicates the company is financing substantial inventory and customer receivables. + +**Capital Allocation:** +- **Shareholder Buybacks (Q3 FY26):** $12.46B (21.8% of revenue) +- **Stock Option Exercises:** $273M +- **Dividends Paid:** $244M (minimal) +- **Debt Issuance/Repayment:** None + +NVDA has returned significant capital to shareholders through buybacks, reducing share count from 24.51B (Q4 FY25) to 24.31B (Q3 FY26). However, buybacks have consumed substantial cash that could have been deployed elsewhere. + +**Investment Activities:** +- **Net Investment Purchase/Sale:** -$6.70B (Q3 FY26) +- **Business Acquisitions:** -$693M (Q3 FY26) +- **Property, Plant & Equipment:** -$1.64B (Q3 FY26) + +The company is actively building its investment portfolio and maintaining strategic M&A activity while limiting capex intensity. + +--- + +### Key Performance Indicators & Trends + +**Earnings Per Share (EPS) Trajectory:** +- **Q3 FY26 Diluted EPS:** $1.30 +- **Q2 FY26 Diluted EPS:** $1.08 +- **Q1 FY26 Diluted EPS:** $0.76 +- **Q4 FY25 Diluted EPS:** $0.89 +- **Q3 FY25 Diluted EPS:** $0.78 +- **TTM EPS:** $4.04 +- **Forward EPS Estimate:** $7.76 + +The sequential progression shows strong recovery from Q1's dip, with Q3 posting the highest quarterly EPS to date. The implied forward 8-quarter EPS run-rate suggests significant earnings growth expectations. + +**Return Metrics:** +- **Return on Equity (TTM):** 107.4% (extraordinarily high) +- **Return on Assets (TTM):** 53.5% (exceptionally high) +- **Return on Invested Capital:** Estimated at 80%+ (not explicitly stated but implied) + +These returns significantly exceed typical technology companies and reflect exceptional capital efficiency. + +--- + +## Valuation Analysis + +**Current Valuation Metrics:** +- **P/E Ratio (TTM):** 46.51x +- **Forward P/E (FY2027 est.):** 24.22x +- **Price to Book:** 38.41x +- **Price to Sales (TTM):** 24.4x ($4.57T market cap / $187.14B revenue) +- **Price to Free Cash Flow:** 86x ($4.57T market cap / $53.28B FCF) + +**Valuation Context:** + +The current valuation presents a complex picture: + +1. **Premium to Market:** NVDA trades at substantial premiums to both the S&P 500 and technology sector peers +2. **Growth Justification:** Forward P/E of 24x suggests ~80% earnings growth embedded in the price, which aligns with the difference between TTM EPS ($4.04) and Forward EPS ($7.76) +3. **Relative Metrics:** + - TTM P/E of 46.5x is elevated but not extreme given 62%+ revenue growth + - PEG Ratio (P/E divided by growth rate) = 46.5 / 62.4 = 0.74, suggesting potentially reasonable value + - Price to Sales of 24.4x is high but reflects unique competitive positioning + +4. **Risk Factors in Valuation:** + - Assumes sustained high growth and margins + - Dependent on continued AI infrastructure investment + - Vulnerable to competitive threats or market saturation + - Market cap concentration risk + +--- + +## Detailed Financial Observations + +### Strengths: + +1. **Unparalleled Profitability:** + - 53% net margin and 70% gross margin represent best-in-class profitability + - Operating leverage evident in expanding margins as revenue scales + - EBITDA margin of 60%+ provides substantial buffer + +2. **Explosive Revenue Growth:** + - 62.4% YoY growth at scale ($57B quarterly revenue) + - Consistent acceleration across major end markets + - Data center segment driving majority of growth + +3. **Fortress Balance Sheet:** + - $50B net cash position provides strategic optionality + - Working capital coverage at 4.47x current ratio + - Minimal financial leverage + +4. **Cash Generation Machine:** + - $94B TTM free cash flow generation + - Operating cash flow conversion of 56% of revenue + - Capital efficient model (2-4% CapEx intensity) + +5. **Shareholder Returns:** + - Aggressive buyback program reducing share count + - Share reduction despite already elevated valuations + - Potential to compound per-share growth + +### Concerns & Risks: + +1. **Valuation Stretched:** + - 46.5x P/E on TTM earnings is premium even for growth + - Significant downside risk if growth decelerates below 20%+ + - Forward P/E of 24x assumes flawless execution + +2. **Working Capital Expansion:** + - Inventory jumped 158% YoY to $19.78B + - Accounts receivable surged 89% YoY to $33.39B + - Recent quarters show negative working capital swing (-$9-11B) + - Suggests company financing customer demand through extended terms + +3. **Execution Risk:** + - Q1 FY26 showed sequential weakness (dip in margins and net income) + - Customer concentration risk with major cloud providers + - Inventory levels suggest some demand uncertainty or supply build + +4. **Competitive Threats:** + - AMD gaining share in data center + - Intel pursuing GPU acceleration + - Custom silicon from hyperscalers (Google TPU, Amazon Trainium) + - Supply chain concentration with TSMC + +5. **Regulatory/Geopolitical:** + - Export controls limiting China sales + - Potential antitrust scrutiny + - ARM acquisition regulatory history + +6. **Growth Sustainability:** + - Current growth rates difficult to sustain at scale + - Market saturation risk in data center + - AI capex cycle potential normalization + +--- + +## Quarterly Trend Analysis (Deep Dive) + +### Revenue Trends: +The progression from Q3 FY25 ($35.08B) to Q3 FY26 ($57.01B) shows a **62.4% YoY increase**. More impressively: +- Q1→Q2 FY26: +6.1% sequential growth +- Q2→Q3 FY26: +22.0% sequential growth + +The acceleration in Q3 suggests: +- Peak seasonal quarter for data center spending +- No signs of demand slowing +- Possible inventory build ahead of supply constraints + +### Profitability Trends: +Net income progression shows stability in margins despite revenue explosion: +- Q3 FY25: 55.0% net margin +- Q4 FY25: 56.1% net margin +- Q1 FY26: 42.6% net margin (anomaly) +- Q2 FY26: 56.6% net margin (recovery) +- Q3 FY26: 56.0% net margin (sustained) + +The recovery from Q1's 42.6% margin to sustained 56%+ margins indicates a one-time issue (likely inventory obsolescence or supply chain normalization) rather than structural profitability decline. + +### Cash Flow Trends: +Operating cash flow shows strong generation with notable working capital impacts: +- Q1 FY26: Strongest FCF at $26.19B (positive working capital change) +- Q2-Q3 FY26: Working capital drain (inventory, receivables buildup) +- Recent trend: Building inventory and extending customer terms + +--- + +## Key Metrics Summary Table + +| Metric | Current | YoY Change | Assessment | +|--------|---------|-----------|-----------| +| **Revenue (TTM)** | $187.14B | +138% | Exceptional growth | +| **Net Income (TTM)** | $99.20B | +165% | Strong profitability expansion | +| **Net Margin** | 53.0% | +520 bps | Margin expansion | +| **Operating Cash Flow (TTM)** | $98.76B | +150% | Strong cash generation | +| **Free Cash Flow (TTM)** | $53.28B | +140% | Robust cash flow | +| **Total Assets** | $161.15B | +67.8% | Significant asset growth | +| **Stockholders' Equity** | $118.90B | +80.2% | Equity expansion | +| **Net Cash Position** | $50.13B | +166% | Fortress balance sheet | +| **Current Ratio** | 4.47x | N/A | Highly liquid | +| **Return on Equity** | 107.4% | N/A | Exceptional returns | +| **P/E Ratio (TTM)** | 46.51x | N/A | Premium valuation | +| **Forward P/E** | 24.22x | N/A | Moderate forward valuation | +| **Book Value per Share** | $4.89 | +68% | Solid equity growth | + +--- + +## Industry Context & Competitive Position + +NVIDIA's financial dominance reflects its leadership in AI infrastructure: + +1. **Market Position:** Controlling estimated 80-85% of high-end AI accelerator market +2. **Technology Advantage:** Generations ahead in GPU architecture (Hopper, next-gen Blackwell) +3. **Software Ecosystem:** CUDA platform creates switching costs and ecosystem lock-in +4. **Customer Relationships:** Deep partnerships with major cloud providers (Amazon, Microsoft, Google, Meta) +5. **Supply Chain:** Exclusive partnership with TSMC for most advanced node production + +The financial metrics reflect these structural advantages through premium margins and pricing power. + +--- + +## Conclusion & Strategic Observations + +**NVIDIA presents a company at a critical inflection point:** + +**Positive Factors:** +- Exceptional profitability and cash generation +- Strong balance sheet providing strategic optionality +- Revenue growth at 62%+ despite massive scale +- Market dominance in strategic AI infrastructure category +- Sustained margin expansion demonstrating operational leverage + +**Concerning Factors:** +- Valuation requiring near-flawless execution (46.5x P/E) +- Elevated inventory levels (158% YoY growth) suggesting demand validation challenges +- Working capital expansion consuming significant cash +- Competitive threats intensifying from multiple angles +- Growth rate sustainability questionable at this scale + +**Assessment:** +NVIDIA's fundamentals are extraordinarily strong with industry-leading profitability, cash generation, and growth rates. However, the valuation of 46.5x P/E on TTM earnings, while potentially justified by forward growth, leaves limited margin for disappointment. The company has demonstrably captured significant value from the AI infrastructure wave, and near-term catalysts remain positive (new product launches, customer wins, expanded margins). However, medium-to-long term risks include competitive pressures, market saturation, and valuation mean reversion. + +The recent inventory buildup and working capital drain warrant monitoring to ensure inventory doesn't become a drag if demand normalizes or competitive products reach market. + +--- + +## Summary Metrics Table + +| Category | Metric | Value | Status | +|----------|--------|-------|--------| +| **Growth** | Revenue YoY Growth | 62.4% | ★★★★★ | +| **Growth** | Net Income YoY Growth | 65.3% | ★★★★★ | +| **Profitability** | Net Profit Margin | 53.0% | ★★★★★ | +| **Profitability** | Operating Margin | 63.2% | ★★★★★ | +| **Profitability** | EBITDA Margin | 60.2% | ★★★★★ | +| **Cash Flow** | Free Cash Flow (TTM) | $53.28B | ★★★★★ | +| **Cash Flow** | Operating Cash Flow Margin | 52.8% | ★★★★★ | +| **Balance Sheet** | Net Cash Position | $50.13B | ★★★★★ | +| **Balance Sheet** | Current Ratio | 4.47x | ★★★★★ | +| **Balance Sheet** | Debt to Equity | 9.10x | ★★☆☆☆ | +| **Returns** | Return on Equity | 107.4% | ★★★★★ | +| **Returns** | Return on Assets | 53.5% | ★★★★★ | +| **Valuation** | P/E Ratio (TTM) | 46.51x | ★★☆☆☆ | +| **Valuation** | Forward P/E | 24.22x | ★★★☆☆ | +| **Valuation** | Price to Sales | 24.4x | ★★☆☆☆ | +| **Risk** | Inventory Growth YoY | 158% | ★★★☆☆ | +| **Risk** | Receivables Growth YoY | 89% | ★★★☆☆ | + +*★★★★★ = Excellent | ★★★★☆ = Very Good | ★★★☆☆ = Good | ★★☆☆☆ = Concerning | ★☆☆☆☆ = Poor* + +--- + +This comprehensive analysis demonstrates that NVIDIA remains a fundamentally strong company with exceptional financial metrics, but valuation represents the primary consideration for new investment decisions at current levels. The company's ability to execute on growth plans while managing competitive threats and maintaining margin discipline will be critical to justifying current premium valuations. +================================ Human Message ================================= + +Continue +================================ Human Message ================================= + +Continue +================================ Human Message ================================= + +Continue +================================ Human Message ================================= + +Continue +================================== Ai Message ================================== + +# NVDA Trading Analysis & Recommendation + +## Critical Assessment of the Proposed Plan + +The investment plan provided demonstrates sophisticated analytical thinking, particularly in its honest evaluation of both bullish and bearish arguments. However, I need to apply independent judgment here rather than simply accept it as established truth. + +### Strengths of the "Hold Until Earnings" Framework: +- **Legitimate catalyst timing**: February 25 earnings are imminent (5 days) +- **Binary event risk is real**: Pre-earnings volatility asymmetrically favors waiting +- **Clear monitoring criteria**: The three specific watch-points (margins, inventory, Blackwell orders) are actionable +- **Intellectually honest**: The analysis acknowledges genuine concerns rather than dismissing them + +### Critical Issues I Must Challenge: + +**1. The Inventory Signal Cannot Be Ignored** +- 158% inventory growth vs 62% revenue growth is not "noisy technical signal" +- This is a working capital red flag that typically *precedes* demand softness, not validates current positioning +- The DSO extension (50→59 days) suggests customers have negotiating leverage—a demand signal inversion + +**2. "Much of the Good News is Already Priced In" Argument** +- This cuts both ways: much of the *bad news* may also be priced in +- Current valuation assumes execution; inventory metrics suggest execution risk +- The 24x forward P/E is "reasonable" only if the $7.76 EPS estimate holds—inventory trends suggest it may not + +**3. The Cisco 2000 Pattern Warning** +- The plan itself identifies this trap: "rationalizing every bearish signal as secretly bullish" +- Then it proceeds to do this: treating inventory buildup as "strategic positioning" +- This is exactly the cognitive error the plan warns against + +**4. Risk-Reward Asymmetry Problem** +- Upside if earnings are good: 10-15% (already partially priced in per the plan's own logic) +- Downside if earnings disappoint on inventory/margins: 15-25% minimum (to $171 stop) +- This is actually *unfavorable* risk-reward pre-earnings, not favorable + +--- + +## My Independent Decision + +Given that: +1. The earnings catalyst is imminent enough that waiting 5 days provides clarity with minimal opportunity cost +2. Working capital metrics are showing deterioration that contradicts the bull narrative +3. The plan itself identifies the trap of "rationalizing bearish signals" but then engages in it +4. Inventory position appears to be the variable that will actually determine earnings outcomes + +**I cannot recommend adding exposure before earnings.** However, I also cannot recommend selling established positions into an imminent catalyst. + +The proposed plan's "Hold" recommendation is correct, but for a different reason than stated: **the risk of making the wrong call on ambiguous signals is higher than the cost of waiting 5 days for clarity.** + +--- + +## My Specific Action Framework: + +- **Current position holders**: HOLD, but reduce position size by 15-20% now as de-risking (not conviction selling) +- **No new additions** before February 25 +- **Set hard monitoring stops**: $171 as plan suggests, plus a mental stop at guidance if inventory language is vague +- **Post-earnings action**: Act decisively on the three metrics identified (margins, inventory, Blackwell specificity) + +The plan is prudent because it acknowledges ambiguity and lets the catalyst resolve it—not because the bull case is compelling. + +--- + +**FINAL TRANSACTION PROPOSAL: HOLD** + +*(With tactical reduction of 15-20% for existing positions and zero new additions until post-earnings clarity)* +================================== Ai Message ================================== + +# NVDA Trading Analysis & Recommendation + +## Critical Assessment of the Proposed Plan + +The investment plan provided demonstrates sophisticated analytical thinking, particularly in its honest evaluation of both bullish and bearish arguments. However, I need to apply independent judgment here rather than simply accept it as established truth. + +### Strengths of the "Hold Until Earnings" Framework: +- **Legitimate catalyst timing**: February 25 earnings are imminent (5 days) +- **Binary event risk is real**: Pre-earnings volatility asymmetrically favors waiting +- **Clear monitoring criteria**: The three specific watch-points (margins, inventory, Blackwell orders) are actionable +- **Intellectually honest**: The analysis acknowledges genuine concerns rather than dismissing them + +### Critical Issues I Must Challenge: + +**1. The Inventory Signal Cannot Be Ignored** +- 158% inventory growth vs 62% revenue growth is not "noisy technical signal" +- This is a working capital red flag that typically *precedes* demand softness, not validates current positioning +- The DSO extension (50→59 days) suggests customers have negotiating leverage—a demand signal inversion + +**2. "Much of the Good News is Already Priced In" Argument** +- This cuts both ways: much of the *bad news* may also be priced in +- Current valuation assumes execution; inventory metrics suggest execution risk +- The 24x forward P/E is "reasonable" only if the $7.76 EPS estimate holds—inventory trends suggest it may not + +**3. The Cisco 2000 Pattern Warning** +- The plan itself identifies this trap: "rationalizing every bearish signal as secretly bullish" +- Then it proceeds to do this: treating inventory buildup as "strategic positioning" +- This is exactly the cognitive error the plan warns against + +**4. Risk-Reward Asymmetry Problem** +- Upside if earnings are good: 10-15% (already partially priced in per the plan's own logic) +- Downside if earnings disappoint on inventory/margins: 15-25% minimum (to $171 stop) +- This is actually *unfavorable* risk-reward pre-earnings, not favorable + +--- + +## My Independent Decision + +Given that: +1. The earnings catalyst is imminent enough that waiting 5 days provides clarity with minimal opportunity cost +2. Working capital metrics are showing deterioration that contradicts the bull narrative +3. The plan itself identifies the trap of "rationalizing bearish signals" but then engages in it +4. Inventory position appears to be the variable that will actually determine earnings outcomes + +**I cannot recommend adding exposure before earnings.** However, I also cannot recommend selling established positions into an imminent catalyst. + +The proposed plan's "Hold" recommendation is correct, but for a different reason than stated: **the risk of making the wrong call on ambiguous signals is higher than the cost of waiting 5 days for clarity.** + +--- + +## My Specific Action Framework: + +- **Current position holders**: HOLD, but reduce position size by 15-20% now as de-risking (not conviction selling) +- **No new additions** before February 25 +- **Set hard monitoring stops**: $171 as plan suggests, plus a mental stop at guidance if inventory language is vague +- **Post-earnings action**: Act decisively on the three metrics identified (margins, inventory, Blackwell specificity) + +The plan is prudent because it acknowledges ambiguity and lets the catalyst resolve it—not because the bull case is compelling. + +--- + +**FINAL TRANSACTION PROPOSAL: HOLD** + +*(With tactical reduction of 15-20% for existing positions and zero new additions until post-earnings clarity)* +================================== Ai Message ================================== + +# NVDA Trading Analysis & Recommendation + +## Critical Assessment of the Proposed Plan + +The investment plan provided demonstrates sophisticated analytical thinking, particularly in its honest evaluation of both bullish and bearish arguments. However, I need to apply independent judgment here rather than simply accept it as established truth. + +### Strengths of the "Hold Until Earnings" Framework: +- **Legitimate catalyst timing**: February 25 earnings are imminent (5 days) +- **Binary event risk is real**: Pre-earnings volatility asymmetrically favors waiting +- **Clear monitoring criteria**: The three specific watch-points (margins, inventory, Blackwell orders) are actionable +- **Intellectually honest**: The analysis acknowledges genuine concerns rather than dismissing them + +### Critical Issues I Must Challenge: + +**1. The Inventory Signal Cannot Be Ignored** +- 158% inventory growth vs 62% revenue growth is not "noisy technical signal" +- This is a working capital red flag that typically *precedes* demand softness, not validates current positioning +- The DSO extension (50→59 days) suggests customers have negotiating leverage—a demand signal inversion + +**2. "Much of the Good News is Already Priced In" Argument** +- This cuts both ways: much of the *bad news* may also be priced in +- Current valuation assumes execution; inventory metrics suggest execution risk +- The 24x forward P/E is "reasonable" only if the $7.76 EPS estimate holds—inventory trends suggest it may not + +**3. The Cisco 2000 Pattern Warning** +- The plan itself identifies this trap: "rationalizing every bearish signal as secretly bullish" +- Then it proceeds to do this: treating inventory buildup as "strategic positioning" +- This is exactly the cognitive error the plan warns against + +**4. Risk-Reward Asymmetry Problem** +- Upside if earnings are good: 10-15% (already partially priced in per the plan's own logic) +- Downside if earnings disappoint on inventory/margins: 15-25% minimum (to $171 stop) +- This is actually *unfavorable* risk-reward pre-earnings, not favorable + +--- + +## My Independent Decision + +Given that: +1. The earnings catalyst is imminent enough that waiting 5 days provides clarity with minimal opportunity cost +2. Working capital metrics are showing deterioration that contradicts the bull narrative +3. The plan itself identifies the trap of "rationalizing bearish signals" but then engages in it +4. Inventory position appears to be the variable that will actually determine earnings outcomes + +**I cannot recommend adding exposure before earnings.** However, I also cannot recommend selling established positions into an imminent catalyst. + +The proposed plan's "Hold" recommendation is correct, but for a different reason than stated: **the risk of making the wrong call on ambiguous signals is higher than the cost of waiting 5 days for clarity.** + +--- + +## My Specific Action Framework: + +- **Current position holders**: HOLD, but reduce position size by 15-20% now as de-risking (not conviction selling) +- **No new additions** before February 25 +- **Set hard monitoring stops**: $171 as plan suggests, plus a mental stop at guidance if inventory language is vague +- **Post-earnings action**: Act decisively on the three metrics identified (margins, inventory, Blackwell specificity) + +The plan is prudent because it acknowledges ambiguity and lets the catalyst resolve it—not because the bull case is compelling. + +--- + +**FINAL TRANSACTION PROPOSAL: HOLD** + +*(With tactical reduction of 15-20% for existing positions and zero new additions until post-earnings clarity)* +================================== Ai Message ================================== + +# NVDA Trading Analysis & Recommendation + +## Critical Assessment of the Proposed Plan + +The investment plan provided demonstrates sophisticated analytical thinking, particularly in its honest evaluation of both bullish and bearish arguments. However, I need to apply independent judgment here rather than simply accept it as established truth. + +### Strengths of the "Hold Until Earnings" Framework: +- **Legitimate catalyst timing**: February 25 earnings are imminent (5 days) +- **Binary event risk is real**: Pre-earnings volatility asymmetrically favors waiting +- **Clear monitoring criteria**: The three specific watch-points (margins, inventory, Blackwell orders) are actionable +- **Intellectually honest**: The analysis acknowledges genuine concerns rather than dismissing them + +### Critical Issues I Must Challenge: + +**1. The Inventory Signal Cannot Be Ignored** +- 158% inventory growth vs 62% revenue growth is not "noisy technical signal" +- This is a working capital red flag that typically *precedes* demand softness, not validates current positioning +- The DSO extension (50→59 days) suggests customers have negotiating leverage—a demand signal inversion + +**2. "Much of the Good News is Already Priced In" Argument** +- This cuts both ways: much of the *bad news* may also be priced in +- Current valuation assumes execution; inventory metrics suggest execution risk +- The 24x forward P/E is "reasonable" only if the $7.76 EPS estimate holds—inventory trends suggest it may not + +**3. The Cisco 2000 Pattern Warning** +- The plan itself identifies this trap: "rationalizing every bearish signal as secretly bullish" +- Then it proceeds to do this: treating inventory buildup as "strategic positioning" +- This is exactly the cognitive error the plan warns against + +**4. Risk-Reward Asymmetry Problem** +- Upside if earnings are good: 10-15% (already partially priced in per the plan's own logic) +- Downside if earnings disappoint on inventory/margins: 15-25% minimum (to $171 stop) +- This is actually *unfavorable* risk-reward pre-earnings, not favorable + +--- + +## My Independent Decision + +Given that: +1. The earnings catalyst is imminent enough that waiting 5 days provides clarity with minimal opportunity cost +2. Working capital metrics are showing deterioration that contradicts the bull narrative +3. The plan itself identifies the trap of "rationalizing bearish signals" but then engages in it +4. Inventory position appears to be the variable that will actually determine earnings outcomes + +**I cannot recommend adding exposure before earnings.** However, I also cannot recommend selling established positions into an imminent catalyst. + +The proposed plan's "Hold" recommendation is correct, but for a different reason than stated: **the risk of making the wrong call on ambiguous signals is higher than the cost of waiting 5 days for clarity.** + +--- + +## My Specific Action Framework: + +- **Current position holders**: HOLD, but reduce position size by 15-20% now as de-risking (not conviction selling) +- **No new additions** before February 25 +- **Set hard monitoring stops**: $171 as plan suggests, plus a mental stop at guidance if inventory language is vague +- **Post-earnings action**: Act decisively on the three metrics identified (margins, inventory, Blackwell specificity) + +The plan is prudent because it acknowledges ambiguity and lets the catalyst resolve it—not because the bull case is compelling. + +--- + +**FINAL TRANSACTION PROPOSAL: HOLD** + +*(With tactical reduction of 15-20% for existing positions and zero new additions until post-earnings clarity)* +================================== Ai Message ================================== + +# NVDA Trading Analysis & Recommendation + +## Critical Assessment of the Proposed Plan + +The investment plan provided demonstrates sophisticated analytical thinking, particularly in its honest evaluation of both bullish and bearish arguments. However, I need to apply independent judgment here rather than simply accept it as established truth. + +### Strengths of the "Hold Until Earnings" Framework: +- **Legitimate catalyst timing**: February 25 earnings are imminent (5 days) +- **Binary event risk is real**: Pre-earnings volatility asymmetrically favors waiting +- **Clear monitoring criteria**: The three specific watch-points (margins, inventory, Blackwell orders) are actionable +- **Intellectually honest**: The analysis acknowledges genuine concerns rather than dismissing them + +### Critical Issues I Must Challenge: + +**1. The Inventory Signal Cannot Be Ignored** +- 158% inventory growth vs 62% revenue growth is not "noisy technical signal" +- This is a working capital red flag that typically *precedes* demand softness, not validates current positioning +- The DSO extension (50→59 days) suggests customers have negotiating leverage—a demand signal inversion + +**2. "Much of the Good News is Already Priced In" Argument** +- This cuts both ways: much of the *bad news* may also be priced in +- Current valuation assumes execution; inventory metrics suggest execution risk +- The 24x forward P/E is "reasonable" only if the $7.76 EPS estimate holds—inventory trends suggest it may not + +**3. The Cisco 2000 Pattern Warning** +- The plan itself identifies this trap: "rationalizing every bearish signal as secretly bullish" +- Then it proceeds to do this: treating inventory buildup as "strategic positioning" +- This is exactly the cognitive error the plan warns against + +**4. Risk-Reward Asymmetry Problem** +- Upside if earnings are good: 10-15% (already partially priced in per the plan's own logic) +- Downside if earnings disappoint on inventory/margins: 15-25% minimum (to $171 stop) +- This is actually *unfavorable* risk-reward pre-earnings, not favorable + +--- + +## My Independent Decision + +Given that: +1. The earnings catalyst is imminent enough that waiting 5 days provides clarity with minimal opportunity cost +2. Working capital metrics are showing deterioration that contradicts the bull narrative +3. The plan itself identifies the trap of "rationalizing bearish signals" but then engages in it +4. Inventory position appears to be the variable that will actually determine earnings outcomes + +**I cannot recommend adding exposure before earnings.** However, I also cannot recommend selling established positions into an imminent catalyst. + +The proposed plan's "Hold" recommendation is correct, but for a different reason than stated: **the risk of making the wrong call on ambiguous signals is higher than the cost of waiting 5 days for clarity.** + +--- + +## My Specific Action Framework: + +- **Current position holders**: HOLD, but reduce position size by 15-20% now as de-risking (not conviction selling) +- **No new additions** before February 25 +- **Set hard monitoring stops**: $171 as plan suggests, plus a mental stop at guidance if inventory language is vague +- **Post-earnings action**: Act decisively on the three metrics identified (margins, inventory, Blackwell specificity) + +The plan is prudent because it acknowledges ambiguity and lets the catalyst resolve it—not because the bull case is compelling. + +--- + +**FINAL TRANSACTION PROPOSAL: HOLD** + +*(With tactical reduction of 15-20% for existing positions and zero new additions until post-earnings clarity)* +HOLD diff --git a/pyproject.toml b/pyproject.toml index 96e464db..624fb868 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,36 +1,36 @@ -[build-system] -requires = ["setuptools>=61.0"] -build-backend = "setuptools.build_meta" - -[project] -name = "tradingagents" -version = "0.2.0" -description = "TradingAgents: Multi-Agents LLM Financial Trading Framework" -readme = "README.md" -requires-python = ">=3.10" -dependencies = [ - "langchain-core>=0.3.81", - "langchain-anthropic>=0.3.15", - "langchain-experimental>=0.3.4", - "langchain-google-genai>=2.1.5", - "langchain-openai>=0.3.23", - "langgraph>=0.4.8", - "pandas>=2.3.0", - "pytz>=2025.2", - "rank-bm25>=0.2.2", - "requests>=2.32.4", - "setuptools>=80.9.0", - "stockstats>=0.6.5", - "tqdm>=4.67.1", - "typing-extensions>=4.14.0", - "yfinance>=0.2.63", - "fastapi>=0.115.0", - "uvicorn[standard]>=0.30.0", - "sse-starlette>=2.0.0", -] - -[project.scripts] -tradingagents = "cli.main:app" - -[tool.setuptools.packages.find] -include = ["tradingagents*", "cli*"] +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + +[project] +name = "tradingagents" +version = "0.2.0" +description = "TradingAgents: Multi-Agents LLM Financial Trading Framework" +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + "langchain-core>=0.3.81", + "langchain-anthropic>=0.3.15", + "langchain-experimental>=0.3.4", + "langchain-google-genai>=2.1.5", + "langchain-openai>=0.3.23", + "langgraph>=0.4.8", + "pandas>=2.3.0", + "pytz>=2025.2", + "rank-bm25>=0.2.2", + "requests>=2.32.4", + "setuptools>=80.9.0", + "stockstats>=0.6.5", + "tqdm>=4.67.1", + "typing-extensions>=4.14.0", + "yfinance>=0.2.63", + "fastapi>=0.115.0", + "uvicorn[standard]>=0.30.0", + "sse-starlette>=2.0.0", +] + +[project.scripts] +tradingagents = "cli.main:app" + +[tool.setuptools.packages.find] +include = ["tradingagents*", "cli*"] diff --git a/test.py b/test.py index b73783e1..c52b917d 100644 --- a/test.py +++ b/test.py @@ -1,11 +1,11 @@ -import time -from tradingagents.dataflows.y_finance import get_YFin_data_online, get_stock_stats_indicators_window, get_balance_sheet as get_yfinance_balance_sheet, get_cashflow as get_yfinance_cashflow, get_income_statement as get_yfinance_income_statement, get_insider_transactions as get_yfinance_insider_transactions - -print("Testing optimized implementation with 30-day lookback:") -start_time = time.time() -result = get_stock_stats_indicators_window("AAPL", "macd", "2024-11-01", 30) -end_time = time.time() - -print(f"Execution time: {end_time - start_time:.2f} seconds") -print(f"Result length: {len(result)} characters") -print(result) +import time +from tradingagents.dataflows.y_finance import get_YFin_data_online, get_stock_stats_indicators_window, get_balance_sheet as get_yfinance_balance_sheet, get_cashflow as get_yfinance_cashflow, get_income_statement as get_yfinance_income_statement, get_insider_transactions as get_yfinance_insider_transactions + +print("Testing optimized implementation with 30-day lookback:") +start_time = time.time() +result = get_stock_stats_indicators_window("AAPL", "macd", "2024-11-01", 30) +end_time = time.time() + +print(f"Execution time: {end_time - start_time:.2f} seconds") +print(f"Result length: {len(result)} characters") +print(result) diff --git a/tradingagents/agents/__init__.py b/tradingagents/agents/__init__.py index 8a169f22..c77a6d55 100644 --- a/tradingagents/agents/__init__.py +++ b/tradingagents/agents/__init__.py @@ -1,40 +1,40 @@ -from .utils.agent_utils import create_msg_delete -from .utils.agent_states import AgentState, InvestDebateState, RiskDebateState -from .utils.memory import FinancialSituationMemory - -from .analysts.fundamentals_analyst import create_fundamentals_analyst -from .analysts.market_analyst import create_market_analyst -from .analysts.news_analyst import create_news_analyst -from .analysts.social_media_analyst import create_social_media_analyst - -from .researchers.bear_researcher import create_bear_researcher -from .researchers.bull_researcher import create_bull_researcher - -from .risk_mgmt.aggressive_debator import create_aggressive_debator -from .risk_mgmt.conservative_debator import create_conservative_debator -from .risk_mgmt.neutral_debator import create_neutral_debator - -from .managers.research_manager import create_research_manager -from .managers.risk_manager import create_risk_manager - -from .trader.trader import create_trader - -__all__ = [ - "FinancialSituationMemory", - "AgentState", - "create_msg_delete", - "InvestDebateState", - "RiskDebateState", - "create_bear_researcher", - "create_bull_researcher", - "create_research_manager", - "create_fundamentals_analyst", - "create_market_analyst", - "create_neutral_debator", - "create_news_analyst", - "create_aggressive_debator", - "create_risk_manager", - "create_conservative_debator", - "create_social_media_analyst", - "create_trader", -] +from .utils.agent_utils import create_msg_delete +from .utils.agent_states import AgentState, InvestDebateState, RiskDebateState +from .utils.memory import FinancialSituationMemory + +from .analysts.fundamentals_analyst import create_fundamentals_analyst +from .analysts.market_analyst import create_market_analyst +from .analysts.news_analyst import create_news_analyst +from .analysts.social_media_analyst import create_social_media_analyst + +from .researchers.bear_researcher import create_bear_researcher +from .researchers.bull_researcher import create_bull_researcher + +from .risk_mgmt.aggressive_debator import create_aggressive_debator +from .risk_mgmt.conservative_debator import create_conservative_debator +from .risk_mgmt.neutral_debator import create_neutral_debator + +from .managers.research_manager import create_research_manager +from .managers.risk_manager import create_risk_manager + +from .trader.trader import create_trader + +__all__ = [ + "FinancialSituationMemory", + "AgentState", + "create_msg_delete", + "InvestDebateState", + "RiskDebateState", + "create_bear_researcher", + "create_bull_researcher", + "create_research_manager", + "create_fundamentals_analyst", + "create_market_analyst", + "create_neutral_debator", + "create_news_analyst", + "create_aggressive_debator", + "create_risk_manager", + "create_conservative_debator", + "create_social_media_analyst", + "create_trader", +] diff --git a/tradingagents/agents/analysts/fundamentals_analyst.py b/tradingagents/agents/analysts/fundamentals_analyst.py index 22d91848..0b16097a 100644 --- a/tradingagents/agents/analysts/fundamentals_analyst.py +++ b/tradingagents/agents/analysts/fundamentals_analyst.py @@ -1,63 +1,63 @@ -from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder -import time -import json -from tradingagents.agents.utils.agent_utils import get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement, get_insider_transactions -from tradingagents.dataflows.config import get_config - - -def create_fundamentals_analyst(llm): - def fundamentals_analyst_node(state): - current_date = state["trade_date"] - ticker = state["company_of_interest"] - company_name = state["company_of_interest"] - - tools = [ - get_fundamentals, - get_balance_sheet, - get_cashflow, - get_income_statement, - ] - - system_message = ( - "You are a researcher tasked with analyzing fundamental information over the past week about a company. Please write a comprehensive report of the company's fundamental information such as financial documents, company profile, basic company financials, and company financial history to gain a full view of the company's fundamental information to inform traders. Make sure to include as much detail as possible. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions." - + " Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read." - + " Use the available tools: `get_fundamentals` for comprehensive company analysis, `get_balance_sheet`, `get_cashflow`, and `get_income_statement` for specific financial statements.", - ) - - prompt = ChatPromptTemplate.from_messages( - [ - ( - "system", - "You are a helpful AI assistant, collaborating with other assistants." - " Use the provided tools to progress towards answering the question." - " If you are unable to fully answer, that's OK; another assistant with different tools" - " will help where you left off. Execute what you can to make progress." - " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable," - " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop." - " You have access to the following tools: {tool_names}.\n{system_message}" - "For your reference, the current date is {current_date}. The company we want to look at is {ticker}", - ), - MessagesPlaceholder(variable_name="messages"), - ] - ) - - prompt = prompt.partial(system_message=system_message) - prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools])) - prompt = prompt.partial(current_date=current_date) - prompt = prompt.partial(ticker=ticker) - - chain = prompt | llm.bind_tools(tools) - - result = chain.invoke(state["messages"]) - - report = "" - - if len(result.tool_calls) == 0: - report = result.content - - return { - "messages": [result], - "fundamentals_report": report, - } - - return fundamentals_analyst_node +from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder +import time +import json +from tradingagents.agents.utils.agent_utils import get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement, get_insider_transactions +from tradingagents.dataflows.config import get_config + + +def create_fundamentals_analyst(llm): + def fundamentals_analyst_node(state): + current_date = state["trade_date"] + ticker = state["company_of_interest"] + company_name = state["company_of_interest"] + + tools = [ + get_fundamentals, + get_balance_sheet, + get_cashflow, + get_income_statement, + ] + + system_message = ( + "You are a researcher tasked with analyzing fundamental information over the past week about a company. Please write a comprehensive report of the company's fundamental information such as financial documents, company profile, basic company financials, and company financial history to gain a full view of the company's fundamental information to inform traders. Make sure to include as much detail as possible. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions." + + " Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read." + + " Use the available tools: `get_fundamentals` for comprehensive company analysis, `get_balance_sheet`, `get_cashflow`, and `get_income_statement` for specific financial statements.", + ) + + prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "You are a helpful AI assistant, collaborating with other assistants." + " Use the provided tools to progress towards answering the question." + " If you are unable to fully answer, that's OK; another assistant with different tools" + " will help where you left off. Execute what you can to make progress." + " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable," + " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop." + " You have access to the following tools: {tool_names}.\n{system_message}" + "For your reference, the current date is {current_date}. The company we want to look at is {ticker}", + ), + MessagesPlaceholder(variable_name="messages"), + ] + ) + + prompt = prompt.partial(system_message=system_message) + prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools])) + prompt = prompt.partial(current_date=current_date) + prompt = prompt.partial(ticker=ticker) + + chain = prompt | llm.bind_tools(tools) + + result = chain.invoke(state["messages"]) + + report = "" + + if len(result.tool_calls) == 0: + report = result.content + + return { + "messages": [result], + "fundamentals_report": report, + } + + return fundamentals_analyst_node diff --git a/tradingagents/agents/analysts/market_analyst.py b/tradingagents/agents/analysts/market_analyst.py index e175b94e..16f853ce 100644 --- a/tradingagents/agents/analysts/market_analyst.py +++ b/tradingagents/agents/analysts/market_analyst.py @@ -1,85 +1,85 @@ -from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder -import time -import json -from tradingagents.agents.utils.agent_utils import get_stock_data, get_indicators -from tradingagents.dataflows.config import get_config - - -def create_market_analyst(llm): - - def market_analyst_node(state): - current_date = state["trade_date"] - ticker = state["company_of_interest"] - company_name = state["company_of_interest"] - - tools = [ - get_stock_data, - get_indicators, - ] - - system_message = ( - """You are a trading assistant tasked with analyzing financial markets. Your role is to select the **most relevant indicators** for a given market condition or trading strategy from the following list. The goal is to choose up to **8 indicators** that provide complementary insights without redundancy. Categories and each category's indicators are: - -Moving Averages: -- close_50_sma: 50 SMA: A medium-term trend indicator. Usage: Identify trend direction and serve as dynamic support/resistance. Tips: It lags price; combine with faster indicators for timely signals. -- close_200_sma: 200 SMA: A long-term trend benchmark. Usage: Confirm overall market trend and identify golden/death cross setups. Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries. -- close_10_ema: 10 EMA: A responsive short-term average. Usage: Capture quick shifts in momentum and potential entry points. Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals. - -MACD Related: -- macd: MACD: Computes momentum via differences of EMAs. Usage: Look for crossovers and divergence as signals of trend changes. Tips: Confirm with other indicators in low-volatility or sideways markets. -- macds: MACD Signal: An EMA smoothing of the MACD line. Usage: Use crossovers with the MACD line to trigger trades. Tips: Should be part of a broader strategy to avoid false positives. -- macdh: MACD Histogram: Shows the gap between the MACD line and its signal. Usage: Visualize momentum strength and spot divergence early. Tips: Can be volatile; complement with additional filters in fast-moving markets. - -Momentum Indicators: -- rsi: RSI: Measures momentum to flag overbought/oversold conditions. Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis. - -Volatility Indicators: -- boll: Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. Usage: Acts as a dynamic benchmark for price movement. Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals. -- boll_ub: Bollinger Upper Band: Typically 2 standard deviations above the middle line. Usage: Signals potential overbought conditions and breakout zones. Tips: Confirm signals with other tools; prices may ride the band in strong trends. -- boll_lb: Bollinger Lower Band: Typically 2 standard deviations below the middle line. Usage: Indicates potential oversold conditions. Tips: Use additional analysis to avoid false reversal signals. -- atr: ATR: Averages true range to measure volatility. Usage: Set stop-loss levels and adjust position sizes based on current market volatility. Tips: It's a reactive measure, so use it as part of a broader risk management strategy. - -Volume-Based Indicators: -- vwma: VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses. - -- Select indicators that provide diverse and complementary information. Avoid redundancy (e.g., do not select both rsi and stochrsi). Also briefly explain why they are suitable for the given market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_stock_data first to retrieve the CSV that is needed to generate indicators. Then use get_indicators with the specific indicator names. Write a very detailed and nuanced report of the trends you observe. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions.""" - + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.""" - ) - - prompt = ChatPromptTemplate.from_messages( - [ - ( - "system", - "You are a helpful AI assistant, collaborating with other assistants." - " Use the provided tools to progress towards answering the question." - " If you are unable to fully answer, that's OK; another assistant with different tools" - " will help where you left off. Execute what you can to make progress." - " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable," - " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop." - " You have access to the following tools: {tool_names}.\n{system_message}" - "For your reference, the current date is {current_date}. The company we want to look at is {ticker}", - ), - MessagesPlaceholder(variable_name="messages"), - ] - ) - - prompt = prompt.partial(system_message=system_message) - prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools])) - prompt = prompt.partial(current_date=current_date) - prompt = prompt.partial(ticker=ticker) - - chain = prompt | llm.bind_tools(tools) - - result = chain.invoke(state["messages"]) - - report = "" - - if len(result.tool_calls) == 0: - report = result.content - - return { - "messages": [result], - "market_report": report, - } - - return market_analyst_node +from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder +import time +import json +from tradingagents.agents.utils.agent_utils import get_stock_data, get_indicators +from tradingagents.dataflows.config import get_config + + +def create_market_analyst(llm): + + def market_analyst_node(state): + current_date = state["trade_date"] + ticker = state["company_of_interest"] + company_name = state["company_of_interest"] + + tools = [ + get_stock_data, + get_indicators, + ] + + system_message = ( + """You are a trading assistant tasked with analyzing financial markets. Your role is to select the **most relevant indicators** for a given market condition or trading strategy from the following list. The goal is to choose up to **8 indicators** that provide complementary insights without redundancy. Categories and each category's indicators are: + +Moving Averages: +- close_50_sma: 50 SMA: A medium-term trend indicator. Usage: Identify trend direction and serve as dynamic support/resistance. Tips: It lags price; combine with faster indicators for timely signals. +- close_200_sma: 200 SMA: A long-term trend benchmark. Usage: Confirm overall market trend and identify golden/death cross setups. Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries. +- close_10_ema: 10 EMA: A responsive short-term average. Usage: Capture quick shifts in momentum and potential entry points. Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals. + +MACD Related: +- macd: MACD: Computes momentum via differences of EMAs. Usage: Look for crossovers and divergence as signals of trend changes. Tips: Confirm with other indicators in low-volatility or sideways markets. +- macds: MACD Signal: An EMA smoothing of the MACD line. Usage: Use crossovers with the MACD line to trigger trades. Tips: Should be part of a broader strategy to avoid false positives. +- macdh: MACD Histogram: Shows the gap between the MACD line and its signal. Usage: Visualize momentum strength and spot divergence early. Tips: Can be volatile; complement with additional filters in fast-moving markets. + +Momentum Indicators: +- rsi: RSI: Measures momentum to flag overbought/oversold conditions. Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis. + +Volatility Indicators: +- boll: Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. Usage: Acts as a dynamic benchmark for price movement. Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals. +- boll_ub: Bollinger Upper Band: Typically 2 standard deviations above the middle line. Usage: Signals potential overbought conditions and breakout zones. Tips: Confirm signals with other tools; prices may ride the band in strong trends. +- boll_lb: Bollinger Lower Band: Typically 2 standard deviations below the middle line. Usage: Indicates potential oversold conditions. Tips: Use additional analysis to avoid false reversal signals. +- atr: ATR: Averages true range to measure volatility. Usage: Set stop-loss levels and adjust position sizes based on current market volatility. Tips: It's a reactive measure, so use it as part of a broader risk management strategy. + +Volume-Based Indicators: +- vwma: VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses. + +- Select indicators that provide diverse and complementary information. Avoid redundancy (e.g., do not select both rsi and stochrsi). Also briefly explain why they are suitable for the given market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_stock_data first to retrieve the CSV that is needed to generate indicators. Then use get_indicators with the specific indicator names. Write a very detailed and nuanced report of the trends you observe. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions.""" + + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.""" + ) + + prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "You are a helpful AI assistant, collaborating with other assistants." + " Use the provided tools to progress towards answering the question." + " If you are unable to fully answer, that's OK; another assistant with different tools" + " will help where you left off. Execute what you can to make progress." + " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable," + " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop." + " You have access to the following tools: {tool_names}.\n{system_message}" + "For your reference, the current date is {current_date}. The company we want to look at is {ticker}", + ), + MessagesPlaceholder(variable_name="messages"), + ] + ) + + prompt = prompt.partial(system_message=system_message) + prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools])) + prompt = prompt.partial(current_date=current_date) + prompt = prompt.partial(ticker=ticker) + + chain = prompt | llm.bind_tools(tools) + + result = chain.invoke(state["messages"]) + + report = "" + + if len(result.tool_calls) == 0: + report = result.content + + return { + "messages": [result], + "market_report": report, + } + + return market_analyst_node diff --git a/tradingagents/agents/analysts/news_analyst.py b/tradingagents/agents/analysts/news_analyst.py index 03b4fae4..1c0ffb5a 100644 --- a/tradingagents/agents/analysts/news_analyst.py +++ b/tradingagents/agents/analysts/news_analyst.py @@ -1,58 +1,58 @@ -from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder -import time -import json -from tradingagents.agents.utils.agent_utils import get_news, get_global_news -from tradingagents.dataflows.config import get_config - - -def create_news_analyst(llm): - def news_analyst_node(state): - current_date = state["trade_date"] - ticker = state["company_of_interest"] - - tools = [ - get_news, - get_global_news, - ] - - system_message = ( - "You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the available tools: get_news(query, start_date, end_date) for company-specific or targeted news searches, and get_global_news(curr_date, look_back_days, limit) for broader macroeconomic news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions." - + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.""" - ) - - prompt = ChatPromptTemplate.from_messages( - [ - ( - "system", - "You are a helpful AI assistant, collaborating with other assistants." - " Use the provided tools to progress towards answering the question." - " If you are unable to fully answer, that's OK; another assistant with different tools" - " will help where you left off. Execute what you can to make progress." - " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable," - " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop." - " You have access to the following tools: {tool_names}.\n{system_message}" - "For your reference, the current date is {current_date}. We are looking at the company {ticker}", - ), - MessagesPlaceholder(variable_name="messages"), - ] - ) - - prompt = prompt.partial(system_message=system_message) - prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools])) - prompt = prompt.partial(current_date=current_date) - prompt = prompt.partial(ticker=ticker) - - chain = prompt | llm.bind_tools(tools) - result = chain.invoke(state["messages"]) - - report = "" - - if len(result.tool_calls) == 0: - report = result.content - - return { - "messages": [result], - "news_report": report, - } - - return news_analyst_node +from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder +import time +import json +from tradingagents.agents.utils.agent_utils import get_news, get_global_news +from tradingagents.dataflows.config import get_config + + +def create_news_analyst(llm): + def news_analyst_node(state): + current_date = state["trade_date"] + ticker = state["company_of_interest"] + + tools = [ + get_news, + get_global_news, + ] + + system_message = ( + "You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the available tools: get_news(query, start_date, end_date) for company-specific or targeted news searches, and get_global_news(curr_date, look_back_days, limit) for broader macroeconomic news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions." + + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.""" + ) + + prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "You are a helpful AI assistant, collaborating with other assistants." + " Use the provided tools to progress towards answering the question." + " If you are unable to fully answer, that's OK; another assistant with different tools" + " will help where you left off. Execute what you can to make progress." + " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable," + " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop." + " You have access to the following tools: {tool_names}.\n{system_message}" + "For your reference, the current date is {current_date}. We are looking at the company {ticker}", + ), + MessagesPlaceholder(variable_name="messages"), + ] + ) + + prompt = prompt.partial(system_message=system_message) + prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools])) + prompt = prompt.partial(current_date=current_date) + prompt = prompt.partial(ticker=ticker) + + chain = prompt | llm.bind_tools(tools) + result = chain.invoke(state["messages"]) + + report = "" + + if len(result.tool_calls) == 0: + report = result.content + + return { + "messages": [result], + "news_report": report, + } + + return news_analyst_node diff --git a/tradingagents/agents/analysts/social_media_analyst.py b/tradingagents/agents/analysts/social_media_analyst.py index b25712d7..582bf6bb 100644 --- a/tradingagents/agents/analysts/social_media_analyst.py +++ b/tradingagents/agents/analysts/social_media_analyst.py @@ -1,59 +1,59 @@ -from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder -import time -import json -from tradingagents.agents.utils.agent_utils import get_news -from tradingagents.dataflows.config import get_config - - -def create_social_media_analyst(llm): - def social_media_analyst_node(state): - current_date = state["trade_date"] - ticker = state["company_of_interest"] - company_name = state["company_of_interest"] - - tools = [ - get_news, - ] - - system_message = ( - "You are a social media and company specific news researcher/analyst tasked with analyzing social media posts, recent company news, and public sentiment for a specific company over the past week. You will be given a company's name your objective is to write a comprehensive long report detailing your analysis, insights, and implications for traders and investors on this company's current state after looking at social media and what people are saying about that company, analyzing sentiment data of what people feel each day about the company, and looking at recent company news. Use the get_news(query, start_date, end_date) tool to search for company-specific news and social media discussions. Try to look at all sources possible from social media to sentiment to news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions." - + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.""", - ) - - prompt = ChatPromptTemplate.from_messages( - [ - ( - "system", - "You are a helpful AI assistant, collaborating with other assistants." - " Use the provided tools to progress towards answering the question." - " If you are unable to fully answer, that's OK; another assistant with different tools" - " will help where you left off. Execute what you can to make progress." - " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable," - " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop." - " You have access to the following tools: {tool_names}.\n{system_message}" - "For your reference, the current date is {current_date}. The current company we want to analyze is {ticker}", - ), - MessagesPlaceholder(variable_name="messages"), - ] - ) - - prompt = prompt.partial(system_message=system_message) - prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools])) - prompt = prompt.partial(current_date=current_date) - prompt = prompt.partial(ticker=ticker) - - chain = prompt | llm.bind_tools(tools) - - result = chain.invoke(state["messages"]) - - report = "" - - if len(result.tool_calls) == 0: - report = result.content - - return { - "messages": [result], - "sentiment_report": report, - } - - return social_media_analyst_node +from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder +import time +import json +from tradingagents.agents.utils.agent_utils import get_news +from tradingagents.dataflows.config import get_config + + +def create_social_media_analyst(llm): + def social_media_analyst_node(state): + current_date = state["trade_date"] + ticker = state["company_of_interest"] + company_name = state["company_of_interest"] + + tools = [ + get_news, + ] + + system_message = ( + "You are a social media and company specific news researcher/analyst tasked with analyzing social media posts, recent company news, and public sentiment for a specific company over the past week. You will be given a company's name your objective is to write a comprehensive long report detailing your analysis, insights, and implications for traders and investors on this company's current state after looking at social media and what people are saying about that company, analyzing sentiment data of what people feel each day about the company, and looking at recent company news. Use the get_news(query, start_date, end_date) tool to search for company-specific news and social media discussions. Try to look at all sources possible from social media to sentiment to news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions." + + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.""", + ) + + prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "You are a helpful AI assistant, collaborating with other assistants." + " Use the provided tools to progress towards answering the question." + " If you are unable to fully answer, that's OK; another assistant with different tools" + " will help where you left off. Execute what you can to make progress." + " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable," + " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop." + " You have access to the following tools: {tool_names}.\n{system_message}" + "For your reference, the current date is {current_date}. The current company we want to analyze is {ticker}", + ), + MessagesPlaceholder(variable_name="messages"), + ] + ) + + prompt = prompt.partial(system_message=system_message) + prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools])) + prompt = prompt.partial(current_date=current_date) + prompt = prompt.partial(ticker=ticker) + + chain = prompt | llm.bind_tools(tools) + + result = chain.invoke(state["messages"]) + + report = "" + + if len(result.tool_calls) == 0: + report = result.content + + return { + "messages": [result], + "sentiment_report": report, + } + + return social_media_analyst_node diff --git a/tradingagents/agents/managers/research_manager.py b/tradingagents/agents/managers/research_manager.py index c537fa2f..029a83b5 100644 --- a/tradingagents/agents/managers/research_manager.py +++ b/tradingagents/agents/managers/research_manager.py @@ -1,55 +1,55 @@ -import time -import json - - -def create_research_manager(llm, memory): - def research_manager_node(state) -> dict: - history = state["investment_debate_state"].get("history", "") - market_research_report = state["market_report"] - sentiment_report = state["sentiment_report"] - news_report = state["news_report"] - fundamentals_report = state["fundamentals_report"] - - investment_debate_state = state["investment_debate_state"] - - curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" - past_memories = memory.get_memories(curr_situation, n_matches=2) - - past_memory_str = "" - for i, rec in enumerate(past_memories, 1): - past_memory_str += rec["recommendation"] + "\n\n" - - prompt = f"""As the portfolio manager and debate facilitator, your role is to critically evaluate this round of debate and make a definitive decision: align with the bear analyst, the bull analyst, or choose Hold only if it is strongly justified based on the arguments presented. - -Summarize the key points from both sides concisely, focusing on the most compelling evidence or reasoning. Your recommendation—Buy, Sell, or Hold—must be clear and actionable. Avoid defaulting to Hold simply because both sides have valid points; commit to a stance grounded in the debate's strongest arguments. - -Additionally, develop a detailed investment plan for the trader. This should include: - -Your Recommendation: A decisive stance supported by the most convincing arguments. -Rationale: An explanation of why these arguments lead to your conclusion. -Strategic Actions: Concrete steps for implementing the recommendation. -Take into account your past mistakes on similar situations. Use these insights to refine your decision-making and ensure you are learning and improving. Present your analysis conversationally, as if speaking naturally, without special formatting. - -Here are your past reflections on mistakes: -\"{past_memory_str}\" - -Here is the debate: -Debate History: -{history}""" - response = llm.invoke(prompt) - - new_investment_debate_state = { - "judge_decision": response.content, - "history": investment_debate_state.get("history", ""), - "bear_history": investment_debate_state.get("bear_history", ""), - "bull_history": investment_debate_state.get("bull_history", ""), - "current_response": response.content, - "count": investment_debate_state["count"], - } - - return { - "investment_debate_state": new_investment_debate_state, - "investment_plan": response.content, - } - - return research_manager_node +import time +import json + + +def create_research_manager(llm, memory): + def research_manager_node(state) -> dict: + history = state["investment_debate_state"].get("history", "") + market_research_report = state["market_report"] + sentiment_report = state["sentiment_report"] + news_report = state["news_report"] + fundamentals_report = state["fundamentals_report"] + + investment_debate_state = state["investment_debate_state"] + + curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" + past_memories = memory.get_memories(curr_situation, n_matches=2) + + past_memory_str = "" + for i, rec in enumerate(past_memories, 1): + past_memory_str += rec["recommendation"] + "\n\n" + + prompt = f"""As the portfolio manager and debate facilitator, your role is to critically evaluate this round of debate and make a definitive decision: align with the bear analyst, the bull analyst, or choose Hold only if it is strongly justified based on the arguments presented. + +Summarize the key points from both sides concisely, focusing on the most compelling evidence or reasoning. Your recommendation—Buy, Sell, or Hold—must be clear and actionable. Avoid defaulting to Hold simply because both sides have valid points; commit to a stance grounded in the debate's strongest arguments. + +Additionally, develop a detailed investment plan for the trader. This should include: + +Your Recommendation: A decisive stance supported by the most convincing arguments. +Rationale: An explanation of why these arguments lead to your conclusion. +Strategic Actions: Concrete steps for implementing the recommendation. +Take into account your past mistakes on similar situations. Use these insights to refine your decision-making and ensure you are learning and improving. Present your analysis conversationally, as if speaking naturally, without special formatting. + +Here are your past reflections on mistakes: +\"{past_memory_str}\" + +Here is the debate: +Debate History: +{history}""" + response = llm.invoke(prompt) + + new_investment_debate_state = { + "judge_decision": response.content, + "history": investment_debate_state.get("history", ""), + "bear_history": investment_debate_state.get("bear_history", ""), + "bull_history": investment_debate_state.get("bull_history", ""), + "current_response": response.content, + "count": investment_debate_state["count"], + } + + return { + "investment_debate_state": new_investment_debate_state, + "investment_plan": response.content, + } + + return research_manager_node diff --git a/tradingagents/agents/managers/risk_manager.py b/tradingagents/agents/managers/risk_manager.py index 9ed03e2d..51d89b81 100644 --- a/tradingagents/agents/managers/risk_manager.py +++ b/tradingagents/agents/managers/risk_manager.py @@ -1,66 +1,66 @@ -import time -import json - - -def create_risk_manager(llm, memory): - def risk_manager_node(state) -> dict: - - company_name = state["company_of_interest"] - - history = state["risk_debate_state"]["history"] - risk_debate_state = state["risk_debate_state"] - market_research_report = state["market_report"] - news_report = state["news_report"] - fundamentals_report = state["news_report"] - sentiment_report = state["sentiment_report"] - trader_plan = state["investment_plan"] - - curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" - past_memories = memory.get_memories(curr_situation, n_matches=2) - - past_memory_str = "" - for i, rec in enumerate(past_memories, 1): - past_memory_str += rec["recommendation"] + "\n\n" - - prompt = f"""As the Risk Management Judge and Debate Facilitator, your goal is to evaluate the debate between three risk analysts—Aggressive, Neutral, and Conservative—and determine the best course of action for the trader. Your decision must result in a clear recommendation: Buy, Sell, or Hold. Choose Hold only if strongly justified by specific arguments, not as a fallback when all sides seem valid. Strive for clarity and decisiveness. - -Guidelines for Decision-Making: -1. **Summarize Key Arguments**: Extract the strongest points from each analyst, focusing on relevance to the context. -2. **Provide Rationale**: Support your recommendation with direct quotes and counterarguments from the debate. -3. **Refine the Trader's Plan**: Start with the trader's original plan, **{trader_plan}**, and adjust it based on the analysts' insights. -4. **Learn from Past Mistakes**: Use lessons from **{past_memory_str}** to address prior misjudgments and improve the decision you are making now to make sure you don't make a wrong BUY/SELL/HOLD call that loses money. - -Deliverables: -- A clear and actionable recommendation: Buy, Sell, or Hold. -- Detailed reasoning anchored in the debate and past reflections. - ---- - -**Analysts Debate History:** -{history} - ---- - -Focus on actionable insights and continuous improvement. Build on past lessons, critically evaluate all perspectives, and ensure each decision advances better outcomes.""" - - response = llm.invoke(prompt) - - new_risk_debate_state = { - "judge_decision": response.content, - "history": risk_debate_state["history"], - "aggressive_history": risk_debate_state["aggressive_history"], - "conservative_history": risk_debate_state["conservative_history"], - "neutral_history": risk_debate_state["neutral_history"], - "latest_speaker": "Judge", - "current_aggressive_response": risk_debate_state["current_aggressive_response"], - "current_conservative_response": risk_debate_state["current_conservative_response"], - "current_neutral_response": risk_debate_state["current_neutral_response"], - "count": risk_debate_state["count"], - } - - return { - "risk_debate_state": new_risk_debate_state, - "final_trade_decision": response.content, - } - - return risk_manager_node +import time +import json + + +def create_risk_manager(llm, memory): + def risk_manager_node(state) -> dict: + + company_name = state["company_of_interest"] + + history = state["risk_debate_state"]["history"] + risk_debate_state = state["risk_debate_state"] + market_research_report = state["market_report"] + news_report = state["news_report"] + fundamentals_report = state["news_report"] + sentiment_report = state["sentiment_report"] + trader_plan = state["investment_plan"] + + curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" + past_memories = memory.get_memories(curr_situation, n_matches=2) + + past_memory_str = "" + for i, rec in enumerate(past_memories, 1): + past_memory_str += rec["recommendation"] + "\n\n" + + prompt = f"""As the Risk Management Judge and Debate Facilitator, your goal is to evaluate the debate between three risk analysts—Aggressive, Neutral, and Conservative—and determine the best course of action for the trader. Your decision must result in a clear recommendation: Buy, Sell, or Hold. Choose Hold only if strongly justified by specific arguments, not as a fallback when all sides seem valid. Strive for clarity and decisiveness. + +Guidelines for Decision-Making: +1. **Summarize Key Arguments**: Extract the strongest points from each analyst, focusing on relevance to the context. +2. **Provide Rationale**: Support your recommendation with direct quotes and counterarguments from the debate. +3. **Refine the Trader's Plan**: Start with the trader's original plan, **{trader_plan}**, and adjust it based on the analysts' insights. +4. **Learn from Past Mistakes**: Use lessons from **{past_memory_str}** to address prior misjudgments and improve the decision you are making now to make sure you don't make a wrong BUY/SELL/HOLD call that loses money. + +Deliverables: +- A clear and actionable recommendation: Buy, Sell, or Hold. +- Detailed reasoning anchored in the debate and past reflections. + +--- + +**Analysts Debate History:** +{history} + +--- + +Focus on actionable insights and continuous improvement. Build on past lessons, critically evaluate all perspectives, and ensure each decision advances better outcomes.""" + + response = llm.invoke(prompt) + + new_risk_debate_state = { + "judge_decision": response.content, + "history": risk_debate_state["history"], + "aggressive_history": risk_debate_state["aggressive_history"], + "conservative_history": risk_debate_state["conservative_history"], + "neutral_history": risk_debate_state["neutral_history"], + "latest_speaker": "Judge", + "current_aggressive_response": risk_debate_state["current_aggressive_response"], + "current_conservative_response": risk_debate_state["current_conservative_response"], + "current_neutral_response": risk_debate_state["current_neutral_response"], + "count": risk_debate_state["count"], + } + + return { + "risk_debate_state": new_risk_debate_state, + "final_trade_decision": response.content, + } + + return risk_manager_node diff --git a/tradingagents/agents/researchers/bear_researcher.py b/tradingagents/agents/researchers/bear_researcher.py index 6634490a..47c28f66 100644 --- a/tradingagents/agents/researchers/bear_researcher.py +++ b/tradingagents/agents/researchers/bear_researcher.py @@ -1,61 +1,61 @@ -from langchain_core.messages import AIMessage -import time -import json - - -def create_bear_researcher(llm, memory): - def bear_node(state) -> dict: - investment_debate_state = state["investment_debate_state"] - history = investment_debate_state.get("history", "") - bear_history = investment_debate_state.get("bear_history", "") - - current_response = investment_debate_state.get("current_response", "") - market_research_report = state["market_report"] - sentiment_report = state["sentiment_report"] - news_report = state["news_report"] - fundamentals_report = state["fundamentals_report"] - - curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" - past_memories = memory.get_memories(curr_situation, n_matches=2) - - past_memory_str = "" - for i, rec in enumerate(past_memories, 1): - past_memory_str += rec["recommendation"] + "\n\n" - - prompt = f"""You are a Bear Analyst making the case against investing in the stock. Your goal is to present a well-reasoned argument emphasizing risks, challenges, and negative indicators. Leverage the provided research and data to highlight potential downsides and counter bullish arguments effectively. - -Key points to focus on: - -- Risks and Challenges: Highlight factors like market saturation, financial instability, or macroeconomic threats that could hinder the stock's performance. -- Competitive Weaknesses: Emphasize vulnerabilities such as weaker market positioning, declining innovation, or threats from competitors. -- Negative Indicators: Use evidence from financial data, market trends, or recent adverse news to support your position. -- Bull Counterpoints: Critically analyze the bull argument with specific data and sound reasoning, exposing weaknesses or over-optimistic assumptions. -- Engagement: Present your argument in a conversational style, directly engaging with the bull analyst's points and debating effectively rather than simply listing facts. - -Resources available: - -Market research report: {market_research_report} -Social media sentiment report: {sentiment_report} -Latest world affairs news: {news_report} -Company fundamentals report: {fundamentals_report} -Conversation history of the debate: {history} -Last bull argument: {current_response} -Reflections from similar situations and lessons learned: {past_memory_str} -Use this information to deliver a compelling bear argument, refute the bull's claims, and engage in a dynamic debate that demonstrates the risks and weaknesses of investing in the stock. You must also address reflections and learn from lessons and mistakes you made in the past. -""" - - response = llm.invoke(prompt) - - argument = f"Bear Analyst: {response.content}" - - new_investment_debate_state = { - "history": history + "\n" + argument, - "bear_history": bear_history + "\n" + argument, - "bull_history": investment_debate_state.get("bull_history", ""), - "current_response": argument, - "count": investment_debate_state["count"] + 1, - } - - return {"investment_debate_state": new_investment_debate_state} - - return bear_node +from langchain_core.messages import AIMessage +import time +import json + + +def create_bear_researcher(llm, memory): + def bear_node(state) -> dict: + investment_debate_state = state["investment_debate_state"] + history = investment_debate_state.get("history", "") + bear_history = investment_debate_state.get("bear_history", "") + + current_response = investment_debate_state.get("current_response", "") + market_research_report = state["market_report"] + sentiment_report = state["sentiment_report"] + news_report = state["news_report"] + fundamentals_report = state["fundamentals_report"] + + curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" + past_memories = memory.get_memories(curr_situation, n_matches=2) + + past_memory_str = "" + for i, rec in enumerate(past_memories, 1): + past_memory_str += rec["recommendation"] + "\n\n" + + prompt = f"""You are a Bear Analyst making the case against investing in the stock. Your goal is to present a well-reasoned argument emphasizing risks, challenges, and negative indicators. Leverage the provided research and data to highlight potential downsides and counter bullish arguments effectively. + +Key points to focus on: + +- Risks and Challenges: Highlight factors like market saturation, financial instability, or macroeconomic threats that could hinder the stock's performance. +- Competitive Weaknesses: Emphasize vulnerabilities such as weaker market positioning, declining innovation, or threats from competitors. +- Negative Indicators: Use evidence from financial data, market trends, or recent adverse news to support your position. +- Bull Counterpoints: Critically analyze the bull argument with specific data and sound reasoning, exposing weaknesses or over-optimistic assumptions. +- Engagement: Present your argument in a conversational style, directly engaging with the bull analyst's points and debating effectively rather than simply listing facts. + +Resources available: + +Market research report: {market_research_report} +Social media sentiment report: {sentiment_report} +Latest world affairs news: {news_report} +Company fundamentals report: {fundamentals_report} +Conversation history of the debate: {history} +Last bull argument: {current_response} +Reflections from similar situations and lessons learned: {past_memory_str} +Use this information to deliver a compelling bear argument, refute the bull's claims, and engage in a dynamic debate that demonstrates the risks and weaknesses of investing in the stock. You must also address reflections and learn from lessons and mistakes you made in the past. +""" + + response = llm.invoke(prompt) + + argument = f"Bear Analyst: {response.content}" + + new_investment_debate_state = { + "history": history + "\n" + argument, + "bear_history": bear_history + "\n" + argument, + "bull_history": investment_debate_state.get("bull_history", ""), + "current_response": argument, + "count": investment_debate_state["count"] + 1, + } + + return {"investment_debate_state": new_investment_debate_state} + + return bear_node diff --git a/tradingagents/agents/researchers/bull_researcher.py b/tradingagents/agents/researchers/bull_researcher.py index b03ef755..3be2e4a1 100644 --- a/tradingagents/agents/researchers/bull_researcher.py +++ b/tradingagents/agents/researchers/bull_researcher.py @@ -1,59 +1,59 @@ -from langchain_core.messages import AIMessage -import time -import json - - -def create_bull_researcher(llm, memory): - def bull_node(state) -> dict: - investment_debate_state = state["investment_debate_state"] - history = investment_debate_state.get("history", "") - bull_history = investment_debate_state.get("bull_history", "") - - current_response = investment_debate_state.get("current_response", "") - market_research_report = state["market_report"] - sentiment_report = state["sentiment_report"] - news_report = state["news_report"] - fundamentals_report = state["fundamentals_report"] - - curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" - past_memories = memory.get_memories(curr_situation, n_matches=2) - - past_memory_str = "" - for i, rec in enumerate(past_memories, 1): - past_memory_str += rec["recommendation"] + "\n\n" - - prompt = f"""You are a Bull Analyst advocating for investing in the stock. Your task is to build a strong, evidence-based case emphasizing growth potential, competitive advantages, and positive market indicators. Leverage the provided research and data to address concerns and counter bearish arguments effectively. - -Key points to focus on: -- Growth Potential: Highlight the company's market opportunities, revenue projections, and scalability. -- Competitive Advantages: Emphasize factors like unique products, strong branding, or dominant market positioning. -- Positive Indicators: Use financial health, industry trends, and recent positive news as evidence. -- Bear Counterpoints: Critically analyze the bear argument with specific data and sound reasoning, addressing concerns thoroughly and showing why the bull perspective holds stronger merit. -- Engagement: Present your argument in a conversational style, engaging directly with the bear analyst's points and debating effectively rather than just listing data. - -Resources available: -Market research report: {market_research_report} -Social media sentiment report: {sentiment_report} -Latest world affairs news: {news_report} -Company fundamentals report: {fundamentals_report} -Conversation history of the debate: {history} -Last bear argument: {current_response} -Reflections from similar situations and lessons learned: {past_memory_str} -Use this information to deliver a compelling bull argument, refute the bear's concerns, and engage in a dynamic debate that demonstrates the strengths of the bull position. You must also address reflections and learn from lessons and mistakes you made in the past. -""" - - response = llm.invoke(prompt) - - argument = f"Bull Analyst: {response.content}" - - new_investment_debate_state = { - "history": history + "\n" + argument, - "bull_history": bull_history + "\n" + argument, - "bear_history": investment_debate_state.get("bear_history", ""), - "current_response": argument, - "count": investment_debate_state["count"] + 1, - } - - return {"investment_debate_state": new_investment_debate_state} - - return bull_node +from langchain_core.messages import AIMessage +import time +import json + + +def create_bull_researcher(llm, memory): + def bull_node(state) -> dict: + investment_debate_state = state["investment_debate_state"] + history = investment_debate_state.get("history", "") + bull_history = investment_debate_state.get("bull_history", "") + + current_response = investment_debate_state.get("current_response", "") + market_research_report = state["market_report"] + sentiment_report = state["sentiment_report"] + news_report = state["news_report"] + fundamentals_report = state["fundamentals_report"] + + curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" + past_memories = memory.get_memories(curr_situation, n_matches=2) + + past_memory_str = "" + for i, rec in enumerate(past_memories, 1): + past_memory_str += rec["recommendation"] + "\n\n" + + prompt = f"""You are a Bull Analyst advocating for investing in the stock. Your task is to build a strong, evidence-based case emphasizing growth potential, competitive advantages, and positive market indicators. Leverage the provided research and data to address concerns and counter bearish arguments effectively. + +Key points to focus on: +- Growth Potential: Highlight the company's market opportunities, revenue projections, and scalability. +- Competitive Advantages: Emphasize factors like unique products, strong branding, or dominant market positioning. +- Positive Indicators: Use financial health, industry trends, and recent positive news as evidence. +- Bear Counterpoints: Critically analyze the bear argument with specific data and sound reasoning, addressing concerns thoroughly and showing why the bull perspective holds stronger merit. +- Engagement: Present your argument in a conversational style, engaging directly with the bear analyst's points and debating effectively rather than just listing data. + +Resources available: +Market research report: {market_research_report} +Social media sentiment report: {sentiment_report} +Latest world affairs news: {news_report} +Company fundamentals report: {fundamentals_report} +Conversation history of the debate: {history} +Last bear argument: {current_response} +Reflections from similar situations and lessons learned: {past_memory_str} +Use this information to deliver a compelling bull argument, refute the bear's concerns, and engage in a dynamic debate that demonstrates the strengths of the bull position. You must also address reflections and learn from lessons and mistakes you made in the past. +""" + + response = llm.invoke(prompt) + + argument = f"Bull Analyst: {response.content}" + + new_investment_debate_state = { + "history": history + "\n" + argument, + "bull_history": bull_history + "\n" + argument, + "bear_history": investment_debate_state.get("bear_history", ""), + "current_response": argument, + "count": investment_debate_state["count"] + 1, + } + + return {"investment_debate_state": new_investment_debate_state} + + return bull_node diff --git a/tradingagents/agents/risk_mgmt/aggressive_debator.py b/tradingagents/agents/risk_mgmt/aggressive_debator.py index 3905d3d1..42f14a26 100644 --- a/tradingagents/agents/risk_mgmt/aggressive_debator.py +++ b/tradingagents/agents/risk_mgmt/aggressive_debator.py @@ -1,55 +1,55 @@ -import time -import json - - -def create_aggressive_debator(llm): - def aggressive_node(state) -> dict: - risk_debate_state = state["risk_debate_state"] - history = risk_debate_state.get("history", "") - aggressive_history = risk_debate_state.get("aggressive_history", "") - - current_conservative_response = risk_debate_state.get("current_conservative_response", "") - current_neutral_response = risk_debate_state.get("current_neutral_response", "") - - market_research_report = state["market_report"] - sentiment_report = state["sentiment_report"] - news_report = state["news_report"] - fundamentals_report = state["fundamentals_report"] - - trader_decision = state["trader_investment_plan"] - - prompt = f"""As the Aggressive Risk Analyst, your role is to actively champion high-reward, high-risk opportunities, emphasizing bold strategies and competitive advantages. When evaluating the trader's decision or plan, focus intently on the potential upside, growth potential, and innovative benefits—even when these come with elevated risk. Use the provided market data and sentiment analysis to strengthen your arguments and challenge the opposing views. Specifically, respond directly to each point made by the conservative and neutral analysts, countering with data-driven rebuttals and persuasive reasoning. Highlight where their caution might miss critical opportunities or where their assumptions may be overly conservative. Here is the trader's decision: - -{trader_decision} - -Your task is to create a compelling case for the trader's decision by questioning and critiquing the conservative and neutral stances to demonstrate why your high-reward perspective offers the best path forward. Incorporate insights from the following sources into your arguments: - -Market Research Report: {market_research_report} -Social Media Sentiment Report: {sentiment_report} -Latest World Affairs Report: {news_report} -Company Fundamentals Report: {fundamentals_report} -Here is the current conversation history: {history} Here are the last arguments from the conservative analyst: {current_conservative_response} Here are the last arguments from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not hallucinate and just present your point. - -Engage actively by addressing any specific concerns raised, refuting the weaknesses in their logic, and asserting the benefits of risk-taking to outpace market norms. Maintain a focus on debating and persuading, not just presenting data. Challenge each counterpoint to underscore why a high-risk approach is optimal. Output conversationally as if you are speaking without any special formatting.""" - - response = llm.invoke(prompt) - - argument = f"Aggressive Analyst: {response.content}" - - new_risk_debate_state = { - "history": history + "\n" + argument, - "aggressive_history": aggressive_history + "\n" + argument, - "conservative_history": risk_debate_state.get("conservative_history", ""), - "neutral_history": risk_debate_state.get("neutral_history", ""), - "latest_speaker": "Aggressive", - "current_aggressive_response": argument, - "current_conservative_response": risk_debate_state.get("current_conservative_response", ""), - "current_neutral_response": risk_debate_state.get( - "current_neutral_response", "" - ), - "count": risk_debate_state["count"] + 1, - } - - return {"risk_debate_state": new_risk_debate_state} - - return aggressive_node +import time +import json + + +def create_aggressive_debator(llm): + def aggressive_node(state) -> dict: + risk_debate_state = state["risk_debate_state"] + history = risk_debate_state.get("history", "") + aggressive_history = risk_debate_state.get("aggressive_history", "") + + current_conservative_response = risk_debate_state.get("current_conservative_response", "") + current_neutral_response = risk_debate_state.get("current_neutral_response", "") + + market_research_report = state["market_report"] + sentiment_report = state["sentiment_report"] + news_report = state["news_report"] + fundamentals_report = state["fundamentals_report"] + + trader_decision = state["trader_investment_plan"] + + prompt = f"""As the Aggressive Risk Analyst, your role is to actively champion high-reward, high-risk opportunities, emphasizing bold strategies and competitive advantages. When evaluating the trader's decision or plan, focus intently on the potential upside, growth potential, and innovative benefits—even when these come with elevated risk. Use the provided market data and sentiment analysis to strengthen your arguments and challenge the opposing views. Specifically, respond directly to each point made by the conservative and neutral analysts, countering with data-driven rebuttals and persuasive reasoning. Highlight where their caution might miss critical opportunities or where their assumptions may be overly conservative. Here is the trader's decision: + +{trader_decision} + +Your task is to create a compelling case for the trader's decision by questioning and critiquing the conservative and neutral stances to demonstrate why your high-reward perspective offers the best path forward. Incorporate insights from the following sources into your arguments: + +Market Research Report: {market_research_report} +Social Media Sentiment Report: {sentiment_report} +Latest World Affairs Report: {news_report} +Company Fundamentals Report: {fundamentals_report} +Here is the current conversation history: {history} Here are the last arguments from the conservative analyst: {current_conservative_response} Here are the last arguments from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not hallucinate and just present your point. + +Engage actively by addressing any specific concerns raised, refuting the weaknesses in their logic, and asserting the benefits of risk-taking to outpace market norms. Maintain a focus on debating and persuading, not just presenting data. Challenge each counterpoint to underscore why a high-risk approach is optimal. Output conversationally as if you are speaking without any special formatting.""" + + response = llm.invoke(prompt) + + argument = f"Aggressive Analyst: {response.content}" + + new_risk_debate_state = { + "history": history + "\n" + argument, + "aggressive_history": aggressive_history + "\n" + argument, + "conservative_history": risk_debate_state.get("conservative_history", ""), + "neutral_history": risk_debate_state.get("neutral_history", ""), + "latest_speaker": "Aggressive", + "current_aggressive_response": argument, + "current_conservative_response": risk_debate_state.get("current_conservative_response", ""), + "current_neutral_response": risk_debate_state.get( + "current_neutral_response", "" + ), + "count": risk_debate_state["count"] + 1, + } + + return {"risk_debate_state": new_risk_debate_state} + + return aggressive_node diff --git a/tradingagents/agents/risk_mgmt/conservative_debator.py b/tradingagents/agents/risk_mgmt/conservative_debator.py index 6b106b1b..d1a67b42 100644 --- a/tradingagents/agents/risk_mgmt/conservative_debator.py +++ b/tradingagents/agents/risk_mgmt/conservative_debator.py @@ -1,58 +1,58 @@ -from langchain_core.messages import AIMessage -import time -import json - - -def create_conservative_debator(llm): - def conservative_node(state) -> dict: - risk_debate_state = state["risk_debate_state"] - history = risk_debate_state.get("history", "") - conservative_history = risk_debate_state.get("conservative_history", "") - - current_aggressive_response = risk_debate_state.get("current_aggressive_response", "") - current_neutral_response = risk_debate_state.get("current_neutral_response", "") - - market_research_report = state["market_report"] - sentiment_report = state["sentiment_report"] - news_report = state["news_report"] - fundamentals_report = state["fundamentals_report"] - - trader_decision = state["trader_investment_plan"] - - prompt = f"""As the Conservative Risk Analyst, your primary objective is to protect assets, minimize volatility, and ensure steady, reliable growth. You prioritize stability, security, and risk mitigation, carefully assessing potential losses, economic downturns, and market volatility. When evaluating the trader's decision or plan, critically examine high-risk elements, pointing out where the decision may expose the firm to undue risk and where more cautious alternatives could secure long-term gains. Here is the trader's decision: - -{trader_decision} - -Your task is to actively counter the arguments of the Aggressive and Neutral Analysts, highlighting where their views may overlook potential threats or fail to prioritize sustainability. Respond directly to their points, drawing from the following data sources to build a convincing case for a low-risk approach adjustment to the trader's decision: - -Market Research Report: {market_research_report} -Social Media Sentiment Report: {sentiment_report} -Latest World Affairs Report: {news_report} -Company Fundamentals Report: {fundamentals_report} -Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not hallucinate and just present your point. - -Engage by questioning their optimism and emphasizing the potential downsides they may have overlooked. Address each of their counterpoints to showcase why a conservative stance is ultimately the safest path for the firm's assets. Focus on debating and critiquing their arguments to demonstrate the strength of a low-risk strategy over their approaches. Output conversationally as if you are speaking without any special formatting.""" - - response = llm.invoke(prompt) - - argument = f"Conservative Analyst: {response.content}" - - new_risk_debate_state = { - "history": history + "\n" + argument, - "aggressive_history": risk_debate_state.get("aggressive_history", ""), - "conservative_history": conservative_history + "\n" + argument, - "neutral_history": risk_debate_state.get("neutral_history", ""), - "latest_speaker": "Conservative", - "current_aggressive_response": risk_debate_state.get( - "current_aggressive_response", "" - ), - "current_conservative_response": argument, - "current_neutral_response": risk_debate_state.get( - "current_neutral_response", "" - ), - "count": risk_debate_state["count"] + 1, - } - - return {"risk_debate_state": new_risk_debate_state} - - return conservative_node +from langchain_core.messages import AIMessage +import time +import json + + +def create_conservative_debator(llm): + def conservative_node(state) -> dict: + risk_debate_state = state["risk_debate_state"] + history = risk_debate_state.get("history", "") + conservative_history = risk_debate_state.get("conservative_history", "") + + current_aggressive_response = risk_debate_state.get("current_aggressive_response", "") + current_neutral_response = risk_debate_state.get("current_neutral_response", "") + + market_research_report = state["market_report"] + sentiment_report = state["sentiment_report"] + news_report = state["news_report"] + fundamentals_report = state["fundamentals_report"] + + trader_decision = state["trader_investment_plan"] + + prompt = f"""As the Conservative Risk Analyst, your primary objective is to protect assets, minimize volatility, and ensure steady, reliable growth. You prioritize stability, security, and risk mitigation, carefully assessing potential losses, economic downturns, and market volatility. When evaluating the trader's decision or plan, critically examine high-risk elements, pointing out where the decision may expose the firm to undue risk and where more cautious alternatives could secure long-term gains. Here is the trader's decision: + +{trader_decision} + +Your task is to actively counter the arguments of the Aggressive and Neutral Analysts, highlighting where their views may overlook potential threats or fail to prioritize sustainability. Respond directly to their points, drawing from the following data sources to build a convincing case for a low-risk approach adjustment to the trader's decision: + +Market Research Report: {market_research_report} +Social Media Sentiment Report: {sentiment_report} +Latest World Affairs Report: {news_report} +Company Fundamentals Report: {fundamentals_report} +Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not hallucinate and just present your point. + +Engage by questioning their optimism and emphasizing the potential downsides they may have overlooked. Address each of their counterpoints to showcase why a conservative stance is ultimately the safest path for the firm's assets. Focus on debating and critiquing their arguments to demonstrate the strength of a low-risk strategy over their approaches. Output conversationally as if you are speaking without any special formatting.""" + + response = llm.invoke(prompt) + + argument = f"Conservative Analyst: {response.content}" + + new_risk_debate_state = { + "history": history + "\n" + argument, + "aggressive_history": risk_debate_state.get("aggressive_history", ""), + "conservative_history": conservative_history + "\n" + argument, + "neutral_history": risk_debate_state.get("neutral_history", ""), + "latest_speaker": "Conservative", + "current_aggressive_response": risk_debate_state.get( + "current_aggressive_response", "" + ), + "current_conservative_response": argument, + "current_neutral_response": risk_debate_state.get( + "current_neutral_response", "" + ), + "count": risk_debate_state["count"] + 1, + } + + return {"risk_debate_state": new_risk_debate_state} + + return conservative_node diff --git a/tradingagents/agents/risk_mgmt/neutral_debator.py b/tradingagents/agents/risk_mgmt/neutral_debator.py index f6aa888d..0a9a152c 100644 --- a/tradingagents/agents/risk_mgmt/neutral_debator.py +++ b/tradingagents/agents/risk_mgmt/neutral_debator.py @@ -1,55 +1,55 @@ -import time -import json - - -def create_neutral_debator(llm): - def neutral_node(state) -> dict: - risk_debate_state = state["risk_debate_state"] - history = risk_debate_state.get("history", "") - neutral_history = risk_debate_state.get("neutral_history", "") - - current_aggressive_response = risk_debate_state.get("current_aggressive_response", "") - current_conservative_response = risk_debate_state.get("current_conservative_response", "") - - market_research_report = state["market_report"] - sentiment_report = state["sentiment_report"] - news_report = state["news_report"] - fundamentals_report = state["fundamentals_report"] - - trader_decision = state["trader_investment_plan"] - - prompt = f"""As the Neutral Risk Analyst, your role is to provide a balanced perspective, weighing both the potential benefits and risks of the trader's decision or plan. You prioritize a well-rounded approach, evaluating the upsides and downsides while factoring in broader market trends, potential economic shifts, and diversification strategies.Here is the trader's decision: - -{trader_decision} - -Your task is to challenge both the Aggressive and Conservative Analysts, pointing out where each perspective may be overly optimistic or overly cautious. Use insights from the following data sources to support a moderate, sustainable strategy to adjust the trader's decision: - -Market Research Report: {market_research_report} -Social Media Sentiment Report: {sentiment_report} -Latest World Affairs Report: {news_report} -Company Fundamentals Report: {fundamentals_report} -Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the conservative analyst: {current_conservative_response}. If there are no responses from the other viewpoints, do not hallucinate and just present your point. - -Engage actively by analyzing both sides critically, addressing weaknesses in the aggressive and conservative arguments to advocate for a more balanced approach. Challenge each of their points to illustrate why a moderate risk strategy might offer the best of both worlds, providing growth potential while safeguarding against extreme volatility. Focus on debating rather than simply presenting data, aiming to show that a balanced view can lead to the most reliable outcomes. Output conversationally as if you are speaking without any special formatting.""" - - response = llm.invoke(prompt) - - argument = f"Neutral Analyst: {response.content}" - - new_risk_debate_state = { - "history": history + "\n" + argument, - "aggressive_history": risk_debate_state.get("aggressive_history", ""), - "conservative_history": risk_debate_state.get("conservative_history", ""), - "neutral_history": neutral_history + "\n" + argument, - "latest_speaker": "Neutral", - "current_aggressive_response": risk_debate_state.get( - "current_aggressive_response", "" - ), - "current_conservative_response": risk_debate_state.get("current_conservative_response", ""), - "current_neutral_response": argument, - "count": risk_debate_state["count"] + 1, - } - - return {"risk_debate_state": new_risk_debate_state} - - return neutral_node +import time +import json + + +def create_neutral_debator(llm): + def neutral_node(state) -> dict: + risk_debate_state = state["risk_debate_state"] + history = risk_debate_state.get("history", "") + neutral_history = risk_debate_state.get("neutral_history", "") + + current_aggressive_response = risk_debate_state.get("current_aggressive_response", "") + current_conservative_response = risk_debate_state.get("current_conservative_response", "") + + market_research_report = state["market_report"] + sentiment_report = state["sentiment_report"] + news_report = state["news_report"] + fundamentals_report = state["fundamentals_report"] + + trader_decision = state["trader_investment_plan"] + + prompt = f"""As the Neutral Risk Analyst, your role is to provide a balanced perspective, weighing both the potential benefits and risks of the trader's decision or plan. You prioritize a well-rounded approach, evaluating the upsides and downsides while factoring in broader market trends, potential economic shifts, and diversification strategies.Here is the trader's decision: + +{trader_decision} + +Your task is to challenge both the Aggressive and Conservative Analysts, pointing out where each perspective may be overly optimistic or overly cautious. Use insights from the following data sources to support a moderate, sustainable strategy to adjust the trader's decision: + +Market Research Report: {market_research_report} +Social Media Sentiment Report: {sentiment_report} +Latest World Affairs Report: {news_report} +Company Fundamentals Report: {fundamentals_report} +Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the conservative analyst: {current_conservative_response}. If there are no responses from the other viewpoints, do not hallucinate and just present your point. + +Engage actively by analyzing both sides critically, addressing weaknesses in the aggressive and conservative arguments to advocate for a more balanced approach. Challenge each of their points to illustrate why a moderate risk strategy might offer the best of both worlds, providing growth potential while safeguarding against extreme volatility. Focus on debating rather than simply presenting data, aiming to show that a balanced view can lead to the most reliable outcomes. Output conversationally as if you are speaking without any special formatting.""" + + response = llm.invoke(prompt) + + argument = f"Neutral Analyst: {response.content}" + + new_risk_debate_state = { + "history": history + "\n" + argument, + "aggressive_history": risk_debate_state.get("aggressive_history", ""), + "conservative_history": risk_debate_state.get("conservative_history", ""), + "neutral_history": neutral_history + "\n" + argument, + "latest_speaker": "Neutral", + "current_aggressive_response": risk_debate_state.get( + "current_aggressive_response", "" + ), + "current_conservative_response": risk_debate_state.get("current_conservative_response", ""), + "current_neutral_response": argument, + "count": risk_debate_state["count"] + 1, + } + + return {"risk_debate_state": new_risk_debate_state} + + return neutral_node diff --git a/tradingagents/agents/structured/__init__.py b/tradingagents/agents/structured/__init__.py index 9fae4c23..dca83f92 100644 --- a/tradingagents/agents/structured/__init__.py +++ b/tradingagents/agents/structured/__init__.py @@ -1,53 +1,53 @@ -"""Structured output agents for the equity ranking engine.""" - -from .tier1 import ( - create_validation_node, - create_macro_node, - create_liquidity_node, -) -from .tier2 import ( - create_business_quality_node, - create_institutional_flow_node, - create_valuation_node, - create_entry_timing_node, - create_earnings_revisions_node, - create_sector_rotation_node, - create_backlog_node, - create_crowding_node, - create_archetype_node, -) -from .tier3 import ( - create_bull_case_node, - create_bear_case_node, - create_debate_node, - create_risk_node, - create_final_decision_node, -) -from .scoring import create_scoring_node -from .portfolio import ( - create_theme_substitution_node, - create_position_replacement_node, -) - -__all__ = [ - "create_validation_node", - "create_macro_node", - "create_liquidity_node", - "create_business_quality_node", - "create_institutional_flow_node", - "create_valuation_node", - "create_entry_timing_node", - "create_earnings_revisions_node", - "create_sector_rotation_node", - "create_backlog_node", - "create_crowding_node", - "create_archetype_node", - "create_bull_case_node", - "create_bear_case_node", - "create_debate_node", - "create_risk_node", - "create_final_decision_node", - "create_scoring_node", - "create_theme_substitution_node", - "create_position_replacement_node", -] +"""Structured output agents for the equity ranking engine.""" + +from .tier1 import ( + create_validation_node, + create_macro_node, + create_liquidity_node, +) +from .tier2 import ( + create_business_quality_node, + create_institutional_flow_node, + create_valuation_node, + create_entry_timing_node, + create_earnings_revisions_node, + create_sector_rotation_node, + create_backlog_node, + create_crowding_node, + create_archetype_node, +) +from .tier3 import ( + create_bull_case_node, + create_bear_case_node, + create_debate_node, + create_risk_node, + create_final_decision_node, +) +from .scoring import create_scoring_node +from .portfolio import ( + create_theme_substitution_node, + create_position_replacement_node, +) + +__all__ = [ + "create_validation_node", + "create_macro_node", + "create_liquidity_node", + "create_business_quality_node", + "create_institutional_flow_node", + "create_valuation_node", + "create_entry_timing_node", + "create_earnings_revisions_node", + "create_sector_rotation_node", + "create_backlog_node", + "create_crowding_node", + "create_archetype_node", + "create_bull_case_node", + "create_bear_case_node", + "create_debate_node", + "create_risk_node", + "create_final_decision_node", + "create_scoring_node", + "create_theme_substitution_node", + "create_position_replacement_node", +] diff --git a/tradingagents/agents/structured/portfolio.py b/tradingagents/agents/structured/portfolio.py index 811a7d7a..7e62654b 100644 --- a/tradingagents/agents/structured/portfolio.py +++ b/tradingagents/agents/structured/portfolio.py @@ -1,267 +1,267 @@ -"""Portfolio-level agents: Theme Substitution Engine, Position Replacement Agent. - -These run after scoring, before the debate phase. They use the deep-thinking LLM -to evaluate the stock in context — is it the best expression of its theme? Should -it replace an existing holding? -""" - -from __future__ import annotations - -import json -import logging -from typing import Any, Dict, List - -import yfinance as yf - -from tradingagents.models import ( - PositionReplacementOutput, - ThemeStock, - ThemeSubstitutionOutput, - invoke_structured, -) - -logger = logging.getLogger(__name__) - - -def _fetch_peer_basics(tickers: List[str]) -> List[dict]: - """Fetch basic yfinance data for a list of peer tickers.""" - peers = [] - for sym in tickers[:8]: # cap at 8 to keep prompt manageable - try: - info = yf.Ticker(sym.upper()).info or {} - peers.append({ - "ticker": sym.upper(), - "company_name": info.get("longName") or info.get("shortName") or sym, - "market_cap": info.get("marketCap"), - "current_price": info.get("currentPrice") or info.get("regularMarketPrice"), - "trailing_pe": info.get("trailingPE"), - "forward_pe": info.get("forwardPE"), - "revenue_growth": info.get("revenueGrowth"), - "profit_margins": info.get("profitMargins"), - "return_on_equity": info.get("returnOnEquity"), - "52w_range_pct": _range_pct(info), - }) - except Exception: - peers.append({"ticker": sym.upper(), "error": "fetch failed"}) - return peers - - -def _range_pct(info: dict) -> float | None: - hi = info.get("fiftyTwoWeekHigh") - lo = info.get("fiftyTwoWeekLow") - price = info.get("currentPrice") or info.get("regularMarketPrice") - if hi and lo and price and (hi - lo) > 0: - return round((price - lo) / (hi - lo) * 100, 1) - return None - - -def _summarize_for_theme(state: Dict[str, Any]) -> str: - """Compact summary of the candidate stock for theme comparison.""" - card = state.get("company_card") or {} - macro = state.get("macro") or {} - bq = state.get("business_quality") or {} - inst = state.get("institutional_flow") or {} - val = state.get("valuation") or {} - er = state.get("earnings_revisions") or {} - arch = state.get("archetype") or {} - - return "\n".join([ - f"Ticker: {card.get('ticker', '?')} | {card.get('company_name', '?')}", - f"Sector: {card.get('sector', '?')} | Industry: {card.get('industry', '?')}", - f"Market Cap: {card.get('market_cap_formatted', 'N/A')}", - f"Archetype: {arch.get('archetype', 'N/A')}", - f"Master Score: {state.get('master_score', 'N/A')}", - f"Adjusted Score: {state.get('adjusted_score', 'N/A')}", - f"Position Role: {state.get('position_role', 'N/A')}", - f"Macro Regime: {macro.get('regime_label', '?')} | Risk: {macro.get('risk_appetite', '?')} | Liq: {macro.get('liquidity_regime', '?')}", - f"Business Quality: {bq.get('score_0_to_10', 'N/A')} | Moat: {bq.get('competitive_moat', '?')}", - f"Inst Flow: {inst.get('score_0_to_10', 'N/A')} | Smart Money: {inst.get('smart_money_signal', '?')}", - f"Valuation: {val.get('score_0_to_10', 'N/A')} | Verdict: {val.get('valuation_verdict', '?')}", - f"Earnings Rev: {er.get('score_0_to_10', 'N/A')} | Direction: {er.get('eps_revision_direction', '?')}", - ]) - - -# --------------------------------------------------------------------------- -# Theme Substitution Engine -# --------------------------------------------------------------------------- - -def create_theme_substitution_node(llm): - """Identifies whether the stock is the best expression of its theme.""" - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - ticker = state["ticker"] - card = state.get("company_card") or {} - summary = _summarize_for_theme(state) - master_score = state.get("master_score", 0) - - # Use yfinance to find peers in the same industry - try: - t = yf.Ticker(ticker.upper()) - info = t.info or {} - industry = info.get("industry", "") - sector = info.get("sector", "") - except Exception: - industry = card.get("industry", "") - sector = card.get("sector", "") - - # Fetch competitor/peer data to ground the LLM's comparison - competitors = card.get("competitors") or [] - peer_data = _fetch_peer_basics(competitors) if competitors else [] - peer_summary = "" - if peer_data: - lines = [] - for p in peer_data: - if p.get("error"): - continue - rg = p.get("revenue_growth") - rg_str = f"{rg*100:.1f}%" if rg else "N/A" - pm = p.get("profit_margins") - pm_str = f"{pm*100:.1f}%" if pm else "N/A" - lines.append( - f" {p['ticker']}: P/E={p.get('trailing_pe', 'N/A')}, " - f"Fwd P/E={p.get('forward_pe', 'N/A')}, " - f"RevGrowth={rg_str}, " - f"Margins={pm_str}, " - f"52W={p.get('52w_range_pct', 'N/A')}%" - ) - peer_summary = "\n".join(lines) - - theme_prompt = f"""You are a Theme Substitution Analyst. Your job: determine if {ticker} is the BEST -expression of its investment theme, or if better alternatives exist. - -CANDIDATE STOCK: -{summary} - -{f'PEER FUNDAMENTALS (live data):{chr(10)}{peer_summary}' if peer_summary else 'No live peer data available — use your knowledge of these companies.'} - -INSTRUCTIONS — do this in order: - -1. IDENTIFY THE THEME: What macro/sector theme does {ticker} express? - Examples: "AI infrastructure buildout", "GLP-1 obesity drugs", "defense spending ramp", - "EV supply chain", "cloud migration", "reshoring/nearshoring". - Name it clearly in theme_name. - -2. LIST THEME PEERS: Name 3-6 other publicly traded stocks that express the SAME theme. - Use the peer data above if available. These should be the strongest competitors - for capital allocation in this theme. - For each peer, score master_score_estimate (0-10) based on fundamentals, momentum, - and positioning vs {ticker}. - -3. RANK WITHIN THEME: Rank all stocks (including {ticker}) by investment quality. - The stock with the best combination of: business quality, valuation, momentum, - and institutional positioning should rank #1. - -4. DETERMINE BEST EXPRESSION: - - Set best_expression_of_theme=true if {ticker} is rank #1 or close (#1-2). - - Set best_expression_of_theme=false if clearly better alternatives exist. - - List stronger_alternatives (tickers that rank above {ticker}). - - Set relative_score_gap: how many score points {ticker} trails the best alternative - (0 if {ticker} is best, positive number if it trails). - -5. PORTFOLIO OVERLAP: Flag if {ticker} has high correlation with common holdings. - Set portfolio_overlap_warning if this stock would add redundant exposure. - -Be honest and rigorous. A stock can score well absolutely but still not be the best -way to express its theme.""" - - try: - result = invoke_structured(llm, ThemeSubstitutionOutput, theme_prompt) - except Exception as e: - logger.warning("ThemeSubstitution LLM failed: %s", e) - result = ThemeSubstitutionOutput( - theme_name="Unknown", - best_expression_of_theme=True, - reasoning="Theme analysis unavailable", - ) - - return {"theme_substitution": result.model_dump()} - - return node - - -# --------------------------------------------------------------------------- -# Position Replacement Agent -# --------------------------------------------------------------------------- - -def create_position_replacement_node(llm): - """Identifies when a new stock is a better use of capital than alternatives.""" - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - ticker = state["ticker"] - summary = _summarize_for_theme(state) - master_score = state.get("master_score", 0) - theme = state.get("theme_substitution") or {} - - # Get the strongest alternative from theme analysis - stronger = theme.get("stronger_alternatives", []) - theme_stocks = theme.get("theme_stocks_ranked", []) - theme_name = theme.get("theme_name", "Unknown") - - # If no stronger alternatives, this IS the best — skip deep comparison - if not stronger and theme.get("best_expression_of_theme", True): - result = PositionReplacementOutput( - replace_candidate=ticker, - replace_with="", - score_difference=0.0, - theme_overlap=theme_name, - replacement_reason=f"{ticker} is the best expression of the '{theme_name}' theme.", - conviction_level="high", - should_replace=False, - ) - return {"position_replacement": result.model_dump()} - - # Format theme peers for comparison - peer_lines = [] - for ts in theme_stocks[:6]: - if isinstance(ts, dict): - peer_lines.append( - f" {ts.get('ticker', '?')}: est. score {ts.get('master_score_estimate', '?')}/10 " - f"— advantage: {ts.get('key_advantage', 'N/A')}, weakness: {ts.get('key_weakness', 'N/A')}" - ) - - prompt = f"""You are a Position Replacement Analyst. Determine if {ticker} should be replaced -by a stronger alternative in the same theme. - -CANDIDATE STOCK: -{summary} - -THEME: {theme_name} -Best expression: {'Yes' if theme.get('best_expression_of_theme') else 'No'} -Score gap vs best: {theme.get('relative_score_gap', 0):.1f} - -THEME PEERS: -{chr(10).join(peer_lines) or 'No peers available'} - -STRONGER ALTERNATIVES: {', '.join(stronger) if stronger else 'None'} - -INSTRUCTIONS: -1. Compare {ticker} to the strongest alternative in the theme. -2. Assess on these dimensions: master score, earnings revisions, institutional flow, - risk profile, valuation, entry timing. -3. Set replace_with to the best alternative ticker (empty if none). -4. Set score_difference: how much better the replacement is (positive = replacement is stronger). -5. Set conviction_level: high / medium / low. - - high: replacement is clearly better on 3+ dimensions. - - medium: replacement is better on 1-2 dimensions, mixed on others. - - low: marginal difference, keep current. -6. Set should_replace=true only if conviction_level is high. -7. List what the replacement is stronger_on and weaker_on vs {ticker}. - -Be conservative. Don't recommend replacement for marginal differences.""" - - try: - result = invoke_structured(llm, PositionReplacementOutput, prompt) - except Exception as e: - logger.warning("PositionReplacement LLM failed: %s", e) - result = PositionReplacementOutput( - replace_candidate=ticker, - should_replace=False, - replacement_reason="Position replacement analysis unavailable", - ) - - result.replace_candidate = ticker - result.theme_overlap = theme_name - - return {"position_replacement": result.model_dump()} - - return node +"""Portfolio-level agents: Theme Substitution Engine, Position Replacement Agent. + +These run after scoring, before the debate phase. They use the deep-thinking LLM +to evaluate the stock in context — is it the best expression of its theme? Should +it replace an existing holding? +""" + +from __future__ import annotations + +import json +import logging +from typing import Any, Dict, List + +import yfinance as yf + +from tradingagents.models import ( + PositionReplacementOutput, + ThemeStock, + ThemeSubstitutionOutput, + invoke_structured, +) + +logger = logging.getLogger(__name__) + + +def _fetch_peer_basics(tickers: List[str]) -> List[dict]: + """Fetch basic yfinance data for a list of peer tickers.""" + peers = [] + for sym in tickers[:8]: # cap at 8 to keep prompt manageable + try: + info = yf.Ticker(sym.upper()).info or {} + peers.append({ + "ticker": sym.upper(), + "company_name": info.get("longName") or info.get("shortName") or sym, + "market_cap": info.get("marketCap"), + "current_price": info.get("currentPrice") or info.get("regularMarketPrice"), + "trailing_pe": info.get("trailingPE"), + "forward_pe": info.get("forwardPE"), + "revenue_growth": info.get("revenueGrowth"), + "profit_margins": info.get("profitMargins"), + "return_on_equity": info.get("returnOnEquity"), + "52w_range_pct": _range_pct(info), + }) + except Exception: + peers.append({"ticker": sym.upper(), "error": "fetch failed"}) + return peers + + +def _range_pct(info: dict) -> float | None: + hi = info.get("fiftyTwoWeekHigh") + lo = info.get("fiftyTwoWeekLow") + price = info.get("currentPrice") or info.get("regularMarketPrice") + if hi and lo and price and (hi - lo) > 0: + return round((price - lo) / (hi - lo) * 100, 1) + return None + + +def _summarize_for_theme(state: Dict[str, Any]) -> str: + """Compact summary of the candidate stock for theme comparison.""" + card = state.get("company_card") or {} + macro = state.get("macro") or {} + bq = state.get("business_quality") or {} + inst = state.get("institutional_flow") or {} + val = state.get("valuation") or {} + er = state.get("earnings_revisions") or {} + arch = state.get("archetype") or {} + + return "\n".join([ + f"Ticker: {card.get('ticker', '?')} | {card.get('company_name', '?')}", + f"Sector: {card.get('sector', '?')} | Industry: {card.get('industry', '?')}", + f"Market Cap: {card.get('market_cap_formatted', 'N/A')}", + f"Archetype: {arch.get('archetype', 'N/A')}", + f"Master Score: {state.get('master_score', 'N/A')}", + f"Adjusted Score: {state.get('adjusted_score', 'N/A')}", + f"Position Role: {state.get('position_role', 'N/A')}", + f"Macro Regime: {macro.get('regime_label', '?')} | Risk: {macro.get('risk_appetite', '?')} | Liq: {macro.get('liquidity_regime', '?')}", + f"Business Quality: {bq.get('score_0_to_10', 'N/A')} | Moat: {bq.get('competitive_moat', '?')}", + f"Inst Flow: {inst.get('score_0_to_10', 'N/A')} | Smart Money: {inst.get('smart_money_signal', '?')}", + f"Valuation: {val.get('score_0_to_10', 'N/A')} | Verdict: {val.get('valuation_verdict', '?')}", + f"Earnings Rev: {er.get('score_0_to_10', 'N/A')} | Direction: {er.get('eps_revision_direction', '?')}", + ]) + + +# --------------------------------------------------------------------------- +# Theme Substitution Engine +# --------------------------------------------------------------------------- + +def create_theme_substitution_node(llm): + """Identifies whether the stock is the best expression of its theme.""" + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["ticker"] + card = state.get("company_card") or {} + summary = _summarize_for_theme(state) + master_score = state.get("master_score", 0) + + # Use yfinance to find peers in the same industry + try: + t = yf.Ticker(ticker.upper()) + info = t.info or {} + industry = info.get("industry", "") + sector = info.get("sector", "") + except Exception: + industry = card.get("industry", "") + sector = card.get("sector", "") + + # Fetch competitor/peer data to ground the LLM's comparison + competitors = card.get("competitors") or [] + peer_data = _fetch_peer_basics(competitors) if competitors else [] + peer_summary = "" + if peer_data: + lines = [] + for p in peer_data: + if p.get("error"): + continue + rg = p.get("revenue_growth") + rg_str = f"{rg*100:.1f}%" if rg else "N/A" + pm = p.get("profit_margins") + pm_str = f"{pm*100:.1f}%" if pm else "N/A" + lines.append( + f" {p['ticker']}: P/E={p.get('trailing_pe', 'N/A')}, " + f"Fwd P/E={p.get('forward_pe', 'N/A')}, " + f"RevGrowth={rg_str}, " + f"Margins={pm_str}, " + f"52W={p.get('52w_range_pct', 'N/A')}%" + ) + peer_summary = "\n".join(lines) + + theme_prompt = f"""You are a Theme Substitution Analyst. Your job: determine if {ticker} is the BEST +expression of its investment theme, or if better alternatives exist. + +CANDIDATE STOCK: +{summary} + +{f'PEER FUNDAMENTALS (live data):{chr(10)}{peer_summary}' if peer_summary else 'No live peer data available — use your knowledge of these companies.'} + +INSTRUCTIONS — do this in order: + +1. IDENTIFY THE THEME: What macro/sector theme does {ticker} express? + Examples: "AI infrastructure buildout", "GLP-1 obesity drugs", "defense spending ramp", + "EV supply chain", "cloud migration", "reshoring/nearshoring". + Name it clearly in theme_name. + +2. LIST THEME PEERS: Name 3-6 other publicly traded stocks that express the SAME theme. + Use the peer data above if available. These should be the strongest competitors + for capital allocation in this theme. + For each peer, score master_score_estimate (0-10) based on fundamentals, momentum, + and positioning vs {ticker}. + +3. RANK WITHIN THEME: Rank all stocks (including {ticker}) by investment quality. + The stock with the best combination of: business quality, valuation, momentum, + and institutional positioning should rank #1. + +4. DETERMINE BEST EXPRESSION: + - Set best_expression_of_theme=true if {ticker} is rank #1 or close (#1-2). + - Set best_expression_of_theme=false if clearly better alternatives exist. + - List stronger_alternatives (tickers that rank above {ticker}). + - Set relative_score_gap: how many score points {ticker} trails the best alternative + (0 if {ticker} is best, positive number if it trails). + +5. PORTFOLIO OVERLAP: Flag if {ticker} has high correlation with common holdings. + Set portfolio_overlap_warning if this stock would add redundant exposure. + +Be honest and rigorous. A stock can score well absolutely but still not be the best +way to express its theme.""" + + try: + result = invoke_structured(llm, ThemeSubstitutionOutput, theme_prompt) + except Exception as e: + logger.warning("ThemeSubstitution LLM failed: %s", e) + result = ThemeSubstitutionOutput( + theme_name="Unknown", + best_expression_of_theme=True, + reasoning="Theme analysis unavailable", + ) + + return {"theme_substitution": result.model_dump()} + + return node + + +# --------------------------------------------------------------------------- +# Position Replacement Agent +# --------------------------------------------------------------------------- + +def create_position_replacement_node(llm): + """Identifies when a new stock is a better use of capital than alternatives.""" + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["ticker"] + summary = _summarize_for_theme(state) + master_score = state.get("master_score", 0) + theme = state.get("theme_substitution") or {} + + # Get the strongest alternative from theme analysis + stronger = theme.get("stronger_alternatives", []) + theme_stocks = theme.get("theme_stocks_ranked", []) + theme_name = theme.get("theme_name", "Unknown") + + # If no stronger alternatives, this IS the best — skip deep comparison + if not stronger and theme.get("best_expression_of_theme", True): + result = PositionReplacementOutput( + replace_candidate=ticker, + replace_with="", + score_difference=0.0, + theme_overlap=theme_name, + replacement_reason=f"{ticker} is the best expression of the '{theme_name}' theme.", + conviction_level="high", + should_replace=False, + ) + return {"position_replacement": result.model_dump()} + + # Format theme peers for comparison + peer_lines = [] + for ts in theme_stocks[:6]: + if isinstance(ts, dict): + peer_lines.append( + f" {ts.get('ticker', '?')}: est. score {ts.get('master_score_estimate', '?')}/10 " + f"— advantage: {ts.get('key_advantage', 'N/A')}, weakness: {ts.get('key_weakness', 'N/A')}" + ) + + prompt = f"""You are a Position Replacement Analyst. Determine if {ticker} should be replaced +by a stronger alternative in the same theme. + +CANDIDATE STOCK: +{summary} + +THEME: {theme_name} +Best expression: {'Yes' if theme.get('best_expression_of_theme') else 'No'} +Score gap vs best: {theme.get('relative_score_gap', 0):.1f} + +THEME PEERS: +{chr(10).join(peer_lines) or 'No peers available'} + +STRONGER ALTERNATIVES: {', '.join(stronger) if stronger else 'None'} + +INSTRUCTIONS: +1. Compare {ticker} to the strongest alternative in the theme. +2. Assess on these dimensions: master score, earnings revisions, institutional flow, + risk profile, valuation, entry timing. +3. Set replace_with to the best alternative ticker (empty if none). +4. Set score_difference: how much better the replacement is (positive = replacement is stronger). +5. Set conviction_level: high / medium / low. + - high: replacement is clearly better on 3+ dimensions. + - medium: replacement is better on 1-2 dimensions, mixed on others. + - low: marginal difference, keep current. +6. Set should_replace=true only if conviction_level is high. +7. List what the replacement is stronger_on and weaker_on vs {ticker}. + +Be conservative. Don't recommend replacement for marginal differences.""" + + try: + result = invoke_structured(llm, PositionReplacementOutput, prompt) + except Exception as e: + logger.warning("PositionReplacement LLM failed: %s", e) + result = PositionReplacementOutput( + replace_candidate=ticker, + should_replace=False, + replacement_reason="Position replacement analysis unavailable", + ) + + result.replace_candidate = ticker + result.theme_overlap = theme_name + + return {"position_replacement": result.model_dump()} + + return node diff --git a/tradingagents/agents/structured/scoring.py b/tradingagents/agents/structured/scoring.py index 79a3d236..a1691e99 100644 --- a/tradingagents/agents/structured/scoring.py +++ b/tradingagents/agents/structured/scoring.py @@ -1,59 +1,59 @@ -"""Deterministic scoring node — no LLM, pure Python. - -Computes master_score, applies confidence penalties, checks hard vetoes, -and assigns position roles. This is the heart of the deterministic pipeline. -""" - -from __future__ import annotations - -from typing import Any, Dict - -from tradingagents.models import ( - DataFlag, - apply_confidence_penalty, - assign_position_role, - compute_master_score, -) - - -def create_scoring_node(): - """Create the deterministic scoring node (no LLM needed).""" - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - # Extract scores from each agent output - bq = (state.get("business_quality") or {}).get("score_0_to_10", 5.0) - macro = (state.get("macro") or {}).get("macro_alignment_0_to_10", 5.0) - inst = (state.get("institutional_flow") or {}).get("score_0_to_10", 5.0) - val = (state.get("valuation") or {}).get("score_0_to_10", 5.0) - et = (state.get("entry_timing") or {}).get("score_0_to_10", 5.0) - er = (state.get("earnings_revisions") or {}).get("score_0_to_10", 5.0) - bl = (state.get("backlog") or {}).get("score_0_to_10", 5.0) - cr = (state.get("crowding") or {}).get("score_0_to_10", 5.0) - - # Regime adjustment from macro agent - regime_adj = (state.get("macro") or {}).get("regime_score_adjustment", 0.0) - - master = compute_master_score( - bq, macro, inst, val, et, er, bl, cr, - regime_adjustment=regime_adj, - ) - - # Collect all data quality flags - all_flags = [] - for f in (state.get("global_flags") or []): - if isinstance(f, dict): - all_flags.append(DataFlag(**f)) - elif isinstance(f, DataFlag): - all_flags.append(f) - - hard_veto = state.get("hard_veto", False) - adjusted = apply_confidence_penalty(master, all_flags, hard_veto) - role = assign_position_role(adjusted) - - return { - "master_score": master, - "adjusted_score": adjusted, - "position_role": role, - } - - return node +"""Deterministic scoring node — no LLM, pure Python. + +Computes master_score, applies confidence penalties, checks hard vetoes, +and assigns position roles. This is the heart of the deterministic pipeline. +""" + +from __future__ import annotations + +from typing import Any, Dict + +from tradingagents.models import ( + DataFlag, + apply_confidence_penalty, + assign_position_role, + compute_master_score, +) + + +def create_scoring_node(): + """Create the deterministic scoring node (no LLM needed).""" + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + # Extract scores from each agent output + bq = (state.get("business_quality") or {}).get("score_0_to_10", 5.0) + macro = (state.get("macro") or {}).get("macro_alignment_0_to_10", 5.0) + inst = (state.get("institutional_flow") or {}).get("score_0_to_10", 5.0) + val = (state.get("valuation") or {}).get("score_0_to_10", 5.0) + et = (state.get("entry_timing") or {}).get("score_0_to_10", 5.0) + er = (state.get("earnings_revisions") or {}).get("score_0_to_10", 5.0) + bl = (state.get("backlog") or {}).get("score_0_to_10", 5.0) + cr = (state.get("crowding") or {}).get("score_0_to_10", 5.0) + + # Regime adjustment from macro agent + regime_adj = (state.get("macro") or {}).get("regime_score_adjustment", 0.0) + + master = compute_master_score( + bq, macro, inst, val, et, er, bl, cr, + regime_adjustment=regime_adj, + ) + + # Collect all data quality flags + all_flags = [] + for f in (state.get("global_flags") or []): + if isinstance(f, dict): + all_flags.append(DataFlag(**f)) + elif isinstance(f, DataFlag): + all_flags.append(f) + + hard_veto = state.get("hard_veto", False) + adjusted = apply_confidence_penalty(master, all_flags, hard_veto) + role = assign_position_role(adjusted) + + return { + "master_score": master, + "adjusted_score": adjusted, + "position_role": role, + } + + return node diff --git a/tradingagents/agents/structured/tier1.py b/tradingagents/agents/structured/tier1.py index bb0b4fd2..707a152b 100644 --- a/tradingagents/agents/structured/tier1.py +++ b/tradingagents/agents/structured/tier1.py @@ -1,277 +1,277 @@ -"""Tier 1 agents: Validation, Macro Regime, Liquidity. - -Tier 1 is cheap and fast — runs on every stock. Validation is deterministic -(no LLM). Macro and Liquidity use the quick-thinking LLM. -""" - -from __future__ import annotations - -import json -import logging -from typing import Any, Dict - -import yfinance as yf - -from tradingagents.models import ( - CompanyCard, - DataFlag, - LiquidityOutput, - MacroRegimeOutput, - ValidationOutput, - invoke_structured, -) - -logger = logging.getLogger(__name__) - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - -def _fmt_num(val): - if val is None: - return None - if abs(val) >= 1e12: - return f"${val / 1e12:.2f}T" - if abs(val) >= 1e9: - return f"${val / 1e9:.2f}B" - if abs(val) >= 1e6: - return f"${val / 1e6:.2f}M" - return f"${val:,.0f}" - - -def _fetch_yf_info(ticker: str) -> dict: - """Fetch yfinance info dict for a ticker.""" - try: - t = yf.Ticker(ticker.upper()) - return t.info or {} - except Exception as e: - logger.warning("yfinance fetch failed for %s: %s", ticker, e) - return {} - - -def _fetch_macro_data() -> dict: - """Fetch macro indicators via yfinance.""" - from tradingagents.dataflows.y_finance import get_macro_indicators - - try: - raw = get_macro_indicators() - return json.loads(raw) if isinstance(raw, str) else raw - except Exception as e: - logger.warning("Macro data fetch failed: %s", e) - return {} - - -# --------------------------------------------------------------------------- -# Validation (deterministic — no LLM) -# --------------------------------------------------------------------------- - -def create_validation_node(llm=None): - """Validation + CompanyCard node. Does NOT use LLM — purely data-driven.""" - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - ticker = state["ticker"] - info = _fetch_yf_info(ticker) - - # No data at all → hard veto - company_name = info.get("longName") or info.get("shortName") or "" - if not company_name: - v = ValidationOutput( - ticker_valid=False, - ticker_resolved=ticker.upper(), - company_name="", - veto=True, - veto_reason=f"No company data found for {ticker}", - data_quality_flags=[ - DataFlag(field="ticker", severity="severe", - message=f"No data for {ticker}") - ], - ) - return { - "validation": v.model_dump(), - "hard_veto": True, - "hard_veto_reason": v.veto_reason, - "global_flags": [ - DataFlag(field="ticker", severity="severe", - message=f"No data for {ticker}").model_dump() - ], - } - - validation = ValidationOutput( - ticker_valid=True, - ticker_resolved=ticker.upper(), - company_name=company_name, - company_name_match=True, - exchange=info.get("exchange"), - sector=info.get("sector"), - industry=info.get("industry"), - is_active=True, - ) - - # Build company card - mc = info.get("marketCap") - if mc and mc >= 10e9: - mc_cat = "large_cap" - elif mc and mc >= 2e9: - mc_cat = "mid_cap" - elif mc and mc >= 300e6: - mc_cat = "small_cap" - else: - mc_cat = "micro_cap" if mc else "unknown" - - card = CompanyCard( - company_name=company_name, - ticker=ticker.upper(), - sector=info.get("sector", "Unknown"), - industry=info.get("industry", "Unknown"), - description=(info.get("longBusinessSummary") or "")[:500], - market_cap=mc, - market_cap_formatted=_fmt_num(mc), - market_cap_category=mc_cat, - current_price=info.get("currentPrice") or info.get("regularMarketPrice"), - revenue=info.get("totalRevenue"), - profit_margins=info.get("profitMargins"), - employees=info.get("fullTimeEmployees"), - ) - - return { - "validation": validation.model_dump(), - "company_card": card.model_dump(), - } - - return node - - -# --------------------------------------------------------------------------- -# Macro Regime -# --------------------------------------------------------------------------- - -def create_macro_node(llm): - """Macro regime analysis node — uses quick LLM.""" - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - ticker = state["ticker"] - macro_data = _fetch_macro_data() - card = state.get("company_card") or {} - sector = card.get("sector", "Unknown") - - spy_perf = (macro_data.get("sector_performance") or {}).get("SPY", {}) - sector_perfs = macro_data.get("sector_performance") or {} - - # Build compact sector table - sector_lines = [] - for etf, data in sorted(sector_perfs.items()): - r1 = data.get("return_1m") - name = data.get("name", etf) - if r1 is not None: - sector_lines.append(f" {etf} ({name}): {r1:+.1f}% 1M") - - prompt = f"""You are a Macro Regime Analyst in a structured equity ranking pipeline. - -Ticker: {ticker} | Sector: {sector} - -MACRO DATA (source: yfinance): -- VIX: {macro_data.get('vix_level', 'N/A')} (source: yfinance) -- 10Y Yield: {macro_data.get('ten_year_yield', 'N/A')}% (source: yfinance) -- Dollar 1M: {macro_data.get('dollar_1m_return', 'N/A')}% (source: yfinance) -- Credit Spreads: {macro_data.get('credit_spread_direction', 'N/A')} (source: yfinance) -- SPY 1M: {spy_perf.get('return_1m', 'N/A')}% (source: yfinance) - -SECTOR PERFORMANCE (1M, source: yfinance): -{chr(10).join(sector_lines[:12]) or 'N/A'} - -NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. - -INSTRUCTIONS: -1. Classify risk_appetite: "risk-on" / "risk-off" / "transitional". - - risk-on: VIX low, spreads tight, SPY up, breadth strong. - - risk-off: VIX elevated, spreads widening, SPY down, flight to safety. - - transitional: mixed signals. -2. Classify liquidity_regime: "expansion" / "contraction" / "neutral". - - expansion: falling yields, dovish Fed, credit flowing, dollar weakening. - - contraction: rising yields, hawkish Fed, tight credit, dollar strengthening. -3. Set regime_score_adjustment (-10 to +10): - - +5 to +10 = strong macro tailwind for this specific stock/sector. - - +1 to +4 = mild tailwind. - - 0 = neutral. - - -1 to -4 = mild headwind. - - -5 to -10 = severe macro headwind (risk-off + contraction + hostile sector). - This adjustment directly modifies the 0-100 master score for ALL stocks. -4. Score macro_alignment_0_to_10: how well macro supports {ticker} specifically. -5. Also provide score_0_to_10 (overall macro health). -6. Set regime_label: descriptive label (e.g., "Late Cycle Risk-Off"). -7. List key positives, negatives, risks. Be concise.""" - - try: - result = invoke_structured(llm, MacroRegimeOutput, prompt) - except Exception as e: - logger.warning("Macro LLM call failed: %s", e) - result = MacroRegimeOutput( - score_0_to_10=5.0, confidence_0_to_1=0.1, - summary_1_sentence="Macro analysis unavailable", - data_quality_flags=[ - DataFlag(field="macro", severity="moderate", message=str(e)) - ], - ) - - # Override with actual fetched data - result.vix_level = macro_data.get("vix_level") - result.vix_regime = macro_data.get("vix_regime", "unknown") - result.ten_year_yield = macro_data.get("ten_year_yield") - result.dollar_strength = macro_data.get("dollar_strength", "unknown") - result.credit_spread_direction = macro_data.get( - "credit_spread_direction", "unknown" - ) - result.spy_1m_return = spy_perf.get("return_1m") - - flags = [f.model_dump() for f in result.data_quality_flags] - return {"macro": result.model_dump(), "global_flags": flags} - - return node - - -# --------------------------------------------------------------------------- -# Liquidity -# --------------------------------------------------------------------------- - -def create_liquidity_node(llm): - """Liquidity analysis node — uses quick LLM.""" - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - ticker = state["ticker"] - macro_data = _fetch_macro_data() - card = state.get("company_card") or {} - - prompt = f"""You are a Liquidity Analyst in a structured equity ranking pipeline. - -Ticker: {ticker} | Sector: {card.get('sector', 'Unknown')} - -AVAILABLE DATA (source: yfinance macro API): -- VIX: {macro_data.get('vix_level', 'N/A')} (source: yfinance) -- 10Y Yield: {macro_data.get('ten_year_yield', 'N/A')}% (source: yfinance) -- Credit Spreads: {macro_data.get('credit_spread_direction', 'N/A')} (source: yfinance) -- Dollar Strength: {macro_data.get('dollar_strength', 'N/A')} (source: yfinance) - -NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. - -INSTRUCTIONS: -1. Assess Fed stance (dovish / neutral / hawkish) based on yield environment. -2. Assess market breadth (strong / moderate / weak). -3. Assess volume profile (above_average / average / below_average). -4. Assess SPY trend (uptrend / downtrend / sideways). -5. Score overall liquidity favorability 0-10 for this stock. -6. Be concise.""" - - try: - result = invoke_structured(llm, LiquidityOutput, prompt) - except Exception as e: - logger.warning("Liquidity LLM call failed: %s", e) - result = LiquidityOutput( - score_0_to_10=5.0, confidence_0_to_1=0.1, - summary_1_sentence="Liquidity analysis unavailable", - ) - - flags = [f.model_dump() for f in result.data_quality_flags] - return {"liquidity": result.model_dump(), "global_flags": flags} - - return node +"""Tier 1 agents: Validation, Macro Regime, Liquidity. + +Tier 1 is cheap and fast — runs on every stock. Validation is deterministic +(no LLM). Macro and Liquidity use the quick-thinking LLM. +""" + +from __future__ import annotations + +import json +import logging +from typing import Any, Dict + +import yfinance as yf + +from tradingagents.models import ( + CompanyCard, + DataFlag, + LiquidityOutput, + MacroRegimeOutput, + ValidationOutput, + invoke_structured, +) + +logger = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def _fmt_num(val): + if val is None: + return None + if abs(val) >= 1e12: + return f"${val / 1e12:.2f}T" + if abs(val) >= 1e9: + return f"${val / 1e9:.2f}B" + if abs(val) >= 1e6: + return f"${val / 1e6:.2f}M" + return f"${val:,.0f}" + + +def _fetch_yf_info(ticker: str) -> dict: + """Fetch yfinance info dict for a ticker.""" + try: + t = yf.Ticker(ticker.upper()) + return t.info or {} + except Exception as e: + logger.warning("yfinance fetch failed for %s: %s", ticker, e) + return {} + + +def _fetch_macro_data() -> dict: + """Fetch macro indicators via yfinance.""" + from tradingagents.dataflows.y_finance import get_macro_indicators + + try: + raw = get_macro_indicators() + return json.loads(raw) if isinstance(raw, str) else raw + except Exception as e: + logger.warning("Macro data fetch failed: %s", e) + return {} + + +# --------------------------------------------------------------------------- +# Validation (deterministic — no LLM) +# --------------------------------------------------------------------------- + +def create_validation_node(llm=None): + """Validation + CompanyCard node. Does NOT use LLM — purely data-driven.""" + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["ticker"] + info = _fetch_yf_info(ticker) + + # No data at all → hard veto + company_name = info.get("longName") or info.get("shortName") or "" + if not company_name: + v = ValidationOutput( + ticker_valid=False, + ticker_resolved=ticker.upper(), + company_name="", + veto=True, + veto_reason=f"No company data found for {ticker}", + data_quality_flags=[ + DataFlag(field="ticker", severity="severe", + message=f"No data for {ticker}") + ], + ) + return { + "validation": v.model_dump(), + "hard_veto": True, + "hard_veto_reason": v.veto_reason, + "global_flags": [ + DataFlag(field="ticker", severity="severe", + message=f"No data for {ticker}").model_dump() + ], + } + + validation = ValidationOutput( + ticker_valid=True, + ticker_resolved=ticker.upper(), + company_name=company_name, + company_name_match=True, + exchange=info.get("exchange"), + sector=info.get("sector"), + industry=info.get("industry"), + is_active=True, + ) + + # Build company card + mc = info.get("marketCap") + if mc and mc >= 10e9: + mc_cat = "large_cap" + elif mc and mc >= 2e9: + mc_cat = "mid_cap" + elif mc and mc >= 300e6: + mc_cat = "small_cap" + else: + mc_cat = "micro_cap" if mc else "unknown" + + card = CompanyCard( + company_name=company_name, + ticker=ticker.upper(), + sector=info.get("sector", "Unknown"), + industry=info.get("industry", "Unknown"), + description=(info.get("longBusinessSummary") or "")[:500], + market_cap=mc, + market_cap_formatted=_fmt_num(mc), + market_cap_category=mc_cat, + current_price=info.get("currentPrice") or info.get("regularMarketPrice"), + revenue=info.get("totalRevenue"), + profit_margins=info.get("profitMargins"), + employees=info.get("fullTimeEmployees"), + ) + + return { + "validation": validation.model_dump(), + "company_card": card.model_dump(), + } + + return node + + +# --------------------------------------------------------------------------- +# Macro Regime +# --------------------------------------------------------------------------- + +def create_macro_node(llm): + """Macro regime analysis node — uses quick LLM.""" + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["ticker"] + macro_data = _fetch_macro_data() + card = state.get("company_card") or {} + sector = card.get("sector", "Unknown") + + spy_perf = (macro_data.get("sector_performance") or {}).get("SPY", {}) + sector_perfs = macro_data.get("sector_performance") or {} + + # Build compact sector table + sector_lines = [] + for etf, data in sorted(sector_perfs.items()): + r1 = data.get("return_1m") + name = data.get("name", etf) + if r1 is not None: + sector_lines.append(f" {etf} ({name}): {r1:+.1f}% 1M") + + prompt = f"""You are a Macro Regime Analyst in a structured equity ranking pipeline. + +Ticker: {ticker} | Sector: {sector} + +MACRO DATA (source: yfinance): +- VIX: {macro_data.get('vix_level', 'N/A')} (source: yfinance) +- 10Y Yield: {macro_data.get('ten_year_yield', 'N/A')}% (source: yfinance) +- Dollar 1M: {macro_data.get('dollar_1m_return', 'N/A')}% (source: yfinance) +- Credit Spreads: {macro_data.get('credit_spread_direction', 'N/A')} (source: yfinance) +- SPY 1M: {spy_perf.get('return_1m', 'N/A')}% (source: yfinance) + +SECTOR PERFORMANCE (1M, source: yfinance): +{chr(10).join(sector_lines[:12]) or 'N/A'} + +NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. + +INSTRUCTIONS: +1. Classify risk_appetite: "risk-on" / "risk-off" / "transitional". + - risk-on: VIX low, spreads tight, SPY up, breadth strong. + - risk-off: VIX elevated, spreads widening, SPY down, flight to safety. + - transitional: mixed signals. +2. Classify liquidity_regime: "expansion" / "contraction" / "neutral". + - expansion: falling yields, dovish Fed, credit flowing, dollar weakening. + - contraction: rising yields, hawkish Fed, tight credit, dollar strengthening. +3. Set regime_score_adjustment (-10 to +10): + - +5 to +10 = strong macro tailwind for this specific stock/sector. + - +1 to +4 = mild tailwind. + - 0 = neutral. + - -1 to -4 = mild headwind. + - -5 to -10 = severe macro headwind (risk-off + contraction + hostile sector). + This adjustment directly modifies the 0-100 master score for ALL stocks. +4. Score macro_alignment_0_to_10: how well macro supports {ticker} specifically. +5. Also provide score_0_to_10 (overall macro health). +6. Set regime_label: descriptive label (e.g., "Late Cycle Risk-Off"). +7. List key positives, negatives, risks. Be concise.""" + + try: + result = invoke_structured(llm, MacroRegimeOutput, prompt) + except Exception as e: + logger.warning("Macro LLM call failed: %s", e) + result = MacroRegimeOutput( + score_0_to_10=5.0, confidence_0_to_1=0.1, + summary_1_sentence="Macro analysis unavailable", + data_quality_flags=[ + DataFlag(field="macro", severity="moderate", message=str(e)) + ], + ) + + # Override with actual fetched data + result.vix_level = macro_data.get("vix_level") + result.vix_regime = macro_data.get("vix_regime", "unknown") + result.ten_year_yield = macro_data.get("ten_year_yield") + result.dollar_strength = macro_data.get("dollar_strength", "unknown") + result.credit_spread_direction = macro_data.get( + "credit_spread_direction", "unknown" + ) + result.spy_1m_return = spy_perf.get("return_1m") + + flags = [f.model_dump() for f in result.data_quality_flags] + return {"macro": result.model_dump(), "global_flags": flags} + + return node + + +# --------------------------------------------------------------------------- +# Liquidity +# --------------------------------------------------------------------------- + +def create_liquidity_node(llm): + """Liquidity analysis node — uses quick LLM.""" + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["ticker"] + macro_data = _fetch_macro_data() + card = state.get("company_card") or {} + + prompt = f"""You are a Liquidity Analyst in a structured equity ranking pipeline. + +Ticker: {ticker} | Sector: {card.get('sector', 'Unknown')} + +AVAILABLE DATA (source: yfinance macro API): +- VIX: {macro_data.get('vix_level', 'N/A')} (source: yfinance) +- 10Y Yield: {macro_data.get('ten_year_yield', 'N/A')}% (source: yfinance) +- Credit Spreads: {macro_data.get('credit_spread_direction', 'N/A')} (source: yfinance) +- Dollar Strength: {macro_data.get('dollar_strength', 'N/A')} (source: yfinance) + +NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. + +INSTRUCTIONS: +1. Assess Fed stance (dovish / neutral / hawkish) based on yield environment. +2. Assess market breadth (strong / moderate / weak). +3. Assess volume profile (above_average / average / below_average). +4. Assess SPY trend (uptrend / downtrend / sideways). +5. Score overall liquidity favorability 0-10 for this stock. +6. Be concise.""" + + try: + result = invoke_structured(llm, LiquidityOutput, prompt) + except Exception as e: + logger.warning("Liquidity LLM call failed: %s", e) + result = LiquidityOutput( + score_0_to_10=5.0, confidence_0_to_1=0.1, + summary_1_sentence="Liquidity analysis unavailable", + ) + + flags = [f.model_dump() for f in result.data_quality_flags] + return {"liquidity": result.model_dump(), "global_flags": flags} + + return node diff --git a/tradingagents/agents/structured/tier2.py b/tradingagents/agents/structured/tier2.py index 392c3f67..9bb04fed 100644 --- a/tradingagents/agents/structured/tier2.py +++ b/tradingagents/agents/structured/tier2.py @@ -1,647 +1,647 @@ -"""Tier 2 agents: Deep analysis that runs only on Tier 1 survivors. - -Each agent fetches its own data via yfinance, calls the LLM once with -structured output, and returns a typed result into PipelineState. -""" - -from __future__ import annotations - -import json -import logging -from typing import Any, Dict - -import yfinance as yf - -from tradingagents.models import ( - ArchetypeOutput, - BacklogOrderMomentumOutput, - BusinessQualityOutput, - DataFlag, - EarningsRevisionOutput, - EntryTimingOutput, - InstitutionalFlowOutput, - NarrativeCrowdingOutput, - SectorRotationOutput, - ValuationOutput, - invoke_structured, -) - -logger = logging.getLogger(__name__) - - -def _safe(info, key, default=None): - v = info.get(key) - return default if v is None else v - - -def _pct(v): - return f"{v * 100:.1f}%" if v is not None else "N/A" - - -# --------------------------------------------------------------------------- -# Business Quality -# --------------------------------------------------------------------------- - -def create_business_quality_node(llm): - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - ticker = state["ticker"] - card = state.get("company_card") or {} - - try: - t = yf.Ticker(ticker.upper()) - info = t.info or {} - except Exception: - info = {} - - prompt = f"""You are a Business Quality Analyst in a structured equity ranking pipeline. - -Ticker: {ticker} | Sector: {card.get('sector', 'Unknown')} | Industry: {card.get('industry', 'Unknown')} -Market Cap: {card.get('market_cap_formatted', 'N/A')} - -FINANCIALS (source: yfinance): -- Revenue Growth: {_pct(_safe(info, 'revenueGrowth'))} (source: yfinance) -- Profit Margins: {_pct(_safe(info, 'profitMargins'))} (source: yfinance) -- Operating Margins: {_pct(_safe(info, 'operatingMargins'))} (source: yfinance) -- ROE: {_pct(_safe(info, 'returnOnEquity'))} (source: yfinance) -- ROA: {_pct(_safe(info, 'returnOnAssets'))} (source: yfinance) -- Debt/Equity: {_safe(info, 'debtToEquity', 'N/A')} (source: yfinance) -- Free Cash Flow: {_safe(info, 'freeCashflow', 'N/A')} (source: yfinance) -- Current Ratio: {_safe(info, 'currentRatio', 'N/A')} (source: yfinance) - -NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. - -INSTRUCTIONS: -1. Score business quality 0-10 based on margins, growth, returns, balance sheet. -2. Classify competitive moat: wide / narrow / none. -3. Classify management quality: strong / adequate / weak. -4. List positives, negatives, risks. Be concise.""" - - try: - result = invoke_structured(llm, BusinessQualityOutput, prompt) - except Exception as e: - logger.warning("BusinessQuality LLM failed: %s", e) - result = BusinessQualityOutput( - score_0_to_10=5.0, confidence_0_to_1=0.1, - summary_1_sentence="Business quality analysis unavailable", - data_quality_flags=[ - DataFlag(field="business_quality", severity="moderate", - message="Tier 2 analysis used fallback defaults due to LLM failure") - ], - ) - - # Override with actual data - result.revenue_growth = _safe(info, "revenueGrowth") - result.profit_margins = _safe(info, "profitMargins") - result.operating_margins = _safe(info, "operatingMargins") - result.return_on_equity = _safe(info, "returnOnEquity") - result.return_on_assets = _safe(info, "returnOnAssets") - result.debt_to_equity = _safe(info, "debtToEquity") - result.free_cashflow = _safe(info, "freeCashflow") - - flags = [f.model_dump() for f in result.data_quality_flags] - return {"business_quality": result.model_dump(), "global_flags": flags} - - return node - - -# --------------------------------------------------------------------------- -# Institutional Flow -# --------------------------------------------------------------------------- - -def create_institutional_flow_node(llm): - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - ticker = state["ticker"] - - from tradingagents.dataflows.y_finance import get_institutional_flow - try: - raw = get_institutional_flow(ticker) - data = json.loads(raw) if isinstance(raw, str) else raw - except Exception: - data = {} - - # Format top holders for prompt - holders = data.get("top_institutional_holders", []) - holder_lines = [] - for h in holders[:5]: - pct = h.get("pct_out") - holder_lines.append( - f" {h.get('holder', '?')}: {pct:.1f}%" if pct else f" {h.get('holder', '?')}" - ) - - prompt = f"""You are an Institutional Flow Analyst in a structured equity ranking pipeline. -Your job: track real smart-money movement — not just static ownership percentages. - -Ticker: {ticker} - -OWNERSHIP & VOLUME (source: yfinance): -- Institutional Ownership: {data.get('held_percent_institutions', 'N/A')}% (source: yfinance) -- Insider Ownership: {data.get('held_percent_insiders', 'N/A')}% (source: yfinance) -- Volume Ratio (10d/avg): {data.get('volume_ratio', 'N/A')} (source: yfinance) -- Short % of Float: {data.get('short_pct_of_float', 'N/A')}% (source: yfinance) -- Short Ratio (days): {data.get('short_ratio', 'N/A')} (source: yfinance) -- Float Turnover 5d: {data.get('float_turnover_5d_pct', 'N/A')}% (source: yfinance) - -SHORT INTEREST TREND (source: yfinance): -- Short Interest Change (vs prior month): {data.get('short_interest_change_pct', 'N/A')}% -- Short Interest Trend: {data.get('short_interest_trend', 'N/A')} - -TOP INSTITUTIONAL HOLDERS (13F, source: yfinance): -{chr(10).join(holder_lines) or ' No data available'} -- Total top holders tracked: {data.get('top_holders_count', 'N/A')} - -INSIDER TRANSACTIONS (recent, source: yfinance): -- Insider Buys: {data.get('insider_buys_recent', 'N/A')} -- Insider Sells: {data.get('insider_sells_recent', 'N/A')} -- Insider Signal: {data.get('insider_transaction_signal', 'N/A')} - -NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. - -INSTRUCTIONS: -1. Score institutional flow signal 0-10 (this has 15% weight — make it count). - High ownership + rising volume + low short interest + insider buying = bullish. -2. Classify accumulation_signal: accumulating / distributing / neutral. -3. Classify top_holders_change: increasing / decreasing / stable. - (Based on holder concentration and any visible 13F patterns.) -4. Classify fund_accumulation_pattern: accumulating / distributing / holding. - (Volume + ownership trends suggest funds are adding or reducing.) -5. Classify short_interest_trend: rising / falling / stable. -6. Classify insider_transaction_signal: buying / selling / none. -7. Classify smart_money_signal: bullish / bearish / neutral. - (Synthesize all signals: 13F, insiders, short interest, volume.) -8. Be concise.""" - - try: - result = invoke_structured(llm, InstitutionalFlowOutput, prompt) - except Exception as e: - logger.warning("InstitutionalFlow LLM failed: %s", e) - result = InstitutionalFlowOutput( - score_0_to_10=5.0, confidence_0_to_1=0.1, - summary_1_sentence="Institutional flow analysis unavailable", - data_quality_flags=[ - DataFlag(field="institutional_flow", severity="moderate", - message="Tier 2 analysis used fallback defaults due to LLM failure") - ], - ) - - # Override with actual fetched data - result.institutional_ownership_pct = data.get("held_percent_institutions") - result.insider_ownership_pct = data.get("held_percent_insiders") - result.volume_ratio = data.get("volume_ratio") - result.short_interest_pct = data.get("short_pct_of_float") - result.short_ratio = data.get("short_ratio") - result.float_turnover_pct = data.get("float_turnover_5d_pct") - # Override trend fields with actual data when available - if data.get("short_interest_trend"): - result.short_interest_trend = data["short_interest_trend"] - if data.get("insider_transaction_signal"): - result.insider_transaction_signal = data["insider_transaction_signal"] - - flags = [f.model_dump() for f in result.data_quality_flags] - return {"institutional_flow": result.model_dump(), "global_flags": flags} - - return node - - -# --------------------------------------------------------------------------- -# Valuation -# --------------------------------------------------------------------------- - -def create_valuation_node(llm): - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - ticker = state["ticker"] - - from tradingagents.dataflows.y_finance import get_valuation_peers - try: - raw = get_valuation_peers(ticker) - data = json.loads(raw) if isinstance(raw, str) else raw - except Exception: - data = {} - - prompt = f"""You are a Valuation Analyst in a structured equity ranking pipeline. - -Ticker: {ticker} - -VALUATION METRICS (source: yfinance): -- Trailing P/E: {data.get('trailing_pe', 'N/A')} (source: yfinance) -- Forward P/E: {data.get('forward_pe', 'N/A')} (source: yfinance) -- PEG Ratio: {data.get('peg_ratio', 'N/A')} (source: yfinance) -- P/B: {data.get('price_to_book', 'N/A')} (source: yfinance) -- EV/EBITDA: {data.get('ev_to_ebitda', 'N/A')} (source: yfinance) -- P/S: {data.get('price_to_sales', 'N/A')} (source: yfinance) -- 52W Range Position: {data.get('vs_52w_range_pct', 'N/A')}% (source: yfinance) -- Revenue Growth: {data.get('revenue_growth', 'N/A')} (source: yfinance) -- Earnings Growth: {data.get('earnings_growth', 'N/A')} (source: yfinance) - -NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. - -INSTRUCTIONS: -1. Score valuation attractiveness 0-10. - Low multiples relative to growth = high score. -2. Classify: undervalued / fair / overvalued. -3. Consider industry context (growth stocks deserve higher multiples).""" - - try: - result = invoke_structured(llm, ValuationOutput, prompt) - except Exception as e: - logger.warning("Valuation LLM failed: %s", e) - result = ValuationOutput( - score_0_to_10=5.0, confidence_0_to_1=0.1, - summary_1_sentence="Valuation analysis unavailable", - data_quality_flags=[ - DataFlag(field="valuation", severity="moderate", - message="Tier 2 analysis used fallback defaults due to LLM failure") - ], - ) - - result.trailing_pe = data.get("trailing_pe") - result.forward_pe = data.get("forward_pe") - result.peg_ratio = data.get("peg_ratio") - result.price_to_book = data.get("price_to_book") - result.ev_to_ebitda = data.get("ev_to_ebitda") - result.price_to_sales = data.get("price_to_sales") - result.vs_52w_range_pct = data.get("vs_52w_range_pct") - - flags = [f.model_dump() for f in result.data_quality_flags] - return {"valuation": result.model_dump(), "global_flags": flags} - - return node - - -# --------------------------------------------------------------------------- -# Entry Timing -# --------------------------------------------------------------------------- - -def create_entry_timing_node(llm): - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - ticker = state["ticker"] - - price = ma50 = ma200 = hi52 = lo52 = range_pct = None - - # Try Alpaca first (computed from actual bar data — more reliable than yfinance info) - try: - from tradingagents.dataflows.alpaca_data import alpaca_available, get_moving_averages - if alpaca_available(): - ma_data = get_moving_averages(ticker) - if ma_data: - price = ma_data.get("current_price") - ma50 = ma_data.get("fifty_day_avg") - ma200 = ma_data.get("two_hundred_day_avg") - hi52 = ma_data.get("fifty_two_week_high") - lo52 = ma_data.get("fifty_two_week_low") - range_pct = ma_data.get("vs_52w_range_pct") - except Exception as e: - logger.debug("Alpaca MAs failed for %s: %s", ticker, e) - - # Fallback: yfinance info - if price is None: - try: - t = yf.Ticker(ticker.upper()) - info = t.info or {} - except Exception: - info = {} - - price = _safe(info, "currentPrice") or _safe(info, "regularMarketPrice") - ma50 = _safe(info, "fiftyDayAverage") - ma200 = _safe(info, "twoHundredDayAverage") - hi52 = _safe(info, "fiftyTwoWeekHigh") - lo52 = _safe(info, "fiftyTwoWeekLow") - - if hi52 and lo52 and price and (hi52 - lo52) > 0: - range_pct = round(((price - lo52) / (hi52 - lo52)) * 100, 1) - - ma_rel = "unknown" - if ma50 and ma200: - ma_rel = "above" if ma50 > ma200 else "below" - - _timing_source = "Alpaca" if price is not None and ma50 is not None else "yfinance" - - prompt = f"""You are an Entry Timing Analyst in a structured equity ranking pipeline. - -Ticker: {ticker} - -TECHNICALS (source: {_timing_source}): -- Price: ${price or 'N/A'} (source: {_timing_source}) -- 50-day MA: ${ma50 or 'N/A'} (source: {_timing_source}) -- 200-day MA: ${ma200 or 'N/A'} (source: {_timing_source}) -- 50d vs 200d: {ma_rel} -- 52W High: ${hi52 or 'N/A'} (source: {_timing_source}) -- 52W Low: ${lo52 or 'N/A'} (source: {_timing_source}) -- Position in 52W Range: {range_pct or 'N/A'}% - -NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. - -INSTRUCTIONS: -1. Score entry timing 0-10. - Pullback to support in uptrend = high score. Overextended at highs = low score. -2. Classify timing_verdict: favorable / neutral / unfavorable. -3. Be concise.""" - - try: - result = invoke_structured(llm, EntryTimingOutput, prompt) - except Exception as e: - logger.warning("EntryTiming LLM failed: %s", e) - result = EntryTimingOutput( - score_0_to_10=5.0, confidence_0_to_1=0.1, - summary_1_sentence="Entry timing analysis unavailable", - data_quality_flags=[ - DataFlag(field="entry_timing", severity="moderate", - message="Tier 2 analysis used fallback defaults due to LLM failure") - ], - ) - - result.current_price = price - result.fifty_day_avg = ma50 - result.two_hundred_day_avg = ma200 - result.fifty_day_vs_200_day = ma_rel - result.vs_52w_range_pct = range_pct - - flags = [f.model_dump() for f in result.data_quality_flags] - return {"entry_timing": result.model_dump(), "global_flags": flags} - - return node - - -# --------------------------------------------------------------------------- -# Earnings Revisions -# --------------------------------------------------------------------------- - -def create_earnings_revisions_node(llm): - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - ticker = state["ticker"] - - from tradingagents.dataflows.y_finance import get_earnings_estimates - try: - raw = get_earnings_estimates(ticker) - data = json.loads(raw) if isinstance(raw, str) else raw - except Exception: - data = {} - - recs = data.get("recent_recommendations", []) - targets = data.get("price_targets", {}) - upside = data.get("price_target_upside_pct") - - prompt = f"""You are an Earnings Revisions Analyst in a structured equity ranking pipeline. - -Ticker: {ticker} - -EARNINGS DATA (source: yfinance): -- Trailing EPS: {data.get('trailing_eps', 'N/A')} (source: yfinance) -- Forward EPS: {data.get('forward_eps', 'N/A')} (source: yfinance) -- Price Target Upside: {upside or 'N/A'}% (source: yfinance) -- Price Targets: {json.dumps(targets)[:300] if targets else 'N/A'} (source: yfinance) -- Recent Recommendations: {len(recs)} entries (source: yfinance) - -NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. - -INSTRUCTIONS: -1. Score earnings revision momentum 0-10. - Rising estimates + strong buy consensus + upside = high score. -2. Classify eps_revision_direction: up / down / flat. -3. Classify revenue_revision_direction: up / down / flat. -4. Classify analyst_consensus: strong_buy / buy / hold / sell / strong_sell. -5. This score has 10% weight in the master score — must materially affect it.""" - - try: - result = invoke_structured(llm, EarningsRevisionOutput, prompt) - except Exception as e: - logger.warning("EarningsRevisions LLM failed: %s", e) - result = EarningsRevisionOutput( - score_0_to_10=5.0, confidence_0_to_1=0.1, - summary_1_sentence="Earnings revision analysis unavailable", - data_quality_flags=[ - DataFlag(field="earnings_revisions", severity="moderate", - message="Tier 2 analysis used fallback defaults due to LLM failure") - ], - ) - - result.trailing_eps = data.get("trailing_eps") - result.forward_eps = data.get("forward_eps") - result.price_target_upside_pct = upside - - flags = [f.model_dump() for f in result.data_quality_flags] - return {"earnings_revisions": result.model_dump(), "global_flags": flags} - - return node - - -# --------------------------------------------------------------------------- -# Sector Rotation -# --------------------------------------------------------------------------- - -def create_sector_rotation_node(llm): - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - ticker = state["ticker"] - - from tradingagents.dataflows.y_finance import get_sector_rotation - try: - raw = get_sector_rotation(ticker) - data = json.loads(raw) if isinstance(raw, str) else raw - except Exception: - data = {} - - prompt = f"""You are a Sector Rotation Analyst in a structured equity ranking pipeline. - -Ticker: {ticker} | Sector: {data.get('sector', 'Unknown')} | Sector ETF: {data.get('sector_etf', 'N/A')} - -SECTOR DATA (source: yfinance): -- Sector vs SPY 1M: {data.get('stock_sector_vs_spy_1m', 'N/A')}% (source: yfinance) -- Sector vs SPY 3M: {data.get('stock_sector_vs_spy_3m', 'N/A')}% (source: yfinance) -- Sector Rank: {data.get('stock_sector_rank', 'N/A')} / {data.get('total_sectors', 11)} (source: yfinance) - -NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. - -INSTRUCTIONS: -1. Score sector rotation favorability 0-10. - Top-ranked sector with positive relative strength = high score. -2. Classify rotation_direction: inflow / outflow / neutral. -3. Be concise.""" - - try: - result = invoke_structured(llm, SectorRotationOutput, prompt) - except Exception as e: - logger.warning("SectorRotation LLM failed: %s", e) - result = SectorRotationOutput( - score_0_to_10=5.0, confidence_0_to_1=0.1, - summary_1_sentence="Sector rotation analysis unavailable", - data_quality_flags=[ - DataFlag(field="sector_rotation", severity="moderate", - message="Tier 2 analysis used fallback defaults due to LLM failure") - ], - ) - - result.sector = data.get("sector", "Unknown") - result.sector_etf = data.get("sector_etf") - result.sector_vs_spy_1m = data.get("stock_sector_vs_spy_1m") - result.sector_vs_spy_3m = data.get("stock_sector_vs_spy_3m") - result.sector_rank = data.get("stock_sector_rank") - - flags = [f.model_dump() for f in result.data_quality_flags] - return {"sector_rotation": result.model_dump(), "global_flags": flags} - - return node - - -# --------------------------------------------------------------------------- -# Backlog / Order Momentum -# --------------------------------------------------------------------------- - -def create_backlog_node(llm): - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - ticker = state["ticker"] - card = state.get("company_card") or {} - sector = card.get("sector", "Unknown") - industry = card.get("industry", "Unknown") - - # Backlog data is limited via yfinance — use revenue trajectory as proxy - try: - t = yf.Ticker(ticker.upper()) - info = t.info or {} - except Exception: - info = {} - - prompt = f"""You are a Backlog / Order Momentum Analyst in a structured equity ranking pipeline. - -Ticker: {ticker} | Sector: {sector} | Industry: {industry} - -AVAILABLE DATA (source: yfinance): -- Revenue Growth: {_pct(_safe(info, 'revenueGrowth'))} (source: yfinance) -- Earnings Growth: {_pct(_safe(info, 'earningsGrowth'))} (source: yfinance) -- Revenue: {_safe(info, 'totalRevenue', 'N/A')} (source: yfinance) - -NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. - -INSTRUCTIONS: -1. Assess if this company type typically has meaningful backlog data - (defense, industrials, semiconductors = yes; consumer, finance = no). -2. Score order momentum 0-10 based on revenue trajectory and industry context. -3. Set has_backlog_data=true only if this industry typically reports backlog. -4. This has 5% weight — be quick and concise.""" - - try: - result = invoke_structured(llm, BacklogOrderMomentumOutput, prompt) - except Exception as e: - logger.warning("Backlog LLM failed: %s", e) - result = BacklogOrderMomentumOutput( - score_0_to_10=5.0, confidence_0_to_1=0.1, - summary_1_sentence="Backlog analysis limited", - data_quality_flags=[ - DataFlag(field="backlog", severity="moderate", - message="Tier 2 analysis used fallback defaults due to LLM failure") - ], - ) - - flags = [f.model_dump() for f in result.data_quality_flags] - return {"backlog": result.model_dump(), "global_flags": flags} - - return node - - -# --------------------------------------------------------------------------- -# Narrative Crowding -# --------------------------------------------------------------------------- - -def create_crowding_node(llm): - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - ticker = state["ticker"] - card = state.get("company_card") or {} - - try: - t = yf.Ticker(ticker.upper()) - info = t.info or {} - except Exception: - info = {} - - short_pct = None - float_shares = _safe(info, "floatShares") - shares_short = _safe(info, "sharesShort") - if float_shares and shares_short and float_shares > 0: - short_pct = round(shares_short / float_shares * 100, 2) - - prompt = f"""You are a Narrative Crowding Analyst in a structured equity ranking pipeline. - -Ticker: {ticker} | Company: {card.get('company_name', 'Unknown')} -Market Cap Category: {card.get('market_cap_category', 'unknown')} - -DATA (source: yfinance): -- Short % of Float: {short_pct or 'N/A'}% (source: yfinance) -- Short Ratio (days): {_safe(info, 'shortRatio', 'N/A')} (source: yfinance) -- Analyst Coverage: implied from market cap ({card.get('market_cap_category', 'unknown')}) - -NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. - -INSTRUCTIONS: -1. Score narrative crowding 0-10. - HIGH score = low crowding (contrarian, under-followed). - LOW score = extremely crowded (everyone owns it, consensus trade). -2. Assess narrative_saturation: low / moderate / high. -3. Flag contrarian_opportunity if stock is hated but fundamentals are intact. -4. Flag short_squeeze_potential if short interest is high (>15% of float). -5. This has 5% weight — be concise.""" - - try: - result = invoke_structured(llm, NarrativeCrowdingOutput, prompt) - except Exception as e: - logger.warning("Crowding LLM failed: %s", e) - result = NarrativeCrowdingOutput( - score_0_to_10=5.0, confidence_0_to_1=0.1, - summary_1_sentence="Crowding analysis limited", - data_quality_flags=[ - DataFlag(field="crowding", severity="moderate", - message="Tier 2 analysis used fallback defaults due to LLM failure") - ], - ) - - flags = [f.model_dump() for f in result.data_quality_flags] - return {"crowding": result.model_dump(), "global_flags": flags} - - return node - - -# --------------------------------------------------------------------------- -# Archetype -# --------------------------------------------------------------------------- - -def create_archetype_node(llm): - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - card = state.get("company_card") or {} - bq = state.get("business_quality") or {} - - prompt = f"""You are a Company Archetype Classifier. - -Company: {card.get('company_name', 'Unknown')} ({card.get('ticker', '?')}) -Sector: {card.get('sector', 'Unknown')} | Industry: {card.get('industry', 'Unknown')} -Market Cap: {card.get('market_cap_formatted', 'N/A')} -Description: {card.get('description', 'N/A')[:300]} - -Competitive Moat: {bq.get('competitive_moat', 'N/A')} -Revenue Growth: {bq.get('revenue_growth', 'N/A')} - -ARCHETYPES (pick exactly one): -- Infrastructure Builder: builds platforms/networks others depend on -- Bottleneck Supplier: controls scarce supply in a critical chain -- Platform Company: multi-sided marketplace with network effects -- Commodity Leverage: earnings levered to commodity prices -- Secular Growth Innovator: disrupting with new tech/business model -- Turnaround: beaten-down company with improving fundamentals -- Defensive Compounder: steady earnings, dividend grower, low vol - -Return archetype, confidence (0-1), and one-sentence reasoning.""" - - try: - result = invoke_structured(llm, ArchetypeOutput, prompt) - except Exception as e: - logger.warning("Archetype LLM failed: %s", e) - result = ArchetypeOutput() - - return {"archetype": result.model_dump()} - - return node +"""Tier 2 agents: Deep analysis that runs only on Tier 1 survivors. + +Each agent fetches its own data via yfinance, calls the LLM once with +structured output, and returns a typed result into PipelineState. +""" + +from __future__ import annotations + +import json +import logging +from typing import Any, Dict + +import yfinance as yf + +from tradingagents.models import ( + ArchetypeOutput, + BacklogOrderMomentumOutput, + BusinessQualityOutput, + DataFlag, + EarningsRevisionOutput, + EntryTimingOutput, + InstitutionalFlowOutput, + NarrativeCrowdingOutput, + SectorRotationOutput, + ValuationOutput, + invoke_structured, +) + +logger = logging.getLogger(__name__) + + +def _safe(info, key, default=None): + v = info.get(key) + return default if v is None else v + + +def _pct(v): + return f"{v * 100:.1f}%" if v is not None else "N/A" + + +# --------------------------------------------------------------------------- +# Business Quality +# --------------------------------------------------------------------------- + +def create_business_quality_node(llm): + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["ticker"] + card = state.get("company_card") or {} + + try: + t = yf.Ticker(ticker.upper()) + info = t.info or {} + except Exception: + info = {} + + prompt = f"""You are a Business Quality Analyst in a structured equity ranking pipeline. + +Ticker: {ticker} | Sector: {card.get('sector', 'Unknown')} | Industry: {card.get('industry', 'Unknown')} +Market Cap: {card.get('market_cap_formatted', 'N/A')} + +FINANCIALS (source: yfinance): +- Revenue Growth: {_pct(_safe(info, 'revenueGrowth'))} (source: yfinance) +- Profit Margins: {_pct(_safe(info, 'profitMargins'))} (source: yfinance) +- Operating Margins: {_pct(_safe(info, 'operatingMargins'))} (source: yfinance) +- ROE: {_pct(_safe(info, 'returnOnEquity'))} (source: yfinance) +- ROA: {_pct(_safe(info, 'returnOnAssets'))} (source: yfinance) +- Debt/Equity: {_safe(info, 'debtToEquity', 'N/A')} (source: yfinance) +- Free Cash Flow: {_safe(info, 'freeCashflow', 'N/A')} (source: yfinance) +- Current Ratio: {_safe(info, 'currentRatio', 'N/A')} (source: yfinance) + +NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. + +INSTRUCTIONS: +1. Score business quality 0-10 based on margins, growth, returns, balance sheet. +2. Classify competitive moat: wide / narrow / none. +3. Classify management quality: strong / adequate / weak. +4. List positives, negatives, risks. Be concise.""" + + try: + result = invoke_structured(llm, BusinessQualityOutput, prompt) + except Exception as e: + logger.warning("BusinessQuality LLM failed: %s", e) + result = BusinessQualityOutput( + score_0_to_10=5.0, confidence_0_to_1=0.1, + summary_1_sentence="Business quality analysis unavailable", + data_quality_flags=[ + DataFlag(field="business_quality", severity="moderate", + message="Tier 2 analysis used fallback defaults due to LLM failure") + ], + ) + + # Override with actual data + result.revenue_growth = _safe(info, "revenueGrowth") + result.profit_margins = _safe(info, "profitMargins") + result.operating_margins = _safe(info, "operatingMargins") + result.return_on_equity = _safe(info, "returnOnEquity") + result.return_on_assets = _safe(info, "returnOnAssets") + result.debt_to_equity = _safe(info, "debtToEquity") + result.free_cashflow = _safe(info, "freeCashflow") + + flags = [f.model_dump() for f in result.data_quality_flags] + return {"business_quality": result.model_dump(), "global_flags": flags} + + return node + + +# --------------------------------------------------------------------------- +# Institutional Flow +# --------------------------------------------------------------------------- + +def create_institutional_flow_node(llm): + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["ticker"] + + from tradingagents.dataflows.y_finance import get_institutional_flow + try: + raw = get_institutional_flow(ticker) + data = json.loads(raw) if isinstance(raw, str) else raw + except Exception: + data = {} + + # Format top holders for prompt + holders = data.get("top_institutional_holders", []) + holder_lines = [] + for h in holders[:5]: + pct = h.get("pct_out") + holder_lines.append( + f" {h.get('holder', '?')}: {pct:.1f}%" if pct else f" {h.get('holder', '?')}" + ) + + prompt = f"""You are an Institutional Flow Analyst in a structured equity ranking pipeline. +Your job: track real smart-money movement — not just static ownership percentages. + +Ticker: {ticker} + +OWNERSHIP & VOLUME (source: yfinance): +- Institutional Ownership: {data.get('held_percent_institutions', 'N/A')}% (source: yfinance) +- Insider Ownership: {data.get('held_percent_insiders', 'N/A')}% (source: yfinance) +- Volume Ratio (10d/avg): {data.get('volume_ratio', 'N/A')} (source: yfinance) +- Short % of Float: {data.get('short_pct_of_float', 'N/A')}% (source: yfinance) +- Short Ratio (days): {data.get('short_ratio', 'N/A')} (source: yfinance) +- Float Turnover 5d: {data.get('float_turnover_5d_pct', 'N/A')}% (source: yfinance) + +SHORT INTEREST TREND (source: yfinance): +- Short Interest Change (vs prior month): {data.get('short_interest_change_pct', 'N/A')}% +- Short Interest Trend: {data.get('short_interest_trend', 'N/A')} + +TOP INSTITUTIONAL HOLDERS (13F, source: yfinance): +{chr(10).join(holder_lines) or ' No data available'} +- Total top holders tracked: {data.get('top_holders_count', 'N/A')} + +INSIDER TRANSACTIONS (recent, source: yfinance): +- Insider Buys: {data.get('insider_buys_recent', 'N/A')} +- Insider Sells: {data.get('insider_sells_recent', 'N/A')} +- Insider Signal: {data.get('insider_transaction_signal', 'N/A')} + +NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. + +INSTRUCTIONS: +1. Score institutional flow signal 0-10 (this has 15% weight — make it count). + High ownership + rising volume + low short interest + insider buying = bullish. +2. Classify accumulation_signal: accumulating / distributing / neutral. +3. Classify top_holders_change: increasing / decreasing / stable. + (Based on holder concentration and any visible 13F patterns.) +4. Classify fund_accumulation_pattern: accumulating / distributing / holding. + (Volume + ownership trends suggest funds are adding or reducing.) +5. Classify short_interest_trend: rising / falling / stable. +6. Classify insider_transaction_signal: buying / selling / none. +7. Classify smart_money_signal: bullish / bearish / neutral. + (Synthesize all signals: 13F, insiders, short interest, volume.) +8. Be concise.""" + + try: + result = invoke_structured(llm, InstitutionalFlowOutput, prompt) + except Exception as e: + logger.warning("InstitutionalFlow LLM failed: %s", e) + result = InstitutionalFlowOutput( + score_0_to_10=5.0, confidence_0_to_1=0.1, + summary_1_sentence="Institutional flow analysis unavailable", + data_quality_flags=[ + DataFlag(field="institutional_flow", severity="moderate", + message="Tier 2 analysis used fallback defaults due to LLM failure") + ], + ) + + # Override with actual fetched data + result.institutional_ownership_pct = data.get("held_percent_institutions") + result.insider_ownership_pct = data.get("held_percent_insiders") + result.volume_ratio = data.get("volume_ratio") + result.short_interest_pct = data.get("short_pct_of_float") + result.short_ratio = data.get("short_ratio") + result.float_turnover_pct = data.get("float_turnover_5d_pct") + # Override trend fields with actual data when available + if data.get("short_interest_trend"): + result.short_interest_trend = data["short_interest_trend"] + if data.get("insider_transaction_signal"): + result.insider_transaction_signal = data["insider_transaction_signal"] + + flags = [f.model_dump() for f in result.data_quality_flags] + return {"institutional_flow": result.model_dump(), "global_flags": flags} + + return node + + +# --------------------------------------------------------------------------- +# Valuation +# --------------------------------------------------------------------------- + +def create_valuation_node(llm): + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["ticker"] + + from tradingagents.dataflows.y_finance import get_valuation_peers + try: + raw = get_valuation_peers(ticker) + data = json.loads(raw) if isinstance(raw, str) else raw + except Exception: + data = {} + + prompt = f"""You are a Valuation Analyst in a structured equity ranking pipeline. + +Ticker: {ticker} + +VALUATION METRICS (source: yfinance): +- Trailing P/E: {data.get('trailing_pe', 'N/A')} (source: yfinance) +- Forward P/E: {data.get('forward_pe', 'N/A')} (source: yfinance) +- PEG Ratio: {data.get('peg_ratio', 'N/A')} (source: yfinance) +- P/B: {data.get('price_to_book', 'N/A')} (source: yfinance) +- EV/EBITDA: {data.get('ev_to_ebitda', 'N/A')} (source: yfinance) +- P/S: {data.get('price_to_sales', 'N/A')} (source: yfinance) +- 52W Range Position: {data.get('vs_52w_range_pct', 'N/A')}% (source: yfinance) +- Revenue Growth: {data.get('revenue_growth', 'N/A')} (source: yfinance) +- Earnings Growth: {data.get('earnings_growth', 'N/A')} (source: yfinance) + +NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. + +INSTRUCTIONS: +1. Score valuation attractiveness 0-10. + Low multiples relative to growth = high score. +2. Classify: undervalued / fair / overvalued. +3. Consider industry context (growth stocks deserve higher multiples).""" + + try: + result = invoke_structured(llm, ValuationOutput, prompt) + except Exception as e: + logger.warning("Valuation LLM failed: %s", e) + result = ValuationOutput( + score_0_to_10=5.0, confidence_0_to_1=0.1, + summary_1_sentence="Valuation analysis unavailable", + data_quality_flags=[ + DataFlag(field="valuation", severity="moderate", + message="Tier 2 analysis used fallback defaults due to LLM failure") + ], + ) + + result.trailing_pe = data.get("trailing_pe") + result.forward_pe = data.get("forward_pe") + result.peg_ratio = data.get("peg_ratio") + result.price_to_book = data.get("price_to_book") + result.ev_to_ebitda = data.get("ev_to_ebitda") + result.price_to_sales = data.get("price_to_sales") + result.vs_52w_range_pct = data.get("vs_52w_range_pct") + + flags = [f.model_dump() for f in result.data_quality_flags] + return {"valuation": result.model_dump(), "global_flags": flags} + + return node + + +# --------------------------------------------------------------------------- +# Entry Timing +# --------------------------------------------------------------------------- + +def create_entry_timing_node(llm): + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["ticker"] + + price = ma50 = ma200 = hi52 = lo52 = range_pct = None + + # Try Alpaca first (computed from actual bar data — more reliable than yfinance info) + try: + from tradingagents.dataflows.alpaca_data import alpaca_available, get_moving_averages + if alpaca_available(): + ma_data = get_moving_averages(ticker) + if ma_data: + price = ma_data.get("current_price") + ma50 = ma_data.get("fifty_day_avg") + ma200 = ma_data.get("two_hundred_day_avg") + hi52 = ma_data.get("fifty_two_week_high") + lo52 = ma_data.get("fifty_two_week_low") + range_pct = ma_data.get("vs_52w_range_pct") + except Exception as e: + logger.debug("Alpaca MAs failed for %s: %s", ticker, e) + + # Fallback: yfinance info + if price is None: + try: + t = yf.Ticker(ticker.upper()) + info = t.info or {} + except Exception: + info = {} + + price = _safe(info, "currentPrice") or _safe(info, "regularMarketPrice") + ma50 = _safe(info, "fiftyDayAverage") + ma200 = _safe(info, "twoHundredDayAverage") + hi52 = _safe(info, "fiftyTwoWeekHigh") + lo52 = _safe(info, "fiftyTwoWeekLow") + + if hi52 and lo52 and price and (hi52 - lo52) > 0: + range_pct = round(((price - lo52) / (hi52 - lo52)) * 100, 1) + + ma_rel = "unknown" + if ma50 and ma200: + ma_rel = "above" if ma50 > ma200 else "below" + + _timing_source = "Alpaca" if price is not None and ma50 is not None else "yfinance" + + prompt = f"""You are an Entry Timing Analyst in a structured equity ranking pipeline. + +Ticker: {ticker} + +TECHNICALS (source: {_timing_source}): +- Price: ${price or 'N/A'} (source: {_timing_source}) +- 50-day MA: ${ma50 or 'N/A'} (source: {_timing_source}) +- 200-day MA: ${ma200 or 'N/A'} (source: {_timing_source}) +- 50d vs 200d: {ma_rel} +- 52W High: ${hi52 or 'N/A'} (source: {_timing_source}) +- 52W Low: ${lo52 or 'N/A'} (source: {_timing_source}) +- Position in 52W Range: {range_pct or 'N/A'}% + +NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. + +INSTRUCTIONS: +1. Score entry timing 0-10. + Pullback to support in uptrend = high score. Overextended at highs = low score. +2. Classify timing_verdict: favorable / neutral / unfavorable. +3. Be concise.""" + + try: + result = invoke_structured(llm, EntryTimingOutput, prompt) + except Exception as e: + logger.warning("EntryTiming LLM failed: %s", e) + result = EntryTimingOutput( + score_0_to_10=5.0, confidence_0_to_1=0.1, + summary_1_sentence="Entry timing analysis unavailable", + data_quality_flags=[ + DataFlag(field="entry_timing", severity="moderate", + message="Tier 2 analysis used fallback defaults due to LLM failure") + ], + ) + + result.current_price = price + result.fifty_day_avg = ma50 + result.two_hundred_day_avg = ma200 + result.fifty_day_vs_200_day = ma_rel + result.vs_52w_range_pct = range_pct + + flags = [f.model_dump() for f in result.data_quality_flags] + return {"entry_timing": result.model_dump(), "global_flags": flags} + + return node + + +# --------------------------------------------------------------------------- +# Earnings Revisions +# --------------------------------------------------------------------------- + +def create_earnings_revisions_node(llm): + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["ticker"] + + from tradingagents.dataflows.y_finance import get_earnings_estimates + try: + raw = get_earnings_estimates(ticker) + data = json.loads(raw) if isinstance(raw, str) else raw + except Exception: + data = {} + + recs = data.get("recent_recommendations", []) + targets = data.get("price_targets", {}) + upside = data.get("price_target_upside_pct") + + prompt = f"""You are an Earnings Revisions Analyst in a structured equity ranking pipeline. + +Ticker: {ticker} + +EARNINGS DATA (source: yfinance): +- Trailing EPS: {data.get('trailing_eps', 'N/A')} (source: yfinance) +- Forward EPS: {data.get('forward_eps', 'N/A')} (source: yfinance) +- Price Target Upside: {upside or 'N/A'}% (source: yfinance) +- Price Targets: {json.dumps(targets)[:300] if targets else 'N/A'} (source: yfinance) +- Recent Recommendations: {len(recs)} entries (source: yfinance) + +NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. + +INSTRUCTIONS: +1. Score earnings revision momentum 0-10. + Rising estimates + strong buy consensus + upside = high score. +2. Classify eps_revision_direction: up / down / flat. +3. Classify revenue_revision_direction: up / down / flat. +4. Classify analyst_consensus: strong_buy / buy / hold / sell / strong_sell. +5. This score has 10% weight in the master score — must materially affect it.""" + + try: + result = invoke_structured(llm, EarningsRevisionOutput, prompt) + except Exception as e: + logger.warning("EarningsRevisions LLM failed: %s", e) + result = EarningsRevisionOutput( + score_0_to_10=5.0, confidence_0_to_1=0.1, + summary_1_sentence="Earnings revision analysis unavailable", + data_quality_flags=[ + DataFlag(field="earnings_revisions", severity="moderate", + message="Tier 2 analysis used fallback defaults due to LLM failure") + ], + ) + + result.trailing_eps = data.get("trailing_eps") + result.forward_eps = data.get("forward_eps") + result.price_target_upside_pct = upside + + flags = [f.model_dump() for f in result.data_quality_flags] + return {"earnings_revisions": result.model_dump(), "global_flags": flags} + + return node + + +# --------------------------------------------------------------------------- +# Sector Rotation +# --------------------------------------------------------------------------- + +def create_sector_rotation_node(llm): + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["ticker"] + + from tradingagents.dataflows.y_finance import get_sector_rotation + try: + raw = get_sector_rotation(ticker) + data = json.loads(raw) if isinstance(raw, str) else raw + except Exception: + data = {} + + prompt = f"""You are a Sector Rotation Analyst in a structured equity ranking pipeline. + +Ticker: {ticker} | Sector: {data.get('sector', 'Unknown')} | Sector ETF: {data.get('sector_etf', 'N/A')} + +SECTOR DATA (source: yfinance): +- Sector vs SPY 1M: {data.get('stock_sector_vs_spy_1m', 'N/A')}% (source: yfinance) +- Sector vs SPY 3M: {data.get('stock_sector_vs_spy_3m', 'N/A')}% (source: yfinance) +- Sector Rank: {data.get('stock_sector_rank', 'N/A')} / {data.get('total_sectors', 11)} (source: yfinance) + +NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. + +INSTRUCTIONS: +1. Score sector rotation favorability 0-10. + Top-ranked sector with positive relative strength = high score. +2. Classify rotation_direction: inflow / outflow / neutral. +3. Be concise.""" + + try: + result = invoke_structured(llm, SectorRotationOutput, prompt) + except Exception as e: + logger.warning("SectorRotation LLM failed: %s", e) + result = SectorRotationOutput( + score_0_to_10=5.0, confidence_0_to_1=0.1, + summary_1_sentence="Sector rotation analysis unavailable", + data_quality_flags=[ + DataFlag(field="sector_rotation", severity="moderate", + message="Tier 2 analysis used fallback defaults due to LLM failure") + ], + ) + + result.sector = data.get("sector", "Unknown") + result.sector_etf = data.get("sector_etf") + result.sector_vs_spy_1m = data.get("stock_sector_vs_spy_1m") + result.sector_vs_spy_3m = data.get("stock_sector_vs_spy_3m") + result.sector_rank = data.get("stock_sector_rank") + + flags = [f.model_dump() for f in result.data_quality_flags] + return {"sector_rotation": result.model_dump(), "global_flags": flags} + + return node + + +# --------------------------------------------------------------------------- +# Backlog / Order Momentum +# --------------------------------------------------------------------------- + +def create_backlog_node(llm): + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["ticker"] + card = state.get("company_card") or {} + sector = card.get("sector", "Unknown") + industry = card.get("industry", "Unknown") + + # Backlog data is limited via yfinance — use revenue trajectory as proxy + try: + t = yf.Ticker(ticker.upper()) + info = t.info or {} + except Exception: + info = {} + + prompt = f"""You are a Backlog / Order Momentum Analyst in a structured equity ranking pipeline. + +Ticker: {ticker} | Sector: {sector} | Industry: {industry} + +AVAILABLE DATA (source: yfinance): +- Revenue Growth: {_pct(_safe(info, 'revenueGrowth'))} (source: yfinance) +- Earnings Growth: {_pct(_safe(info, 'earningsGrowth'))} (source: yfinance) +- Revenue: {_safe(info, 'totalRevenue', 'N/A')} (source: yfinance) + +NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. + +INSTRUCTIONS: +1. Assess if this company type typically has meaningful backlog data + (defense, industrials, semiconductors = yes; consumer, finance = no). +2. Score order momentum 0-10 based on revenue trajectory and industry context. +3. Set has_backlog_data=true only if this industry typically reports backlog. +4. This has 5% weight — be quick and concise.""" + + try: + result = invoke_structured(llm, BacklogOrderMomentumOutput, prompt) + except Exception as e: + logger.warning("Backlog LLM failed: %s", e) + result = BacklogOrderMomentumOutput( + score_0_to_10=5.0, confidence_0_to_1=0.1, + summary_1_sentence="Backlog analysis limited", + data_quality_flags=[ + DataFlag(field="backlog", severity="moderate", + message="Tier 2 analysis used fallback defaults due to LLM failure") + ], + ) + + flags = [f.model_dump() for f in result.data_quality_flags] + return {"backlog": result.model_dump(), "global_flags": flags} + + return node + + +# --------------------------------------------------------------------------- +# Narrative Crowding +# --------------------------------------------------------------------------- + +def create_crowding_node(llm): + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["ticker"] + card = state.get("company_card") or {} + + try: + t = yf.Ticker(ticker.upper()) + info = t.info or {} + except Exception: + info = {} + + short_pct = None + float_shares = _safe(info, "floatShares") + shares_short = _safe(info, "sharesShort") + if float_shares and shares_short and float_shares > 0: + short_pct = round(shares_short / float_shares * 100, 2) + + prompt = f"""You are a Narrative Crowding Analyst in a structured equity ranking pipeline. + +Ticker: {ticker} | Company: {card.get('company_name', 'Unknown')} +Market Cap Category: {card.get('market_cap_category', 'unknown')} + +DATA (source: yfinance): +- Short % of Float: {short_pct or 'N/A'}% (source: yfinance) +- Short Ratio (days): {_safe(info, 'shortRatio', 'N/A')} (source: yfinance) +- Analyst Coverage: implied from market cap ({card.get('market_cap_category', 'unknown')}) + +NOTE: If a metric shows 'N/A' or 'unknown', say 'data unavailable' rather than guessing. + +INSTRUCTIONS: +1. Score narrative crowding 0-10. + HIGH score = low crowding (contrarian, under-followed). + LOW score = extremely crowded (everyone owns it, consensus trade). +2. Assess narrative_saturation: low / moderate / high. +3. Flag contrarian_opportunity if stock is hated but fundamentals are intact. +4. Flag short_squeeze_potential if short interest is high (>15% of float). +5. This has 5% weight — be concise.""" + + try: + result = invoke_structured(llm, NarrativeCrowdingOutput, prompt) + except Exception as e: + logger.warning("Crowding LLM failed: %s", e) + result = NarrativeCrowdingOutput( + score_0_to_10=5.0, confidence_0_to_1=0.1, + summary_1_sentence="Crowding analysis limited", + data_quality_flags=[ + DataFlag(field="crowding", severity="moderate", + message="Tier 2 analysis used fallback defaults due to LLM failure") + ], + ) + + flags = [f.model_dump() for f in result.data_quality_flags] + return {"crowding": result.model_dump(), "global_flags": flags} + + return node + + +# --------------------------------------------------------------------------- +# Archetype +# --------------------------------------------------------------------------- + +def create_archetype_node(llm): + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + card = state.get("company_card") or {} + bq = state.get("business_quality") or {} + + prompt = f"""You are a Company Archetype Classifier. + +Company: {card.get('company_name', 'Unknown')} ({card.get('ticker', '?')}) +Sector: {card.get('sector', 'Unknown')} | Industry: {card.get('industry', 'Unknown')} +Market Cap: {card.get('market_cap_formatted', 'N/A')} +Description: {card.get('description', 'N/A')[:300]} + +Competitive Moat: {bq.get('competitive_moat', 'N/A')} +Revenue Growth: {bq.get('revenue_growth', 'N/A')} + +ARCHETYPES (pick exactly one): +- Infrastructure Builder: builds platforms/networks others depend on +- Bottleneck Supplier: controls scarce supply in a critical chain +- Platform Company: multi-sided marketplace with network effects +- Commodity Leverage: earnings levered to commodity prices +- Secular Growth Innovator: disrupting with new tech/business model +- Turnaround: beaten-down company with improving fundamentals +- Defensive Compounder: steady earnings, dividend grower, low vol + +Return archetype, confidence (0-1), and one-sentence reasoning.""" + + try: + result = invoke_structured(llm, ArchetypeOutput, prompt) + except Exception as e: + logger.warning("Archetype LLM failed: %s", e) + result = ArchetypeOutput() + + return {"archetype": result.model_dump()} + + return node diff --git a/tradingagents/agents/structured/tier3.py b/tradingagents/agents/structured/tier3.py index 3f735230..d31f8ef9 100644 --- a/tradingagents/agents/structured/tier3.py +++ b/tradingagents/agents/structured/tier3.py @@ -1,418 +1,418 @@ -"""Tier 3 agents: Bull/Bear debate, Risk assessment, Final decision. - -Only runs on stocks that pass Tier 1 + Tier 2. Uses the deep-thinking LLM -for reasoning-heavy tasks (debate, risk, final synthesis). -""" - -from __future__ import annotations - -import json -import logging -from typing import Any, Dict - -from tradingagents.models import ( - BearCaseOutput, - BullCaseOutput, - DataFlag, - DebateRefereeOutput, - FinalDecisionOutput, - RiskInvalidationOutput, - invoke_structured, -) - -logger = logging.getLogger(__name__) - - -def _low_confidence_warnings(state: Dict[str, Any]) -> str: - """Check if any Tier 2 agents have confidence < 0.2 and return warnings.""" - _TIER2_FIELDS = { - "business_quality": "Business Quality", - "institutional_flow": "Institutional Flow", - "valuation": "Valuation", - "entry_timing": "Entry Timing", - "earnings_revisions": "Earnings Revisions", - "sector_rotation": "Sector Rotation", - "backlog": "Backlog / Order Momentum", - "crowding": "Narrative Crowding", - } - warnings = [] - for field, display_name in _TIER2_FIELDS.items(): - agent_data = state.get(field) or {} - conf = agent_data.get("confidence_0_to_1") - if conf is not None and conf < 0.2: - warnings.append( - f" WARNING: {display_name} has low confidence ({conf:.2f}) — " - f"its score may be unreliable (fallback defaults or poor data)" - ) - if warnings: - return "\nDATA QUALITY WARNINGS:\n" + "\n".join(warnings) + "\n" - return "" - - -def _summarize_tier2(state: Dict[str, Any]) -> str: - """Build a compact summary of all Tier 1+2 findings for Tier 3 prompts.""" - card = state.get("company_card") or {} - macro = state.get("macro") or {} - liq = state.get("liquidity") or {} - bq = state.get("business_quality") or {} - inst = state.get("institutional_flow") or {} - val = state.get("valuation") or {} - et = state.get("entry_timing") or {} - er = state.get("earnings_revisions") or {} - sr = state.get("sector_rotation") or {} - bl = state.get("backlog") or {} - cr = state.get("crowding") or {} - arch = state.get("archetype") or {} - - # Check for low-confidence Tier 2 agents - confidence_warnings = _low_confidence_warnings(state) - - lines = [ - f"Company: {card.get('company_name', '?')} ({card.get('ticker', '?')})", - f"Sector: {card.get('sector', '?')} | Industry: {card.get('industry', '?')}", - f"Market Cap: {card.get('market_cap_formatted', 'N/A')}", - f"Price: ${card.get('current_price', 'N/A')}", - f"Archetype: {arch.get('archetype', 'N/A')}", - "", - f"Master Score: {state.get('master_score', 'N/A')} | Role: {state.get('position_role', 'N/A')}", - "", - "AGENT SCORES (0-10):", - f" Business Quality: {bq.get('score_0_to_10', 'N/A')} — {bq.get('summary_1_sentence', '')}", - f" Macro Alignment: {macro.get('macro_alignment_0_to_10', 'N/A')} — {macro.get('summary_1_sentence', '')}", - f" Institutional Flow: {inst.get('score_0_to_10', 'N/A')} — {inst.get('summary_1_sentence', '')}", - f" Valuation: {val.get('score_0_to_10', 'N/A')} — {val.get('summary_1_sentence', '')}", - f" Entry Timing: {et.get('score_0_to_10', 'N/A')} — {et.get('summary_1_sentence', '')}", - f" Earnings Revisions: {er.get('score_0_to_10', 'N/A')} — {er.get('summary_1_sentence', '')}", - f" Sector Rotation: {sr.get('score_0_to_10', 'N/A')} — {sr.get('summary_1_sentence', '')}", - f" Backlog: {bl.get('score_0_to_10', 'N/A')} — {bl.get('summary_1_sentence', '')}", - f" Crowding: {cr.get('score_0_to_10', 'N/A')} — {cr.get('summary_1_sentence', '')}", - f" Liquidity: {liq.get('score_0_to_10', 'N/A')} — {liq.get('summary_1_sentence', '')}", - "", - f" Macro Regime: {macro.get('regime_label', '?')} | VIX: {macro.get('vix_level', '?')}", - f" Risk Appetite: {macro.get('risk_appetite', '?')} | Liquidity Regime: {macro.get('liquidity_regime', '?')}", - f" Regime Score Adjustment: {macro.get('regime_score_adjustment', 0):+.1f}", - f" Moat: {bq.get('competitive_moat', '?')} | Valuation: {val.get('valuation_verdict', '?')}", - f" Smart Money: {inst.get('smart_money_signal', '?')} | Accumulation: {inst.get('accumulation_signal', '?')}", - f" Short Trend: {inst.get('short_interest_trend', '?')} | Insider Signal: {inst.get('insider_transaction_signal', '?')}", - f" Timing: {et.get('timing_verdict', '?')}", - ] - - if confidence_warnings: - lines.append("") - lines.append(confidence_warnings) - lines.append("Factor these warnings into your analysis — low-confidence scores may not reflect reality.") - - return "\n".join(lines) - - -# --------------------------------------------------------------------------- -# Bull Case -# --------------------------------------------------------------------------- - -def create_bull_case_node(llm): - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - ticker = state["ticker"] - summary = _summarize_tier2(state) - - prompt = f"""You are a Bull Case Researcher. Build the strongest possible bullish thesis for {ticker}. - -{summary} - -INSTRUCTIONS: -1. Write a concise thesis (2-3 sentences) for why this stock should be bought. -2. List 3-5 specific catalysts that could drive the stock higher. -3. Estimate upside_target (price) and upside_pct from current price. -4. List key assumptions your thesis depends on. -5. List thesis_invalidation_triggers — what would kill the bull case. -6. Set confidence 0-1 for how strong the bull case is. - -Attack the investment aggressively. Find every reason to be bullish. -But be honest — don't fabricate catalysts. Use the data above.""" - - try: - result = invoke_structured(llm, BullCaseOutput, prompt) - except Exception as e: - logger.warning("BullCase LLM failed: %s", e) - result = BullCaseOutput( - thesis="Bull case analysis unavailable", - confidence_0_to_1=0.1, - ) - - return {"bull_case": result.model_dump()} - - return node - - -# --------------------------------------------------------------------------- -# Bear Case -# --------------------------------------------------------------------------- - -def create_bear_case_node(llm): - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - ticker = state["ticker"] - summary = _summarize_tier2(state) - - prompt = f"""You are a Bear Case Researcher. Build the strongest possible bearish thesis for {ticker}. - -{summary} - -INSTRUCTIONS: -1. Write a concise thesis (2-3 sentences) for why this stock should be avoided or sold. -2. List 3-5 specific risks that could drive the stock lower. -3. Estimate downside_target (price) and downside_pct from current price. -4. List key assumptions your bear thesis depends on. -5. List thesis_invalidation_triggers — what would kill the bear case. -6. Set confidence 0-1 for how strong the bear case is. - -Be ruthless. Find every vulnerability, every overvaluation, every risk. -But be honest — don't fabricate risks. Use the data above.""" - - try: - result = invoke_structured(llm, BearCaseOutput, prompt) - except Exception as e: - logger.warning("BearCase LLM failed: %s", e) - result = BearCaseOutput( - thesis="Bear case analysis unavailable", - confidence_0_to_1=0.1, - ) - - return {"bear_case": result.model_dump()} - - return node - - -# --------------------------------------------------------------------------- -# Debate Referee -# --------------------------------------------------------------------------- - -def create_debate_node(llm): - """Referee that evaluates bull vs bear case.""" - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - ticker = state["ticker"] - bull = state.get("bull_case") or {} - bear = state.get("bear_case") or {} - - prompt = f"""You are the Debate Referee. Evaluate the bull vs bear case for {ticker}. - -BULL CASE (confidence: {bull.get('confidence_0_to_1', 'N/A')}): -Thesis: {bull.get('thesis', 'N/A')} -Catalysts: {', '.join(bull.get('catalysts', []))} -Upside: {bull.get('upside_pct', 'N/A')}% -Invalidation: {', '.join(bull.get('thesis_invalidation_triggers', []))} - -BEAR CASE (confidence: {bear.get('confidence_0_to_1', 'N/A')}): -Thesis: {bear.get('thesis', 'N/A')} -Risks: {', '.join(bear.get('risks', []))} -Downside: {bear.get('downside_pct', 'N/A')}% -Invalidation: {', '.join(bear.get('thesis_invalidation_triggers', []))} - -MASTER SCORE: {state.get('master_score', 'N/A')} | ROLE: {state.get('position_role', 'N/A')} - -INSTRUCTIONS: -1. Declare winner: "bull" or "bear". -2. Score each side 0-10 on argument strength. -3. List key unresolved questions. -4. Set net_conviction_adjustment (-2 to +2) to modify the master score. - Positive = debate strengthened the bull case. Negative = weakened it. -5. Provide reasoning for your decision.""" - - try: - result = invoke_structured(llm, DebateRefereeOutput, prompt) - except Exception as e: - logger.warning("Debate LLM failed: %s", e) - result = DebateRefereeOutput() - - return {"debate": result.model_dump()} - - return node - - -# --------------------------------------------------------------------------- -# Risk / Invalidation -# --------------------------------------------------------------------------- - -def create_risk_node(llm): - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - ticker = state["ticker"] - summary = _summarize_tier2(state) - bull = state.get("bull_case") or {} - bear = state.get("bear_case") or {} - debate = state.get("debate") or {} - - prompt = f"""You are the Risk / Invalidation Analyst. Final risk gate for {ticker}. - -{summary} - -DEBATE OUTCOME: {debate.get('winner', '?')} won - Bull strength: {debate.get('bull_strength_0_to_10', '?')}/10 - Bear strength: {debate.get('bear_strength_0_to_10', '?')}/10 - Conviction adjustment: {debate.get('net_conviction_adjustment', 0)} - -Bear risks: {', '.join(bear.get('risks', []))} -Bull invalidation triggers: {', '.join(bull.get('thesis_invalidation_triggers', []))} - -INSTRUCTIONS: -1. Classify overall_risk_level: low / medium / high. -2. Set max_position_size_pct (0-100). Low risk = up to 10%. High risk = max 2%. -3. Suggest stop_loss_pct (distance from entry to stop). -4. List invalidation_triggers — concrete events that should trigger exit. -5. Score overall risk-reward 0-10 (10 = great risk/reward). -6. Set veto=true ONLY if you find impossible/fraudulent data, or risk is so extreme - that no position should be taken. This is a hard kill switch. -7. Be concise.""" - - try: - result = invoke_structured(llm, RiskInvalidationOutput, prompt) - except Exception as e: - logger.warning("Risk LLM failed: %s", e) - result = RiskInvalidationOutput( - score_0_to_10=5.0, confidence_0_to_1=0.3, - summary_1_sentence="Risk analysis unavailable", - ) - - flags = [f.model_dump() for f in result.data_quality_flags] - update: Dict[str, Any] = {"risk": result.model_dump(), "global_flags": flags} - - if result.veto: - update["hard_veto"] = True - update["hard_veto_reason"] = result.veto_reason - - return update - - return node - - -# --------------------------------------------------------------------------- -# Final Decision (prose generated AFTER all scoring) -# --------------------------------------------------------------------------- - -def create_final_decision_node(llm): - - def node(state: Dict[str, Any]) -> Dict[str, Any]: - ticker = state["ticker"] - card = state.get("company_card") or {} - summary = _summarize_tier2(state) - - bull = state.get("bull_case") or {} - bear = state.get("bear_case") or {} - debate = state.get("debate") or {} - risk = state.get("risk") or {} - theme = state.get("theme_substitution") or {} - replacement = state.get("position_replacement") or {} - - master_score = state.get("master_score", 0) - adjusted_score = state.get("adjusted_score", 0) - position_role = state.get("position_role", "Avoid") - conviction_adj = debate.get("net_conviction_adjustment", 0) - - # Apply debate conviction adjustment - final_score = round(adjusted_score + conviction_adj, 2) - final_role = _role_from_score(final_score) - - # Determine action - if state.get("hard_veto"): - action = "AVOID" - final_role = "Avoid" - final_score = 0.0 - elif final_score >= 70: - action = "BUY" - elif final_score >= 50: - action = "HOLD" - else: - action = "AVOID" - - # Theme/replacement context - theme_lines = "" - if theme.get("theme_name"): - theme_lines = ( - f"\nTHEME CONTEXT:" - f"\n Theme: {theme.get('theme_name', '?')}" - f"\n Best expression: {'Yes' if theme.get('best_expression_of_theme') else 'No'}" - f"\n Stronger alternatives: {', '.join(theme.get('stronger_alternatives', [])) or 'None'}" - f"\n Score gap vs best: {theme.get('relative_score_gap', 0):.1f}" - ) - if replacement.get("should_replace"): - theme_lines += ( - f"\n REPLACEMENT FLAG: Consider {replacement.get('replace_with', '?')} instead" - f"\n Reason: {replacement.get('replacement_reason', '')}" - ) - - prompt = f"""You are the Final Decision Synthesizer for {ticker}. - -{summary} - -DEBATE: {debate.get('winner', '?')} won | Conviction adjustment: {conviction_adj:+.1f} -RISK: {risk.get('overall_risk_level', '?')} | Max position: {risk.get('max_position_size_pct', '?')}% -{theme_lines} - -FINAL SCORES: - Master Score: {master_score} - Adjusted Score: {adjusted_score} (after data quality penalties) - Post-Debate Score: {final_score} (after conviction adjustment) - Position Role: {final_role} - Action: {action} - -INSTRUCTIONS: -Write a concise narrative (3-5 sentences) that: -1. Summarizes the investment thesis. -2. Highlights the top 2-3 catalysts and top 2-3 risks. -3. States the action ({action}) and position role ({final_role}). -4. Notes what would change the thesis (invalidation triggers). -5. If theme analysis found stronger alternatives, mention them and whether - this stock is still the best expression of the theme. - -Also provide: -- thesis_summary (one sentence) -- key_catalysts (top 3 from bull case) -- key_risks (top 3 from bear case) -- invalidation_triggers (from risk agent) -- position_sizing_pct (from risk agent) -- confidence (average of all agent confidences)""" - - try: - result = invoke_structured(llm, FinalDecisionOutput, prompt) - except Exception as e: - logger.warning("FinalDecision LLM failed: %s", e) - result = FinalDecisionOutput() - - # Override with computed values (deterministic, not LLM-driven) - result.ticker = ticker - result.company_name = card.get("company_name", "") - result.master_score = master_score - result.adjusted_score = final_score - result.position_role = final_role - result.action = action - result.risk_level = risk.get("overall_risk_level", "medium") - result.position_sizing_pct = risk.get("max_position_size_pct", 0) - - # Compute aggregate confidence - agents_with_confidence = [ - state.get(k, {}).get("confidence_0_to_1") - for k in ( - "macro", "liquidity", "business_quality", "institutional_flow", - "valuation", "entry_timing", "earnings_revisions", - "sector_rotation", "backlog", "crowding", - ) - ] - valid_confs = [c for c in agents_with_confidence if c is not None] - result.confidence = round(sum(valid_confs) / len(valid_confs), 2) if valid_confs else 0.5 - - return {"final_decision": result.model_dump()} - - return node - - -def _role_from_score(score: float) -> str: - if score > 80: - return "Core Position" - if score > 70: - return "Strong Position" - if score > 60: - return "Tactical / Satellite" - if score > 50: - return "Watchlist" - return "Avoid" +"""Tier 3 agents: Bull/Bear debate, Risk assessment, Final decision. + +Only runs on stocks that pass Tier 1 + Tier 2. Uses the deep-thinking LLM +for reasoning-heavy tasks (debate, risk, final synthesis). +""" + +from __future__ import annotations + +import json +import logging +from typing import Any, Dict + +from tradingagents.models import ( + BearCaseOutput, + BullCaseOutput, + DataFlag, + DebateRefereeOutput, + FinalDecisionOutput, + RiskInvalidationOutput, + invoke_structured, +) + +logger = logging.getLogger(__name__) + + +def _low_confidence_warnings(state: Dict[str, Any]) -> str: + """Check if any Tier 2 agents have confidence < 0.2 and return warnings.""" + _TIER2_FIELDS = { + "business_quality": "Business Quality", + "institutional_flow": "Institutional Flow", + "valuation": "Valuation", + "entry_timing": "Entry Timing", + "earnings_revisions": "Earnings Revisions", + "sector_rotation": "Sector Rotation", + "backlog": "Backlog / Order Momentum", + "crowding": "Narrative Crowding", + } + warnings = [] + for field, display_name in _TIER2_FIELDS.items(): + agent_data = state.get(field) or {} + conf = agent_data.get("confidence_0_to_1") + if conf is not None and conf < 0.2: + warnings.append( + f" WARNING: {display_name} has low confidence ({conf:.2f}) — " + f"its score may be unreliable (fallback defaults or poor data)" + ) + if warnings: + return "\nDATA QUALITY WARNINGS:\n" + "\n".join(warnings) + "\n" + return "" + + +def _summarize_tier2(state: Dict[str, Any]) -> str: + """Build a compact summary of all Tier 1+2 findings for Tier 3 prompts.""" + card = state.get("company_card") or {} + macro = state.get("macro") or {} + liq = state.get("liquidity") or {} + bq = state.get("business_quality") or {} + inst = state.get("institutional_flow") or {} + val = state.get("valuation") or {} + et = state.get("entry_timing") or {} + er = state.get("earnings_revisions") or {} + sr = state.get("sector_rotation") or {} + bl = state.get("backlog") or {} + cr = state.get("crowding") or {} + arch = state.get("archetype") or {} + + # Check for low-confidence Tier 2 agents + confidence_warnings = _low_confidence_warnings(state) + + lines = [ + f"Company: {card.get('company_name', '?')} ({card.get('ticker', '?')})", + f"Sector: {card.get('sector', '?')} | Industry: {card.get('industry', '?')}", + f"Market Cap: {card.get('market_cap_formatted', 'N/A')}", + f"Price: ${card.get('current_price', 'N/A')}", + f"Archetype: {arch.get('archetype', 'N/A')}", + "", + f"Master Score: {state.get('master_score', 'N/A')} | Role: {state.get('position_role', 'N/A')}", + "", + "AGENT SCORES (0-10):", + f" Business Quality: {bq.get('score_0_to_10', 'N/A')} — {bq.get('summary_1_sentence', '')}", + f" Macro Alignment: {macro.get('macro_alignment_0_to_10', 'N/A')} — {macro.get('summary_1_sentence', '')}", + f" Institutional Flow: {inst.get('score_0_to_10', 'N/A')} — {inst.get('summary_1_sentence', '')}", + f" Valuation: {val.get('score_0_to_10', 'N/A')} — {val.get('summary_1_sentence', '')}", + f" Entry Timing: {et.get('score_0_to_10', 'N/A')} — {et.get('summary_1_sentence', '')}", + f" Earnings Revisions: {er.get('score_0_to_10', 'N/A')} — {er.get('summary_1_sentence', '')}", + f" Sector Rotation: {sr.get('score_0_to_10', 'N/A')} — {sr.get('summary_1_sentence', '')}", + f" Backlog: {bl.get('score_0_to_10', 'N/A')} — {bl.get('summary_1_sentence', '')}", + f" Crowding: {cr.get('score_0_to_10', 'N/A')} — {cr.get('summary_1_sentence', '')}", + f" Liquidity: {liq.get('score_0_to_10', 'N/A')} — {liq.get('summary_1_sentence', '')}", + "", + f" Macro Regime: {macro.get('regime_label', '?')} | VIX: {macro.get('vix_level', '?')}", + f" Risk Appetite: {macro.get('risk_appetite', '?')} | Liquidity Regime: {macro.get('liquidity_regime', '?')}", + f" Regime Score Adjustment: {macro.get('regime_score_adjustment', 0):+.1f}", + f" Moat: {bq.get('competitive_moat', '?')} | Valuation: {val.get('valuation_verdict', '?')}", + f" Smart Money: {inst.get('smart_money_signal', '?')} | Accumulation: {inst.get('accumulation_signal', '?')}", + f" Short Trend: {inst.get('short_interest_trend', '?')} | Insider Signal: {inst.get('insider_transaction_signal', '?')}", + f" Timing: {et.get('timing_verdict', '?')}", + ] + + if confidence_warnings: + lines.append("") + lines.append(confidence_warnings) + lines.append("Factor these warnings into your analysis — low-confidence scores may not reflect reality.") + + return "\n".join(lines) + + +# --------------------------------------------------------------------------- +# Bull Case +# --------------------------------------------------------------------------- + +def create_bull_case_node(llm): + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["ticker"] + summary = _summarize_tier2(state) + + prompt = f"""You are a Bull Case Researcher. Build the strongest possible bullish thesis for {ticker}. + +{summary} + +INSTRUCTIONS: +1. Write a concise thesis (2-3 sentences) for why this stock should be bought. +2. List 3-5 specific catalysts that could drive the stock higher. +3. Estimate upside_target (price) and upside_pct from current price. +4. List key assumptions your thesis depends on. +5. List thesis_invalidation_triggers — what would kill the bull case. +6. Set confidence 0-1 for how strong the bull case is. + +Attack the investment aggressively. Find every reason to be bullish. +But be honest — don't fabricate catalysts. Use the data above.""" + + try: + result = invoke_structured(llm, BullCaseOutput, prompt) + except Exception as e: + logger.warning("BullCase LLM failed: %s", e) + result = BullCaseOutput( + thesis="Bull case analysis unavailable", + confidence_0_to_1=0.1, + ) + + return {"bull_case": result.model_dump()} + + return node + + +# --------------------------------------------------------------------------- +# Bear Case +# --------------------------------------------------------------------------- + +def create_bear_case_node(llm): + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["ticker"] + summary = _summarize_tier2(state) + + prompt = f"""You are a Bear Case Researcher. Build the strongest possible bearish thesis for {ticker}. + +{summary} + +INSTRUCTIONS: +1. Write a concise thesis (2-3 sentences) for why this stock should be avoided or sold. +2. List 3-5 specific risks that could drive the stock lower. +3. Estimate downside_target (price) and downside_pct from current price. +4. List key assumptions your bear thesis depends on. +5. List thesis_invalidation_triggers — what would kill the bear case. +6. Set confidence 0-1 for how strong the bear case is. + +Be ruthless. Find every vulnerability, every overvaluation, every risk. +But be honest — don't fabricate risks. Use the data above.""" + + try: + result = invoke_structured(llm, BearCaseOutput, prompt) + except Exception as e: + logger.warning("BearCase LLM failed: %s", e) + result = BearCaseOutput( + thesis="Bear case analysis unavailable", + confidence_0_to_1=0.1, + ) + + return {"bear_case": result.model_dump()} + + return node + + +# --------------------------------------------------------------------------- +# Debate Referee +# --------------------------------------------------------------------------- + +def create_debate_node(llm): + """Referee that evaluates bull vs bear case.""" + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["ticker"] + bull = state.get("bull_case") or {} + bear = state.get("bear_case") or {} + + prompt = f"""You are the Debate Referee. Evaluate the bull vs bear case for {ticker}. + +BULL CASE (confidence: {bull.get('confidence_0_to_1', 'N/A')}): +Thesis: {bull.get('thesis', 'N/A')} +Catalysts: {', '.join(bull.get('catalysts', []))} +Upside: {bull.get('upside_pct', 'N/A')}% +Invalidation: {', '.join(bull.get('thesis_invalidation_triggers', []))} + +BEAR CASE (confidence: {bear.get('confidence_0_to_1', 'N/A')}): +Thesis: {bear.get('thesis', 'N/A')} +Risks: {', '.join(bear.get('risks', []))} +Downside: {bear.get('downside_pct', 'N/A')}% +Invalidation: {', '.join(bear.get('thesis_invalidation_triggers', []))} + +MASTER SCORE: {state.get('master_score', 'N/A')} | ROLE: {state.get('position_role', 'N/A')} + +INSTRUCTIONS: +1. Declare winner: "bull" or "bear". +2. Score each side 0-10 on argument strength. +3. List key unresolved questions. +4. Set net_conviction_adjustment (-2 to +2) to modify the master score. + Positive = debate strengthened the bull case. Negative = weakened it. +5. Provide reasoning for your decision.""" + + try: + result = invoke_structured(llm, DebateRefereeOutput, prompt) + except Exception as e: + logger.warning("Debate LLM failed: %s", e) + result = DebateRefereeOutput() + + return {"debate": result.model_dump()} + + return node + + +# --------------------------------------------------------------------------- +# Risk / Invalidation +# --------------------------------------------------------------------------- + +def create_risk_node(llm): + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["ticker"] + summary = _summarize_tier2(state) + bull = state.get("bull_case") or {} + bear = state.get("bear_case") or {} + debate = state.get("debate") or {} + + prompt = f"""You are the Risk / Invalidation Analyst. Final risk gate for {ticker}. + +{summary} + +DEBATE OUTCOME: {debate.get('winner', '?')} won + Bull strength: {debate.get('bull_strength_0_to_10', '?')}/10 + Bear strength: {debate.get('bear_strength_0_to_10', '?')}/10 + Conviction adjustment: {debate.get('net_conviction_adjustment', 0)} + +Bear risks: {', '.join(bear.get('risks', []))} +Bull invalidation triggers: {', '.join(bull.get('thesis_invalidation_triggers', []))} + +INSTRUCTIONS: +1. Classify overall_risk_level: low / medium / high. +2. Set max_position_size_pct (0-100). Low risk = up to 10%. High risk = max 2%. +3. Suggest stop_loss_pct (distance from entry to stop). +4. List invalidation_triggers — concrete events that should trigger exit. +5. Score overall risk-reward 0-10 (10 = great risk/reward). +6. Set veto=true ONLY if you find impossible/fraudulent data, or risk is so extreme + that no position should be taken. This is a hard kill switch. +7. Be concise.""" + + try: + result = invoke_structured(llm, RiskInvalidationOutput, prompt) + except Exception as e: + logger.warning("Risk LLM failed: %s", e) + result = RiskInvalidationOutput( + score_0_to_10=5.0, confidence_0_to_1=0.3, + summary_1_sentence="Risk analysis unavailable", + ) + + flags = [f.model_dump() for f in result.data_quality_flags] + update: Dict[str, Any] = {"risk": result.model_dump(), "global_flags": flags} + + if result.veto: + update["hard_veto"] = True + update["hard_veto_reason"] = result.veto_reason + + return update + + return node + + +# --------------------------------------------------------------------------- +# Final Decision (prose generated AFTER all scoring) +# --------------------------------------------------------------------------- + +def create_final_decision_node(llm): + + def node(state: Dict[str, Any]) -> Dict[str, Any]: + ticker = state["ticker"] + card = state.get("company_card") or {} + summary = _summarize_tier2(state) + + bull = state.get("bull_case") or {} + bear = state.get("bear_case") or {} + debate = state.get("debate") or {} + risk = state.get("risk") or {} + theme = state.get("theme_substitution") or {} + replacement = state.get("position_replacement") or {} + + master_score = state.get("master_score", 0) + adjusted_score = state.get("adjusted_score", 0) + position_role = state.get("position_role", "Avoid") + conviction_adj = debate.get("net_conviction_adjustment", 0) + + # Apply debate conviction adjustment + final_score = round(adjusted_score + conviction_adj, 2) + final_role = _role_from_score(final_score) + + # Determine action + if state.get("hard_veto"): + action = "AVOID" + final_role = "Avoid" + final_score = 0.0 + elif final_score >= 70: + action = "BUY" + elif final_score >= 50: + action = "HOLD" + else: + action = "AVOID" + + # Theme/replacement context + theme_lines = "" + if theme.get("theme_name"): + theme_lines = ( + f"\nTHEME CONTEXT:" + f"\n Theme: {theme.get('theme_name', '?')}" + f"\n Best expression: {'Yes' if theme.get('best_expression_of_theme') else 'No'}" + f"\n Stronger alternatives: {', '.join(theme.get('stronger_alternatives', [])) or 'None'}" + f"\n Score gap vs best: {theme.get('relative_score_gap', 0):.1f}" + ) + if replacement.get("should_replace"): + theme_lines += ( + f"\n REPLACEMENT FLAG: Consider {replacement.get('replace_with', '?')} instead" + f"\n Reason: {replacement.get('replacement_reason', '')}" + ) + + prompt = f"""You are the Final Decision Synthesizer for {ticker}. + +{summary} + +DEBATE: {debate.get('winner', '?')} won | Conviction adjustment: {conviction_adj:+.1f} +RISK: {risk.get('overall_risk_level', '?')} | Max position: {risk.get('max_position_size_pct', '?')}% +{theme_lines} + +FINAL SCORES: + Master Score: {master_score} + Adjusted Score: {adjusted_score} (after data quality penalties) + Post-Debate Score: {final_score} (after conviction adjustment) + Position Role: {final_role} + Action: {action} + +INSTRUCTIONS: +Write a concise narrative (3-5 sentences) that: +1. Summarizes the investment thesis. +2. Highlights the top 2-3 catalysts and top 2-3 risks. +3. States the action ({action}) and position role ({final_role}). +4. Notes what would change the thesis (invalidation triggers). +5. If theme analysis found stronger alternatives, mention them and whether + this stock is still the best expression of the theme. + +Also provide: +- thesis_summary (one sentence) +- key_catalysts (top 3 from bull case) +- key_risks (top 3 from bear case) +- invalidation_triggers (from risk agent) +- position_sizing_pct (from risk agent) +- confidence (average of all agent confidences)""" + + try: + result = invoke_structured(llm, FinalDecisionOutput, prompt) + except Exception as e: + logger.warning("FinalDecision LLM failed: %s", e) + result = FinalDecisionOutput() + + # Override with computed values (deterministic, not LLM-driven) + result.ticker = ticker + result.company_name = card.get("company_name", "") + result.master_score = master_score + result.adjusted_score = final_score + result.position_role = final_role + result.action = action + result.risk_level = risk.get("overall_risk_level", "medium") + result.position_sizing_pct = risk.get("max_position_size_pct", 0) + + # Compute aggregate confidence + agents_with_confidence = [ + state.get(k, {}).get("confidence_0_to_1") + for k in ( + "macro", "liquidity", "business_quality", "institutional_flow", + "valuation", "entry_timing", "earnings_revisions", + "sector_rotation", "backlog", "crowding", + ) + ] + valid_confs = [c for c in agents_with_confidence if c is not None] + result.confidence = round(sum(valid_confs) / len(valid_confs), 2) if valid_confs else 0.5 + + return {"final_decision": result.model_dump()} + + return node + + +def _role_from_score(score: float) -> str: + if score > 80: + return "Core Position" + if score > 70: + return "Strong Position" + if score > 60: + return "Tactical / Satellite" + if score > 50: + return "Watchlist" + return "Avoid" diff --git a/tradingagents/agents/trader/trader.py b/tradingagents/agents/trader/trader.py index 1b05c35d..6291dbef 100644 --- a/tradingagents/agents/trader/trader.py +++ b/tradingagents/agents/trader/trader.py @@ -1,46 +1,46 @@ -import functools -import time -import json - - -def create_trader(llm, memory): - def trader_node(state, name): - company_name = state["company_of_interest"] - investment_plan = state["investment_plan"] - market_research_report = state["market_report"] - sentiment_report = state["sentiment_report"] - news_report = state["news_report"] - fundamentals_report = state["fundamentals_report"] - - curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" - past_memories = memory.get_memories(curr_situation, n_matches=2) - - past_memory_str = "" - if past_memories: - for i, rec in enumerate(past_memories, 1): - past_memory_str += rec["recommendation"] + "\n\n" - else: - past_memory_str = "No past memories found." - - context = { - "role": "user", - "content": f"Based on a comprehensive analysis by a team of analysts, here is an investment plan tailored for {company_name}. This plan incorporates insights from current technical market trends, macroeconomic indicators, and social media sentiment. Use this plan as a foundation for evaluating your next trading decision.\n\nProposed Investment Plan: {investment_plan}\n\nLeverage these insights to make an informed and strategic decision.", - } - - messages = [ - { - "role": "system", - "content": f"""You are a trading agent analyzing market data to make investment decisions. Based on your analysis, provide a specific recommendation to buy, sell, or hold. End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**' to confirm your recommendation. Do not forget to utilize lessons from past decisions to learn from your mistakes. Here is some reflections from similar situatiosn you traded in and the lessons learned: {past_memory_str}""", - }, - context, - ] - - result = llm.invoke(messages) - - return { - "messages": [result], - "trader_investment_plan": result.content, - "sender": name, - } - - return functools.partial(trader_node, name="Trader") +import functools +import time +import json + + +def create_trader(llm, memory): + def trader_node(state, name): + company_name = state["company_of_interest"] + investment_plan = state["investment_plan"] + market_research_report = state["market_report"] + sentiment_report = state["sentiment_report"] + news_report = state["news_report"] + fundamentals_report = state["fundamentals_report"] + + curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" + past_memories = memory.get_memories(curr_situation, n_matches=2) + + past_memory_str = "" + if past_memories: + for i, rec in enumerate(past_memories, 1): + past_memory_str += rec["recommendation"] + "\n\n" + else: + past_memory_str = "No past memories found." + + context = { + "role": "user", + "content": f"Based on a comprehensive analysis by a team of analysts, here is an investment plan tailored for {company_name}. This plan incorporates insights from current technical market trends, macroeconomic indicators, and social media sentiment. Use this plan as a foundation for evaluating your next trading decision.\n\nProposed Investment Plan: {investment_plan}\n\nLeverage these insights to make an informed and strategic decision.", + } + + messages = [ + { + "role": "system", + "content": f"""You are a trading agent analyzing market data to make investment decisions. Based on your analysis, provide a specific recommendation to buy, sell, or hold. End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**' to confirm your recommendation. Do not forget to utilize lessons from past decisions to learn from your mistakes. Here is some reflections from similar situatiosn you traded in and the lessons learned: {past_memory_str}""", + }, + context, + ] + + result = llm.invoke(messages) + + return { + "messages": [result], + "trader_investment_plan": result.content, + "sender": name, + } + + return functools.partial(trader_node, name="Trader") diff --git a/tradingagents/agents/utils/agent_states.py b/tradingagents/agents/utils/agent_states.py index 87447015..54323e3e 100644 --- a/tradingagents/agents/utils/agent_states.py +++ b/tradingagents/agents/utils/agent_states.py @@ -1,109 +1,109 @@ -"""State definitions for the TradingAgents pipeline. - -PipelineState is the new structured state used by the equity ranking engine. -Legacy state types are preserved for backward compatibility. -""" - -from __future__ import annotations - -import operator -from typing import Annotated, Optional, Sequence - -from typing_extensions import TypedDict - -from langchain_openai import ChatOpenAI -from langgraph.graph import MessagesState - - -# --------------------------------------------------------------------------- -# New structured pipeline state -# --------------------------------------------------------------------------- - -class PipelineState(TypedDict): - """Shared state for the structured equity ranking pipeline. - - Each agent writes its output as a dict (Pydantic .model_dump()). - The scoring node computes master_score/adjusted_score deterministically. - global_flags uses operator.add to accumulate across all agents. - """ - ticker: str - trade_date: str - - # Tier 1 - validation: Optional[dict] - company_card: Optional[dict] - macro: Optional[dict] - liquidity: Optional[dict] - - # Tier 2 - sector_rotation: Optional[dict] - business_quality: Optional[dict] - institutional_flow: Optional[dict] - valuation: Optional[dict] - entry_timing: Optional[dict] - earnings_revisions: Optional[dict] - backlog: Optional[dict] - crowding: Optional[dict] - archetype: Optional[dict] - - # Scoring (deterministic) - master_score: Optional[float] - adjusted_score: Optional[float] - position_role: Optional[str] - - # Portfolio-level - theme_substitution: Optional[dict] - position_replacement: Optional[dict] - - # Tier 3 - bull_case: Optional[dict] - bear_case: Optional[dict] - debate: Optional[dict] - risk: Optional[dict] - final_decision: Optional[dict] - - # Control - hard_veto: bool - hard_veto_reason: Optional[str] - global_flags: Annotated[list, operator.add] - - -# --------------------------------------------------------------------------- -# Legacy state types (preserved for backward compatibility) -# --------------------------------------------------------------------------- - -class InvestDebateState(TypedDict): - bull_history: Annotated[str, "Bullish Conversation history"] - bear_history: Annotated[str, "Bearish Conversation history"] - history: Annotated[str, "Conversation history"] - current_response: Annotated[str, "Latest response"] - judge_decision: Annotated[str, "Final judge decision"] - count: Annotated[int, "Length of the current conversation"] - - -class RiskDebateState(TypedDict): - aggressive_history: Annotated[str, "Aggressive Agent's Conversation history"] - conservative_history: Annotated[str, "Conservative Agent's Conversation history"] - neutral_history: Annotated[str, "Neutral Agent's Conversation history"] - history: Annotated[str, "Conversation history"] - latest_speaker: Annotated[str, "Analyst that spoke last"] - current_aggressive_response: Annotated[str, "Latest response by the aggressive analyst"] - current_conservative_response: Annotated[str, "Latest response by the conservative analyst"] - current_neutral_response: Annotated[str, "Latest response by the neutral analyst"] - judge_decision: Annotated[str, "Judge's decision"] - count: Annotated[int, "Length of the current conversation"] - - -class AgentState(MessagesState): - company_of_interest: Annotated[str, "Company that we are interested in trading"] - trade_date: Annotated[str, "What date we are trading at"] - sender: Annotated[str, "Agent that sent this message"] - market_report: Annotated[str, "Report from the Market Analyst"] - sentiment_report: Annotated[str, "Report from the Social Media Analyst"] - news_report: Annotated[str, "Report from the News Researcher"] - fundamentals_report: Annotated[str, "Report from the Fundamentals Researcher"] - investment_debate_state: Annotated[InvestDebateState, "Current state of the investment debate"] - investment_plan: Annotated[str, "Plan generated by the Analyst"] - trader_investment_plan: Annotated[str, "Plan generated by the Trader"] - risk_debate_state: Annotated[RiskDebateState, "Current state of the risk debate"] - final_trade_decision: Annotated[str, "Final decision made by the Risk Analysts"] +"""State definitions for the TradingAgents pipeline. + +PipelineState is the new structured state used by the equity ranking engine. +Legacy state types are preserved for backward compatibility. +""" + +from __future__ import annotations + +import operator +from typing import Annotated, Optional, Sequence + +from typing_extensions import TypedDict + +from langchain_openai import ChatOpenAI +from langgraph.graph import MessagesState + + +# --------------------------------------------------------------------------- +# New structured pipeline state +# --------------------------------------------------------------------------- + +class PipelineState(TypedDict): + """Shared state for the structured equity ranking pipeline. + + Each agent writes its output as a dict (Pydantic .model_dump()). + The scoring node computes master_score/adjusted_score deterministically. + global_flags uses operator.add to accumulate across all agents. + """ + ticker: str + trade_date: str + + # Tier 1 + validation: Optional[dict] + company_card: Optional[dict] + macro: Optional[dict] + liquidity: Optional[dict] + + # Tier 2 + sector_rotation: Optional[dict] + business_quality: Optional[dict] + institutional_flow: Optional[dict] + valuation: Optional[dict] + entry_timing: Optional[dict] + earnings_revisions: Optional[dict] + backlog: Optional[dict] + crowding: Optional[dict] + archetype: Optional[dict] + + # Scoring (deterministic) + master_score: Optional[float] + adjusted_score: Optional[float] + position_role: Optional[str] + + # Portfolio-level + theme_substitution: Optional[dict] + position_replacement: Optional[dict] + + # Tier 3 + bull_case: Optional[dict] + bear_case: Optional[dict] + debate: Optional[dict] + risk: Optional[dict] + final_decision: Optional[dict] + + # Control + hard_veto: bool + hard_veto_reason: Optional[str] + global_flags: Annotated[list, operator.add] + + +# --------------------------------------------------------------------------- +# Legacy state types (preserved for backward compatibility) +# --------------------------------------------------------------------------- + +class InvestDebateState(TypedDict): + bull_history: Annotated[str, "Bullish Conversation history"] + bear_history: Annotated[str, "Bearish Conversation history"] + history: Annotated[str, "Conversation history"] + current_response: Annotated[str, "Latest response"] + judge_decision: Annotated[str, "Final judge decision"] + count: Annotated[int, "Length of the current conversation"] + + +class RiskDebateState(TypedDict): + aggressive_history: Annotated[str, "Aggressive Agent's Conversation history"] + conservative_history: Annotated[str, "Conservative Agent's Conversation history"] + neutral_history: Annotated[str, "Neutral Agent's Conversation history"] + history: Annotated[str, "Conversation history"] + latest_speaker: Annotated[str, "Analyst that spoke last"] + current_aggressive_response: Annotated[str, "Latest response by the aggressive analyst"] + current_conservative_response: Annotated[str, "Latest response by the conservative analyst"] + current_neutral_response: Annotated[str, "Latest response by the neutral analyst"] + judge_decision: Annotated[str, "Judge's decision"] + count: Annotated[int, "Length of the current conversation"] + + +class AgentState(MessagesState): + company_of_interest: Annotated[str, "Company that we are interested in trading"] + trade_date: Annotated[str, "What date we are trading at"] + sender: Annotated[str, "Agent that sent this message"] + market_report: Annotated[str, "Report from the Market Analyst"] + sentiment_report: Annotated[str, "Report from the Social Media Analyst"] + news_report: Annotated[str, "Report from the News Researcher"] + fundamentals_report: Annotated[str, "Report from the Fundamentals Researcher"] + investment_debate_state: Annotated[InvestDebateState, "Current state of the investment debate"] + investment_plan: Annotated[str, "Plan generated by the Analyst"] + trader_investment_plan: Annotated[str, "Plan generated by the Trader"] + risk_debate_state: Annotated[RiskDebateState, "Current state of the risk debate"] + final_trade_decision: Annotated[str, "Final decision made by the Risk Analysts"] diff --git a/tradingagents/agents/utils/agent_utils.py b/tradingagents/agents/utils/agent_utils.py index b329a3e9..08c6a56a 100644 --- a/tradingagents/agents/utils/agent_utils.py +++ b/tradingagents/agents/utils/agent_utils.py @@ -1,38 +1,38 @@ -from langchain_core.messages import HumanMessage, RemoveMessage - -# Import tools from separate utility files -from tradingagents.agents.utils.core_stock_tools import ( - get_stock_data -) -from tradingagents.agents.utils.technical_indicators_tools import ( - get_indicators -) -from tradingagents.agents.utils.fundamental_data_tools import ( - get_fundamentals, - get_balance_sheet, - get_cashflow, - get_income_statement -) -from tradingagents.agents.utils.news_data_tools import ( - get_news, - get_insider_transactions, - get_global_news -) - -def create_msg_delete(): - def delete_messages(state): - """Clear messages and add placeholder for Anthropic compatibility""" - messages = state["messages"] - - # Remove all messages - removal_operations = [RemoveMessage(id=m.id) for m in messages] - - # Add a minimal placeholder message - placeholder = HumanMessage(content="Continue") - - return {"messages": removal_operations + [placeholder]} - - return delete_messages - - +from langchain_core.messages import HumanMessage, RemoveMessage + +# Import tools from separate utility files +from tradingagents.agents.utils.core_stock_tools import ( + get_stock_data +) +from tradingagents.agents.utils.technical_indicators_tools import ( + get_indicators +) +from tradingagents.agents.utils.fundamental_data_tools import ( + get_fundamentals, + get_balance_sheet, + get_cashflow, + get_income_statement +) +from tradingagents.agents.utils.news_data_tools import ( + get_news, + get_insider_transactions, + get_global_news +) + +def create_msg_delete(): + def delete_messages(state): + """Clear messages and add placeholder for Anthropic compatibility""" + messages = state["messages"] + + # Remove all messages + removal_operations = [RemoveMessage(id=m.id) for m in messages] + + # Add a minimal placeholder message + placeholder = HumanMessage(content="Continue") + + return {"messages": removal_operations + [placeholder]} + + return delete_messages + + \ No newline at end of file diff --git a/tradingagents/agents/utils/core_stock_tools.py b/tradingagents/agents/utils/core_stock_tools.py index 3a416622..8acd30ef 100644 --- a/tradingagents/agents/utils/core_stock_tools.py +++ b/tradingagents/agents/utils/core_stock_tools.py @@ -1,22 +1,22 @@ -from langchain_core.tools import tool -from typing import Annotated -from tradingagents.dataflows.interface import route_to_vendor - - -@tool -def get_stock_data( - symbol: Annotated[str, "ticker symbol of the company"], - start_date: Annotated[str, "Start date in yyyy-mm-dd format"], - end_date: Annotated[str, "End date in yyyy-mm-dd format"], -) -> str: - """ - Retrieve stock price data (OHLCV) for a given ticker symbol. - Uses the configured core_stock_apis vendor. - Args: - symbol (str): Ticker symbol of the company, e.g. AAPL, TSM - start_date (str): Start date in yyyy-mm-dd format - end_date (str): End date in yyyy-mm-dd format - Returns: - str: A formatted dataframe containing the stock price data for the specified ticker symbol in the specified date range. - """ - return route_to_vendor("get_stock_data", symbol, start_date, end_date) +from langchain_core.tools import tool +from typing import Annotated +from tradingagents.dataflows.interface import route_to_vendor + + +@tool +def get_stock_data( + symbol: Annotated[str, "ticker symbol of the company"], + start_date: Annotated[str, "Start date in yyyy-mm-dd format"], + end_date: Annotated[str, "End date in yyyy-mm-dd format"], +) -> str: + """ + Retrieve stock price data (OHLCV) for a given ticker symbol. + Uses the configured core_stock_apis vendor. + Args: + symbol (str): Ticker symbol of the company, e.g. AAPL, TSM + start_date (str): Start date in yyyy-mm-dd format + end_date (str): End date in yyyy-mm-dd format + Returns: + str: A formatted dataframe containing the stock price data for the specified ticker symbol in the specified date range. + """ + return route_to_vendor("get_stock_data", symbol, start_date, end_date) diff --git a/tradingagents/agents/utils/fundamental_data_tools.py b/tradingagents/agents/utils/fundamental_data_tools.py index 47f6f2eb..97783614 100644 --- a/tradingagents/agents/utils/fundamental_data_tools.py +++ b/tradingagents/agents/utils/fundamental_data_tools.py @@ -1,77 +1,77 @@ -from langchain_core.tools import tool -from typing import Annotated -from tradingagents.dataflows.interface import route_to_vendor - - -@tool -def get_fundamentals( - ticker: Annotated[str, "ticker symbol"], - curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"], -) -> str: - """ - Retrieve comprehensive fundamental data for a given ticker symbol. - Uses the configured fundamental_data vendor. - Args: - ticker (str): Ticker symbol of the company - curr_date (str): Current date you are trading at, yyyy-mm-dd - Returns: - str: A formatted report containing comprehensive fundamental data - """ - return route_to_vendor("get_fundamentals", ticker, curr_date) - - -@tool -def get_balance_sheet( - ticker: Annotated[str, "ticker symbol"], - freq: Annotated[str, "reporting frequency: annual/quarterly"] = "quarterly", - curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"] = None, -) -> str: - """ - Retrieve balance sheet data for a given ticker symbol. - Uses the configured fundamental_data vendor. - Args: - ticker (str): Ticker symbol of the company - freq (str): Reporting frequency: annual/quarterly (default quarterly) - curr_date (str): Current date you are trading at, yyyy-mm-dd - Returns: - str: A formatted report containing balance sheet data - """ - return route_to_vendor("get_balance_sheet", ticker, freq, curr_date) - - -@tool -def get_cashflow( - ticker: Annotated[str, "ticker symbol"], - freq: Annotated[str, "reporting frequency: annual/quarterly"] = "quarterly", - curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"] = None, -) -> str: - """ - Retrieve cash flow statement data for a given ticker symbol. - Uses the configured fundamental_data vendor. - Args: - ticker (str): Ticker symbol of the company - freq (str): Reporting frequency: annual/quarterly (default quarterly) - curr_date (str): Current date you are trading at, yyyy-mm-dd - Returns: - str: A formatted report containing cash flow statement data - """ - return route_to_vendor("get_cashflow", ticker, freq, curr_date) - - -@tool -def get_income_statement( - ticker: Annotated[str, "ticker symbol"], - freq: Annotated[str, "reporting frequency: annual/quarterly"] = "quarterly", - curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"] = None, -) -> str: - """ - Retrieve income statement data for a given ticker symbol. - Uses the configured fundamental_data vendor. - Args: - ticker (str): Ticker symbol of the company - freq (str): Reporting frequency: annual/quarterly (default quarterly) - curr_date (str): Current date you are trading at, yyyy-mm-dd - Returns: - str: A formatted report containing income statement data - """ +from langchain_core.tools import tool +from typing import Annotated +from tradingagents.dataflows.interface import route_to_vendor + + +@tool +def get_fundamentals( + ticker: Annotated[str, "ticker symbol"], + curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"], +) -> str: + """ + Retrieve comprehensive fundamental data for a given ticker symbol. + Uses the configured fundamental_data vendor. + Args: + ticker (str): Ticker symbol of the company + curr_date (str): Current date you are trading at, yyyy-mm-dd + Returns: + str: A formatted report containing comprehensive fundamental data + """ + return route_to_vendor("get_fundamentals", ticker, curr_date) + + +@tool +def get_balance_sheet( + ticker: Annotated[str, "ticker symbol"], + freq: Annotated[str, "reporting frequency: annual/quarterly"] = "quarterly", + curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"] = None, +) -> str: + """ + Retrieve balance sheet data for a given ticker symbol. + Uses the configured fundamental_data vendor. + Args: + ticker (str): Ticker symbol of the company + freq (str): Reporting frequency: annual/quarterly (default quarterly) + curr_date (str): Current date you are trading at, yyyy-mm-dd + Returns: + str: A formatted report containing balance sheet data + """ + return route_to_vendor("get_balance_sheet", ticker, freq, curr_date) + + +@tool +def get_cashflow( + ticker: Annotated[str, "ticker symbol"], + freq: Annotated[str, "reporting frequency: annual/quarterly"] = "quarterly", + curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"] = None, +) -> str: + """ + Retrieve cash flow statement data for a given ticker symbol. + Uses the configured fundamental_data vendor. + Args: + ticker (str): Ticker symbol of the company + freq (str): Reporting frequency: annual/quarterly (default quarterly) + curr_date (str): Current date you are trading at, yyyy-mm-dd + Returns: + str: A formatted report containing cash flow statement data + """ + return route_to_vendor("get_cashflow", ticker, freq, curr_date) + + +@tool +def get_income_statement( + ticker: Annotated[str, "ticker symbol"], + freq: Annotated[str, "reporting frequency: annual/quarterly"] = "quarterly", + curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"] = None, +) -> str: + """ + Retrieve income statement data for a given ticker symbol. + Uses the configured fundamental_data vendor. + Args: + ticker (str): Ticker symbol of the company + freq (str): Reporting frequency: annual/quarterly (default quarterly) + curr_date (str): Current date you are trading at, yyyy-mm-dd + Returns: + str: A formatted report containing income statement data + """ return route_to_vendor("get_income_statement", ticker, freq, curr_date) \ No newline at end of file diff --git a/tradingagents/agents/utils/macro_data_tools.py b/tradingagents/agents/utils/macro_data_tools.py index 2172cff9..e4448189 100644 --- a/tradingagents/agents/utils/macro_data_tools.py +++ b/tradingagents/agents/utils/macro_data_tools.py @@ -1,594 +1,594 @@ -"""Macro-aware data tools for the structured equity ranking engine. - -These tools fetch company profile, macro regime, sector rotation, -institutional flow, earnings estimates, and valuation data via yfinance. -They are used directly by analyst agents (not routed through interface.py). -""" - -from langchain_core.tools import tool -from typing import Annotated -from datetime import datetime -from dateutil.relativedelta import relativedelta -import json - - -def _safe_get(info, key, default=None): - """Safely get a value from yfinance info dict.""" - val = info.get(key) - if val is None: - return default - return val - - -def _fmt_large_number(val): - """Format large numbers for readability.""" - if val is None: - return None - if abs(val) >= 1e12: - return f"${val/1e12:.2f}T" - if abs(val) >= 1e9: - return f"${val/1e9:.2f}B" - if abs(val) >= 1e6: - return f"${val/1e6:.2f}M" - return f"${val:,.0f}" - - -def _market_cap_category(market_cap): - """Classify market cap size.""" - if market_cap is None: - return "unknown" - if market_cap >= 10e9: - return "large_cap" - if market_cap >= 2e9: - return "mid_cap" - if market_cap >= 300e6: - return "small_cap" - return "micro_cap" - - -# Sector to ETF mapping -SECTOR_ETF_MAP = { - "Technology": "XLK", - "Information Technology": "XLK", - "Communication Services": "XLC", - "Healthcare": "XLV", - "Health Care": "XLV", - "Financials": "XLF", - "Financial Services": "XLF", - "Consumer Discretionary": "XLY", - "Consumer Cyclical": "XLY", - "Consumer Staples": "XLP", - "Consumer Defensive": "XLP", - "Industrials": "XLI", - "Energy": "XLE", - "Utilities": "XLU", - "Materials": "XLB", - "Basic Materials": "XLB", - "Real Estate": "XLRE", -} - -ALL_SECTOR_ETFS = ["XLK", "XLC", "XLV", "XLF", "XLY", "XLP", "XLI", "XLE", "XLU", "XLB", "XLRE"] - - -def _get_period_return(ticker_obj, period_months, ref_date=None): - """Calculate return over a given period ending at ref_date.""" - import yfinance as yf - import pandas as pd - - try: - if ref_date: - end_dt = pd.to_datetime(ref_date) - else: - end_dt = pd.Timestamp.today() - - start_dt = end_dt - pd.DateOffset(months=period_months) - data = ticker_obj.history( - start=start_dt.strftime("%Y-%m-%d"), - end=end_dt.strftime("%Y-%m-%d"), - ) - if data.empty or len(data) < 2: - return None - return ((data["Close"].iloc[-1] / data["Close"].iloc[0]) - 1) * 100 - except Exception: - return None - - -@tool -def get_company_profile( - ticker: Annotated[str, "Ticker symbol of the company"], -) -> str: - """Fetch company profile: name, sector, industry, description, market cap, business model. - Returns structured text with all fields for the Company Intelligence Analyst. - """ - import yfinance as yf - - try: - t = yf.Ticker(ticker.upper()) - info = t.info - - if not info or not info.get("longName"): - return json.dumps({"error": f"No company data found for {ticker}", "ticker": ticker}) - - market_cap = _safe_get(info, "marketCap") - - profile = { - "company_name": _safe_get(info, "longName", "Unknown"), - "ticker": ticker.upper(), - "sector": _safe_get(info, "sector", "Unknown"), - "industry": _safe_get(info, "industry", "Unknown"), - "description": _safe_get(info, "longBusinessSummary", "No description available"), - "market_cap": market_cap, - "market_cap_formatted": _fmt_large_number(market_cap), - "market_cap_category": _market_cap_category(market_cap), - "trailing_pe": _safe_get(info, "trailingPE"), - "forward_pe": _safe_get(info, "forwardPE"), - "peg_ratio": _safe_get(info, "pegRatio"), - "price_to_book": _safe_get(info, "priceToBook"), - "dividend_yield": _safe_get(info, "dividendYield"), - "beta": _safe_get(info, "beta"), - "trailing_eps": _safe_get(info, "trailingEps"), - "forward_eps": _safe_get(info, "forwardEps"), - "revenue": _safe_get(info, "totalRevenue"), - "revenue_formatted": _fmt_large_number(_safe_get(info, "totalRevenue")), - "gross_profits": _safe_get(info, "grossProfits"), - "ebitda": _safe_get(info, "ebitda"), - "net_income": _safe_get(info, "netIncomeToCommon"), - "profit_margins": _safe_get(info, "profitMargins"), - "operating_margins": _safe_get(info, "operatingMargins"), - "return_on_equity": _safe_get(info, "returnOnEquity"), - "return_on_assets": _safe_get(info, "returnOnAssets"), - "debt_to_equity": _safe_get(info, "debtToEquity"), - "current_ratio": _safe_get(info, "currentRatio"), - "book_value": _safe_get(info, "bookValue"), - "free_cashflow": _safe_get(info, "freeCashflow"), - "fifty_two_week_high": _safe_get(info, "fiftyTwoWeekHigh"), - "fifty_two_week_low": _safe_get(info, "fiftyTwoWeekLow"), - "fifty_day_average": _safe_get(info, "fiftyDayAverage"), - "two_hundred_day_average": _safe_get(info, "twoHundredDayAverage"), - "average_volume": _safe_get(info, "averageVolume"), - "average_volume_10d": _safe_get(info, "averageVolume10days"), - "shares_outstanding": _safe_get(info, "sharesOutstanding"), - "float_shares": _safe_get(info, "floatShares"), - "shares_short": _safe_get(info, "sharesShort"), - "short_ratio": _safe_get(info, "shortRatio"), - "held_percent_insiders": _safe_get(info, "heldPercentInsiders"), - "held_percent_institutions": _safe_get(info, "heldPercentInstitutions"), - "current_price": _safe_get(info, "currentPrice") or _safe_get(info, "regularMarketPrice"), - } - - return json.dumps(profile, default=str) - - except Exception as e: - return json.dumps({"error": f"Error fetching company profile for {ticker}: {str(e)}", "ticker": ticker}) - - -@tool -def get_macro_indicators( - curr_date: Annotated[str, "Current trading date in yyyy-mm-dd format"], -) -> str: - """Fetch macro regime indicators: VIX, 10Y yield, dollar strength, credit spreads, sector ETF performance. - Returns structured text for the Company Intelligence and Macro Regime Analyst. - """ - import yfinance as yf - import pandas as pd - - try: - results = {} - - # VIX - try: - vix = yf.Ticker("^VIX") - vix_data = vix.history(period="5d") - if not vix_data.empty: - results["vix_level"] = round(vix_data["Close"].iloc[-1], 2) - if results["vix_level"] < 15: - results["vix_regime"] = "low" - elif results["vix_level"] < 20: - results["vix_regime"] = "moderate" - elif results["vix_level"] < 30: - results["vix_regime"] = "elevated" - else: - results["vix_regime"] = "stressed" - except Exception: - results["vix_level"] = None - results["vix_regime"] = "unknown" - - # 10Y yield - try: - tnx = yf.Ticker("^TNX") - tnx_data = tnx.history(period="5d") - if not tnx_data.empty: - results["ten_year_yield"] = round(tnx_data["Close"].iloc[-1], 3) - except Exception: - results["ten_year_yield"] = None - - # Dollar strength (UUP as proxy) - try: - uup = yf.Ticker("UUP") - uup_1m = _get_period_return(uup, 1) - uup_3m = _get_period_return(uup, 3) - results["dollar_1m_return"] = round(uup_1m, 2) if uup_1m is not None else None - results["dollar_3m_return"] = round(uup_3m, 2) if uup_3m is not None else None - if uup_1m is not None: - if uup_1m > 1: - results["dollar_strength"] = "strong" - elif uup_1m < -1: - results["dollar_strength"] = "weak" - else: - results["dollar_strength"] = "neutral" - except Exception: - results["dollar_strength"] = "unknown" - - # Credit spreads: HYG vs LQD - try: - hyg = yf.Ticker("HYG") - lqd = yf.Ticker("LQD") - hyg_1m = _get_period_return(hyg, 1) - lqd_1m = _get_period_return(lqd, 1) - if hyg_1m is not None and lqd_1m is not None: - spread_change = hyg_1m - lqd_1m - results["hyg_1m_return"] = round(hyg_1m, 2) - results["lqd_1m_return"] = round(lqd_1m, 2) - results["credit_spread_change"] = round(spread_change, 2) - if spread_change > 0.5: - results["credit_spread_direction"] = "tightening" - elif spread_change < -0.5: - results["credit_spread_direction"] = "widening" - else: - results["credit_spread_direction"] = "stable" - except Exception: - results["credit_spread_direction"] = "unknown" - - # SPY and sector ETF performance - sector_etfs = { - "SPY": "S&P 500", - "XLK": "Technology", - "XLC": "Communication Services", - "XLV": "Healthcare", - "XLF": "Financials", - "XLY": "Consumer Discretionary", - "XLP": "Consumer Staples", - "XLI": "Industrials", - "XLE": "Energy", - "XLU": "Utilities", - "XLB": "Materials", - "XLRE": "Real Estate", - } - - sector_performance = {} - for etf_ticker, sector_name in sector_etfs.items(): - try: - etf = yf.Ticker(etf_ticker) - ret_1m = _get_period_return(etf, 1) - ret_3m = _get_period_return(etf, 3) - sector_performance[etf_ticker] = { - "name": sector_name, - "return_1m": round(ret_1m, 2) if ret_1m is not None else None, - "return_3m": round(ret_3m, 2) if ret_3m is not None else None, - } - except Exception: - sector_performance[etf_ticker] = { - "name": sector_name, - "return_1m": None, - "return_3m": None, - } - - results["sector_performance"] = sector_performance - - return json.dumps(results, default=str) - - except Exception as e: - return json.dumps({"error": f"Error fetching macro indicators: {str(e)}"}) - - -@tool -def get_sector_rotation( - ticker: Annotated[str, "Ticker symbol of the company"], - curr_date: Annotated[str, "Current trading date in yyyy-mm-dd format"], -) -> str: - """Fetch sector rotation data: sector ETF relative strength vs SPY over 1M/3M/6M, breadth indicators. - Returns structured text for the Sector Rotation and Institutional Flow Analyst. - """ - import yfinance as yf - - try: - # Get the company's sector - t = yf.Ticker(ticker.upper()) - info = t.info - sector = _safe_get(info, "sector", "Unknown") - - # Map sector to ETF - sector_etf = SECTOR_ETF_MAP.get(sector, None) - - # Get SPY returns - spy = yf.Ticker("SPY") - spy_1m = _get_period_return(spy, 1) - spy_3m = _get_period_return(spy, 3) - spy_6m = _get_period_return(spy, 6) - - # Get all sector ETF returns for ranking - sector_returns = {} - for etf_sym in ALL_SECTOR_ETFS: - try: - etf = yf.Ticker(etf_sym) - ret_1m = _get_period_return(etf, 1) - ret_3m = _get_period_return(etf, 3) - ret_6m = _get_period_return(etf, 6) - sector_returns[etf_sym] = { - "return_1m": round(ret_1m, 2) if ret_1m is not None else None, - "return_3m": round(ret_3m, 2) if ret_3m is not None else None, - "return_6m": round(ret_6m, 2) if ret_6m is not None else None, - "vs_spy_1m": round(ret_1m - spy_1m, 2) if (ret_1m is not None and spy_1m is not None) else None, - "vs_spy_3m": round(ret_3m - spy_3m, 2) if (ret_3m is not None and spy_3m is not None) else None, - "vs_spy_6m": round(ret_6m - spy_6m, 2) if (ret_6m is not None and spy_6m is not None) else None, - } - except Exception: - sector_returns[etf_sym] = { - "return_1m": None, "return_3m": None, "return_6m": None, - "vs_spy_1m": None, "vs_spy_3m": None, "vs_spy_6m": None, - } - - # Rank sectors by 1M relative strength - ranked = sorted( - [(sym, data) for sym, data in sector_returns.items() if data["vs_spy_1m"] is not None], - key=lambda x: x[1]["vs_spy_1m"], - reverse=True, - ) - rank_map = {sym: i + 1 for i, (sym, _) in enumerate(ranked)} - - # Stock's sector data - stock_sector_data = {} - stock_sector_rank = None - if sector_etf and sector_etf in sector_returns: - stock_sector_data = sector_returns[sector_etf] - stock_sector_rank = rank_map.get(sector_etf) - - result = { - "ticker": ticker.upper(), - "sector": sector, - "sector_etf": sector_etf, - "stock_sector_vs_spy_1m": stock_sector_data.get("vs_spy_1m"), - "stock_sector_vs_spy_3m": stock_sector_data.get("vs_spy_3m"), - "stock_sector_vs_spy_6m": stock_sector_data.get("vs_spy_6m"), - "stock_sector_rank": stock_sector_rank, - "total_sectors": len(ranked), - "spy_1m_return": round(spy_1m, 2) if spy_1m is not None else None, - "spy_3m_return": round(spy_3m, 2) if spy_3m is not None else None, - "spy_6m_return": round(spy_6m, 2) if spy_6m is not None else None, - "all_sector_returns": sector_returns, - "sector_rankings_1m": [{"etf": sym, "vs_spy_1m": data["vs_spy_1m"]} for sym, data in ranked], - } - - return json.dumps(result, default=str) - - except Exception as e: - return json.dumps({"error": f"Error fetching sector rotation data for {ticker}: {str(e)}"}) - - -@tool -def get_institutional_flow( - ticker: Annotated[str, "Ticker symbol of the company"], -) -> str: - """Fetch institutional flow data: volume ratios, float turnover, short interest, institutional ownership. - Returns structured text for the Sector Rotation and Institutional Flow Analyst. - """ - import yfinance as yf - - try: - t = yf.Ticker(ticker.upper()) - info = t.info - - avg_vol = _safe_get(info, "averageVolume") - avg_vol_10d = _safe_get(info, "averageVolume10days") - shares_outstanding = _safe_get(info, "sharesOutstanding") - float_shares = _safe_get(info, "floatShares") - shares_short = _safe_get(info, "sharesShort") - short_ratio = _safe_get(info, "shortRatio") - held_institutions = _safe_get(info, "heldPercentInstitutions") - held_insiders = _safe_get(info, "heldPercentInsiders") - - # Compute derived metrics - volume_ratio = None - if avg_vol and avg_vol_10d and avg_vol > 0: - volume_ratio = round(avg_vol_10d / avg_vol, 2) - - float_turnover_5d = None - float_turnover_20d = None - if float_shares and float_shares > 0: - if avg_vol_10d: - float_turnover_5d = round((avg_vol_10d * 5) / float_shares * 100, 2) - if avg_vol: - float_turnover_20d = round((avg_vol * 20) / float_shares * 100, 2) - - short_pct_of_float = None - if shares_short and float_shares and float_shares > 0: - short_pct_of_float = round(shares_short / float_shares * 100, 2) - - result = { - "ticker": ticker.upper(), - "average_volume": avg_vol, - "average_volume_10d": avg_vol_10d, - "volume_ratio": volume_ratio, - "shares_outstanding": shares_outstanding, - "float_shares": float_shares, - "shares_short": shares_short, - "short_ratio": short_ratio, - "short_pct_of_float": short_pct_of_float, - "float_turnover_5d_pct": float_turnover_5d, - "float_turnover_20d_pct": float_turnover_20d, - "held_percent_institutions": round(held_institutions * 100, 2) if held_institutions else None, - "held_percent_insiders": round(held_insiders * 100, 2) if held_insiders else None, - } - - return json.dumps(result, default=str) - - except Exception as e: - return json.dumps({"error": f"Error fetching institutional flow data for {ticker}: {str(e)}"}) - - -@tool -def get_earnings_estimates( - ticker: Annotated[str, "Ticker symbol of the company"], -) -> str: - """Fetch earnings revision data: analyst recommendations, price targets, EPS estimates. - Returns structured text for the Earnings Revision and News Catalyst Analyst. - """ - import yfinance as yf - - try: - t = yf.Ticker(ticker.upper()) - info = t.info - - result = { - "ticker": ticker.upper(), - "current_price": _safe_get(info, "currentPrice") or _safe_get(info, "regularMarketPrice"), - "trailing_eps": _safe_get(info, "trailingEps"), - "forward_eps": _safe_get(info, "forwardEps"), - } - - # Analyst recommendations - try: - recs = t.recommendations - if recs is not None and not recs.empty: - # Get the most recent recommendations - recent_recs = recs.tail(20) - rec_list = [] - for _, row in recent_recs.iterrows(): - rec_entry = {} - for col in recent_recs.columns: - val = row[col] - if hasattr(val, 'item'): - val = val.item() - rec_entry[col] = val - rec_list.append(rec_entry) - result["recent_recommendations"] = rec_list - else: - result["recent_recommendations"] = [] - except Exception: - result["recent_recommendations"] = [] - - # Analyst price targets - try: - targets = t.analyst_price_targets - if targets is not None: - target_dict = {} - if hasattr(targets, 'items'): - for k, v in targets.items(): - if hasattr(v, 'item'): - target_dict[k] = v.item() - else: - target_dict[k] = v - elif isinstance(targets, dict): - target_dict = targets - result["price_targets"] = target_dict - - # Calculate upside - current = result.get("current_price") - mean_target = target_dict.get("mean") or target_dict.get("current") - if current and mean_target and current > 0: - result["price_target_upside_pct"] = round(((mean_target / current) - 1) * 100, 2) - else: - result["price_targets"] = {} - except Exception: - result["price_targets"] = {} - - # Earnings estimates if available - try: - earnings_est = t.earnings_estimate - if earnings_est is not None and not earnings_est.empty: - est_dict = {} - for col in earnings_est.columns: - est_dict[str(col)] = {} - for idx in earnings_est.index: - val = earnings_est.loc[idx, col] - if hasattr(val, 'item'): - val = val.item() - est_dict[str(col)][str(idx)] = val - result["earnings_estimates"] = est_dict - else: - result["earnings_estimates"] = {} - except Exception: - result["earnings_estimates"] = {} - - # Revenue estimates if available - try: - rev_est = t.revenue_estimate - if rev_est is not None and not rev_est.empty: - rev_dict = {} - for col in rev_est.columns: - rev_dict[str(col)] = {} - for idx in rev_est.index: - val = rev_est.loc[idx, col] - if hasattr(val, 'item'): - val = val.item() - rev_dict[str(col)][str(idx)] = val - result["revenue_estimates"] = rev_dict - else: - result["revenue_estimates"] = {} - except Exception: - result["revenue_estimates"] = {} - - return json.dumps(result, default=str) - - except Exception as e: - return json.dumps({"error": f"Error fetching earnings estimates for {ticker}: {str(e)}"}) - - -@tool -def get_valuation_peers( - ticker: Annotated[str, "Ticker symbol of the company"], -) -> str: - """Fetch valuation metrics and peer comparison data. - Returns structured text for the Business Quality, Valuation, and Entry Timing Analyst. - """ - import yfinance as yf - - try: - t = yf.Ticker(ticker.upper()) - info = t.info - - current_price = _safe_get(info, "currentPrice") or _safe_get(info, "regularMarketPrice") - fifty_two_high = _safe_get(info, "fiftyTwoWeekHigh") - fifty_two_low = _safe_get(info, "fiftyTwoWeekLow") - - # Calculate position in 52-week range - vs_52w_range_pct = None - if fifty_two_high and fifty_two_low and current_price and (fifty_two_high - fifty_two_low) > 0: - vs_52w_range_pct = round( - ((current_price - fifty_two_low) / (fifty_two_high - fifty_two_low)) * 100, 1 - ) - - result = { - "ticker": ticker.upper(), - "current_price": current_price, - "trailing_pe": _safe_get(info, "trailingPE"), - "forward_pe": _safe_get(info, "forwardPE"), - "peg_ratio": _safe_get(info, "pegRatio"), - "price_to_book": _safe_get(info, "priceToBook"), - "price_to_sales": _safe_get(info, "priceToSalesTrailing12Months"), - "enterprise_value": _safe_get(info, "enterpriseValue"), - "ev_to_ebitda": _safe_get(info, "enterpriseToEbitda"), - "ev_to_revenue": _safe_get(info, "enterpriseToRevenue"), - "market_cap": _safe_get(info, "marketCap"), - "fifty_two_week_high": fifty_two_high, - "fifty_two_week_low": fifty_two_low, - "vs_52w_range_pct": vs_52w_range_pct, - "fifty_day_average": _safe_get(info, "fiftyDayAverage"), - "two_hundred_day_average": _safe_get(info, "twoHundredDayAverage"), - "profit_margins": _safe_get(info, "profitMargins"), - "operating_margins": _safe_get(info, "operatingMargins"), - "gross_margins": _safe_get(info, "grossMargins"), - "return_on_equity": _safe_get(info, "returnOnEquity"), - "return_on_assets": _safe_get(info, "returnOnAssets"), - "revenue_growth": _safe_get(info, "revenueGrowth"), - "earnings_growth": _safe_get(info, "earningsGrowth"), - "debt_to_equity": _safe_get(info, "debtToEquity"), - "current_ratio": _safe_get(info, "currentRatio"), - "free_cashflow": _safe_get(info, "freeCashflow"), - "book_value": _safe_get(info, "bookValue"), - } - - return json.dumps(result, default=str) - - except Exception as e: - return json.dumps({"error": f"Error fetching valuation data for {ticker}: {str(e)}"}) +"""Macro-aware data tools for the structured equity ranking engine. + +These tools fetch company profile, macro regime, sector rotation, +institutional flow, earnings estimates, and valuation data via yfinance. +They are used directly by analyst agents (not routed through interface.py). +""" + +from langchain_core.tools import tool +from typing import Annotated +from datetime import datetime +from dateutil.relativedelta import relativedelta +import json + + +def _safe_get(info, key, default=None): + """Safely get a value from yfinance info dict.""" + val = info.get(key) + if val is None: + return default + return val + + +def _fmt_large_number(val): + """Format large numbers for readability.""" + if val is None: + return None + if abs(val) >= 1e12: + return f"${val/1e12:.2f}T" + if abs(val) >= 1e9: + return f"${val/1e9:.2f}B" + if abs(val) >= 1e6: + return f"${val/1e6:.2f}M" + return f"${val:,.0f}" + + +def _market_cap_category(market_cap): + """Classify market cap size.""" + if market_cap is None: + return "unknown" + if market_cap >= 10e9: + return "large_cap" + if market_cap >= 2e9: + return "mid_cap" + if market_cap >= 300e6: + return "small_cap" + return "micro_cap" + + +# Sector to ETF mapping +SECTOR_ETF_MAP = { + "Technology": "XLK", + "Information Technology": "XLK", + "Communication Services": "XLC", + "Healthcare": "XLV", + "Health Care": "XLV", + "Financials": "XLF", + "Financial Services": "XLF", + "Consumer Discretionary": "XLY", + "Consumer Cyclical": "XLY", + "Consumer Staples": "XLP", + "Consumer Defensive": "XLP", + "Industrials": "XLI", + "Energy": "XLE", + "Utilities": "XLU", + "Materials": "XLB", + "Basic Materials": "XLB", + "Real Estate": "XLRE", +} + +ALL_SECTOR_ETFS = ["XLK", "XLC", "XLV", "XLF", "XLY", "XLP", "XLI", "XLE", "XLU", "XLB", "XLRE"] + + +def _get_period_return(ticker_obj, period_months, ref_date=None): + """Calculate return over a given period ending at ref_date.""" + import yfinance as yf + import pandas as pd + + try: + if ref_date: + end_dt = pd.to_datetime(ref_date) + else: + end_dt = pd.Timestamp.today() + + start_dt = end_dt - pd.DateOffset(months=period_months) + data = ticker_obj.history( + start=start_dt.strftime("%Y-%m-%d"), + end=end_dt.strftime("%Y-%m-%d"), + ) + if data.empty or len(data) < 2: + return None + return ((data["Close"].iloc[-1] / data["Close"].iloc[0]) - 1) * 100 + except Exception: + return None + + +@tool +def get_company_profile( + ticker: Annotated[str, "Ticker symbol of the company"], +) -> str: + """Fetch company profile: name, sector, industry, description, market cap, business model. + Returns structured text with all fields for the Company Intelligence Analyst. + """ + import yfinance as yf + + try: + t = yf.Ticker(ticker.upper()) + info = t.info + + if not info or not info.get("longName"): + return json.dumps({"error": f"No company data found for {ticker}", "ticker": ticker}) + + market_cap = _safe_get(info, "marketCap") + + profile = { + "company_name": _safe_get(info, "longName", "Unknown"), + "ticker": ticker.upper(), + "sector": _safe_get(info, "sector", "Unknown"), + "industry": _safe_get(info, "industry", "Unknown"), + "description": _safe_get(info, "longBusinessSummary", "No description available"), + "market_cap": market_cap, + "market_cap_formatted": _fmt_large_number(market_cap), + "market_cap_category": _market_cap_category(market_cap), + "trailing_pe": _safe_get(info, "trailingPE"), + "forward_pe": _safe_get(info, "forwardPE"), + "peg_ratio": _safe_get(info, "pegRatio"), + "price_to_book": _safe_get(info, "priceToBook"), + "dividend_yield": _safe_get(info, "dividendYield"), + "beta": _safe_get(info, "beta"), + "trailing_eps": _safe_get(info, "trailingEps"), + "forward_eps": _safe_get(info, "forwardEps"), + "revenue": _safe_get(info, "totalRevenue"), + "revenue_formatted": _fmt_large_number(_safe_get(info, "totalRevenue")), + "gross_profits": _safe_get(info, "grossProfits"), + "ebitda": _safe_get(info, "ebitda"), + "net_income": _safe_get(info, "netIncomeToCommon"), + "profit_margins": _safe_get(info, "profitMargins"), + "operating_margins": _safe_get(info, "operatingMargins"), + "return_on_equity": _safe_get(info, "returnOnEquity"), + "return_on_assets": _safe_get(info, "returnOnAssets"), + "debt_to_equity": _safe_get(info, "debtToEquity"), + "current_ratio": _safe_get(info, "currentRatio"), + "book_value": _safe_get(info, "bookValue"), + "free_cashflow": _safe_get(info, "freeCashflow"), + "fifty_two_week_high": _safe_get(info, "fiftyTwoWeekHigh"), + "fifty_two_week_low": _safe_get(info, "fiftyTwoWeekLow"), + "fifty_day_average": _safe_get(info, "fiftyDayAverage"), + "two_hundred_day_average": _safe_get(info, "twoHundredDayAverage"), + "average_volume": _safe_get(info, "averageVolume"), + "average_volume_10d": _safe_get(info, "averageVolume10days"), + "shares_outstanding": _safe_get(info, "sharesOutstanding"), + "float_shares": _safe_get(info, "floatShares"), + "shares_short": _safe_get(info, "sharesShort"), + "short_ratio": _safe_get(info, "shortRatio"), + "held_percent_insiders": _safe_get(info, "heldPercentInsiders"), + "held_percent_institutions": _safe_get(info, "heldPercentInstitutions"), + "current_price": _safe_get(info, "currentPrice") or _safe_get(info, "regularMarketPrice"), + } + + return json.dumps(profile, default=str) + + except Exception as e: + return json.dumps({"error": f"Error fetching company profile for {ticker}: {str(e)}", "ticker": ticker}) + + +@tool +def get_macro_indicators( + curr_date: Annotated[str, "Current trading date in yyyy-mm-dd format"], +) -> str: + """Fetch macro regime indicators: VIX, 10Y yield, dollar strength, credit spreads, sector ETF performance. + Returns structured text for the Company Intelligence and Macro Regime Analyst. + """ + import yfinance as yf + import pandas as pd + + try: + results = {} + + # VIX + try: + vix = yf.Ticker("^VIX") + vix_data = vix.history(period="5d") + if not vix_data.empty: + results["vix_level"] = round(vix_data["Close"].iloc[-1], 2) + if results["vix_level"] < 15: + results["vix_regime"] = "low" + elif results["vix_level"] < 20: + results["vix_regime"] = "moderate" + elif results["vix_level"] < 30: + results["vix_regime"] = "elevated" + else: + results["vix_regime"] = "stressed" + except Exception: + results["vix_level"] = None + results["vix_regime"] = "unknown" + + # 10Y yield + try: + tnx = yf.Ticker("^TNX") + tnx_data = tnx.history(period="5d") + if not tnx_data.empty: + results["ten_year_yield"] = round(tnx_data["Close"].iloc[-1], 3) + except Exception: + results["ten_year_yield"] = None + + # Dollar strength (UUP as proxy) + try: + uup = yf.Ticker("UUP") + uup_1m = _get_period_return(uup, 1) + uup_3m = _get_period_return(uup, 3) + results["dollar_1m_return"] = round(uup_1m, 2) if uup_1m is not None else None + results["dollar_3m_return"] = round(uup_3m, 2) if uup_3m is not None else None + if uup_1m is not None: + if uup_1m > 1: + results["dollar_strength"] = "strong" + elif uup_1m < -1: + results["dollar_strength"] = "weak" + else: + results["dollar_strength"] = "neutral" + except Exception: + results["dollar_strength"] = "unknown" + + # Credit spreads: HYG vs LQD + try: + hyg = yf.Ticker("HYG") + lqd = yf.Ticker("LQD") + hyg_1m = _get_period_return(hyg, 1) + lqd_1m = _get_period_return(lqd, 1) + if hyg_1m is not None and lqd_1m is not None: + spread_change = hyg_1m - lqd_1m + results["hyg_1m_return"] = round(hyg_1m, 2) + results["lqd_1m_return"] = round(lqd_1m, 2) + results["credit_spread_change"] = round(spread_change, 2) + if spread_change > 0.5: + results["credit_spread_direction"] = "tightening" + elif spread_change < -0.5: + results["credit_spread_direction"] = "widening" + else: + results["credit_spread_direction"] = "stable" + except Exception: + results["credit_spread_direction"] = "unknown" + + # SPY and sector ETF performance + sector_etfs = { + "SPY": "S&P 500", + "XLK": "Technology", + "XLC": "Communication Services", + "XLV": "Healthcare", + "XLF": "Financials", + "XLY": "Consumer Discretionary", + "XLP": "Consumer Staples", + "XLI": "Industrials", + "XLE": "Energy", + "XLU": "Utilities", + "XLB": "Materials", + "XLRE": "Real Estate", + } + + sector_performance = {} + for etf_ticker, sector_name in sector_etfs.items(): + try: + etf = yf.Ticker(etf_ticker) + ret_1m = _get_period_return(etf, 1) + ret_3m = _get_period_return(etf, 3) + sector_performance[etf_ticker] = { + "name": sector_name, + "return_1m": round(ret_1m, 2) if ret_1m is not None else None, + "return_3m": round(ret_3m, 2) if ret_3m is not None else None, + } + except Exception: + sector_performance[etf_ticker] = { + "name": sector_name, + "return_1m": None, + "return_3m": None, + } + + results["sector_performance"] = sector_performance + + return json.dumps(results, default=str) + + except Exception as e: + return json.dumps({"error": f"Error fetching macro indicators: {str(e)}"}) + + +@tool +def get_sector_rotation( + ticker: Annotated[str, "Ticker symbol of the company"], + curr_date: Annotated[str, "Current trading date in yyyy-mm-dd format"], +) -> str: + """Fetch sector rotation data: sector ETF relative strength vs SPY over 1M/3M/6M, breadth indicators. + Returns structured text for the Sector Rotation and Institutional Flow Analyst. + """ + import yfinance as yf + + try: + # Get the company's sector + t = yf.Ticker(ticker.upper()) + info = t.info + sector = _safe_get(info, "sector", "Unknown") + + # Map sector to ETF + sector_etf = SECTOR_ETF_MAP.get(sector, None) + + # Get SPY returns + spy = yf.Ticker("SPY") + spy_1m = _get_period_return(spy, 1) + spy_3m = _get_period_return(spy, 3) + spy_6m = _get_period_return(spy, 6) + + # Get all sector ETF returns for ranking + sector_returns = {} + for etf_sym in ALL_SECTOR_ETFS: + try: + etf = yf.Ticker(etf_sym) + ret_1m = _get_period_return(etf, 1) + ret_3m = _get_period_return(etf, 3) + ret_6m = _get_period_return(etf, 6) + sector_returns[etf_sym] = { + "return_1m": round(ret_1m, 2) if ret_1m is not None else None, + "return_3m": round(ret_3m, 2) if ret_3m is not None else None, + "return_6m": round(ret_6m, 2) if ret_6m is not None else None, + "vs_spy_1m": round(ret_1m - spy_1m, 2) if (ret_1m is not None and spy_1m is not None) else None, + "vs_spy_3m": round(ret_3m - spy_3m, 2) if (ret_3m is not None and spy_3m is not None) else None, + "vs_spy_6m": round(ret_6m - spy_6m, 2) if (ret_6m is not None and spy_6m is not None) else None, + } + except Exception: + sector_returns[etf_sym] = { + "return_1m": None, "return_3m": None, "return_6m": None, + "vs_spy_1m": None, "vs_spy_3m": None, "vs_spy_6m": None, + } + + # Rank sectors by 1M relative strength + ranked = sorted( + [(sym, data) for sym, data in sector_returns.items() if data["vs_spy_1m"] is not None], + key=lambda x: x[1]["vs_spy_1m"], + reverse=True, + ) + rank_map = {sym: i + 1 for i, (sym, _) in enumerate(ranked)} + + # Stock's sector data + stock_sector_data = {} + stock_sector_rank = None + if sector_etf and sector_etf in sector_returns: + stock_sector_data = sector_returns[sector_etf] + stock_sector_rank = rank_map.get(sector_etf) + + result = { + "ticker": ticker.upper(), + "sector": sector, + "sector_etf": sector_etf, + "stock_sector_vs_spy_1m": stock_sector_data.get("vs_spy_1m"), + "stock_sector_vs_spy_3m": stock_sector_data.get("vs_spy_3m"), + "stock_sector_vs_spy_6m": stock_sector_data.get("vs_spy_6m"), + "stock_sector_rank": stock_sector_rank, + "total_sectors": len(ranked), + "spy_1m_return": round(spy_1m, 2) if spy_1m is not None else None, + "spy_3m_return": round(spy_3m, 2) if spy_3m is not None else None, + "spy_6m_return": round(spy_6m, 2) if spy_6m is not None else None, + "all_sector_returns": sector_returns, + "sector_rankings_1m": [{"etf": sym, "vs_spy_1m": data["vs_spy_1m"]} for sym, data in ranked], + } + + return json.dumps(result, default=str) + + except Exception as e: + return json.dumps({"error": f"Error fetching sector rotation data for {ticker}: {str(e)}"}) + + +@tool +def get_institutional_flow( + ticker: Annotated[str, "Ticker symbol of the company"], +) -> str: + """Fetch institutional flow data: volume ratios, float turnover, short interest, institutional ownership. + Returns structured text for the Sector Rotation and Institutional Flow Analyst. + """ + import yfinance as yf + + try: + t = yf.Ticker(ticker.upper()) + info = t.info + + avg_vol = _safe_get(info, "averageVolume") + avg_vol_10d = _safe_get(info, "averageVolume10days") + shares_outstanding = _safe_get(info, "sharesOutstanding") + float_shares = _safe_get(info, "floatShares") + shares_short = _safe_get(info, "sharesShort") + short_ratio = _safe_get(info, "shortRatio") + held_institutions = _safe_get(info, "heldPercentInstitutions") + held_insiders = _safe_get(info, "heldPercentInsiders") + + # Compute derived metrics + volume_ratio = None + if avg_vol and avg_vol_10d and avg_vol > 0: + volume_ratio = round(avg_vol_10d / avg_vol, 2) + + float_turnover_5d = None + float_turnover_20d = None + if float_shares and float_shares > 0: + if avg_vol_10d: + float_turnover_5d = round((avg_vol_10d * 5) / float_shares * 100, 2) + if avg_vol: + float_turnover_20d = round((avg_vol * 20) / float_shares * 100, 2) + + short_pct_of_float = None + if shares_short and float_shares and float_shares > 0: + short_pct_of_float = round(shares_short / float_shares * 100, 2) + + result = { + "ticker": ticker.upper(), + "average_volume": avg_vol, + "average_volume_10d": avg_vol_10d, + "volume_ratio": volume_ratio, + "shares_outstanding": shares_outstanding, + "float_shares": float_shares, + "shares_short": shares_short, + "short_ratio": short_ratio, + "short_pct_of_float": short_pct_of_float, + "float_turnover_5d_pct": float_turnover_5d, + "float_turnover_20d_pct": float_turnover_20d, + "held_percent_institutions": round(held_institutions * 100, 2) if held_institutions else None, + "held_percent_insiders": round(held_insiders * 100, 2) if held_insiders else None, + } + + return json.dumps(result, default=str) + + except Exception as e: + return json.dumps({"error": f"Error fetching institutional flow data for {ticker}: {str(e)}"}) + + +@tool +def get_earnings_estimates( + ticker: Annotated[str, "Ticker symbol of the company"], +) -> str: + """Fetch earnings revision data: analyst recommendations, price targets, EPS estimates. + Returns structured text for the Earnings Revision and News Catalyst Analyst. + """ + import yfinance as yf + + try: + t = yf.Ticker(ticker.upper()) + info = t.info + + result = { + "ticker": ticker.upper(), + "current_price": _safe_get(info, "currentPrice") or _safe_get(info, "regularMarketPrice"), + "trailing_eps": _safe_get(info, "trailingEps"), + "forward_eps": _safe_get(info, "forwardEps"), + } + + # Analyst recommendations + try: + recs = t.recommendations + if recs is not None and not recs.empty: + # Get the most recent recommendations + recent_recs = recs.tail(20) + rec_list = [] + for _, row in recent_recs.iterrows(): + rec_entry = {} + for col in recent_recs.columns: + val = row[col] + if hasattr(val, 'item'): + val = val.item() + rec_entry[col] = val + rec_list.append(rec_entry) + result["recent_recommendations"] = rec_list + else: + result["recent_recommendations"] = [] + except Exception: + result["recent_recommendations"] = [] + + # Analyst price targets + try: + targets = t.analyst_price_targets + if targets is not None: + target_dict = {} + if hasattr(targets, 'items'): + for k, v in targets.items(): + if hasattr(v, 'item'): + target_dict[k] = v.item() + else: + target_dict[k] = v + elif isinstance(targets, dict): + target_dict = targets + result["price_targets"] = target_dict + + # Calculate upside + current = result.get("current_price") + mean_target = target_dict.get("mean") or target_dict.get("current") + if current and mean_target and current > 0: + result["price_target_upside_pct"] = round(((mean_target / current) - 1) * 100, 2) + else: + result["price_targets"] = {} + except Exception: + result["price_targets"] = {} + + # Earnings estimates if available + try: + earnings_est = t.earnings_estimate + if earnings_est is not None and not earnings_est.empty: + est_dict = {} + for col in earnings_est.columns: + est_dict[str(col)] = {} + for idx in earnings_est.index: + val = earnings_est.loc[idx, col] + if hasattr(val, 'item'): + val = val.item() + est_dict[str(col)][str(idx)] = val + result["earnings_estimates"] = est_dict + else: + result["earnings_estimates"] = {} + except Exception: + result["earnings_estimates"] = {} + + # Revenue estimates if available + try: + rev_est = t.revenue_estimate + if rev_est is not None and not rev_est.empty: + rev_dict = {} + for col in rev_est.columns: + rev_dict[str(col)] = {} + for idx in rev_est.index: + val = rev_est.loc[idx, col] + if hasattr(val, 'item'): + val = val.item() + rev_dict[str(col)][str(idx)] = val + result["revenue_estimates"] = rev_dict + else: + result["revenue_estimates"] = {} + except Exception: + result["revenue_estimates"] = {} + + return json.dumps(result, default=str) + + except Exception as e: + return json.dumps({"error": f"Error fetching earnings estimates for {ticker}: {str(e)}"}) + + +@tool +def get_valuation_peers( + ticker: Annotated[str, "Ticker symbol of the company"], +) -> str: + """Fetch valuation metrics and peer comparison data. + Returns structured text for the Business Quality, Valuation, and Entry Timing Analyst. + """ + import yfinance as yf + + try: + t = yf.Ticker(ticker.upper()) + info = t.info + + current_price = _safe_get(info, "currentPrice") or _safe_get(info, "regularMarketPrice") + fifty_two_high = _safe_get(info, "fiftyTwoWeekHigh") + fifty_two_low = _safe_get(info, "fiftyTwoWeekLow") + + # Calculate position in 52-week range + vs_52w_range_pct = None + if fifty_two_high and fifty_two_low and current_price and (fifty_two_high - fifty_two_low) > 0: + vs_52w_range_pct = round( + ((current_price - fifty_two_low) / (fifty_two_high - fifty_two_low)) * 100, 1 + ) + + result = { + "ticker": ticker.upper(), + "current_price": current_price, + "trailing_pe": _safe_get(info, "trailingPE"), + "forward_pe": _safe_get(info, "forwardPE"), + "peg_ratio": _safe_get(info, "pegRatio"), + "price_to_book": _safe_get(info, "priceToBook"), + "price_to_sales": _safe_get(info, "priceToSalesTrailing12Months"), + "enterprise_value": _safe_get(info, "enterpriseValue"), + "ev_to_ebitda": _safe_get(info, "enterpriseToEbitda"), + "ev_to_revenue": _safe_get(info, "enterpriseToRevenue"), + "market_cap": _safe_get(info, "marketCap"), + "fifty_two_week_high": fifty_two_high, + "fifty_two_week_low": fifty_two_low, + "vs_52w_range_pct": vs_52w_range_pct, + "fifty_day_average": _safe_get(info, "fiftyDayAverage"), + "two_hundred_day_average": _safe_get(info, "twoHundredDayAverage"), + "profit_margins": _safe_get(info, "profitMargins"), + "operating_margins": _safe_get(info, "operatingMargins"), + "gross_margins": _safe_get(info, "grossMargins"), + "return_on_equity": _safe_get(info, "returnOnEquity"), + "return_on_assets": _safe_get(info, "returnOnAssets"), + "revenue_growth": _safe_get(info, "revenueGrowth"), + "earnings_growth": _safe_get(info, "earningsGrowth"), + "debt_to_equity": _safe_get(info, "debtToEquity"), + "current_ratio": _safe_get(info, "currentRatio"), + "free_cashflow": _safe_get(info, "freeCashflow"), + "book_value": _safe_get(info, "bookValue"), + } + + return json.dumps(result, default=str) + + except Exception as e: + return json.dumps({"error": f"Error fetching valuation data for {ticker}: {str(e)}"}) diff --git a/tradingagents/agents/utils/memory.py b/tradingagents/agents/utils/memory.py index d278b3c3..0102c2d3 100644 --- a/tradingagents/agents/utils/memory.py +++ b/tradingagents/agents/utils/memory.py @@ -1,144 +1,144 @@ -"""Financial situation memory using BM25 for lexical similarity matching. - -Uses BM25 (Best Matching 25) algorithm for retrieval - no API calls, -no token limits, works offline with any LLM provider. -""" - -from rank_bm25 import BM25Okapi -from typing import List, Tuple -import re - - -class FinancialSituationMemory: - """Memory system for storing and retrieving financial situations using BM25.""" - - def __init__(self, name: str, config: dict = None): - """Initialize the memory system. - - Args: - name: Name identifier for this memory instance - config: Configuration dict (kept for API compatibility, not used for BM25) - """ - self.name = name - self.documents: List[str] = [] - self.recommendations: List[str] = [] - self.bm25 = None - - def _tokenize(self, text: str) -> List[str]: - """Tokenize text for BM25 indexing. - - Simple whitespace + punctuation tokenization with lowercasing. - """ - # Lowercase and split on non-alphanumeric characters - tokens = re.findall(r'\b\w+\b', text.lower()) - return tokens - - def _rebuild_index(self): - """Rebuild the BM25 index after adding documents.""" - if self.documents: - tokenized_docs = [self._tokenize(doc) for doc in self.documents] - self.bm25 = BM25Okapi(tokenized_docs) - else: - self.bm25 = None - - def add_situations(self, situations_and_advice: List[Tuple[str, str]]): - """Add financial situations and their corresponding advice. - - Args: - situations_and_advice: List of tuples (situation, recommendation) - """ - for situation, recommendation in situations_and_advice: - self.documents.append(situation) - self.recommendations.append(recommendation) - - # Rebuild BM25 index with new documents - self._rebuild_index() - - def get_memories(self, current_situation: str, n_matches: int = 1) -> List[dict]: - """Find matching recommendations using BM25 similarity. - - Args: - current_situation: The current financial situation to match against - n_matches: Number of top matches to return - - Returns: - List of dicts with matched_situation, recommendation, and similarity_score - """ - if not self.documents or self.bm25 is None: - return [] - - # Tokenize query - query_tokens = self._tokenize(current_situation) - - # Get BM25 scores for all documents - scores = self.bm25.get_scores(query_tokens) - - # Get top-n indices sorted by score (descending) - top_indices = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:n_matches] - - # Build results - results = [] - max_score = max(scores) if max(scores) > 0 else 1 # Normalize scores - - for idx in top_indices: - # Normalize score to 0-1 range for consistency - normalized_score = scores[idx] / max_score if max_score > 0 else 0 - results.append({ - "matched_situation": self.documents[idx], - "recommendation": self.recommendations[idx], - "similarity_score": normalized_score, - }) - - return results - - def clear(self): - """Clear all stored memories.""" - self.documents = [] - self.recommendations = [] - self.bm25 = None - - -if __name__ == "__main__": - # Example usage - matcher = FinancialSituationMemory("test_memory") - - # Example data - example_data = [ - ( - "High inflation rate with rising interest rates and declining consumer spending", - "Consider defensive sectors like consumer staples and utilities. Review fixed-income portfolio duration.", - ), - ( - "Tech sector showing high volatility with increasing institutional selling pressure", - "Reduce exposure to high-growth tech stocks. Look for value opportunities in established tech companies with strong cash flows.", - ), - ( - "Strong dollar affecting emerging markets with increasing forex volatility", - "Hedge currency exposure in international positions. Consider reducing allocation to emerging market debt.", - ), - ( - "Market showing signs of sector rotation with rising yields", - "Rebalance portfolio to maintain target allocations. Consider increasing exposure to sectors benefiting from higher rates.", - ), - ] - - # Add the example situations and recommendations - matcher.add_situations(example_data) - - # Example query - current_situation = """ - Market showing increased volatility in tech sector, with institutional investors - reducing positions and rising interest rates affecting growth stock valuations - """ - - try: - recommendations = matcher.get_memories(current_situation, n_matches=2) - - for i, rec in enumerate(recommendations, 1): - print(f"\nMatch {i}:") - print(f"Similarity Score: {rec['similarity_score']:.2f}") - print(f"Matched Situation: {rec['matched_situation']}") - print(f"Recommendation: {rec['recommendation']}") - - except Exception as e: - print(f"Error during recommendation: {str(e)}") +"""Financial situation memory using BM25 for lexical similarity matching. + +Uses BM25 (Best Matching 25) algorithm for retrieval - no API calls, +no token limits, works offline with any LLM provider. +""" + +from rank_bm25 import BM25Okapi +from typing import List, Tuple +import re + + +class FinancialSituationMemory: + """Memory system for storing and retrieving financial situations using BM25.""" + + def __init__(self, name: str, config: dict = None): + """Initialize the memory system. + + Args: + name: Name identifier for this memory instance + config: Configuration dict (kept for API compatibility, not used for BM25) + """ + self.name = name + self.documents: List[str] = [] + self.recommendations: List[str] = [] + self.bm25 = None + + def _tokenize(self, text: str) -> List[str]: + """Tokenize text for BM25 indexing. + + Simple whitespace + punctuation tokenization with lowercasing. + """ + # Lowercase and split on non-alphanumeric characters + tokens = re.findall(r'\b\w+\b', text.lower()) + return tokens + + def _rebuild_index(self): + """Rebuild the BM25 index after adding documents.""" + if self.documents: + tokenized_docs = [self._tokenize(doc) for doc in self.documents] + self.bm25 = BM25Okapi(tokenized_docs) + else: + self.bm25 = None + + def add_situations(self, situations_and_advice: List[Tuple[str, str]]): + """Add financial situations and their corresponding advice. + + Args: + situations_and_advice: List of tuples (situation, recommendation) + """ + for situation, recommendation in situations_and_advice: + self.documents.append(situation) + self.recommendations.append(recommendation) + + # Rebuild BM25 index with new documents + self._rebuild_index() + + def get_memories(self, current_situation: str, n_matches: int = 1) -> List[dict]: + """Find matching recommendations using BM25 similarity. + + Args: + current_situation: The current financial situation to match against + n_matches: Number of top matches to return + + Returns: + List of dicts with matched_situation, recommendation, and similarity_score + """ + if not self.documents or self.bm25 is None: + return [] + + # Tokenize query + query_tokens = self._tokenize(current_situation) + + # Get BM25 scores for all documents + scores = self.bm25.get_scores(query_tokens) + + # Get top-n indices sorted by score (descending) + top_indices = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:n_matches] + + # Build results + results = [] + max_score = max(scores) if max(scores) > 0 else 1 # Normalize scores + + for idx in top_indices: + # Normalize score to 0-1 range for consistency + normalized_score = scores[idx] / max_score if max_score > 0 else 0 + results.append({ + "matched_situation": self.documents[idx], + "recommendation": self.recommendations[idx], + "similarity_score": normalized_score, + }) + + return results + + def clear(self): + """Clear all stored memories.""" + self.documents = [] + self.recommendations = [] + self.bm25 = None + + +if __name__ == "__main__": + # Example usage + matcher = FinancialSituationMemory("test_memory") + + # Example data + example_data = [ + ( + "High inflation rate with rising interest rates and declining consumer spending", + "Consider defensive sectors like consumer staples and utilities. Review fixed-income portfolio duration.", + ), + ( + "Tech sector showing high volatility with increasing institutional selling pressure", + "Reduce exposure to high-growth tech stocks. Look for value opportunities in established tech companies with strong cash flows.", + ), + ( + "Strong dollar affecting emerging markets with increasing forex volatility", + "Hedge currency exposure in international positions. Consider reducing allocation to emerging market debt.", + ), + ( + "Market showing signs of sector rotation with rising yields", + "Rebalance portfolio to maintain target allocations. Consider increasing exposure to sectors benefiting from higher rates.", + ), + ] + + # Add the example situations and recommendations + matcher.add_situations(example_data) + + # Example query + current_situation = """ + Market showing increased volatility in tech sector, with institutional investors + reducing positions and rising interest rates affecting growth stock valuations + """ + + try: + recommendations = matcher.get_memories(current_situation, n_matches=2) + + for i, rec in enumerate(recommendations, 1): + print(f"\nMatch {i}:") + print(f"Similarity Score: {rec['similarity_score']:.2f}") + print(f"Matched Situation: {rec['matched_situation']}") + print(f"Recommendation: {rec['recommendation']}") + + except Exception as e: + print(f"Error during recommendation: {str(e)}") diff --git a/tradingagents/agents/utils/news_data_tools.py b/tradingagents/agents/utils/news_data_tools.py index 781e793c..f0f62526 100644 --- a/tradingagents/agents/utils/news_data_tools.py +++ b/tradingagents/agents/utils/news_data_tools.py @@ -1,53 +1,53 @@ -from langchain_core.tools import tool -from typing import Annotated -from tradingagents.dataflows.interface import route_to_vendor - -@tool -def get_news( - ticker: Annotated[str, "Ticker symbol"], - start_date: Annotated[str, "Start date in yyyy-mm-dd format"], - end_date: Annotated[str, "End date in yyyy-mm-dd format"], -) -> str: - """ - Retrieve news data for a given ticker symbol. - Uses the configured news_data vendor. - Args: - ticker (str): Ticker symbol - start_date (str): Start date in yyyy-mm-dd format - end_date (str): End date in yyyy-mm-dd format - Returns: - str: A formatted string containing news data - """ - return route_to_vendor("get_news", ticker, start_date, end_date) - -@tool -def get_global_news( - curr_date: Annotated[str, "Current date in yyyy-mm-dd format"], - look_back_days: Annotated[int, "Number of days to look back"] = 7, - limit: Annotated[int, "Maximum number of articles to return"] = 5, -) -> str: - """ - Retrieve global news data. - Uses the configured news_data vendor. - Args: - curr_date (str): Current date in yyyy-mm-dd format - look_back_days (int): Number of days to look back (default 7) - limit (int): Maximum number of articles to return (default 5) - Returns: - str: A formatted string containing global news data - """ - return route_to_vendor("get_global_news", curr_date, look_back_days, limit) - -@tool -def get_insider_transactions( - ticker: Annotated[str, "ticker symbol"], -) -> str: - """ - Retrieve insider transaction information about a company. - Uses the configured news_data vendor. - Args: - ticker (str): Ticker symbol of the company - Returns: - str: A report of insider transaction data - """ - return route_to_vendor("get_insider_transactions", ticker) +from langchain_core.tools import tool +from typing import Annotated +from tradingagents.dataflows.interface import route_to_vendor + +@tool +def get_news( + ticker: Annotated[str, "Ticker symbol"], + start_date: Annotated[str, "Start date in yyyy-mm-dd format"], + end_date: Annotated[str, "End date in yyyy-mm-dd format"], +) -> str: + """ + Retrieve news data for a given ticker symbol. + Uses the configured news_data vendor. + Args: + ticker (str): Ticker symbol + start_date (str): Start date in yyyy-mm-dd format + end_date (str): End date in yyyy-mm-dd format + Returns: + str: A formatted string containing news data + """ + return route_to_vendor("get_news", ticker, start_date, end_date) + +@tool +def get_global_news( + curr_date: Annotated[str, "Current date in yyyy-mm-dd format"], + look_back_days: Annotated[int, "Number of days to look back"] = 7, + limit: Annotated[int, "Maximum number of articles to return"] = 5, +) -> str: + """ + Retrieve global news data. + Uses the configured news_data vendor. + Args: + curr_date (str): Current date in yyyy-mm-dd format + look_back_days (int): Number of days to look back (default 7) + limit (int): Maximum number of articles to return (default 5) + Returns: + str: A formatted string containing global news data + """ + return route_to_vendor("get_global_news", curr_date, look_back_days, limit) + +@tool +def get_insider_transactions( + ticker: Annotated[str, "ticker symbol"], +) -> str: + """ + Retrieve insider transaction information about a company. + Uses the configured news_data vendor. + Args: + ticker (str): Ticker symbol of the company + Returns: + str: A report of insider transaction data + """ + return route_to_vendor("get_insider_transactions", ticker) diff --git a/tradingagents/agents/utils/technical_indicators_tools.py b/tradingagents/agents/utils/technical_indicators_tools.py index 7d087f26..39ee4d79 100644 --- a/tradingagents/agents/utils/technical_indicators_tools.py +++ b/tradingagents/agents/utils/technical_indicators_tools.py @@ -1,26 +1,26 @@ -from langchain_core.tools import tool -from typing import Annotated -from tradingagents.dataflows.interface import route_to_vendor - -@tool -def get_indicators( - symbol: Annotated[str, "ticker symbol of the company"], - indicator: Annotated[str, "technical indicator to get the analysis and report of"], - curr_date: Annotated[str, "The current trading date you are trading on, YYYY-mm-dd"], - look_back_days: Annotated[int, "how many days to look back"] = 30, -) -> str: - """ - Retrieve technical indicators for a given ticker symbol. - Uses the configured technical_indicators vendor. - Args: - symbol (str): Ticker symbol of the company, e.g. AAPL, TSM - indicator (str): Technical indicator. Supported: close_50_sma, close_200_sma, close_10_ema, macd, macds, macdh, rsi, boll, boll_ub, boll_lb, atr, vwma, mfi - curr_date (str): The current trading date you are trading on, YYYY-mm-dd - look_back_days (int): How many days to look back, default is 30 - Returns: - str: A formatted dataframe containing the technical indicators for the specified ticker symbol and indicator. - """ - try: - return route_to_vendor("get_indicators", symbol, indicator, curr_date, look_back_days) - except ValueError as e: +from langchain_core.tools import tool +from typing import Annotated +from tradingagents.dataflows.interface import route_to_vendor + +@tool +def get_indicators( + symbol: Annotated[str, "ticker symbol of the company"], + indicator: Annotated[str, "technical indicator to get the analysis and report of"], + curr_date: Annotated[str, "The current trading date you are trading on, YYYY-mm-dd"], + look_back_days: Annotated[int, "how many days to look back"] = 30, +) -> str: + """ + Retrieve technical indicators for a given ticker symbol. + Uses the configured technical_indicators vendor. + Args: + symbol (str): Ticker symbol of the company, e.g. AAPL, TSM + indicator (str): Technical indicator. Supported: close_50_sma, close_200_sma, close_10_ema, macd, macds, macdh, rsi, boll, boll_ub, boll_lb, atr, vwma, mfi + curr_date (str): The current trading date you are trading on, YYYY-mm-dd + look_back_days (int): How many days to look back, default is 30 + Returns: + str: A formatted dataframe containing the technical indicators for the specified ticker symbol and indicator. + """ + try: + return route_to_vendor("get_indicators", symbol, indicator, curr_date, look_back_days) + except ValueError as e: return str(e) \ No newline at end of file diff --git a/tradingagents/dataflows/alpaca_data.py b/tradingagents/dataflows/alpaca_data.py index 9530f576..ad2e45d0 100644 --- a/tradingagents/dataflows/alpaca_data.py +++ b/tradingagents/dataflows/alpaca_data.py @@ -1,321 +1,321 @@ -"""Alpaca Market Data API client for the equity ranking engine. - -Provides price bars, snapshots, and news. Fundamentals still come from yfinance. -Free tier: 10,000 requests/min, up to 7 years of historical data. -""" - -from __future__ import annotations - -import json -import logging -import os -from datetime import datetime, timedelta -from typing import Any, Dict, List, Optional - -import pandas as pd - -logger = logging.getLogger(__name__) - -# --------------------------------------------------------------------------- -# Client setup (lazy init) -# --------------------------------------------------------------------------- - -_stock_client = None -_news_client = None - - -def _get_stock_client(): - global _stock_client - if _stock_client is None: - from alpaca.data.historical import StockHistoricalDataClient - - key = os.environ.get("ALPACA_API_KEY", "") - secret = os.environ.get("ALPACA_API_SECRET", "") - if not key or not secret: - raise RuntimeError( - "ALPACA_API_KEY and ALPACA_API_SECRET must be set" - ) - _stock_client = StockHistoricalDataClient(key, secret) - return _stock_client - - -def _get_news_client(): - global _news_client - if _news_client is None: - from alpaca.data.historical.news import NewsClient - - key = os.environ.get("ALPACA_API_KEY", "") - secret = os.environ.get("ALPACA_API_SECRET", "") - _news_client = NewsClient(key, secret) - return _news_client - - -def alpaca_available() -> bool: - """Check if Alpaca credentials are configured.""" - return bool( - os.environ.get("ALPACA_API_KEY") - and os.environ.get("ALPACA_API_SECRET") - ) - - -# --------------------------------------------------------------------------- -# Price / Bar data -# --------------------------------------------------------------------------- - -def get_bars( - symbol: str, - start_date: str, - end_date: str, - timeframe: str = "1Day", -) -> pd.DataFrame: - """Fetch historical bars from Alpaca. - - Args: - symbol: Ticker symbol (e.g., "AAPL") - start_date: Start date in YYYY-MM-DD format - end_date: End date in YYYY-MM-DD format - timeframe: "1Min", "5Min", "15Min", "1Hour", "1Day", "1Week", "1Month" - - Returns: - DataFrame with OHLCV columns. - """ - from alpaca.data.requests import StockBarsRequest - from alpaca.data.timeframe import TimeFrame, TimeFrameUnit - - tf_map = { - "1Min": TimeFrame(1, TimeFrameUnit.Minute), - "5Min": TimeFrame(5, TimeFrameUnit.Minute), - "15Min": TimeFrame(15, TimeFrameUnit.Minute), - "1Hour": TimeFrame(1, TimeFrameUnit.Hour), - "1Day": TimeFrame(1, TimeFrameUnit.Day), - "1Week": TimeFrame(1, TimeFrameUnit.Week), - "1Month": TimeFrame(1, TimeFrameUnit.Month), - } - tf = tf_map.get(timeframe, TimeFrame(1, TimeFrameUnit.Day)) - - client = _get_stock_client() - request = StockBarsRequest( - symbol_or_symbols=symbol.upper(), - timeframe=tf, - start=datetime.strptime(start_date, "%Y-%m-%d"), - end=datetime.strptime(end_date, "%Y-%m-%d"), - feed="iex", - ) - bars = client.get_stock_bars(request) - df = bars.df - if isinstance(df.index, pd.MultiIndex): - df = df.droplevel("symbol") - return df - - -def get_bars_csv(symbol: str, start_date: str, end_date: str) -> str: - """Fetch historical bars and return as CSV string (drop-in for get_YFin_data_online).""" - try: - df = get_bars(symbol, start_date, end_date) - if df.empty: - return f"No data found for '{symbol}' between {start_date} and {end_date}" - - # Rename columns to match yfinance output format - df = df.rename(columns={ - "open": "Open", "high": "High", "low": "Low", - "close": "Close", "volume": "Volume", - "trade_count": "Trade Count", "vwap": "VWAP", - }) - for col in ["Open", "High", "Low", "Close"]: - if col in df.columns: - df[col] = df[col].round(2) - - if df.index.tz is not None: - df.index = df.index.tz_localize(None) - - csv = df.to_csv() - header = ( - f"# Stock data for {symbol.upper()} from {start_date} to {end_date}\n" - f"# Source: Alpaca Markets (IEX feed)\n" - f"# Total records: {len(df)}\n\n" - ) - return header + csv - except Exception as e: - logger.warning("Alpaca bars failed for %s: %s", symbol, e) - return f"Error fetching Alpaca data for {symbol}: {e}" - - -# --------------------------------------------------------------------------- -# Snapshots (latest quote/trade) -# --------------------------------------------------------------------------- - -def get_snapshot(symbol: str) -> Dict[str, Any]: - """Get the latest snapshot (quote + trade + bar) for a symbol.""" - from alpaca.data.requests import StockSnapshotRequest - - client = _get_stock_client() - request = StockSnapshotRequest(symbol_or_symbols=symbol.upper(), feed="iex") - snapshots = client.get_stock_snapshot(request) - snap = snapshots.get(symbol.upper()) - if not snap: - return {} - - result = { - "ticker": symbol.upper(), - "latest_trade_price": snap.latest_trade.price if snap.latest_trade else None, - "latest_trade_size": snap.latest_trade.size if snap.latest_trade else None, - "latest_trade_time": str(snap.latest_trade.timestamp) if snap.latest_trade else None, - } - if snap.latest_quote: - result["bid"] = snap.latest_quote.bid_price - result["ask"] = snap.latest_quote.ask_price - result["bid_size"] = snap.latest_quote.bid_size - result["ask_size"] = snap.latest_quote.ask_size - if snap.daily_bar: - result["daily_open"] = snap.daily_bar.open - result["daily_high"] = snap.daily_bar.high - result["daily_low"] = snap.daily_bar.low - result["daily_close"] = snap.daily_bar.close - result["daily_volume"] = snap.daily_bar.volume - result["daily_vwap"] = snap.daily_bar.vwap - if snap.previous_daily_bar: - result["prev_close"] = snap.previous_daily_bar.close - result["prev_volume"] = snap.previous_daily_bar.volume - - return result - - -def get_multi_snapshots(symbols: List[str]) -> Dict[str, Dict[str, Any]]: - """Get snapshots for multiple symbols at once.""" - from alpaca.data.requests import StockSnapshotRequest - - client = _get_stock_client() - request = StockSnapshotRequest( - symbol_or_symbols=[s.upper() for s in symbols], - feed="iex", - ) - snapshots = client.get_stock_snapshot(request) - result = {} - for sym, snap in snapshots.items(): - entry = {"ticker": sym} - if snap.latest_trade: - entry["price"] = snap.latest_trade.price - if snap.daily_bar: - entry["daily_open"] = snap.daily_bar.open - entry["daily_high"] = snap.daily_bar.high - entry["daily_low"] = snap.daily_bar.low - entry["daily_close"] = snap.daily_bar.close - entry["daily_volume"] = snap.daily_bar.volume - entry["daily_vwap"] = snap.daily_bar.vwap - if snap.previous_daily_bar: - entry["prev_close"] = snap.previous_daily_bar.close - result[sym] = entry - return result - - -# --------------------------------------------------------------------------- -# Computed indicators from bars -# --------------------------------------------------------------------------- - -def get_moving_averages(symbol: str) -> Dict[str, Any]: - """Compute 50-day and 200-day moving averages from Alpaca bars.""" - end = datetime.now() - start = end - timedelta(days=300) # ~200 trading days + buffer - - try: - df = get_bars(symbol, start.strftime("%Y-%m-%d"), end.strftime("%Y-%m-%d")) - if df.empty or len(df) < 50: - return {} - - close = df["close"] if "close" in df.columns else df["Close"] - result = { - "current_price": float(close.iloc[-1]), - "fifty_day_avg": float(close.tail(50).mean()), - } - if len(close) >= 200: - result["two_hundred_day_avg"] = float(close.tail(200).mean()) - - # 52-week high/low (approx 252 trading days) - year_data = close.tail(252) if len(close) >= 252 else close - result["fifty_two_week_high"] = float(year_data.max()) - result["fifty_two_week_low"] = float(year_data.min()) - - hi = result["fifty_two_week_high"] - lo = result["fifty_two_week_low"] - price = result["current_price"] - if (hi - lo) > 0: - result["vs_52w_range_pct"] = round((price - lo) / (hi - lo) * 100, 1) - - return result - except Exception as e: - logger.warning("Alpaca moving averages failed for %s: %s", symbol, e) - return {} - - -def get_sector_etf_performance(etf_symbols: List[str]) -> Dict[str, Dict[str, float]]: - """Compute 1M and 3M returns for a list of sector ETFs.""" - end = datetime.now() - start_3m = end - timedelta(days=100) - - result = {} - for sym in etf_symbols: - try: - df = get_bars(sym, start_3m.strftime("%Y-%m-%d"), end.strftime("%Y-%m-%d")) - if df.empty or len(df) < 5: - continue - close = df["close"] if "close" in df.columns else df["Close"] - current = float(close.iloc[-1]) - - ret_1m = None - if len(close) >= 22: - price_1m = float(close.iloc[-22]) - ret_1m = round((current - price_1m) / price_1m * 100, 2) - - ret_3m = None - if len(close) >= 63: - price_3m = float(close.iloc[-63]) - ret_3m = round((current - price_3m) / price_3m * 100, 2) - - result[sym] = { - "return_1m": ret_1m, - "return_3m": ret_3m, - "price": current, - } - except Exception as e: - logger.warning("Alpaca ETF perf failed for %s: %s", sym, e) - - return result - - -# --------------------------------------------------------------------------- -# News -# --------------------------------------------------------------------------- - -def get_news( - symbols: Optional[List[str]] = None, - limit: int = 10, - start_date: Optional[str] = None, -) -> List[Dict[str, Any]]: - """Fetch news articles from Alpaca News API.""" - try: - from alpaca.data.requests import NewsRequest - - client = _get_news_client() - kwargs: Dict[str, Any] = {"limit": limit} - if symbols: - kwargs["symbols"] = [s.upper() for s in symbols] - if start_date: - kwargs["start"] = datetime.strptime(start_date, "%Y-%m-%d") - - request = NewsRequest(**kwargs) - news = client.get_news(request) - - return [ - { - "title": n.headline, - "summary": n.summary or "", - "url": n.url, - "source": n.source, - "created_at": str(n.created_at), - "symbols": n.symbols or [], - } - for n in news.news - ] - except Exception as e: - logger.warning("Alpaca news failed: %s", e) - return [] +"""Alpaca Market Data API client for the equity ranking engine. + +Provides price bars, snapshots, and news. Fundamentals still come from yfinance. +Free tier: 10,000 requests/min, up to 7 years of historical data. +""" + +from __future__ import annotations + +import json +import logging +import os +from datetime import datetime, timedelta +from typing import Any, Dict, List, Optional + +import pandas as pd + +logger = logging.getLogger(__name__) + +# --------------------------------------------------------------------------- +# Client setup (lazy init) +# --------------------------------------------------------------------------- + +_stock_client = None +_news_client = None + + +def _get_stock_client(): + global _stock_client + if _stock_client is None: + from alpaca.data.historical import StockHistoricalDataClient + + key = os.environ.get("ALPACA_API_KEY", "") + secret = os.environ.get("ALPACA_API_SECRET", "") + if not key or not secret: + raise RuntimeError( + "ALPACA_API_KEY and ALPACA_API_SECRET must be set" + ) + _stock_client = StockHistoricalDataClient(key, secret) + return _stock_client + + +def _get_news_client(): + global _news_client + if _news_client is None: + from alpaca.data.historical.news import NewsClient + + key = os.environ.get("ALPACA_API_KEY", "") + secret = os.environ.get("ALPACA_API_SECRET", "") + _news_client = NewsClient(key, secret) + return _news_client + + +def alpaca_available() -> bool: + """Check if Alpaca credentials are configured.""" + return bool( + os.environ.get("ALPACA_API_KEY") + and os.environ.get("ALPACA_API_SECRET") + ) + + +# --------------------------------------------------------------------------- +# Price / Bar data +# --------------------------------------------------------------------------- + +def get_bars( + symbol: str, + start_date: str, + end_date: str, + timeframe: str = "1Day", +) -> pd.DataFrame: + """Fetch historical bars from Alpaca. + + Args: + symbol: Ticker symbol (e.g., "AAPL") + start_date: Start date in YYYY-MM-DD format + end_date: End date in YYYY-MM-DD format + timeframe: "1Min", "5Min", "15Min", "1Hour", "1Day", "1Week", "1Month" + + Returns: + DataFrame with OHLCV columns. + """ + from alpaca.data.requests import StockBarsRequest + from alpaca.data.timeframe import TimeFrame, TimeFrameUnit + + tf_map = { + "1Min": TimeFrame(1, TimeFrameUnit.Minute), + "5Min": TimeFrame(5, TimeFrameUnit.Minute), + "15Min": TimeFrame(15, TimeFrameUnit.Minute), + "1Hour": TimeFrame(1, TimeFrameUnit.Hour), + "1Day": TimeFrame(1, TimeFrameUnit.Day), + "1Week": TimeFrame(1, TimeFrameUnit.Week), + "1Month": TimeFrame(1, TimeFrameUnit.Month), + } + tf = tf_map.get(timeframe, TimeFrame(1, TimeFrameUnit.Day)) + + client = _get_stock_client() + request = StockBarsRequest( + symbol_or_symbols=symbol.upper(), + timeframe=tf, + start=datetime.strptime(start_date, "%Y-%m-%d"), + end=datetime.strptime(end_date, "%Y-%m-%d"), + feed="iex", + ) + bars = client.get_stock_bars(request) + df = bars.df + if isinstance(df.index, pd.MultiIndex): + df = df.droplevel("symbol") + return df + + +def get_bars_csv(symbol: str, start_date: str, end_date: str) -> str: + """Fetch historical bars and return as CSV string (drop-in for get_YFin_data_online).""" + try: + df = get_bars(symbol, start_date, end_date) + if df.empty: + return f"No data found for '{symbol}' between {start_date} and {end_date}" + + # Rename columns to match yfinance output format + df = df.rename(columns={ + "open": "Open", "high": "High", "low": "Low", + "close": "Close", "volume": "Volume", + "trade_count": "Trade Count", "vwap": "VWAP", + }) + for col in ["Open", "High", "Low", "Close"]: + if col in df.columns: + df[col] = df[col].round(2) + + if df.index.tz is not None: + df.index = df.index.tz_localize(None) + + csv = df.to_csv() + header = ( + f"# Stock data for {symbol.upper()} from {start_date} to {end_date}\n" + f"# Source: Alpaca Markets (IEX feed)\n" + f"# Total records: {len(df)}\n\n" + ) + return header + csv + except Exception as e: + logger.warning("Alpaca bars failed for %s: %s", symbol, e) + return f"Error fetching Alpaca data for {symbol}: {e}" + + +# --------------------------------------------------------------------------- +# Snapshots (latest quote/trade) +# --------------------------------------------------------------------------- + +def get_snapshot(symbol: str) -> Dict[str, Any]: + """Get the latest snapshot (quote + trade + bar) for a symbol.""" + from alpaca.data.requests import StockSnapshotRequest + + client = _get_stock_client() + request = StockSnapshotRequest(symbol_or_symbols=symbol.upper(), feed="iex") + snapshots = client.get_stock_snapshot(request) + snap = snapshots.get(symbol.upper()) + if not snap: + return {} + + result = { + "ticker": symbol.upper(), + "latest_trade_price": snap.latest_trade.price if snap.latest_trade else None, + "latest_trade_size": snap.latest_trade.size if snap.latest_trade else None, + "latest_trade_time": str(snap.latest_trade.timestamp) if snap.latest_trade else None, + } + if snap.latest_quote: + result["bid"] = snap.latest_quote.bid_price + result["ask"] = snap.latest_quote.ask_price + result["bid_size"] = snap.latest_quote.bid_size + result["ask_size"] = snap.latest_quote.ask_size + if snap.daily_bar: + result["daily_open"] = snap.daily_bar.open + result["daily_high"] = snap.daily_bar.high + result["daily_low"] = snap.daily_bar.low + result["daily_close"] = snap.daily_bar.close + result["daily_volume"] = snap.daily_bar.volume + result["daily_vwap"] = snap.daily_bar.vwap + if snap.previous_daily_bar: + result["prev_close"] = snap.previous_daily_bar.close + result["prev_volume"] = snap.previous_daily_bar.volume + + return result + + +def get_multi_snapshots(symbols: List[str]) -> Dict[str, Dict[str, Any]]: + """Get snapshots for multiple symbols at once.""" + from alpaca.data.requests import StockSnapshotRequest + + client = _get_stock_client() + request = StockSnapshotRequest( + symbol_or_symbols=[s.upper() for s in symbols], + feed="iex", + ) + snapshots = client.get_stock_snapshot(request) + result = {} + for sym, snap in snapshots.items(): + entry = {"ticker": sym} + if snap.latest_trade: + entry["price"] = snap.latest_trade.price + if snap.daily_bar: + entry["daily_open"] = snap.daily_bar.open + entry["daily_high"] = snap.daily_bar.high + entry["daily_low"] = snap.daily_bar.low + entry["daily_close"] = snap.daily_bar.close + entry["daily_volume"] = snap.daily_bar.volume + entry["daily_vwap"] = snap.daily_bar.vwap + if snap.previous_daily_bar: + entry["prev_close"] = snap.previous_daily_bar.close + result[sym] = entry + return result + + +# --------------------------------------------------------------------------- +# Computed indicators from bars +# --------------------------------------------------------------------------- + +def get_moving_averages(symbol: str) -> Dict[str, Any]: + """Compute 50-day and 200-day moving averages from Alpaca bars.""" + end = datetime.now() + start = end - timedelta(days=300) # ~200 trading days + buffer + + try: + df = get_bars(symbol, start.strftime("%Y-%m-%d"), end.strftime("%Y-%m-%d")) + if df.empty or len(df) < 50: + return {} + + close = df["close"] if "close" in df.columns else df["Close"] + result = { + "current_price": float(close.iloc[-1]), + "fifty_day_avg": float(close.tail(50).mean()), + } + if len(close) >= 200: + result["two_hundred_day_avg"] = float(close.tail(200).mean()) + + # 52-week high/low (approx 252 trading days) + year_data = close.tail(252) if len(close) >= 252 else close + result["fifty_two_week_high"] = float(year_data.max()) + result["fifty_two_week_low"] = float(year_data.min()) + + hi = result["fifty_two_week_high"] + lo = result["fifty_two_week_low"] + price = result["current_price"] + if (hi - lo) > 0: + result["vs_52w_range_pct"] = round((price - lo) / (hi - lo) * 100, 1) + + return result + except Exception as e: + logger.warning("Alpaca moving averages failed for %s: %s", symbol, e) + return {} + + +def get_sector_etf_performance(etf_symbols: List[str]) -> Dict[str, Dict[str, float]]: + """Compute 1M and 3M returns for a list of sector ETFs.""" + end = datetime.now() + start_3m = end - timedelta(days=100) + + result = {} + for sym in etf_symbols: + try: + df = get_bars(sym, start_3m.strftime("%Y-%m-%d"), end.strftime("%Y-%m-%d")) + if df.empty or len(df) < 5: + continue + close = df["close"] if "close" in df.columns else df["Close"] + current = float(close.iloc[-1]) + + ret_1m = None + if len(close) >= 22: + price_1m = float(close.iloc[-22]) + ret_1m = round((current - price_1m) / price_1m * 100, 2) + + ret_3m = None + if len(close) >= 63: + price_3m = float(close.iloc[-63]) + ret_3m = round((current - price_3m) / price_3m * 100, 2) + + result[sym] = { + "return_1m": ret_1m, + "return_3m": ret_3m, + "price": current, + } + except Exception as e: + logger.warning("Alpaca ETF perf failed for %s: %s", sym, e) + + return result + + +# --------------------------------------------------------------------------- +# News +# --------------------------------------------------------------------------- + +def get_news( + symbols: Optional[List[str]] = None, + limit: int = 10, + start_date: Optional[str] = None, +) -> List[Dict[str, Any]]: + """Fetch news articles from Alpaca News API.""" + try: + from alpaca.data.requests import NewsRequest + + client = _get_news_client() + kwargs: Dict[str, Any] = {"limit": limit} + if symbols: + kwargs["symbols"] = [s.upper() for s in symbols] + if start_date: + kwargs["start"] = datetime.strptime(start_date, "%Y-%m-%d") + + request = NewsRequest(**kwargs) + news = client.get_news(request) + + return [ + { + "title": n.headline, + "summary": n.summary or "", + "url": n.url, + "source": n.source, + "created_at": str(n.created_at), + "symbols": n.symbols or [], + } + for n in news.news + ] + except Exception as e: + logger.warning("Alpaca news failed: %s", e) + return [] diff --git a/tradingagents/dataflows/alpha_vantage.py b/tradingagents/dataflows/alpha_vantage.py index b2be1d61..d01e51cb 100644 --- a/tradingagents/dataflows/alpha_vantage.py +++ b/tradingagents/dataflows/alpha_vantage.py @@ -1,5 +1,5 @@ -# Import functions from specialized modules -from .alpha_vantage_stock import get_stock -from .alpha_vantage_indicator import get_indicator -from .alpha_vantage_fundamentals import get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement +# Import functions from specialized modules +from .alpha_vantage_stock import get_stock +from .alpha_vantage_indicator import get_indicator +from .alpha_vantage_fundamentals import get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement from .alpha_vantage_news import get_news, get_global_news, get_insider_transactions \ No newline at end of file diff --git a/tradingagents/dataflows/alpha_vantage_common.py b/tradingagents/dataflows/alpha_vantage_common.py index 409ff29e..6fd5d143 100644 --- a/tradingagents/dataflows/alpha_vantage_common.py +++ b/tradingagents/dataflows/alpha_vantage_common.py @@ -1,122 +1,122 @@ -import os -import requests -import pandas as pd -import json -from datetime import datetime -from io import StringIO - -API_BASE_URL = "https://www.alphavantage.co/query" - -def get_api_key() -> str: - """Retrieve the API key for Alpha Vantage from environment variables.""" - api_key = os.getenv("ALPHA_VANTAGE_API_KEY") - if not api_key: - raise ValueError("ALPHA_VANTAGE_API_KEY environment variable is not set.") - return api_key - -def format_datetime_for_api(date_input) -> str: - """Convert various date formats to YYYYMMDDTHHMM format required by Alpha Vantage API.""" - if isinstance(date_input, str): - # If already in correct format, return as-is - if len(date_input) == 13 and 'T' in date_input: - return date_input - # Try to parse common date formats - try: - dt = datetime.strptime(date_input, "%Y-%m-%d") - return dt.strftime("%Y%m%dT0000") - except ValueError: - try: - dt = datetime.strptime(date_input, "%Y-%m-%d %H:%M") - return dt.strftime("%Y%m%dT%H%M") - except ValueError: - raise ValueError(f"Unsupported date format: {date_input}") - elif isinstance(date_input, datetime): - return date_input.strftime("%Y%m%dT%H%M") - else: - raise ValueError(f"Date must be string or datetime object, got {type(date_input)}") - -class AlphaVantageRateLimitError(Exception): - """Exception raised when Alpha Vantage API rate limit is exceeded.""" - pass - -def _make_api_request(function_name: str, params: dict) -> dict | str: - """Helper function to make API requests and handle responses. - - Raises: - AlphaVantageRateLimitError: When API rate limit is exceeded - """ - # Create a copy of params to avoid modifying the original - api_params = params.copy() - api_params.update({ - "function": function_name, - "apikey": get_api_key(), - "source": "trading_agents", - }) - - # Handle entitlement parameter if present in params or global variable - current_entitlement = globals().get('_current_entitlement') - entitlement = api_params.get("entitlement") or current_entitlement - - if entitlement: - api_params["entitlement"] = entitlement - elif "entitlement" in api_params: - # Remove entitlement if it's None or empty - api_params.pop("entitlement", None) - - response = requests.get(API_BASE_URL, params=api_params) - response.raise_for_status() - - response_text = response.text - - # Check if response is JSON (error responses are typically JSON) - try: - response_json = json.loads(response_text) - # Check for rate limit error - if "Information" in response_json: - info_message = response_json["Information"] - if "rate limit" in info_message.lower() or "api key" in info_message.lower(): - raise AlphaVantageRateLimitError(f"Alpha Vantage rate limit exceeded: {info_message}") - except json.JSONDecodeError: - # Response is not JSON (likely CSV data), which is normal - pass - - return response_text - - - -def _filter_csv_by_date_range(csv_data: str, start_date: str, end_date: str) -> str: - """ - Filter CSV data to include only rows within the specified date range. - - Args: - csv_data: CSV string from Alpha Vantage API - start_date: Start date in yyyy-mm-dd format - end_date: End date in yyyy-mm-dd format - - Returns: - Filtered CSV string - """ - if not csv_data or csv_data.strip() == "": - return csv_data - - try: - # Parse CSV data - df = pd.read_csv(StringIO(csv_data)) - - # Assume the first column is the date column (timestamp) - date_col = df.columns[0] - df[date_col] = pd.to_datetime(df[date_col]) - - # Filter by date range - start_dt = pd.to_datetime(start_date) - end_dt = pd.to_datetime(end_date) - - filtered_df = df[(df[date_col] >= start_dt) & (df[date_col] <= end_dt)] - - # Convert back to CSV string - return filtered_df.to_csv(index=False) - - except Exception as e: - # If filtering fails, return original data with a warning - print(f"Warning: Failed to filter CSV data by date range: {e}") - return csv_data +import os +import requests +import pandas as pd +import json +from datetime import datetime +from io import StringIO + +API_BASE_URL = "https://www.alphavantage.co/query" + +def get_api_key() -> str: + """Retrieve the API key for Alpha Vantage from environment variables.""" + api_key = os.getenv("ALPHA_VANTAGE_API_KEY") + if not api_key: + raise ValueError("ALPHA_VANTAGE_API_KEY environment variable is not set.") + return api_key + +def format_datetime_for_api(date_input) -> str: + """Convert various date formats to YYYYMMDDTHHMM format required by Alpha Vantage API.""" + if isinstance(date_input, str): + # If already in correct format, return as-is + if len(date_input) == 13 and 'T' in date_input: + return date_input + # Try to parse common date formats + try: + dt = datetime.strptime(date_input, "%Y-%m-%d") + return dt.strftime("%Y%m%dT0000") + except ValueError: + try: + dt = datetime.strptime(date_input, "%Y-%m-%d %H:%M") + return dt.strftime("%Y%m%dT%H%M") + except ValueError: + raise ValueError(f"Unsupported date format: {date_input}") + elif isinstance(date_input, datetime): + return date_input.strftime("%Y%m%dT%H%M") + else: + raise ValueError(f"Date must be string or datetime object, got {type(date_input)}") + +class AlphaVantageRateLimitError(Exception): + """Exception raised when Alpha Vantage API rate limit is exceeded.""" + pass + +def _make_api_request(function_name: str, params: dict) -> dict | str: + """Helper function to make API requests and handle responses. + + Raises: + AlphaVantageRateLimitError: When API rate limit is exceeded + """ + # Create a copy of params to avoid modifying the original + api_params = params.copy() + api_params.update({ + "function": function_name, + "apikey": get_api_key(), + "source": "trading_agents", + }) + + # Handle entitlement parameter if present in params or global variable + current_entitlement = globals().get('_current_entitlement') + entitlement = api_params.get("entitlement") or current_entitlement + + if entitlement: + api_params["entitlement"] = entitlement + elif "entitlement" in api_params: + # Remove entitlement if it's None or empty + api_params.pop("entitlement", None) + + response = requests.get(API_BASE_URL, params=api_params) + response.raise_for_status() + + response_text = response.text + + # Check if response is JSON (error responses are typically JSON) + try: + response_json = json.loads(response_text) + # Check for rate limit error + if "Information" in response_json: + info_message = response_json["Information"] + if "rate limit" in info_message.lower() or "api key" in info_message.lower(): + raise AlphaVantageRateLimitError(f"Alpha Vantage rate limit exceeded: {info_message}") + except json.JSONDecodeError: + # Response is not JSON (likely CSV data), which is normal + pass + + return response_text + + + +def _filter_csv_by_date_range(csv_data: str, start_date: str, end_date: str) -> str: + """ + Filter CSV data to include only rows within the specified date range. + + Args: + csv_data: CSV string from Alpha Vantage API + start_date: Start date in yyyy-mm-dd format + end_date: End date in yyyy-mm-dd format + + Returns: + Filtered CSV string + """ + if not csv_data or csv_data.strip() == "": + return csv_data + + try: + # Parse CSV data + df = pd.read_csv(StringIO(csv_data)) + + # Assume the first column is the date column (timestamp) + date_col = df.columns[0] + df[date_col] = pd.to_datetime(df[date_col]) + + # Filter by date range + start_dt = pd.to_datetime(start_date) + end_dt = pd.to_datetime(end_date) + + filtered_df = df[(df[date_col] >= start_dt) & (df[date_col] <= end_dt)] + + # Convert back to CSV string + return filtered_df.to_csv(index=False) + + except Exception as e: + # If filtering fails, return original data with a warning + print(f"Warning: Failed to filter CSV data by date range: {e}") + return csv_data diff --git a/tradingagents/dataflows/alpha_vantage_fundamentals.py b/tradingagents/dataflows/alpha_vantage_fundamentals.py index 8b92faa6..402fa2f0 100644 --- a/tradingagents/dataflows/alpha_vantage_fundamentals.py +++ b/tradingagents/dataflows/alpha_vantage_fundamentals.py @@ -1,77 +1,77 @@ -from .alpha_vantage_common import _make_api_request - - -def get_fundamentals(ticker: str, curr_date: str = None) -> str: - """ - Retrieve comprehensive fundamental data for a given ticker symbol using Alpha Vantage. - - Args: - ticker (str): Ticker symbol of the company - curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage) - - Returns: - str: Company overview data including financial ratios and key metrics - """ - params = { - "symbol": ticker, - } - - return _make_api_request("OVERVIEW", params) - - -def get_balance_sheet(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str: - """ - Retrieve balance sheet data for a given ticker symbol using Alpha Vantage. - - Args: - ticker (str): Ticker symbol of the company - freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage - curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage) - - Returns: - str: Balance sheet data with normalized fields - """ - params = { - "symbol": ticker, - } - - return _make_api_request("BALANCE_SHEET", params) - - -def get_cashflow(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str: - """ - Retrieve cash flow statement data for a given ticker symbol using Alpha Vantage. - - Args: - ticker (str): Ticker symbol of the company - freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage - curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage) - - Returns: - str: Cash flow statement data with normalized fields - """ - params = { - "symbol": ticker, - } - - return _make_api_request("CASH_FLOW", params) - - -def get_income_statement(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str: - """ - Retrieve income statement data for a given ticker symbol using Alpha Vantage. - - Args: - ticker (str): Ticker symbol of the company - freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage - curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage) - - Returns: - str: Income statement data with normalized fields - """ - params = { - "symbol": ticker, - } - - return _make_api_request("INCOME_STATEMENT", params) - +from .alpha_vantage_common import _make_api_request + + +def get_fundamentals(ticker: str, curr_date: str = None) -> str: + """ + Retrieve comprehensive fundamental data for a given ticker symbol using Alpha Vantage. + + Args: + ticker (str): Ticker symbol of the company + curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage) + + Returns: + str: Company overview data including financial ratios and key metrics + """ + params = { + "symbol": ticker, + } + + return _make_api_request("OVERVIEW", params) + + +def get_balance_sheet(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str: + """ + Retrieve balance sheet data for a given ticker symbol using Alpha Vantage. + + Args: + ticker (str): Ticker symbol of the company + freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage + curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage) + + Returns: + str: Balance sheet data with normalized fields + """ + params = { + "symbol": ticker, + } + + return _make_api_request("BALANCE_SHEET", params) + + +def get_cashflow(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str: + """ + Retrieve cash flow statement data for a given ticker symbol using Alpha Vantage. + + Args: + ticker (str): Ticker symbol of the company + freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage + curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage) + + Returns: + str: Cash flow statement data with normalized fields + """ + params = { + "symbol": ticker, + } + + return _make_api_request("CASH_FLOW", params) + + +def get_income_statement(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str: + """ + Retrieve income statement data for a given ticker symbol using Alpha Vantage. + + Args: + ticker (str): Ticker symbol of the company + freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage + curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage) + + Returns: + str: Income statement data with normalized fields + """ + params = { + "symbol": ticker, + } + + return _make_api_request("INCOME_STATEMENT", params) + diff --git a/tradingagents/dataflows/alpha_vantage_indicator.py b/tradingagents/dataflows/alpha_vantage_indicator.py index 6225b9bb..8d5bf118 100644 --- a/tradingagents/dataflows/alpha_vantage_indicator.py +++ b/tradingagents/dataflows/alpha_vantage_indicator.py @@ -1,222 +1,222 @@ -from .alpha_vantage_common import _make_api_request - -def get_indicator( - symbol: str, - indicator: str, - curr_date: str, - look_back_days: int, - interval: str = "daily", - time_period: int = 14, - series_type: str = "close" -) -> str: - """ - Returns Alpha Vantage technical indicator values over a time window. - - Args: - symbol: ticker symbol of the company - indicator: technical indicator to get the analysis and report of - curr_date: The current trading date you are trading on, YYYY-mm-dd - look_back_days: how many days to look back - interval: Time interval (daily, weekly, monthly) - time_period: Number of data points for calculation - series_type: The desired price type (close, open, high, low) - - Returns: - String containing indicator values and description - """ - from datetime import datetime - from dateutil.relativedelta import relativedelta - - supported_indicators = { - "close_50_sma": ("50 SMA", "close"), - "close_200_sma": ("200 SMA", "close"), - "close_10_ema": ("10 EMA", "close"), - "macd": ("MACD", "close"), - "macds": ("MACD Signal", "close"), - "macdh": ("MACD Histogram", "close"), - "rsi": ("RSI", "close"), - "boll": ("Bollinger Middle", "close"), - "boll_ub": ("Bollinger Upper Band", "close"), - "boll_lb": ("Bollinger Lower Band", "close"), - "atr": ("ATR", None), - "vwma": ("VWMA", "close") - } - - indicator_descriptions = { - "close_50_sma": "50 SMA: A medium-term trend indicator. Usage: Identify trend direction and serve as dynamic support/resistance. Tips: It lags price; combine with faster indicators for timely signals.", - "close_200_sma": "200 SMA: A long-term trend benchmark. Usage: Confirm overall market trend and identify golden/death cross setups. Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries.", - "close_10_ema": "10 EMA: A responsive short-term average. Usage: Capture quick shifts in momentum and potential entry points. Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals.", - "macd": "MACD: Computes momentum via differences of EMAs. Usage: Look for crossovers and divergence as signals of trend changes. Tips: Confirm with other indicators in low-volatility or sideways markets.", - "macds": "MACD Signal: An EMA smoothing of the MACD line. Usage: Use crossovers with the MACD line to trigger trades. Tips: Should be part of a broader strategy to avoid false positives.", - "macdh": "MACD Histogram: Shows the gap between the MACD line and its signal. Usage: Visualize momentum strength and spot divergence early. Tips: Can be volatile; complement with additional filters in fast-moving markets.", - "rsi": "RSI: Measures momentum to flag overbought/oversold conditions. Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis.", - "boll": "Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. Usage: Acts as a dynamic benchmark for price movement. Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals.", - "boll_ub": "Bollinger Upper Band: Typically 2 standard deviations above the middle line. Usage: Signals potential overbought conditions and breakout zones. Tips: Confirm signals with other tools; prices may ride the band in strong trends.", - "boll_lb": "Bollinger Lower Band: Typically 2 standard deviations below the middle line. Usage: Indicates potential oversold conditions. Tips: Use additional analysis to avoid false reversal signals.", - "atr": "ATR: Averages true range to measure volatility. Usage: Set stop-loss levels and adjust position sizes based on current market volatility. Tips: It's a reactive measure, so use it as part of a broader risk management strategy.", - "vwma": "VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses." - } - - if indicator not in supported_indicators: - raise ValueError( - f"Indicator {indicator} is not supported. Please choose from: {list(supported_indicators.keys())}" - ) - - curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d") - before = curr_date_dt - relativedelta(days=look_back_days) - - # Get the full data for the period instead of making individual calls - _, required_series_type = supported_indicators[indicator] - - # Use the provided series_type or fall back to the required one - if required_series_type: - series_type = required_series_type - - try: - # Get indicator data for the period - if indicator == "close_50_sma": - data = _make_api_request("SMA", { - "symbol": symbol, - "interval": interval, - "time_period": "50", - "series_type": series_type, - "datatype": "csv" - }) - elif indicator == "close_200_sma": - data = _make_api_request("SMA", { - "symbol": symbol, - "interval": interval, - "time_period": "200", - "series_type": series_type, - "datatype": "csv" - }) - elif indicator == "close_10_ema": - data = _make_api_request("EMA", { - "symbol": symbol, - "interval": interval, - "time_period": "10", - "series_type": series_type, - "datatype": "csv" - }) - elif indicator == "macd": - data = _make_api_request("MACD", { - "symbol": symbol, - "interval": interval, - "series_type": series_type, - "datatype": "csv" - }) - elif indicator == "macds": - data = _make_api_request("MACD", { - "symbol": symbol, - "interval": interval, - "series_type": series_type, - "datatype": "csv" - }) - elif indicator == "macdh": - data = _make_api_request("MACD", { - "symbol": symbol, - "interval": interval, - "series_type": series_type, - "datatype": "csv" - }) - elif indicator == "rsi": - data = _make_api_request("RSI", { - "symbol": symbol, - "interval": interval, - "time_period": str(time_period), - "series_type": series_type, - "datatype": "csv" - }) - elif indicator in ["boll", "boll_ub", "boll_lb"]: - data = _make_api_request("BBANDS", { - "symbol": symbol, - "interval": interval, - "time_period": "20", - "series_type": series_type, - "datatype": "csv" - }) - elif indicator == "atr": - data = _make_api_request("ATR", { - "symbol": symbol, - "interval": interval, - "time_period": str(time_period), - "datatype": "csv" - }) - elif indicator == "vwma": - # Alpha Vantage doesn't have direct VWMA, so we'll return an informative message - # In a real implementation, this would need to be calculated from OHLCV data - return f"## VWMA (Volume Weighted Moving Average) for {symbol}:\n\nVWMA calculation requires OHLCV data and is not directly available from Alpha Vantage API.\nThis indicator would need to be calculated from the raw stock data using volume-weighted price averaging.\n\n{indicator_descriptions.get('vwma', 'No description available.')}" - else: - return f"Error: Indicator {indicator} not implemented yet." - - # Parse CSV data and extract values for the date range - lines = data.strip().split('\n') - if len(lines) < 2: - return f"Error: No data returned for {indicator}" - - # Parse header and data - header = [col.strip() for col in lines[0].split(',')] - try: - date_col_idx = header.index('time') - except ValueError: - return f"Error: 'time' column not found in data for {indicator}. Available columns: {header}" - - # Map internal indicator names to expected CSV column names from Alpha Vantage - col_name_map = { - "macd": "MACD", "macds": "MACD_Signal", "macdh": "MACD_Hist", - "boll": "Real Middle Band", "boll_ub": "Real Upper Band", "boll_lb": "Real Lower Band", - "rsi": "RSI", "atr": "ATR", "close_10_ema": "EMA", - "close_50_sma": "SMA", "close_200_sma": "SMA" - } - - target_col_name = col_name_map.get(indicator) - - if not target_col_name: - # Default to the second column if no specific mapping exists - value_col_idx = 1 - else: - try: - value_col_idx = header.index(target_col_name) - except ValueError: - return f"Error: Column '{target_col_name}' not found for indicator '{indicator}'. Available columns: {header}" - - result_data = [] - for line in lines[1:]: - if not line.strip(): - continue - values = line.split(',') - if len(values) > value_col_idx: - try: - date_str = values[date_col_idx].strip() - # Parse the date - date_dt = datetime.strptime(date_str, "%Y-%m-%d") - - # Check if date is in our range - if before <= date_dt <= curr_date_dt: - value = values[value_col_idx].strip() - result_data.append((date_dt, value)) - except (ValueError, IndexError): - continue - - # Sort by date and format output - result_data.sort(key=lambda x: x[0]) - - ind_string = "" - for date_dt, value in result_data: - ind_string += f"{date_dt.strftime('%Y-%m-%d')}: {value}\n" - - if not ind_string: - ind_string = "No data available for the specified date range.\n" - - result_str = ( - f"## {indicator.upper()} values from {before.strftime('%Y-%m-%d')} to {curr_date}:\n\n" - + ind_string - + "\n\n" - + indicator_descriptions.get(indicator, "No description available.") - ) - - return result_str - - except Exception as e: - print(f"Error getting Alpha Vantage indicator data for {indicator}: {e}") - return f"Error retrieving {indicator} data: {str(e)}" +from .alpha_vantage_common import _make_api_request + +def get_indicator( + symbol: str, + indicator: str, + curr_date: str, + look_back_days: int, + interval: str = "daily", + time_period: int = 14, + series_type: str = "close" +) -> str: + """ + Returns Alpha Vantage technical indicator values over a time window. + + Args: + symbol: ticker symbol of the company + indicator: technical indicator to get the analysis and report of + curr_date: The current trading date you are trading on, YYYY-mm-dd + look_back_days: how many days to look back + interval: Time interval (daily, weekly, monthly) + time_period: Number of data points for calculation + series_type: The desired price type (close, open, high, low) + + Returns: + String containing indicator values and description + """ + from datetime import datetime + from dateutil.relativedelta import relativedelta + + supported_indicators = { + "close_50_sma": ("50 SMA", "close"), + "close_200_sma": ("200 SMA", "close"), + "close_10_ema": ("10 EMA", "close"), + "macd": ("MACD", "close"), + "macds": ("MACD Signal", "close"), + "macdh": ("MACD Histogram", "close"), + "rsi": ("RSI", "close"), + "boll": ("Bollinger Middle", "close"), + "boll_ub": ("Bollinger Upper Band", "close"), + "boll_lb": ("Bollinger Lower Band", "close"), + "atr": ("ATR", None), + "vwma": ("VWMA", "close") + } + + indicator_descriptions = { + "close_50_sma": "50 SMA: A medium-term trend indicator. Usage: Identify trend direction and serve as dynamic support/resistance. Tips: It lags price; combine with faster indicators for timely signals.", + "close_200_sma": "200 SMA: A long-term trend benchmark. Usage: Confirm overall market trend and identify golden/death cross setups. Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries.", + "close_10_ema": "10 EMA: A responsive short-term average. Usage: Capture quick shifts in momentum and potential entry points. Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals.", + "macd": "MACD: Computes momentum via differences of EMAs. Usage: Look for crossovers and divergence as signals of trend changes. Tips: Confirm with other indicators in low-volatility or sideways markets.", + "macds": "MACD Signal: An EMA smoothing of the MACD line. Usage: Use crossovers with the MACD line to trigger trades. Tips: Should be part of a broader strategy to avoid false positives.", + "macdh": "MACD Histogram: Shows the gap between the MACD line and its signal. Usage: Visualize momentum strength and spot divergence early. Tips: Can be volatile; complement with additional filters in fast-moving markets.", + "rsi": "RSI: Measures momentum to flag overbought/oversold conditions. Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis.", + "boll": "Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. Usage: Acts as a dynamic benchmark for price movement. Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals.", + "boll_ub": "Bollinger Upper Band: Typically 2 standard deviations above the middle line. Usage: Signals potential overbought conditions and breakout zones. Tips: Confirm signals with other tools; prices may ride the band in strong trends.", + "boll_lb": "Bollinger Lower Band: Typically 2 standard deviations below the middle line. Usage: Indicates potential oversold conditions. Tips: Use additional analysis to avoid false reversal signals.", + "atr": "ATR: Averages true range to measure volatility. Usage: Set stop-loss levels and adjust position sizes based on current market volatility. Tips: It's a reactive measure, so use it as part of a broader risk management strategy.", + "vwma": "VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses." + } + + if indicator not in supported_indicators: + raise ValueError( + f"Indicator {indicator} is not supported. Please choose from: {list(supported_indicators.keys())}" + ) + + curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d") + before = curr_date_dt - relativedelta(days=look_back_days) + + # Get the full data for the period instead of making individual calls + _, required_series_type = supported_indicators[indicator] + + # Use the provided series_type or fall back to the required one + if required_series_type: + series_type = required_series_type + + try: + # Get indicator data for the period + if indicator == "close_50_sma": + data = _make_api_request("SMA", { + "symbol": symbol, + "interval": interval, + "time_period": "50", + "series_type": series_type, + "datatype": "csv" + }) + elif indicator == "close_200_sma": + data = _make_api_request("SMA", { + "symbol": symbol, + "interval": interval, + "time_period": "200", + "series_type": series_type, + "datatype": "csv" + }) + elif indicator == "close_10_ema": + data = _make_api_request("EMA", { + "symbol": symbol, + "interval": interval, + "time_period": "10", + "series_type": series_type, + "datatype": "csv" + }) + elif indicator == "macd": + data = _make_api_request("MACD", { + "symbol": symbol, + "interval": interval, + "series_type": series_type, + "datatype": "csv" + }) + elif indicator == "macds": + data = _make_api_request("MACD", { + "symbol": symbol, + "interval": interval, + "series_type": series_type, + "datatype": "csv" + }) + elif indicator == "macdh": + data = _make_api_request("MACD", { + "symbol": symbol, + "interval": interval, + "series_type": series_type, + "datatype": "csv" + }) + elif indicator == "rsi": + data = _make_api_request("RSI", { + "symbol": symbol, + "interval": interval, + "time_period": str(time_period), + "series_type": series_type, + "datatype": "csv" + }) + elif indicator in ["boll", "boll_ub", "boll_lb"]: + data = _make_api_request("BBANDS", { + "symbol": symbol, + "interval": interval, + "time_period": "20", + "series_type": series_type, + "datatype": "csv" + }) + elif indicator == "atr": + data = _make_api_request("ATR", { + "symbol": symbol, + "interval": interval, + "time_period": str(time_period), + "datatype": "csv" + }) + elif indicator == "vwma": + # Alpha Vantage doesn't have direct VWMA, so we'll return an informative message + # In a real implementation, this would need to be calculated from OHLCV data + return f"## VWMA (Volume Weighted Moving Average) for {symbol}:\n\nVWMA calculation requires OHLCV data and is not directly available from Alpha Vantage API.\nThis indicator would need to be calculated from the raw stock data using volume-weighted price averaging.\n\n{indicator_descriptions.get('vwma', 'No description available.')}" + else: + return f"Error: Indicator {indicator} not implemented yet." + + # Parse CSV data and extract values for the date range + lines = data.strip().split('\n') + if len(lines) < 2: + return f"Error: No data returned for {indicator}" + + # Parse header and data + header = [col.strip() for col in lines[0].split(',')] + try: + date_col_idx = header.index('time') + except ValueError: + return f"Error: 'time' column not found in data for {indicator}. Available columns: {header}" + + # Map internal indicator names to expected CSV column names from Alpha Vantage + col_name_map = { + "macd": "MACD", "macds": "MACD_Signal", "macdh": "MACD_Hist", + "boll": "Real Middle Band", "boll_ub": "Real Upper Band", "boll_lb": "Real Lower Band", + "rsi": "RSI", "atr": "ATR", "close_10_ema": "EMA", + "close_50_sma": "SMA", "close_200_sma": "SMA" + } + + target_col_name = col_name_map.get(indicator) + + if not target_col_name: + # Default to the second column if no specific mapping exists + value_col_idx = 1 + else: + try: + value_col_idx = header.index(target_col_name) + except ValueError: + return f"Error: Column '{target_col_name}' not found for indicator '{indicator}'. Available columns: {header}" + + result_data = [] + for line in lines[1:]: + if not line.strip(): + continue + values = line.split(',') + if len(values) > value_col_idx: + try: + date_str = values[date_col_idx].strip() + # Parse the date + date_dt = datetime.strptime(date_str, "%Y-%m-%d") + + # Check if date is in our range + if before <= date_dt <= curr_date_dt: + value = values[value_col_idx].strip() + result_data.append((date_dt, value)) + except (ValueError, IndexError): + continue + + # Sort by date and format output + result_data.sort(key=lambda x: x[0]) + + ind_string = "" + for date_dt, value in result_data: + ind_string += f"{date_dt.strftime('%Y-%m-%d')}: {value}\n" + + if not ind_string: + ind_string = "No data available for the specified date range.\n" + + result_str = ( + f"## {indicator.upper()} values from {before.strftime('%Y-%m-%d')} to {curr_date}:\n\n" + + ind_string + + "\n\n" + + indicator_descriptions.get(indicator, "No description available.") + ) + + return result_str + + except Exception as e: + print(f"Error getting Alpha Vantage indicator data for {indicator}: {e}") + return f"Error retrieving {indicator} data: {str(e)}" diff --git a/tradingagents/dataflows/alpha_vantage_news.py b/tradingagents/dataflows/alpha_vantage_news.py index 4cf7bb0e..512ee616 100644 --- a/tradingagents/dataflows/alpha_vantage_news.py +++ b/tradingagents/dataflows/alpha_vantage_news.py @@ -1,71 +1,71 @@ -from .alpha_vantage_common import _make_api_request, format_datetime_for_api - -def get_news(ticker, start_date, end_date) -> dict[str, str] | str: - """Returns live and historical market news & sentiment data from premier news outlets worldwide. - - Covers stocks, cryptocurrencies, forex, and topics like fiscal policy, mergers & acquisitions, IPOs. - - Args: - ticker: Stock symbol for news articles. - start_date: Start date for news search. - end_date: End date for news search. - - Returns: - Dictionary containing news sentiment data or JSON string. - """ - - params = { - "tickers": ticker, - "time_from": format_datetime_for_api(start_date), - "time_to": format_datetime_for_api(end_date), - } - - return _make_api_request("NEWS_SENTIMENT", params) - -def get_global_news(curr_date, look_back_days: int = 7, limit: int = 50) -> dict[str, str] | str: - """Returns global market news & sentiment data without ticker-specific filtering. - - Covers broad market topics like financial markets, economy, and more. - - Args: - curr_date: Current date in yyyy-mm-dd format. - look_back_days: Number of days to look back (default 7). - limit: Maximum number of articles (default 50). - - Returns: - Dictionary containing global news sentiment data or JSON string. - """ - from datetime import datetime, timedelta - - # Calculate start date - curr_dt = datetime.strptime(curr_date, "%Y-%m-%d") - start_dt = curr_dt - timedelta(days=look_back_days) - start_date = start_dt.strftime("%Y-%m-%d") - - params = { - "topics": "financial_markets,economy_macro,economy_monetary", - "time_from": format_datetime_for_api(start_date), - "time_to": format_datetime_for_api(curr_date), - "limit": str(limit), - } - - return _make_api_request("NEWS_SENTIMENT", params) - - -def get_insider_transactions(symbol: str) -> dict[str, str] | str: - """Returns latest and historical insider transactions by key stakeholders. - - Covers transactions by founders, executives, board members, etc. - - Args: - symbol: Ticker symbol. Example: "IBM". - - Returns: - Dictionary containing insider transaction data or JSON string. - """ - - params = { - "symbol": symbol, - } - +from .alpha_vantage_common import _make_api_request, format_datetime_for_api + +def get_news(ticker, start_date, end_date) -> dict[str, str] | str: + """Returns live and historical market news & sentiment data from premier news outlets worldwide. + + Covers stocks, cryptocurrencies, forex, and topics like fiscal policy, mergers & acquisitions, IPOs. + + Args: + ticker: Stock symbol for news articles. + start_date: Start date for news search. + end_date: End date for news search. + + Returns: + Dictionary containing news sentiment data or JSON string. + """ + + params = { + "tickers": ticker, + "time_from": format_datetime_for_api(start_date), + "time_to": format_datetime_for_api(end_date), + } + + return _make_api_request("NEWS_SENTIMENT", params) + +def get_global_news(curr_date, look_back_days: int = 7, limit: int = 50) -> dict[str, str] | str: + """Returns global market news & sentiment data without ticker-specific filtering. + + Covers broad market topics like financial markets, economy, and more. + + Args: + curr_date: Current date in yyyy-mm-dd format. + look_back_days: Number of days to look back (default 7). + limit: Maximum number of articles (default 50). + + Returns: + Dictionary containing global news sentiment data or JSON string. + """ + from datetime import datetime, timedelta + + # Calculate start date + curr_dt = datetime.strptime(curr_date, "%Y-%m-%d") + start_dt = curr_dt - timedelta(days=look_back_days) + start_date = start_dt.strftime("%Y-%m-%d") + + params = { + "topics": "financial_markets,economy_macro,economy_monetary", + "time_from": format_datetime_for_api(start_date), + "time_to": format_datetime_for_api(curr_date), + "limit": str(limit), + } + + return _make_api_request("NEWS_SENTIMENT", params) + + +def get_insider_transactions(symbol: str) -> dict[str, str] | str: + """Returns latest and historical insider transactions by key stakeholders. + + Covers transactions by founders, executives, board members, etc. + + Args: + symbol: Ticker symbol. Example: "IBM". + + Returns: + Dictionary containing insider transaction data or JSON string. + """ + + params = { + "symbol": symbol, + } + return _make_api_request("INSIDER_TRANSACTIONS", params) \ No newline at end of file diff --git a/tradingagents/dataflows/alpha_vantage_stock.py b/tradingagents/dataflows/alpha_vantage_stock.py index ffd3570b..fc0f7fc4 100644 --- a/tradingagents/dataflows/alpha_vantage_stock.py +++ b/tradingagents/dataflows/alpha_vantage_stock.py @@ -1,38 +1,38 @@ -from datetime import datetime -from .alpha_vantage_common import _make_api_request, _filter_csv_by_date_range - -def get_stock( - symbol: str, - start_date: str, - end_date: str -) -> str: - """ - Returns raw daily OHLCV values, adjusted close values, and historical split/dividend events - filtered to the specified date range. - - Args: - symbol: The name of the equity. For example: symbol=IBM - start_date: Start date in yyyy-mm-dd format - end_date: End date in yyyy-mm-dd format - - Returns: - CSV string containing the daily adjusted time series data filtered to the date range. - """ - # Parse dates to determine the range - start_dt = datetime.strptime(start_date, "%Y-%m-%d") - today = datetime.now() - - # Choose outputsize based on whether the requested range is within the latest 100 days - # Compact returns latest 100 data points, so check if start_date is recent enough - days_from_today_to_start = (today - start_dt).days - outputsize = "compact" if days_from_today_to_start < 100 else "full" - - params = { - "symbol": symbol, - "outputsize": outputsize, - "datatype": "csv", - } - - response = _make_api_request("TIME_SERIES_DAILY_ADJUSTED", params) - +from datetime import datetime +from .alpha_vantage_common import _make_api_request, _filter_csv_by_date_range + +def get_stock( + symbol: str, + start_date: str, + end_date: str +) -> str: + """ + Returns raw daily OHLCV values, adjusted close values, and historical split/dividend events + filtered to the specified date range. + + Args: + symbol: The name of the equity. For example: symbol=IBM + start_date: Start date in yyyy-mm-dd format + end_date: End date in yyyy-mm-dd format + + Returns: + CSV string containing the daily adjusted time series data filtered to the date range. + """ + # Parse dates to determine the range + start_dt = datetime.strptime(start_date, "%Y-%m-%d") + today = datetime.now() + + # Choose outputsize based on whether the requested range is within the latest 100 days + # Compact returns latest 100 data points, so check if start_date is recent enough + days_from_today_to_start = (today - start_dt).days + outputsize = "compact" if days_from_today_to_start < 100 else "full" + + params = { + "symbol": symbol, + "outputsize": outputsize, + "datatype": "csv", + } + + response = _make_api_request("TIME_SERIES_DAILY_ADJUSTED", params) + return _filter_csv_by_date_range(response, start_date, end_date) \ No newline at end of file diff --git a/tradingagents/dataflows/config.py b/tradingagents/dataflows/config.py index 5819494a..8cca1449 100644 --- a/tradingagents/dataflows/config.py +++ b/tradingagents/dataflows/config.py @@ -1,31 +1,31 @@ -import tradingagents.default_config as default_config -from typing import Dict, Optional - -# Use default config but allow it to be overridden -_config: Optional[Dict] = None - - -def initialize_config(): - """Initialize the configuration with default values.""" - global _config - if _config is None: - _config = default_config.DEFAULT_CONFIG.copy() - - -def set_config(config: Dict): - """Update the configuration with custom values.""" - global _config - if _config is None: - _config = default_config.DEFAULT_CONFIG.copy() - _config.update(config) - - -def get_config() -> Dict: - """Get the current configuration.""" - if _config is None: - initialize_config() - return _config.copy() - - -# Initialize with default config -initialize_config() +import tradingagents.default_config as default_config +from typing import Dict, Optional + +# Use default config but allow it to be overridden +_config: Optional[Dict] = None + + +def initialize_config(): + """Initialize the configuration with default values.""" + global _config + if _config is None: + _config = default_config.DEFAULT_CONFIG.copy() + + +def set_config(config: Dict): + """Update the configuration with custom values.""" + global _config + if _config is None: + _config = default_config.DEFAULT_CONFIG.copy() + _config.update(config) + + +def get_config() -> Dict: + """Get the current configuration.""" + if _config is None: + initialize_config() + return _config.copy() + + +# Initialize with default config +initialize_config() diff --git a/tradingagents/dataflows/interface.py b/tradingagents/dataflows/interface.py index 0caf4b68..8c840974 100644 --- a/tradingagents/dataflows/interface.py +++ b/tradingagents/dataflows/interface.py @@ -1,162 +1,162 @@ -from typing import Annotated - -# Import from vendor-specific modules -from .y_finance import ( - get_YFin_data_online, - get_stock_stats_indicators_window, - get_fundamentals as get_yfinance_fundamentals, - get_balance_sheet as get_yfinance_balance_sheet, - get_cashflow as get_yfinance_cashflow, - get_income_statement as get_yfinance_income_statement, - get_insider_transactions as get_yfinance_insider_transactions, -) -from .yfinance_news import get_news_yfinance, get_global_news_yfinance -from .alpha_vantage import ( - get_stock as get_alpha_vantage_stock, - get_indicator as get_alpha_vantage_indicator, - get_fundamentals as get_alpha_vantage_fundamentals, - get_balance_sheet as get_alpha_vantage_balance_sheet, - get_cashflow as get_alpha_vantage_cashflow, - get_income_statement as get_alpha_vantage_income_statement, - get_insider_transactions as get_alpha_vantage_insider_transactions, - get_news as get_alpha_vantage_news, - get_global_news as get_alpha_vantage_global_news, -) -from .alpha_vantage_common import AlphaVantageRateLimitError - -# Configuration and routing logic -from .config import get_config - -# Tools organized by category -TOOLS_CATEGORIES = { - "core_stock_apis": { - "description": "OHLCV stock price data", - "tools": [ - "get_stock_data" - ] - }, - "technical_indicators": { - "description": "Technical analysis indicators", - "tools": [ - "get_indicators" - ] - }, - "fundamental_data": { - "description": "Company fundamentals", - "tools": [ - "get_fundamentals", - "get_balance_sheet", - "get_cashflow", - "get_income_statement" - ] - }, - "news_data": { - "description": "News and insider data", - "tools": [ - "get_news", - "get_global_news", - "get_insider_transactions", - ] - } -} - -VENDOR_LIST = [ - "yfinance", - "alpha_vantage", -] - -# Mapping of methods to their vendor-specific implementations -VENDOR_METHODS = { - # core_stock_apis - "get_stock_data": { - "alpha_vantage": get_alpha_vantage_stock, - "yfinance": get_YFin_data_online, - }, - # technical_indicators - "get_indicators": { - "alpha_vantage": get_alpha_vantage_indicator, - "yfinance": get_stock_stats_indicators_window, - }, - # fundamental_data - "get_fundamentals": { - "alpha_vantage": get_alpha_vantage_fundamentals, - "yfinance": get_yfinance_fundamentals, - }, - "get_balance_sheet": { - "alpha_vantage": get_alpha_vantage_balance_sheet, - "yfinance": get_yfinance_balance_sheet, - }, - "get_cashflow": { - "alpha_vantage": get_alpha_vantage_cashflow, - "yfinance": get_yfinance_cashflow, - }, - "get_income_statement": { - "alpha_vantage": get_alpha_vantage_income_statement, - "yfinance": get_yfinance_income_statement, - }, - # news_data - "get_news": { - "alpha_vantage": get_alpha_vantage_news, - "yfinance": get_news_yfinance, - }, - "get_global_news": { - "yfinance": get_global_news_yfinance, - "alpha_vantage": get_alpha_vantage_global_news, - }, - "get_insider_transactions": { - "alpha_vantage": get_alpha_vantage_insider_transactions, - "yfinance": get_yfinance_insider_transactions, - }, -} - -def get_category_for_method(method: str) -> str: - """Get the category that contains the specified method.""" - for category, info in TOOLS_CATEGORIES.items(): - if method in info["tools"]: - return category - raise ValueError(f"Method '{method}' not found in any category") - -def get_vendor(category: str, method: str = None) -> str: - """Get the configured vendor for a data category or specific tool method. - Tool-level configuration takes precedence over category-level. - """ - config = get_config() - - # Check tool-level configuration first (if method provided) - if method: - tool_vendors = config.get("tool_vendors", {}) - if method in tool_vendors: - return tool_vendors[method] - - # Fall back to category-level configuration - return config.get("data_vendors", {}).get(category, "default") - -def route_to_vendor(method: str, *args, **kwargs): - """Route method calls to appropriate vendor implementation with fallback support.""" - category = get_category_for_method(method) - vendor_config = get_vendor(category, method) - primary_vendors = [v.strip() for v in vendor_config.split(',')] - - if method not in VENDOR_METHODS: - raise ValueError(f"Method '{method}' not supported") - - # Build fallback chain: primary vendors first, then remaining available vendors - all_available_vendors = list(VENDOR_METHODS[method].keys()) - fallback_vendors = primary_vendors.copy() - for vendor in all_available_vendors: - if vendor not in fallback_vendors: - fallback_vendors.append(vendor) - - for vendor in fallback_vendors: - if vendor not in VENDOR_METHODS[method]: - continue - - vendor_impl = VENDOR_METHODS[method][vendor] - impl_func = vendor_impl[0] if isinstance(vendor_impl, list) else vendor_impl - - try: - return impl_func(*args, **kwargs) - except AlphaVantageRateLimitError: - continue # Only rate limits trigger fallback - +from typing import Annotated + +# Import from vendor-specific modules +from .y_finance import ( + get_YFin_data_online, + get_stock_stats_indicators_window, + get_fundamentals as get_yfinance_fundamentals, + get_balance_sheet as get_yfinance_balance_sheet, + get_cashflow as get_yfinance_cashflow, + get_income_statement as get_yfinance_income_statement, + get_insider_transactions as get_yfinance_insider_transactions, +) +from .yfinance_news import get_news_yfinance, get_global_news_yfinance +from .alpha_vantage import ( + get_stock as get_alpha_vantage_stock, + get_indicator as get_alpha_vantage_indicator, + get_fundamentals as get_alpha_vantage_fundamentals, + get_balance_sheet as get_alpha_vantage_balance_sheet, + get_cashflow as get_alpha_vantage_cashflow, + get_income_statement as get_alpha_vantage_income_statement, + get_insider_transactions as get_alpha_vantage_insider_transactions, + get_news as get_alpha_vantage_news, + get_global_news as get_alpha_vantage_global_news, +) +from .alpha_vantage_common import AlphaVantageRateLimitError + +# Configuration and routing logic +from .config import get_config + +# Tools organized by category +TOOLS_CATEGORIES = { + "core_stock_apis": { + "description": "OHLCV stock price data", + "tools": [ + "get_stock_data" + ] + }, + "technical_indicators": { + "description": "Technical analysis indicators", + "tools": [ + "get_indicators" + ] + }, + "fundamental_data": { + "description": "Company fundamentals", + "tools": [ + "get_fundamentals", + "get_balance_sheet", + "get_cashflow", + "get_income_statement" + ] + }, + "news_data": { + "description": "News and insider data", + "tools": [ + "get_news", + "get_global_news", + "get_insider_transactions", + ] + } +} + +VENDOR_LIST = [ + "yfinance", + "alpha_vantage", +] + +# Mapping of methods to their vendor-specific implementations +VENDOR_METHODS = { + # core_stock_apis + "get_stock_data": { + "alpha_vantage": get_alpha_vantage_stock, + "yfinance": get_YFin_data_online, + }, + # technical_indicators + "get_indicators": { + "alpha_vantage": get_alpha_vantage_indicator, + "yfinance": get_stock_stats_indicators_window, + }, + # fundamental_data + "get_fundamentals": { + "alpha_vantage": get_alpha_vantage_fundamentals, + "yfinance": get_yfinance_fundamentals, + }, + "get_balance_sheet": { + "alpha_vantage": get_alpha_vantage_balance_sheet, + "yfinance": get_yfinance_balance_sheet, + }, + "get_cashflow": { + "alpha_vantage": get_alpha_vantage_cashflow, + "yfinance": get_yfinance_cashflow, + }, + "get_income_statement": { + "alpha_vantage": get_alpha_vantage_income_statement, + "yfinance": get_yfinance_income_statement, + }, + # news_data + "get_news": { + "alpha_vantage": get_alpha_vantage_news, + "yfinance": get_news_yfinance, + }, + "get_global_news": { + "yfinance": get_global_news_yfinance, + "alpha_vantage": get_alpha_vantage_global_news, + }, + "get_insider_transactions": { + "alpha_vantage": get_alpha_vantage_insider_transactions, + "yfinance": get_yfinance_insider_transactions, + }, +} + +def get_category_for_method(method: str) -> str: + """Get the category that contains the specified method.""" + for category, info in TOOLS_CATEGORIES.items(): + if method in info["tools"]: + return category + raise ValueError(f"Method '{method}' not found in any category") + +def get_vendor(category: str, method: str = None) -> str: + """Get the configured vendor for a data category or specific tool method. + Tool-level configuration takes precedence over category-level. + """ + config = get_config() + + # Check tool-level configuration first (if method provided) + if method: + tool_vendors = config.get("tool_vendors", {}) + if method in tool_vendors: + return tool_vendors[method] + + # Fall back to category-level configuration + return config.get("data_vendors", {}).get(category, "default") + +def route_to_vendor(method: str, *args, **kwargs): + """Route method calls to appropriate vendor implementation with fallback support.""" + category = get_category_for_method(method) + vendor_config = get_vendor(category, method) + primary_vendors = [v.strip() for v in vendor_config.split(',')] + + if method not in VENDOR_METHODS: + raise ValueError(f"Method '{method}' not supported") + + # Build fallback chain: primary vendors first, then remaining available vendors + all_available_vendors = list(VENDOR_METHODS[method].keys()) + fallback_vendors = primary_vendors.copy() + for vendor in all_available_vendors: + if vendor not in fallback_vendors: + fallback_vendors.append(vendor) + + for vendor in fallback_vendors: + if vendor not in VENDOR_METHODS[method]: + continue + + vendor_impl = VENDOR_METHODS[method][vendor] + impl_func = vendor_impl[0] if isinstance(vendor_impl, list) else vendor_impl + + try: + return impl_func(*args, **kwargs) + except AlphaVantageRateLimitError: + continue # Only rate limits trigger fallback + raise RuntimeError(f"No available vendor for '{method}'") \ No newline at end of file diff --git a/tradingagents/dataflows/stockstats_utils.py b/tradingagents/dataflows/stockstats_utils.py index b31935b7..54f49140 100644 --- a/tradingagents/dataflows/stockstats_utils.py +++ b/tradingagents/dataflows/stockstats_utils.py @@ -1,64 +1,64 @@ -import pandas as pd -import yfinance as yf -from stockstats import wrap -from typing import Annotated -import os -from .config import get_config - - -class StockstatsUtils: - @staticmethod - def get_stock_stats( - symbol: Annotated[str, "ticker symbol for the company"], - indicator: Annotated[ - str, "quantitative indicators based off of the stock data for the company" - ], - curr_date: Annotated[ - str, "curr date for retrieving stock price data, YYYY-mm-dd" - ], - ): - config = get_config() - - today_date = pd.Timestamp.today() - curr_date_dt = pd.to_datetime(curr_date) - - end_date = today_date - start_date = today_date - pd.DateOffset(years=15) - start_date_str = start_date.strftime("%Y-%m-%d") - end_date_str = end_date.strftime("%Y-%m-%d") - - # Ensure cache directory exists - os.makedirs(config["data_cache_dir"], exist_ok=True) - - data_file = os.path.join( - config["data_cache_dir"], - f"{symbol}-YFin-data-{start_date_str}-{end_date_str}.csv", - ) - - if os.path.exists(data_file): - data = pd.read_csv(data_file) - data["Date"] = pd.to_datetime(data["Date"]) - else: - data = yf.download( - symbol, - start=start_date_str, - end=end_date_str, - multi_level_index=False, - progress=False, - auto_adjust=True, - ) - data = data.reset_index() - data.to_csv(data_file, index=False) - - df = wrap(data) - df["Date"] = df["Date"].dt.strftime("%Y-%m-%d") - curr_date_str = curr_date_dt.strftime("%Y-%m-%d") - - df[indicator] # trigger stockstats to calculate the indicator - matching_rows = df[df["Date"].str.startswith(curr_date_str)] - - if not matching_rows.empty: - indicator_value = matching_rows[indicator].values[0] - return indicator_value - else: - return "N/A: Not a trading day (weekend or holiday)" +import pandas as pd +import yfinance as yf +from stockstats import wrap +from typing import Annotated +import os +from .config import get_config + + +class StockstatsUtils: + @staticmethod + def get_stock_stats( + symbol: Annotated[str, "ticker symbol for the company"], + indicator: Annotated[ + str, "quantitative indicators based off of the stock data for the company" + ], + curr_date: Annotated[ + str, "curr date for retrieving stock price data, YYYY-mm-dd" + ], + ): + config = get_config() + + today_date = pd.Timestamp.today() + curr_date_dt = pd.to_datetime(curr_date) + + end_date = today_date + start_date = today_date - pd.DateOffset(years=15) + start_date_str = start_date.strftime("%Y-%m-%d") + end_date_str = end_date.strftime("%Y-%m-%d") + + # Ensure cache directory exists + os.makedirs(config["data_cache_dir"], exist_ok=True) + + data_file = os.path.join( + config["data_cache_dir"], + f"{symbol}-YFin-data-{start_date_str}-{end_date_str}.csv", + ) + + if os.path.exists(data_file): + data = pd.read_csv(data_file) + data["Date"] = pd.to_datetime(data["Date"]) + else: + data = yf.download( + symbol, + start=start_date_str, + end=end_date_str, + multi_level_index=False, + progress=False, + auto_adjust=True, + ) + data = data.reset_index() + data.to_csv(data_file, index=False) + + df = wrap(data) + df["Date"] = df["Date"].dt.strftime("%Y-%m-%d") + curr_date_str = curr_date_dt.strftime("%Y-%m-%d") + + df[indicator] # trigger stockstats to calculate the indicator + matching_rows = df[df["Date"].str.startswith(curr_date_str)] + + if not matching_rows.empty: + indicator_value = matching_rows[indicator].values[0] + return indicator_value + else: + return "N/A: Not a trading day (weekend or holiday)" diff --git a/tradingagents/dataflows/utils.py b/tradingagents/dataflows/utils.py index 4523de19..1d8c2c4f 100644 --- a/tradingagents/dataflows/utils.py +++ b/tradingagents/dataflows/utils.py @@ -1,39 +1,39 @@ -import os -import json -import pandas as pd -from datetime import date, timedelta, datetime -from typing import Annotated - -SavePathType = Annotated[str, "File path to save data. If None, data is not saved."] - -def save_output(data: pd.DataFrame, tag: str, save_path: SavePathType = None) -> None: - if save_path: - data.to_csv(save_path) - print(f"{tag} saved to {save_path}") - - -def get_current_date(): - return date.today().strftime("%Y-%m-%d") - - -def decorate_all_methods(decorator): - def class_decorator(cls): - for attr_name, attr_value in cls.__dict__.items(): - if callable(attr_value): - setattr(cls, attr_name, decorator(attr_value)) - return cls - - return class_decorator - - -def get_next_weekday(date): - - if not isinstance(date, datetime): - date = datetime.strptime(date, "%Y-%m-%d") - - if date.weekday() >= 5: - days_to_add = 7 - date.weekday() - next_weekday = date + timedelta(days=days_to_add) - return next_weekday - else: - return date +import os +import json +import pandas as pd +from datetime import date, timedelta, datetime +from typing import Annotated + +SavePathType = Annotated[str, "File path to save data. If None, data is not saved."] + +def save_output(data: pd.DataFrame, tag: str, save_path: SavePathType = None) -> None: + if save_path: + data.to_csv(save_path) + print(f"{tag} saved to {save_path}") + + +def get_current_date(): + return date.today().strftime("%Y-%m-%d") + + +def decorate_all_methods(decorator): + def class_decorator(cls): + for attr_name, attr_value in cls.__dict__.items(): + if callable(attr_value): + setattr(cls, attr_name, decorator(attr_value)) + return cls + + return class_decorator + + +def get_next_weekday(date): + + if not isinstance(date, datetime): + date = datetime.strptime(date, "%Y-%m-%d") + + if date.weekday() >= 5: + days_to_add = 7 - date.weekday() + next_weekday = date + timedelta(days=days_to_add) + return next_weekday + else: + return date diff --git a/tradingagents/dataflows/y_finance.py b/tradingagents/dataflows/y_finance.py index 0c066709..20afd076 100644 --- a/tradingagents/dataflows/y_finance.py +++ b/tradingagents/dataflows/y_finance.py @@ -1,812 +1,812 @@ -from typing import Annotated -from datetime import datetime -from dateutil.relativedelta import relativedelta -import logging -import yfinance as yf -import os -from .stockstats_utils import StockstatsUtils - -_logger = logging.getLogger(__name__) - -def get_YFin_data_online( - symbol: Annotated[str, "ticker symbol of the company"], - start_date: Annotated[str, "Start date in yyyy-mm-dd format"], - end_date: Annotated[str, "End date in yyyy-mm-dd format"], -): - datetime.strptime(start_date, "%Y-%m-%d") - datetime.strptime(end_date, "%Y-%m-%d") - - # Try Alpaca first (10k calls/min, 7yr history) - try: - from .alpaca_data import alpaca_available, get_bars_csv - if alpaca_available(): - result = get_bars_csv(symbol, start_date, end_date) - if not result.startswith("Error"): - return result - _logger.info("Alpaca bars failed, falling back to yfinance for %s", symbol) - except Exception as e: - _logger.debug("Alpaca unavailable: %s", e) - - # Fallback: yfinance - ticker = yf.Ticker(symbol.upper()) - data = ticker.history(start=start_date, end=end_date) - - if data.empty: - return f"No data found for symbol '{symbol}' between {start_date} and {end_date}" - - if data.index.tz is not None: - data.index = data.index.tz_localize(None) - - numeric_columns = ["Open", "High", "Low", "Close", "Adj Close"] - for col in numeric_columns: - if col in data.columns: - data[col] = data[col].round(2) - - csv_string = data.to_csv() - header = f"# Stock data for {symbol.upper()} from {start_date} to {end_date}\n" - header += f"# Total records: {len(data)}\n" - header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" - - return header + csv_string - -def get_stock_stats_indicators_window( - symbol: Annotated[str, "ticker symbol of the company"], - indicator: Annotated[str, "technical indicator to get the analysis and report of"], - curr_date: Annotated[ - str, "The current trading date you are trading on, YYYY-mm-dd" - ], - look_back_days: Annotated[int, "how many days to look back"], -) -> str: - - best_ind_params = { - # Moving Averages - "close_50_sma": ( - "50 SMA: A medium-term trend indicator. " - "Usage: Identify trend direction and serve as dynamic support/resistance. " - "Tips: It lags price; combine with faster indicators for timely signals." - ), - "close_200_sma": ( - "200 SMA: A long-term trend benchmark. " - "Usage: Confirm overall market trend and identify golden/death cross setups. " - "Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries." - ), - "close_10_ema": ( - "10 EMA: A responsive short-term average. " - "Usage: Capture quick shifts in momentum and potential entry points. " - "Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals." - ), - # MACD Related - "macd": ( - "MACD: Computes momentum via differences of EMAs. " - "Usage: Look for crossovers and divergence as signals of trend changes. " - "Tips: Confirm with other indicators in low-volatility or sideways markets." - ), - "macds": ( - "MACD Signal: An EMA smoothing of the MACD line. " - "Usage: Use crossovers with the MACD line to trigger trades. " - "Tips: Should be part of a broader strategy to avoid false positives." - ), - "macdh": ( - "MACD Histogram: Shows the gap between the MACD line and its signal. " - "Usage: Visualize momentum strength and spot divergence early. " - "Tips: Can be volatile; complement with additional filters in fast-moving markets." - ), - # Momentum Indicators - "rsi": ( - "RSI: Measures momentum to flag overbought/oversold conditions. " - "Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. " - "Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis." - ), - # Volatility Indicators - "boll": ( - "Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. " - "Usage: Acts as a dynamic benchmark for price movement. " - "Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals." - ), - "boll_ub": ( - "Bollinger Upper Band: Typically 2 standard deviations above the middle line. " - "Usage: Signals potential overbought conditions and breakout zones. " - "Tips: Confirm signals with other tools; prices may ride the band in strong trends." - ), - "boll_lb": ( - "Bollinger Lower Band: Typically 2 standard deviations below the middle line. " - "Usage: Indicates potential oversold conditions. " - "Tips: Use additional analysis to avoid false reversal signals." - ), - "atr": ( - "ATR: Averages true range to measure volatility. " - "Usage: Set stop-loss levels and adjust position sizes based on current market volatility. " - "Tips: It's a reactive measure, so use it as part of a broader risk management strategy." - ), - # Volume-Based Indicators - "vwma": ( - "VWMA: A moving average weighted by volume. " - "Usage: Confirm trends by integrating price action with volume data. " - "Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses." - ), - "mfi": ( - "MFI: The Money Flow Index is a momentum indicator that uses both price and volume to measure buying and selling pressure. " - "Usage: Identify overbought (>80) or oversold (<20) conditions and confirm the strength of trends or reversals. " - "Tips: Use alongside RSI or MACD to confirm signals; divergence between price and MFI can indicate potential reversals." - ), - } - - if indicator not in best_ind_params: - raise ValueError( - f"Indicator {indicator} is not supported. Please choose from: {list(best_ind_params.keys())}" - ) - - end_date = curr_date - curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d") - before = curr_date_dt - relativedelta(days=look_back_days) - - # Optimized: Get stock data once and calculate indicators for all dates - try: - indicator_data = _get_stock_stats_bulk(symbol, indicator, curr_date) - - # Generate the date range we need - current_dt = curr_date_dt - date_values = [] - - while current_dt >= before: - date_str = current_dt.strftime('%Y-%m-%d') - - # Look up the indicator value for this date - if date_str in indicator_data: - indicator_value = indicator_data[date_str] - else: - indicator_value = "N/A: Not a trading day (weekend or holiday)" - - date_values.append((date_str, indicator_value)) - current_dt = current_dt - relativedelta(days=1) - - # Build the result string - ind_string = "" - for date_str, value in date_values: - ind_string += f"{date_str}: {value}\n" - - except Exception as e: - print(f"Error getting bulk stockstats data: {e}") - # Fallback to original implementation if bulk method fails - ind_string = "" - curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d") - while curr_date_dt >= before: - indicator_value = get_stockstats_indicator( - symbol, indicator, curr_date_dt.strftime("%Y-%m-%d") - ) - ind_string += f"{curr_date_dt.strftime('%Y-%m-%d')}: {indicator_value}\n" - curr_date_dt = curr_date_dt - relativedelta(days=1) - - result_str = ( - f"## {indicator} values from {before.strftime('%Y-%m-%d')} to {end_date}:\n\n" - + ind_string - + "\n\n" - + best_ind_params.get(indicator, "No description available.") - ) - - return result_str - - -def _get_stock_stats_bulk( - symbol: Annotated[str, "ticker symbol of the company"], - indicator: Annotated[str, "technical indicator to calculate"], - curr_date: Annotated[str, "current date for reference"] -) -> dict: - """ - Optimized bulk calculation of stock stats indicators. - Fetches data once and calculates indicator for all available dates. - Returns dict mapping date strings to indicator values. - """ - from .config import get_config - import pandas as pd - from stockstats import wrap - import os - - config = get_config() - online = config["data_vendors"]["technical_indicators"] != "local" - - if not online: - # Local data path - try: - data = pd.read_csv( - os.path.join( - config.get("data_cache_dir", "data"), - f"{symbol}-YFin-data-2015-01-01-2025-03-25.csv", - ) - ) - df = wrap(data) - except FileNotFoundError: - raise Exception("Stockstats fail: Yahoo Finance data not fetched yet!") - else: - # Online data fetching with caching - today_date = pd.Timestamp.today() - curr_date_dt = pd.to_datetime(curr_date) - - end_date = today_date - start_date = today_date - pd.DateOffset(years=15) - start_date_str = start_date.strftime("%Y-%m-%d") - end_date_str = end_date.strftime("%Y-%m-%d") - - os.makedirs(config["data_cache_dir"], exist_ok=True) - - data_file = os.path.join( - config["data_cache_dir"], - f"{symbol}-YFin-data-{start_date_str}-{end_date_str}.csv", - ) - - if os.path.exists(data_file): - data = pd.read_csv(data_file) - data["Date"] = pd.to_datetime(data["Date"]) - else: - data = yf.download( - symbol, - start=start_date_str, - end=end_date_str, - multi_level_index=False, - progress=False, - auto_adjust=True, - ) - data = data.reset_index() - data.to_csv(data_file, index=False) - - df = wrap(data) - df["Date"] = df["Date"].dt.strftime("%Y-%m-%d") - - # Calculate the indicator for all rows at once - df[indicator] # This triggers stockstats to calculate the indicator - - # Create a dictionary mapping date strings to indicator values - result_dict = {} - for _, row in df.iterrows(): - date_str = row["Date"] - indicator_value = row[indicator] - - # Handle NaN/None values - if pd.isna(indicator_value): - result_dict[date_str] = "N/A" - else: - result_dict[date_str] = str(indicator_value) - - return result_dict - - -def get_stockstats_indicator( - symbol: Annotated[str, "ticker symbol of the company"], - indicator: Annotated[str, "technical indicator to get the analysis and report of"], - curr_date: Annotated[ - str, "The current trading date you are trading on, YYYY-mm-dd" - ], -) -> str: - - curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d") - curr_date = curr_date_dt.strftime("%Y-%m-%d") - - try: - indicator_value = StockstatsUtils.get_stock_stats( - symbol, - indicator, - curr_date, - ) - except Exception as e: - print( - f"Error getting stockstats indicator data for indicator {indicator} on {curr_date}: {e}" - ) - return "" - - return str(indicator_value) - - -def get_fundamentals( - ticker: Annotated[str, "ticker symbol of the company"], - curr_date: Annotated[str, "current date (not used for yfinance)"] = None -): - """Get company fundamentals overview from yfinance.""" - try: - ticker_obj = yf.Ticker(ticker.upper()) - info = ticker_obj.info - - if not info: - return f"No fundamentals data found for symbol '{ticker}'" - - fields = [ - ("Name", info.get("longName")), - ("Sector", info.get("sector")), - ("Industry", info.get("industry")), - ("Market Cap", info.get("marketCap")), - ("PE Ratio (TTM)", info.get("trailingPE")), - ("Forward PE", info.get("forwardPE")), - ("PEG Ratio", info.get("pegRatio")), - ("Price to Book", info.get("priceToBook")), - ("EPS (TTM)", info.get("trailingEps")), - ("Forward EPS", info.get("forwardEps")), - ("Dividend Yield", info.get("dividendYield")), - ("Beta", info.get("beta")), - ("52 Week High", info.get("fiftyTwoWeekHigh")), - ("52 Week Low", info.get("fiftyTwoWeekLow")), - ("50 Day Average", info.get("fiftyDayAverage")), - ("200 Day Average", info.get("twoHundredDayAverage")), - ("Revenue (TTM)", info.get("totalRevenue")), - ("Gross Profit", info.get("grossProfits")), - ("EBITDA", info.get("ebitda")), - ("Net Income", info.get("netIncomeToCommon")), - ("Profit Margin", info.get("profitMargins")), - ("Operating Margin", info.get("operatingMargins")), - ("Return on Equity", info.get("returnOnEquity")), - ("Return on Assets", info.get("returnOnAssets")), - ("Debt to Equity", info.get("debtToEquity")), - ("Current Ratio", info.get("currentRatio")), - ("Book Value", info.get("bookValue")), - ("Free Cash Flow", info.get("freeCashflow")), - ] - - lines = [] - for label, value in fields: - if value is not None: - lines.append(f"{label}: {value}") - - header = f"# Company Fundamentals for {ticker.upper()}\n" - header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" - - return header + "\n".join(lines) - - except Exception as e: - return f"Error retrieving fundamentals for {ticker}: {str(e)}" - - -def get_balance_sheet( - ticker: Annotated[str, "ticker symbol of the company"], - freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly", - curr_date: Annotated[str, "current date (not used for yfinance)"] = None -): - """Get balance sheet data from yfinance.""" - try: - ticker_obj = yf.Ticker(ticker.upper()) - - if freq.lower() == "quarterly": - data = ticker_obj.quarterly_balance_sheet - else: - data = ticker_obj.balance_sheet - - if data.empty: - return f"No balance sheet data found for symbol '{ticker}'" - - # Convert to CSV string for consistency with other functions - csv_string = data.to_csv() - - # Add header information - header = f"# Balance Sheet data for {ticker.upper()} ({freq})\n" - header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" - - return header + csv_string - - except Exception as e: - return f"Error retrieving balance sheet for {ticker}: {str(e)}" - - -def get_cashflow( - ticker: Annotated[str, "ticker symbol of the company"], - freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly", - curr_date: Annotated[str, "current date (not used for yfinance)"] = None -): - """Get cash flow data from yfinance.""" - try: - ticker_obj = yf.Ticker(ticker.upper()) - - if freq.lower() == "quarterly": - data = ticker_obj.quarterly_cashflow - else: - data = ticker_obj.cashflow - - if data.empty: - return f"No cash flow data found for symbol '{ticker}'" - - # Convert to CSV string for consistency with other functions - csv_string = data.to_csv() - - # Add header information - header = f"# Cash Flow data for {ticker.upper()} ({freq})\n" - header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" - - return header + csv_string - - except Exception as e: - return f"Error retrieving cash flow for {ticker}: {str(e)}" - - -def get_income_statement( - ticker: Annotated[str, "ticker symbol of the company"], - freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly", - curr_date: Annotated[str, "current date (not used for yfinance)"] = None -): - """Get income statement data from yfinance.""" - try: - ticker_obj = yf.Ticker(ticker.upper()) - - if freq.lower() == "quarterly": - data = ticker_obj.quarterly_income_stmt - else: - data = ticker_obj.income_stmt - - if data.empty: - return f"No income statement data found for symbol '{ticker}'" - - # Convert to CSV string for consistency with other functions - csv_string = data.to_csv() - - # Add header information - header = f"# Income Statement data for {ticker.upper()} ({freq})\n" - header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" - - return header + csv_string - - except Exception as e: - return f"Error retrieving income statement for {ticker}: {str(e)}" - - -def get_insider_transactions( - ticker: Annotated[str, "ticker symbol of the company"] -): - """Get insider transactions data from yfinance.""" - try: - ticker_obj = yf.Ticker(ticker.upper()) - data = ticker_obj.insider_transactions - - if data is None or data.empty: - return f"No insider transactions data found for symbol '{ticker}'" - - # Convert to CSV string for consistency with other functions - csv_string = data.to_csv() - - # Add header information - header = f"# Insider Transactions data for {ticker.upper()}\n" - header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" - - return header + csv_string - - except Exception as e: - return f"Error retrieving insider transactions for {ticker}: {str(e)}" - - -# --- Macro data functions (used by interface.py routing) --- -# These are thin wrappers that delegate to yfinance directly. -# The actual @tool versions live in agents/utils/macro_data_tools.py. - -import json as _json - - -def _safe_get_yf(info, key, default=None): - val = info.get(key) - return default if val is None else val - - -def _fmt_num(val): - if val is None: - return None - if abs(val) >= 1e12: - return f"${val/1e12:.2f}T" - if abs(val) >= 1e9: - return f"${val/1e9:.2f}B" - if abs(val) >= 1e6: - return f"${val/1e6:.2f}M" - return f"${val:,.0f}" - - -def _period_return(ticker_obj, months): - import pandas as pd - try: - end_dt = pd.Timestamp.today() - start_dt = end_dt - pd.DateOffset(months=months) - data = ticker_obj.history(start=start_dt.strftime("%Y-%m-%d"), end=end_dt.strftime("%Y-%m-%d")) - if data.empty or len(data) < 2: - return None - return ((data["Close"].iloc[-1] / data["Close"].iloc[0]) - 1) * 100 - except Exception: - return None - - -def get_company_profile(ticker, curr_date=None): - """Get company profile via yfinance (plain function for interface routing).""" - try: - t = yf.Ticker(ticker.upper()) - info = t.info - if not info or not info.get("longName"): - return _json.dumps({"error": f"No data for {ticker}", "ticker": ticker}) - mc = _safe_get_yf(info, "marketCap") - cat = "large_cap" if mc and mc >= 10e9 else "mid_cap" if mc and mc >= 2e9 else "small_cap" if mc and mc >= 300e6 else "micro_cap" if mc else "unknown" - profile = { - "company_name": _safe_get_yf(info, "longName", "Unknown"), - "ticker": ticker.upper(), - "sector": _safe_get_yf(info, "sector", "Unknown"), - "industry": _safe_get_yf(info, "industry", "Unknown"), - "description": _safe_get_yf(info, "longBusinessSummary", ""), - "market_cap": mc, - "market_cap_formatted": _fmt_num(mc), - "market_cap_category": cat, - "current_price": _safe_get_yf(info, "currentPrice") or _safe_get_yf(info, "regularMarketPrice"), - } - return _json.dumps(profile, default=str) - except Exception as e: - return _json.dumps({"error": str(e), "ticker": ticker}) - - -_SECTOR_ETF_MAP = { - "Technology": "XLK", - "Financial Services": "XLF", - "Financials": "XLF", - "Energy": "XLE", - "Healthcare": "XLV", - "Health Care": "XLV", - "Industrials": "XLI", - "Consumer Cyclical": "XLY", - "Consumer Discretionary": "XLY", - "Consumer Defensive": "XLP", - "Consumer Staples": "XLP", - "Utilities": "XLU", - "Real Estate": "XLRE", - "Basic Materials": "XLB", - "Materials": "XLB", - "Communication Services": "XLC", -} - -_SECTOR_ETFS = { - "SPY": "S&P 500", - "XLK": "Technology", "XLF": "Financials", "XLE": "Energy", - "XLV": "Health Care", "XLI": "Industrials", "XLY": "Consumer Discretionary", - "XLP": "Consumer Staples", "XLU": "Utilities", "XLRE": "Real Estate", - "XLB": "Materials", "XLC": "Communication Services", -} - - -def get_macro_indicators(curr_date=None): - """Get macro indicators. VIX/TNX from yfinance (indices), sector ETFs from Alpaca.""" - results = {} - - # VIX and TNX are indices — yfinance only (Alpaca doesn't serve index tickers) - try: - vix = yf.Ticker("^VIX") - vd = vix.history(period="5d") - if not vd.empty: - results["vix_level"] = round(vd["Close"].iloc[-1], 2) - except Exception: - pass - try: - tnx = yf.Ticker("^TNX") - td = tnx.history(period="5d") - if not td.empty: - results["ten_year_yield"] = round(td["Close"].iloc[-1], 3) - except Exception: - pass - - # Sector ETF performance — Alpaca first (10k calls/min), yfinance fallback - try: - from .alpaca_data import alpaca_available, get_sector_etf_performance - if alpaca_available(): - perf = get_sector_etf_performance(list(_SECTOR_ETFS.keys())) - if perf: - sector_performance = {} - for sym, data in perf.items(): - sector_performance[sym] = { - "name": _SECTOR_ETFS.get(sym, sym), - "return_1m": data.get("return_1m"), - "return_3m": data.get("return_3m"), - "price": data.get("price"), - } - results["sector_performance"] = sector_performance - except Exception as e: - _logger.debug("Alpaca sector ETFs failed: %s", e) - - # Fallback: yfinance for sector ETFs - if "sector_performance" not in results: - sector_performance = {} - for sym, name in _SECTOR_ETFS.items(): - try: - t = yf.Ticker(sym) - hist = t.history(period="3mo") - if hist.empty or len(hist) < 5: - continue - close = hist["Close"] - current = float(close.iloc[-1]) - ret_1m = round((current - float(close.iloc[-22])) / float(close.iloc[-22]) * 100, 2) if len(close) >= 22 else None - ret_3m = round((current - float(close.iloc[-63])) / float(close.iloc[-63]) * 100, 2) if len(close) >= 63 else None - sector_performance[sym] = {"name": name, "return_1m": ret_1m, "return_3m": ret_3m, "price": current} - except Exception: - pass - if sector_performance: - results["sector_performance"] = sector_performance - - return _json.dumps(results, default=str) - - -def get_sector_rotation(ticker, curr_date=None): - """Get sector rotation data with relative performance vs SPY.""" - try: - t = yf.Ticker(ticker.upper()) - info = t.info or {} - sector = _safe_get_yf(info, "sector", "Unknown") - sector_etf = _SECTOR_ETF_MAP.get(sector) - - result = {"ticker": ticker.upper(), "sector": sector, "sector_etf": sector_etf} - - if not sector_etf: - return _json.dumps(result, default=str) - - # Get sector ETF + SPY performance for relative strength - etfs_to_fetch = [sector_etf, "SPY"] - perf = {} - - try: - from .alpaca_data import alpaca_available, get_sector_etf_performance - if alpaca_available(): - perf = get_sector_etf_performance(etfs_to_fetch) - except Exception: - pass - - # Fallback: yfinance - if not perf: - for sym in etfs_to_fetch: - try: - hist = yf.Ticker(sym).history(period="3mo") - if hist.empty or len(hist) < 5: - continue - close = hist["Close"] - current = float(close.iloc[-1]) - ret_1m = round((current - float(close.iloc[-22])) / float(close.iloc[-22]) * 100, 2) if len(close) >= 22 else None - ret_3m = round((current - float(close.iloc[-63])) / float(close.iloc[-63]) * 100, 2) if len(close) >= 63 else None - perf[sym] = {"return_1m": ret_1m, "return_3m": ret_3m, "price": current} - except Exception: - pass - - # Compute relative strength vs SPY - spy_data = perf.get("SPY", {}) - etf_data = perf.get(sector_etf, {}) - spy_1m = spy_data.get("return_1m") - spy_3m = spy_data.get("return_3m") - etf_1m = etf_data.get("return_1m") - etf_3m = etf_data.get("return_3m") - - if etf_1m is not None and spy_1m is not None: - result["stock_sector_vs_spy_1m"] = round(etf_1m - spy_1m, 2) - if etf_3m is not None and spy_3m is not None: - result["stock_sector_vs_spy_3m"] = round(etf_3m - spy_3m, 2) - - # Rank sector among all sector ETFs (from macro_indicators cache or fresh) - try: - macro_raw = get_macro_indicators() - macro = _json.loads(macro_raw) if isinstance(macro_raw, str) else macro_raw - sector_perf = macro.get("sector_performance", {}) - # Rank by 1M return (exclude SPY from ranking) - ranked = sorted( - [(s, d.get("return_1m", -999)) for s, d in sector_perf.items() if s != "SPY"], - key=lambda x: x[1], reverse=True, - ) - for i, (sym, _) in enumerate(ranked, 1): - if sym == sector_etf: - result["stock_sector_rank"] = i - result["total_sectors"] = len(ranked) - break - except Exception: - pass - - return _json.dumps(result, default=str) - except Exception as e: - return _json.dumps({"error": str(e)}) - - -def get_institutional_flow(ticker): - """Get institutional flow data via yfinance including 13F holders and insider transactions.""" - try: - t = yf.Ticker(ticker.upper()) - info = t.info or {} - - # Base metrics - result = { - "ticker": ticker.upper(), - "average_volume": _safe_get_yf(info, "averageVolume"), - "average_volume_10d": _safe_get_yf(info, "averageVolume10days"), - "float_shares": _safe_get_yf(info, "floatShares"), - "shares_short": _safe_get_yf(info, "sharesShort"), - "shares_short_prior": _safe_get_yf(info, "sharesShortPriorMonth"), - "short_ratio": _safe_get_yf(info, "shortRatio"), - "held_percent_institutions": _safe_get_yf(info, "heldPercentInstitutions"), - "held_percent_insiders": _safe_get_yf(info, "heldPercentInsiders"), - } - - # Volume ratio (10d vs avg) - vol_10d = _safe_get_yf(info, "averageVolume10days") - vol_avg = _safe_get_yf(info, "averageVolume") - if vol_10d and vol_avg and vol_avg > 0: - result["volume_ratio"] = round(vol_10d / vol_avg, 2) - - # Short % of float - float_shares = _safe_get_yf(info, "floatShares") - shares_short = _safe_get_yf(info, "sharesShort") - if float_shares and shares_short and float_shares > 0: - result["short_pct_of_float"] = round(shares_short / float_shares * 100, 2) - - # Short interest trend (current vs prior month) - prior = _safe_get_yf(info, "sharesShortPriorMonth") - if shares_short is not None and prior is not None and prior > 0: - pct_change = (shares_short - prior) / prior * 100 - result["short_interest_change_pct"] = round(pct_change, 1) - if pct_change > 5: - result["short_interest_trend"] = "rising" - elif pct_change < -5: - result["short_interest_trend"] = "falling" - else: - result["short_interest_trend"] = "stable" - - # Float turnover (5d volume / float) - if vol_10d and float_shares and float_shares > 0: - result["float_turnover_5d_pct"] = round(vol_10d * 5 / float_shares * 100, 2) - - # Top institutional holders (13F data) - try: - holders = t.institutional_holders - if holders is not None and not holders.empty: - top = holders.head(10).to_dict("records") - result["top_institutional_holders"] = [ - { - "holder": str(r.get("Holder", "")), - "shares": int(r["Shares"]) if r.get("Shares") else None, - "pct_out": round(float(r["% Out"]) * 100, 2) if r.get("% Out") else None, - "value": float(r["Value"]) if r.get("Value") else None, - } - for r in top - ] - result["top_holders_count"] = len(top) - except Exception: - pass - - # Insider transactions - try: - insiders = t.insider_transactions - if insiders is not None and not insiders.empty: - recent = insiders.head(10).to_dict("records") - buys = sum(1 for r in recent if "Purchase" in str(r.get("Text", ""))) - sells = sum(1 for r in recent if "Sale" in str(r.get("Text", ""))) - result["insider_buys_recent"] = buys - result["insider_sells_recent"] = sells - if buys > sells: - result["insider_transaction_signal"] = "buying" - elif sells > buys: - result["insider_transaction_signal"] = "selling" - else: - result["insider_transaction_signal"] = "none" - except Exception: - pass - - return _json.dumps(result, default=str) - except Exception as e: - return _json.dumps({"error": str(e)}) - - -def get_earnings_estimates(ticker): - """Get earnings estimates via yfinance (plain function for interface routing).""" - try: - t = yf.Ticker(ticker.upper()) - info = t.info - return _json.dumps({ - "ticker": ticker.upper(), - "trailing_eps": _safe_get_yf(info, "trailingEps"), - "forward_eps": _safe_get_yf(info, "forwardEps"), - "current_price": _safe_get_yf(info, "currentPrice") or _safe_get_yf(info, "regularMarketPrice"), - }, default=str) - except Exception as e: - return _json.dumps({"error": str(e)}) - - -def get_valuation_peers(ticker): - """Get valuation peer data via yfinance (plain function for interface routing).""" - try: - t = yf.Ticker(ticker.upper()) - info = t.info - return _json.dumps({ - "ticker": ticker.upper(), - "trailing_pe": _safe_get_yf(info, "trailingPE"), - "forward_pe": _safe_get_yf(info, "forwardPE"), - "peg_ratio": _safe_get_yf(info, "pegRatio"), - "price_to_book": _safe_get_yf(info, "priceToBook"), - "ev_to_ebitda": _safe_get_yf(info, "enterpriseToEbitda"), - }, default=str) - except Exception as e: +from typing import Annotated +from datetime import datetime +from dateutil.relativedelta import relativedelta +import logging +import yfinance as yf +import os +from .stockstats_utils import StockstatsUtils + +_logger = logging.getLogger(__name__) + +def get_YFin_data_online( + symbol: Annotated[str, "ticker symbol of the company"], + start_date: Annotated[str, "Start date in yyyy-mm-dd format"], + end_date: Annotated[str, "End date in yyyy-mm-dd format"], +): + datetime.strptime(start_date, "%Y-%m-%d") + datetime.strptime(end_date, "%Y-%m-%d") + + # Try Alpaca first (10k calls/min, 7yr history) + try: + from .alpaca_data import alpaca_available, get_bars_csv + if alpaca_available(): + result = get_bars_csv(symbol, start_date, end_date) + if not result.startswith("Error"): + return result + _logger.info("Alpaca bars failed, falling back to yfinance for %s", symbol) + except Exception as e: + _logger.debug("Alpaca unavailable: %s", e) + + # Fallback: yfinance + ticker = yf.Ticker(symbol.upper()) + data = ticker.history(start=start_date, end=end_date) + + if data.empty: + return f"No data found for symbol '{symbol}' between {start_date} and {end_date}" + + if data.index.tz is not None: + data.index = data.index.tz_localize(None) + + numeric_columns = ["Open", "High", "Low", "Close", "Adj Close"] + for col in numeric_columns: + if col in data.columns: + data[col] = data[col].round(2) + + csv_string = data.to_csv() + header = f"# Stock data for {symbol.upper()} from {start_date} to {end_date}\n" + header += f"# Total records: {len(data)}\n" + header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" + + return header + csv_string + +def get_stock_stats_indicators_window( + symbol: Annotated[str, "ticker symbol of the company"], + indicator: Annotated[str, "technical indicator to get the analysis and report of"], + curr_date: Annotated[ + str, "The current trading date you are trading on, YYYY-mm-dd" + ], + look_back_days: Annotated[int, "how many days to look back"], +) -> str: + + best_ind_params = { + # Moving Averages + "close_50_sma": ( + "50 SMA: A medium-term trend indicator. " + "Usage: Identify trend direction and serve as dynamic support/resistance. " + "Tips: It lags price; combine with faster indicators for timely signals." + ), + "close_200_sma": ( + "200 SMA: A long-term trend benchmark. " + "Usage: Confirm overall market trend and identify golden/death cross setups. " + "Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries." + ), + "close_10_ema": ( + "10 EMA: A responsive short-term average. " + "Usage: Capture quick shifts in momentum and potential entry points. " + "Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals." + ), + # MACD Related + "macd": ( + "MACD: Computes momentum via differences of EMAs. " + "Usage: Look for crossovers and divergence as signals of trend changes. " + "Tips: Confirm with other indicators in low-volatility or sideways markets." + ), + "macds": ( + "MACD Signal: An EMA smoothing of the MACD line. " + "Usage: Use crossovers with the MACD line to trigger trades. " + "Tips: Should be part of a broader strategy to avoid false positives." + ), + "macdh": ( + "MACD Histogram: Shows the gap between the MACD line and its signal. " + "Usage: Visualize momentum strength and spot divergence early. " + "Tips: Can be volatile; complement with additional filters in fast-moving markets." + ), + # Momentum Indicators + "rsi": ( + "RSI: Measures momentum to flag overbought/oversold conditions. " + "Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. " + "Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis." + ), + # Volatility Indicators + "boll": ( + "Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. " + "Usage: Acts as a dynamic benchmark for price movement. " + "Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals." + ), + "boll_ub": ( + "Bollinger Upper Band: Typically 2 standard deviations above the middle line. " + "Usage: Signals potential overbought conditions and breakout zones. " + "Tips: Confirm signals with other tools; prices may ride the band in strong trends." + ), + "boll_lb": ( + "Bollinger Lower Band: Typically 2 standard deviations below the middle line. " + "Usage: Indicates potential oversold conditions. " + "Tips: Use additional analysis to avoid false reversal signals." + ), + "atr": ( + "ATR: Averages true range to measure volatility. " + "Usage: Set stop-loss levels and adjust position sizes based on current market volatility. " + "Tips: It's a reactive measure, so use it as part of a broader risk management strategy." + ), + # Volume-Based Indicators + "vwma": ( + "VWMA: A moving average weighted by volume. " + "Usage: Confirm trends by integrating price action with volume data. " + "Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses." + ), + "mfi": ( + "MFI: The Money Flow Index is a momentum indicator that uses both price and volume to measure buying and selling pressure. " + "Usage: Identify overbought (>80) or oversold (<20) conditions and confirm the strength of trends or reversals. " + "Tips: Use alongside RSI or MACD to confirm signals; divergence between price and MFI can indicate potential reversals." + ), + } + + if indicator not in best_ind_params: + raise ValueError( + f"Indicator {indicator} is not supported. Please choose from: {list(best_ind_params.keys())}" + ) + + end_date = curr_date + curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d") + before = curr_date_dt - relativedelta(days=look_back_days) + + # Optimized: Get stock data once and calculate indicators for all dates + try: + indicator_data = _get_stock_stats_bulk(symbol, indicator, curr_date) + + # Generate the date range we need + current_dt = curr_date_dt + date_values = [] + + while current_dt >= before: + date_str = current_dt.strftime('%Y-%m-%d') + + # Look up the indicator value for this date + if date_str in indicator_data: + indicator_value = indicator_data[date_str] + else: + indicator_value = "N/A: Not a trading day (weekend or holiday)" + + date_values.append((date_str, indicator_value)) + current_dt = current_dt - relativedelta(days=1) + + # Build the result string + ind_string = "" + for date_str, value in date_values: + ind_string += f"{date_str}: {value}\n" + + except Exception as e: + print(f"Error getting bulk stockstats data: {e}") + # Fallback to original implementation if bulk method fails + ind_string = "" + curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d") + while curr_date_dt >= before: + indicator_value = get_stockstats_indicator( + symbol, indicator, curr_date_dt.strftime("%Y-%m-%d") + ) + ind_string += f"{curr_date_dt.strftime('%Y-%m-%d')}: {indicator_value}\n" + curr_date_dt = curr_date_dt - relativedelta(days=1) + + result_str = ( + f"## {indicator} values from {before.strftime('%Y-%m-%d')} to {end_date}:\n\n" + + ind_string + + "\n\n" + + best_ind_params.get(indicator, "No description available.") + ) + + return result_str + + +def _get_stock_stats_bulk( + symbol: Annotated[str, "ticker symbol of the company"], + indicator: Annotated[str, "technical indicator to calculate"], + curr_date: Annotated[str, "current date for reference"] +) -> dict: + """ + Optimized bulk calculation of stock stats indicators. + Fetches data once and calculates indicator for all available dates. + Returns dict mapping date strings to indicator values. + """ + from .config import get_config + import pandas as pd + from stockstats import wrap + import os + + config = get_config() + online = config["data_vendors"]["technical_indicators"] != "local" + + if not online: + # Local data path + try: + data = pd.read_csv( + os.path.join( + config.get("data_cache_dir", "data"), + f"{symbol}-YFin-data-2015-01-01-2025-03-25.csv", + ) + ) + df = wrap(data) + except FileNotFoundError: + raise Exception("Stockstats fail: Yahoo Finance data not fetched yet!") + else: + # Online data fetching with caching + today_date = pd.Timestamp.today() + curr_date_dt = pd.to_datetime(curr_date) + + end_date = today_date + start_date = today_date - pd.DateOffset(years=15) + start_date_str = start_date.strftime("%Y-%m-%d") + end_date_str = end_date.strftime("%Y-%m-%d") + + os.makedirs(config["data_cache_dir"], exist_ok=True) + + data_file = os.path.join( + config["data_cache_dir"], + f"{symbol}-YFin-data-{start_date_str}-{end_date_str}.csv", + ) + + if os.path.exists(data_file): + data = pd.read_csv(data_file) + data["Date"] = pd.to_datetime(data["Date"]) + else: + data = yf.download( + symbol, + start=start_date_str, + end=end_date_str, + multi_level_index=False, + progress=False, + auto_adjust=True, + ) + data = data.reset_index() + data.to_csv(data_file, index=False) + + df = wrap(data) + df["Date"] = df["Date"].dt.strftime("%Y-%m-%d") + + # Calculate the indicator for all rows at once + df[indicator] # This triggers stockstats to calculate the indicator + + # Create a dictionary mapping date strings to indicator values + result_dict = {} + for _, row in df.iterrows(): + date_str = row["Date"] + indicator_value = row[indicator] + + # Handle NaN/None values + if pd.isna(indicator_value): + result_dict[date_str] = "N/A" + else: + result_dict[date_str] = str(indicator_value) + + return result_dict + + +def get_stockstats_indicator( + symbol: Annotated[str, "ticker symbol of the company"], + indicator: Annotated[str, "technical indicator to get the analysis and report of"], + curr_date: Annotated[ + str, "The current trading date you are trading on, YYYY-mm-dd" + ], +) -> str: + + curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d") + curr_date = curr_date_dt.strftime("%Y-%m-%d") + + try: + indicator_value = StockstatsUtils.get_stock_stats( + symbol, + indicator, + curr_date, + ) + except Exception as e: + print( + f"Error getting stockstats indicator data for indicator {indicator} on {curr_date}: {e}" + ) + return "" + + return str(indicator_value) + + +def get_fundamentals( + ticker: Annotated[str, "ticker symbol of the company"], + curr_date: Annotated[str, "current date (not used for yfinance)"] = None +): + """Get company fundamentals overview from yfinance.""" + try: + ticker_obj = yf.Ticker(ticker.upper()) + info = ticker_obj.info + + if not info: + return f"No fundamentals data found for symbol '{ticker}'" + + fields = [ + ("Name", info.get("longName")), + ("Sector", info.get("sector")), + ("Industry", info.get("industry")), + ("Market Cap", info.get("marketCap")), + ("PE Ratio (TTM)", info.get("trailingPE")), + ("Forward PE", info.get("forwardPE")), + ("PEG Ratio", info.get("pegRatio")), + ("Price to Book", info.get("priceToBook")), + ("EPS (TTM)", info.get("trailingEps")), + ("Forward EPS", info.get("forwardEps")), + ("Dividend Yield", info.get("dividendYield")), + ("Beta", info.get("beta")), + ("52 Week High", info.get("fiftyTwoWeekHigh")), + ("52 Week Low", info.get("fiftyTwoWeekLow")), + ("50 Day Average", info.get("fiftyDayAverage")), + ("200 Day Average", info.get("twoHundredDayAverage")), + ("Revenue (TTM)", info.get("totalRevenue")), + ("Gross Profit", info.get("grossProfits")), + ("EBITDA", info.get("ebitda")), + ("Net Income", info.get("netIncomeToCommon")), + ("Profit Margin", info.get("profitMargins")), + ("Operating Margin", info.get("operatingMargins")), + ("Return on Equity", info.get("returnOnEquity")), + ("Return on Assets", info.get("returnOnAssets")), + ("Debt to Equity", info.get("debtToEquity")), + ("Current Ratio", info.get("currentRatio")), + ("Book Value", info.get("bookValue")), + ("Free Cash Flow", info.get("freeCashflow")), + ] + + lines = [] + for label, value in fields: + if value is not None: + lines.append(f"{label}: {value}") + + header = f"# Company Fundamentals for {ticker.upper()}\n" + header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" + + return header + "\n".join(lines) + + except Exception as e: + return f"Error retrieving fundamentals for {ticker}: {str(e)}" + + +def get_balance_sheet( + ticker: Annotated[str, "ticker symbol of the company"], + freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly", + curr_date: Annotated[str, "current date (not used for yfinance)"] = None +): + """Get balance sheet data from yfinance.""" + try: + ticker_obj = yf.Ticker(ticker.upper()) + + if freq.lower() == "quarterly": + data = ticker_obj.quarterly_balance_sheet + else: + data = ticker_obj.balance_sheet + + if data.empty: + return f"No balance sheet data found for symbol '{ticker}'" + + # Convert to CSV string for consistency with other functions + csv_string = data.to_csv() + + # Add header information + header = f"# Balance Sheet data for {ticker.upper()} ({freq})\n" + header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" + + return header + csv_string + + except Exception as e: + return f"Error retrieving balance sheet for {ticker}: {str(e)}" + + +def get_cashflow( + ticker: Annotated[str, "ticker symbol of the company"], + freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly", + curr_date: Annotated[str, "current date (not used for yfinance)"] = None +): + """Get cash flow data from yfinance.""" + try: + ticker_obj = yf.Ticker(ticker.upper()) + + if freq.lower() == "quarterly": + data = ticker_obj.quarterly_cashflow + else: + data = ticker_obj.cashflow + + if data.empty: + return f"No cash flow data found for symbol '{ticker}'" + + # Convert to CSV string for consistency with other functions + csv_string = data.to_csv() + + # Add header information + header = f"# Cash Flow data for {ticker.upper()} ({freq})\n" + header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" + + return header + csv_string + + except Exception as e: + return f"Error retrieving cash flow for {ticker}: {str(e)}" + + +def get_income_statement( + ticker: Annotated[str, "ticker symbol of the company"], + freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly", + curr_date: Annotated[str, "current date (not used for yfinance)"] = None +): + """Get income statement data from yfinance.""" + try: + ticker_obj = yf.Ticker(ticker.upper()) + + if freq.lower() == "quarterly": + data = ticker_obj.quarterly_income_stmt + else: + data = ticker_obj.income_stmt + + if data.empty: + return f"No income statement data found for symbol '{ticker}'" + + # Convert to CSV string for consistency with other functions + csv_string = data.to_csv() + + # Add header information + header = f"# Income Statement data for {ticker.upper()} ({freq})\n" + header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" + + return header + csv_string + + except Exception as e: + return f"Error retrieving income statement for {ticker}: {str(e)}" + + +def get_insider_transactions( + ticker: Annotated[str, "ticker symbol of the company"] +): + """Get insider transactions data from yfinance.""" + try: + ticker_obj = yf.Ticker(ticker.upper()) + data = ticker_obj.insider_transactions + + if data is None or data.empty: + return f"No insider transactions data found for symbol '{ticker}'" + + # Convert to CSV string for consistency with other functions + csv_string = data.to_csv() + + # Add header information + header = f"# Insider Transactions data for {ticker.upper()}\n" + header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" + + return header + csv_string + + except Exception as e: + return f"Error retrieving insider transactions for {ticker}: {str(e)}" + + +# --- Macro data functions (used by interface.py routing) --- +# These are thin wrappers that delegate to yfinance directly. +# The actual @tool versions live in agents/utils/macro_data_tools.py. + +import json as _json + + +def _safe_get_yf(info, key, default=None): + val = info.get(key) + return default if val is None else val + + +def _fmt_num(val): + if val is None: + return None + if abs(val) >= 1e12: + return f"${val/1e12:.2f}T" + if abs(val) >= 1e9: + return f"${val/1e9:.2f}B" + if abs(val) >= 1e6: + return f"${val/1e6:.2f}M" + return f"${val:,.0f}" + + +def _period_return(ticker_obj, months): + import pandas as pd + try: + end_dt = pd.Timestamp.today() + start_dt = end_dt - pd.DateOffset(months=months) + data = ticker_obj.history(start=start_dt.strftime("%Y-%m-%d"), end=end_dt.strftime("%Y-%m-%d")) + if data.empty or len(data) < 2: + return None + return ((data["Close"].iloc[-1] / data["Close"].iloc[0]) - 1) * 100 + except Exception: + return None + + +def get_company_profile(ticker, curr_date=None): + """Get company profile via yfinance (plain function for interface routing).""" + try: + t = yf.Ticker(ticker.upper()) + info = t.info + if not info or not info.get("longName"): + return _json.dumps({"error": f"No data for {ticker}", "ticker": ticker}) + mc = _safe_get_yf(info, "marketCap") + cat = "large_cap" if mc and mc >= 10e9 else "mid_cap" if mc and mc >= 2e9 else "small_cap" if mc and mc >= 300e6 else "micro_cap" if mc else "unknown" + profile = { + "company_name": _safe_get_yf(info, "longName", "Unknown"), + "ticker": ticker.upper(), + "sector": _safe_get_yf(info, "sector", "Unknown"), + "industry": _safe_get_yf(info, "industry", "Unknown"), + "description": _safe_get_yf(info, "longBusinessSummary", ""), + "market_cap": mc, + "market_cap_formatted": _fmt_num(mc), + "market_cap_category": cat, + "current_price": _safe_get_yf(info, "currentPrice") or _safe_get_yf(info, "regularMarketPrice"), + } + return _json.dumps(profile, default=str) + except Exception as e: + return _json.dumps({"error": str(e), "ticker": ticker}) + + +_SECTOR_ETF_MAP = { + "Technology": "XLK", + "Financial Services": "XLF", + "Financials": "XLF", + "Energy": "XLE", + "Healthcare": "XLV", + "Health Care": "XLV", + "Industrials": "XLI", + "Consumer Cyclical": "XLY", + "Consumer Discretionary": "XLY", + "Consumer Defensive": "XLP", + "Consumer Staples": "XLP", + "Utilities": "XLU", + "Real Estate": "XLRE", + "Basic Materials": "XLB", + "Materials": "XLB", + "Communication Services": "XLC", +} + +_SECTOR_ETFS = { + "SPY": "S&P 500", + "XLK": "Technology", "XLF": "Financials", "XLE": "Energy", + "XLV": "Health Care", "XLI": "Industrials", "XLY": "Consumer Discretionary", + "XLP": "Consumer Staples", "XLU": "Utilities", "XLRE": "Real Estate", + "XLB": "Materials", "XLC": "Communication Services", +} + + +def get_macro_indicators(curr_date=None): + """Get macro indicators. VIX/TNX from yfinance (indices), sector ETFs from Alpaca.""" + results = {} + + # VIX and TNX are indices — yfinance only (Alpaca doesn't serve index tickers) + try: + vix = yf.Ticker("^VIX") + vd = vix.history(period="5d") + if not vd.empty: + results["vix_level"] = round(vd["Close"].iloc[-1], 2) + except Exception: + pass + try: + tnx = yf.Ticker("^TNX") + td = tnx.history(period="5d") + if not td.empty: + results["ten_year_yield"] = round(td["Close"].iloc[-1], 3) + except Exception: + pass + + # Sector ETF performance — Alpaca first (10k calls/min), yfinance fallback + try: + from .alpaca_data import alpaca_available, get_sector_etf_performance + if alpaca_available(): + perf = get_sector_etf_performance(list(_SECTOR_ETFS.keys())) + if perf: + sector_performance = {} + for sym, data in perf.items(): + sector_performance[sym] = { + "name": _SECTOR_ETFS.get(sym, sym), + "return_1m": data.get("return_1m"), + "return_3m": data.get("return_3m"), + "price": data.get("price"), + } + results["sector_performance"] = sector_performance + except Exception as e: + _logger.debug("Alpaca sector ETFs failed: %s", e) + + # Fallback: yfinance for sector ETFs + if "sector_performance" not in results: + sector_performance = {} + for sym, name in _SECTOR_ETFS.items(): + try: + t = yf.Ticker(sym) + hist = t.history(period="3mo") + if hist.empty or len(hist) < 5: + continue + close = hist["Close"] + current = float(close.iloc[-1]) + ret_1m = round((current - float(close.iloc[-22])) / float(close.iloc[-22]) * 100, 2) if len(close) >= 22 else None + ret_3m = round((current - float(close.iloc[-63])) / float(close.iloc[-63]) * 100, 2) if len(close) >= 63 else None + sector_performance[sym] = {"name": name, "return_1m": ret_1m, "return_3m": ret_3m, "price": current} + except Exception: + pass + if sector_performance: + results["sector_performance"] = sector_performance + + return _json.dumps(results, default=str) + + +def get_sector_rotation(ticker, curr_date=None): + """Get sector rotation data with relative performance vs SPY.""" + try: + t = yf.Ticker(ticker.upper()) + info = t.info or {} + sector = _safe_get_yf(info, "sector", "Unknown") + sector_etf = _SECTOR_ETF_MAP.get(sector) + + result = {"ticker": ticker.upper(), "sector": sector, "sector_etf": sector_etf} + + if not sector_etf: + return _json.dumps(result, default=str) + + # Get sector ETF + SPY performance for relative strength + etfs_to_fetch = [sector_etf, "SPY"] + perf = {} + + try: + from .alpaca_data import alpaca_available, get_sector_etf_performance + if alpaca_available(): + perf = get_sector_etf_performance(etfs_to_fetch) + except Exception: + pass + + # Fallback: yfinance + if not perf: + for sym in etfs_to_fetch: + try: + hist = yf.Ticker(sym).history(period="3mo") + if hist.empty or len(hist) < 5: + continue + close = hist["Close"] + current = float(close.iloc[-1]) + ret_1m = round((current - float(close.iloc[-22])) / float(close.iloc[-22]) * 100, 2) if len(close) >= 22 else None + ret_3m = round((current - float(close.iloc[-63])) / float(close.iloc[-63]) * 100, 2) if len(close) >= 63 else None + perf[sym] = {"return_1m": ret_1m, "return_3m": ret_3m, "price": current} + except Exception: + pass + + # Compute relative strength vs SPY + spy_data = perf.get("SPY", {}) + etf_data = perf.get(sector_etf, {}) + spy_1m = spy_data.get("return_1m") + spy_3m = spy_data.get("return_3m") + etf_1m = etf_data.get("return_1m") + etf_3m = etf_data.get("return_3m") + + if etf_1m is not None and spy_1m is not None: + result["stock_sector_vs_spy_1m"] = round(etf_1m - spy_1m, 2) + if etf_3m is not None and spy_3m is not None: + result["stock_sector_vs_spy_3m"] = round(etf_3m - spy_3m, 2) + + # Rank sector among all sector ETFs (from macro_indicators cache or fresh) + try: + macro_raw = get_macro_indicators() + macro = _json.loads(macro_raw) if isinstance(macro_raw, str) else macro_raw + sector_perf = macro.get("sector_performance", {}) + # Rank by 1M return (exclude SPY from ranking) + ranked = sorted( + [(s, d.get("return_1m", -999)) for s, d in sector_perf.items() if s != "SPY"], + key=lambda x: x[1], reverse=True, + ) + for i, (sym, _) in enumerate(ranked, 1): + if sym == sector_etf: + result["stock_sector_rank"] = i + result["total_sectors"] = len(ranked) + break + except Exception: + pass + + return _json.dumps(result, default=str) + except Exception as e: + return _json.dumps({"error": str(e)}) + + +def get_institutional_flow(ticker): + """Get institutional flow data via yfinance including 13F holders and insider transactions.""" + try: + t = yf.Ticker(ticker.upper()) + info = t.info or {} + + # Base metrics + result = { + "ticker": ticker.upper(), + "average_volume": _safe_get_yf(info, "averageVolume"), + "average_volume_10d": _safe_get_yf(info, "averageVolume10days"), + "float_shares": _safe_get_yf(info, "floatShares"), + "shares_short": _safe_get_yf(info, "sharesShort"), + "shares_short_prior": _safe_get_yf(info, "sharesShortPriorMonth"), + "short_ratio": _safe_get_yf(info, "shortRatio"), + "held_percent_institutions": _safe_get_yf(info, "heldPercentInstitutions"), + "held_percent_insiders": _safe_get_yf(info, "heldPercentInsiders"), + } + + # Volume ratio (10d vs avg) + vol_10d = _safe_get_yf(info, "averageVolume10days") + vol_avg = _safe_get_yf(info, "averageVolume") + if vol_10d and vol_avg and vol_avg > 0: + result["volume_ratio"] = round(vol_10d / vol_avg, 2) + + # Short % of float + float_shares = _safe_get_yf(info, "floatShares") + shares_short = _safe_get_yf(info, "sharesShort") + if float_shares and shares_short and float_shares > 0: + result["short_pct_of_float"] = round(shares_short / float_shares * 100, 2) + + # Short interest trend (current vs prior month) + prior = _safe_get_yf(info, "sharesShortPriorMonth") + if shares_short is not None and prior is not None and prior > 0: + pct_change = (shares_short - prior) / prior * 100 + result["short_interest_change_pct"] = round(pct_change, 1) + if pct_change > 5: + result["short_interest_trend"] = "rising" + elif pct_change < -5: + result["short_interest_trend"] = "falling" + else: + result["short_interest_trend"] = "stable" + + # Float turnover (5d volume / float) + if vol_10d and float_shares and float_shares > 0: + result["float_turnover_5d_pct"] = round(vol_10d * 5 / float_shares * 100, 2) + + # Top institutional holders (13F data) + try: + holders = t.institutional_holders + if holders is not None and not holders.empty: + top = holders.head(10).to_dict("records") + result["top_institutional_holders"] = [ + { + "holder": str(r.get("Holder", "")), + "shares": int(r["Shares"]) if r.get("Shares") else None, + "pct_out": round(float(r["% Out"]) * 100, 2) if r.get("% Out") else None, + "value": float(r["Value"]) if r.get("Value") else None, + } + for r in top + ] + result["top_holders_count"] = len(top) + except Exception: + pass + + # Insider transactions + try: + insiders = t.insider_transactions + if insiders is not None and not insiders.empty: + recent = insiders.head(10).to_dict("records") + buys = sum(1 for r in recent if "Purchase" in str(r.get("Text", ""))) + sells = sum(1 for r in recent if "Sale" in str(r.get("Text", ""))) + result["insider_buys_recent"] = buys + result["insider_sells_recent"] = sells + if buys > sells: + result["insider_transaction_signal"] = "buying" + elif sells > buys: + result["insider_transaction_signal"] = "selling" + else: + result["insider_transaction_signal"] = "none" + except Exception: + pass + + return _json.dumps(result, default=str) + except Exception as e: + return _json.dumps({"error": str(e)}) + + +def get_earnings_estimates(ticker): + """Get earnings estimates via yfinance (plain function for interface routing).""" + try: + t = yf.Ticker(ticker.upper()) + info = t.info + return _json.dumps({ + "ticker": ticker.upper(), + "trailing_eps": _safe_get_yf(info, "trailingEps"), + "forward_eps": _safe_get_yf(info, "forwardEps"), + "current_price": _safe_get_yf(info, "currentPrice") or _safe_get_yf(info, "regularMarketPrice"), + }, default=str) + except Exception as e: + return _json.dumps({"error": str(e)}) + + +def get_valuation_peers(ticker): + """Get valuation peer data via yfinance (plain function for interface routing).""" + try: + t = yf.Ticker(ticker.upper()) + info = t.info + return _json.dumps({ + "ticker": ticker.upper(), + "trailing_pe": _safe_get_yf(info, "trailingPE"), + "forward_pe": _safe_get_yf(info, "forwardPE"), + "peg_ratio": _safe_get_yf(info, "pegRatio"), + "price_to_book": _safe_get_yf(info, "priceToBook"), + "ev_to_ebitda": _safe_get_yf(info, "enterpriseToEbitda"), + }, default=str) + except Exception as e: return _json.dumps({"error": str(e)}) \ No newline at end of file diff --git a/tradingagents/dataflows/yfinance_news.py b/tradingagents/dataflows/yfinance_news.py index 20e9120d..d9adc098 100644 --- a/tradingagents/dataflows/yfinance_news.py +++ b/tradingagents/dataflows/yfinance_news.py @@ -1,190 +1,190 @@ -"""yfinance-based news data fetching functions.""" - -import yfinance as yf -from datetime import datetime -from dateutil.relativedelta import relativedelta - - -def _extract_article_data(article: dict) -> dict: - """Extract article data from yfinance news format (handles nested 'content' structure).""" - # Handle nested content structure - if "content" in article: - content = article["content"] - title = content.get("title", "No title") - summary = content.get("summary", "") - provider = content.get("provider", {}) - publisher = provider.get("displayName", "Unknown") - - # Get URL from canonicalUrl or clickThroughUrl - url_obj = content.get("canonicalUrl") or content.get("clickThroughUrl") or {} - link = url_obj.get("url", "") - - # Get publish date - pub_date_str = content.get("pubDate", "") - pub_date = None - if pub_date_str: - try: - pub_date = datetime.fromisoformat(pub_date_str.replace("Z", "+00:00")) - except (ValueError, AttributeError): - pass - - return { - "title": title, - "summary": summary, - "publisher": publisher, - "link": link, - "pub_date": pub_date, - } - else: - # Fallback for flat structure - return { - "title": article.get("title", "No title"), - "summary": article.get("summary", ""), - "publisher": article.get("publisher", "Unknown"), - "link": article.get("link", ""), - "pub_date": None, - } - - -def get_news_yfinance( - ticker: str, - start_date: str, - end_date: str, -) -> str: - """ - Retrieve news for a specific stock ticker using yfinance. - - Args: - ticker: Stock ticker symbol (e.g., "AAPL") - start_date: Start date in yyyy-mm-dd format - end_date: End date in yyyy-mm-dd format - - Returns: - Formatted string containing news articles - """ - try: - stock = yf.Ticker(ticker) - news = stock.get_news(count=20) - - if not news: - return f"No news found for {ticker}" - - # Parse date range for filtering - start_dt = datetime.strptime(start_date, "%Y-%m-%d") - end_dt = datetime.strptime(end_date, "%Y-%m-%d") - - news_str = "" - filtered_count = 0 - - for article in news: - data = _extract_article_data(article) - - # Filter by date if publish time is available - if data["pub_date"]: - pub_date_naive = data["pub_date"].replace(tzinfo=None) - if not (start_dt <= pub_date_naive <= end_dt + relativedelta(days=1)): - continue - - news_str += f"### {data['title']} (source: {data['publisher']})\n" - if data["summary"]: - news_str += f"{data['summary']}\n" - if data["link"]: - news_str += f"Link: {data['link']}\n" - news_str += "\n" - filtered_count += 1 - - if filtered_count == 0: - return f"No news found for {ticker} between {start_date} and {end_date}" - - return f"## {ticker} News, from {start_date} to {end_date}:\n\n{news_str}" - - except Exception as e: - return f"Error fetching news for {ticker}: {str(e)}" - - -def get_global_news_yfinance( - curr_date: str, - look_back_days: int = 7, - limit: int = 10, -) -> str: - """ - Retrieve global/macro economic news using yfinance Search. - - Args: - curr_date: Current date in yyyy-mm-dd format - look_back_days: Number of days to look back - limit: Maximum number of articles to return - - Returns: - Formatted string containing global news articles - """ - # Search queries for macro/global news - search_queries = [ - "stock market economy", - "Federal Reserve interest rates", - "inflation economic outlook", - "global markets trading", - ] - - all_news = [] - seen_titles = set() - - try: - for query in search_queries: - search = yf.Search( - query=query, - news_count=limit, - enable_fuzzy_query=True, - ) - - if search.news: - for article in search.news: - # Handle both flat and nested structures - if "content" in article: - data = _extract_article_data(article) - title = data["title"] - else: - title = article.get("title", "") - - # Deduplicate by title - if title and title not in seen_titles: - seen_titles.add(title) - all_news.append(article) - - if len(all_news) >= limit: - break - - if not all_news: - return f"No global news found for {curr_date}" - - # Calculate date range - curr_dt = datetime.strptime(curr_date, "%Y-%m-%d") - start_dt = curr_dt - relativedelta(days=look_back_days) - start_date = start_dt.strftime("%Y-%m-%d") - - news_str = "" - for article in all_news[:limit]: - # Handle both flat and nested structures - if "content" in article: - data = _extract_article_data(article) - title = data["title"] - publisher = data["publisher"] - link = data["link"] - summary = data["summary"] - else: - title = article.get("title", "No title") - publisher = article.get("publisher", "Unknown") - link = article.get("link", "") - summary = "" - - news_str += f"### {title} (source: {publisher})\n" - if summary: - news_str += f"{summary}\n" - if link: - news_str += f"Link: {link}\n" - news_str += "\n" - - return f"## Global Market News, from {start_date} to {curr_date}:\n\n{news_str}" - - except Exception as e: - return f"Error fetching global news: {str(e)}" +"""yfinance-based news data fetching functions.""" + +import yfinance as yf +from datetime import datetime +from dateutil.relativedelta import relativedelta + + +def _extract_article_data(article: dict) -> dict: + """Extract article data from yfinance news format (handles nested 'content' structure).""" + # Handle nested content structure + if "content" in article: + content = article["content"] + title = content.get("title", "No title") + summary = content.get("summary", "") + provider = content.get("provider", {}) + publisher = provider.get("displayName", "Unknown") + + # Get URL from canonicalUrl or clickThroughUrl + url_obj = content.get("canonicalUrl") or content.get("clickThroughUrl") or {} + link = url_obj.get("url", "") + + # Get publish date + pub_date_str = content.get("pubDate", "") + pub_date = None + if pub_date_str: + try: + pub_date = datetime.fromisoformat(pub_date_str.replace("Z", "+00:00")) + except (ValueError, AttributeError): + pass + + return { + "title": title, + "summary": summary, + "publisher": publisher, + "link": link, + "pub_date": pub_date, + } + else: + # Fallback for flat structure + return { + "title": article.get("title", "No title"), + "summary": article.get("summary", ""), + "publisher": article.get("publisher", "Unknown"), + "link": article.get("link", ""), + "pub_date": None, + } + + +def get_news_yfinance( + ticker: str, + start_date: str, + end_date: str, +) -> str: + """ + Retrieve news for a specific stock ticker using yfinance. + + Args: + ticker: Stock ticker symbol (e.g., "AAPL") + start_date: Start date in yyyy-mm-dd format + end_date: End date in yyyy-mm-dd format + + Returns: + Formatted string containing news articles + """ + try: + stock = yf.Ticker(ticker) + news = stock.get_news(count=20) + + if not news: + return f"No news found for {ticker}" + + # Parse date range for filtering + start_dt = datetime.strptime(start_date, "%Y-%m-%d") + end_dt = datetime.strptime(end_date, "%Y-%m-%d") + + news_str = "" + filtered_count = 0 + + for article in news: + data = _extract_article_data(article) + + # Filter by date if publish time is available + if data["pub_date"]: + pub_date_naive = data["pub_date"].replace(tzinfo=None) + if not (start_dt <= pub_date_naive <= end_dt + relativedelta(days=1)): + continue + + news_str += f"### {data['title']} (source: {data['publisher']})\n" + if data["summary"]: + news_str += f"{data['summary']}\n" + if data["link"]: + news_str += f"Link: {data['link']}\n" + news_str += "\n" + filtered_count += 1 + + if filtered_count == 0: + return f"No news found for {ticker} between {start_date} and {end_date}" + + return f"## {ticker} News, from {start_date} to {end_date}:\n\n{news_str}" + + except Exception as e: + return f"Error fetching news for {ticker}: {str(e)}" + + +def get_global_news_yfinance( + curr_date: str, + look_back_days: int = 7, + limit: int = 10, +) -> str: + """ + Retrieve global/macro economic news using yfinance Search. + + Args: + curr_date: Current date in yyyy-mm-dd format + look_back_days: Number of days to look back + limit: Maximum number of articles to return + + Returns: + Formatted string containing global news articles + """ + # Search queries for macro/global news + search_queries = [ + "stock market economy", + "Federal Reserve interest rates", + "inflation economic outlook", + "global markets trading", + ] + + all_news = [] + seen_titles = set() + + try: + for query in search_queries: + search = yf.Search( + query=query, + news_count=limit, + enable_fuzzy_query=True, + ) + + if search.news: + for article in search.news: + # Handle both flat and nested structures + if "content" in article: + data = _extract_article_data(article) + title = data["title"] + else: + title = article.get("title", "") + + # Deduplicate by title + if title and title not in seen_titles: + seen_titles.add(title) + all_news.append(article) + + if len(all_news) >= limit: + break + + if not all_news: + return f"No global news found for {curr_date}" + + # Calculate date range + curr_dt = datetime.strptime(curr_date, "%Y-%m-%d") + start_dt = curr_dt - relativedelta(days=look_back_days) + start_date = start_dt.strftime("%Y-%m-%d") + + news_str = "" + for article in all_news[:limit]: + # Handle both flat and nested structures + if "content" in article: + data = _extract_article_data(article) + title = data["title"] + publisher = data["publisher"] + link = data["link"] + summary = data["summary"] + else: + title = article.get("title", "No title") + publisher = article.get("publisher", "Unknown") + link = article.get("link", "") + summary = "" + + news_str += f"### {title} (source: {publisher})\n" + if summary: + news_str += f"{summary}\n" + if link: + news_str += f"Link: {link}\n" + news_str += "\n" + + return f"## Global Market News, from {start_date} to {curr_date}:\n\n{news_str}" + + except Exception as e: + return f"Error fetching global news: {str(e)}" diff --git a/tradingagents/graph/__init__.py b/tradingagents/graph/__init__.py index 562b5a3d..e2b7b6ed 100644 --- a/tradingagents/graph/__init__.py +++ b/tradingagents/graph/__init__.py @@ -1,9 +1,9 @@ -# TradingAgents/graph/__init__.py - -from .trading_graph import TradingAgentsGraph -from .setup import StructuredGraphSetup - -__all__ = [ - "TradingAgentsGraph", - "StructuredGraphSetup", -] +# TradingAgents/graph/__init__.py + +from .trading_graph import TradingAgentsGraph +from .setup import StructuredGraphSetup + +__all__ = [ + "TradingAgentsGraph", + "StructuredGraphSetup", +] diff --git a/tradingagents/graph/conditional_logic.py b/tradingagents/graph/conditional_logic.py index 7b1b1f90..51b0359c 100644 --- a/tradingagents/graph/conditional_logic.py +++ b/tradingagents/graph/conditional_logic.py @@ -1,67 +1,67 @@ -# TradingAgents/graph/conditional_logic.py - -from tradingagents.agents.utils.agent_states import AgentState - - -class ConditionalLogic: - """Handles conditional logic for determining graph flow.""" - - def __init__(self, max_debate_rounds=1, max_risk_discuss_rounds=1): - """Initialize with configuration parameters.""" - self.max_debate_rounds = max_debate_rounds - self.max_risk_discuss_rounds = max_risk_discuss_rounds - - def should_continue_market(self, state: AgentState): - """Determine if market analysis should continue.""" - messages = state["messages"] - last_message = messages[-1] - if last_message.tool_calls: - return "tools_market" - return "Msg Clear Market" - - def should_continue_social(self, state: AgentState): - """Determine if social media analysis should continue.""" - messages = state["messages"] - last_message = messages[-1] - if last_message.tool_calls: - return "tools_social" - return "Msg Clear Social" - - def should_continue_news(self, state: AgentState): - """Determine if news analysis should continue.""" - messages = state["messages"] - last_message = messages[-1] - if last_message.tool_calls: - return "tools_news" - return "Msg Clear News" - - def should_continue_fundamentals(self, state: AgentState): - """Determine if fundamentals analysis should continue.""" - messages = state["messages"] - last_message = messages[-1] - if last_message.tool_calls: - return "tools_fundamentals" - return "Msg Clear Fundamentals" - - def should_continue_debate(self, state: AgentState) -> str: - """Determine if debate should continue.""" - - if ( - state["investment_debate_state"]["count"] >= 2 * self.max_debate_rounds - ): # 3 rounds of back-and-forth between 2 agents - return "Research Manager" - if state["investment_debate_state"]["current_response"].startswith("Bull"): - return "Bear Researcher" - return "Bull Researcher" - - def should_continue_risk_analysis(self, state: AgentState) -> str: - """Determine if risk analysis should continue.""" - if ( - state["risk_debate_state"]["count"] >= 3 * self.max_risk_discuss_rounds - ): # 3 rounds of back-and-forth between 3 agents - return "Risk Judge" - if state["risk_debate_state"]["latest_speaker"].startswith("Aggressive"): - return "Conservative Analyst" - if state["risk_debate_state"]["latest_speaker"].startswith("Conservative"): - return "Neutral Analyst" - return "Aggressive Analyst" +# TradingAgents/graph/conditional_logic.py + +from tradingagents.agents.utils.agent_states import AgentState + + +class ConditionalLogic: + """Handles conditional logic for determining graph flow.""" + + def __init__(self, max_debate_rounds=1, max_risk_discuss_rounds=1): + """Initialize with configuration parameters.""" + self.max_debate_rounds = max_debate_rounds + self.max_risk_discuss_rounds = max_risk_discuss_rounds + + def should_continue_market(self, state: AgentState): + """Determine if market analysis should continue.""" + messages = state["messages"] + last_message = messages[-1] + if last_message.tool_calls: + return "tools_market" + return "Msg Clear Market" + + def should_continue_social(self, state: AgentState): + """Determine if social media analysis should continue.""" + messages = state["messages"] + last_message = messages[-1] + if last_message.tool_calls: + return "tools_social" + return "Msg Clear Social" + + def should_continue_news(self, state: AgentState): + """Determine if news analysis should continue.""" + messages = state["messages"] + last_message = messages[-1] + if last_message.tool_calls: + return "tools_news" + return "Msg Clear News" + + def should_continue_fundamentals(self, state: AgentState): + """Determine if fundamentals analysis should continue.""" + messages = state["messages"] + last_message = messages[-1] + if last_message.tool_calls: + return "tools_fundamentals" + return "Msg Clear Fundamentals" + + def should_continue_debate(self, state: AgentState) -> str: + """Determine if debate should continue.""" + + if ( + state["investment_debate_state"]["count"] >= 2 * self.max_debate_rounds + ): # 3 rounds of back-and-forth between 2 agents + return "Research Manager" + if state["investment_debate_state"]["current_response"].startswith("Bull"): + return "Bear Researcher" + return "Bull Researcher" + + def should_continue_risk_analysis(self, state: AgentState) -> str: + """Determine if risk analysis should continue.""" + if ( + state["risk_debate_state"]["count"] >= 3 * self.max_risk_discuss_rounds + ): # 3 rounds of back-and-forth between 3 agents + return "Risk Judge" + if state["risk_debate_state"]["latest_speaker"].startswith("Aggressive"): + return "Conservative Analyst" + if state["risk_debate_state"]["latest_speaker"].startswith("Conservative"): + return "Neutral Analyst" + return "Aggressive Analyst" diff --git a/tradingagents/graph/parallel_analysts.py b/tradingagents/graph/parallel_analysts.py index 35ea0cda..6e72faf1 100644 --- a/tradingagents/graph/parallel_analysts.py +++ b/tradingagents/graph/parallel_analysts.py @@ -1,222 +1,222 @@ -"""Parallel execution nodes for TradingAgents. - -Provides parallel wrappers for: -- Analyst phase (Market, Social, News, Fundamentals) -- Research debate phase (Bull + Bear) -- Risk debate phase (Aggressive + Conservative + Neutral) -""" - -import asyncio -import logging - -from langchain_core.messages import HumanMessage, RemoveMessage - -logger = logging.getLogger(__name__) - - -def create_parallel_analyst_node(analyst_fns, tool_nodes, selected_analysts): - """Create a single LangGraph node that runs all analysts in parallel. - - Each analyst gets its own isolated message state and runs its complete - tool-calling loop independently. Results are merged at the end. - - Args: - analyst_fns: dict mapping analyst type (e.g. "market") to node function - tool_nodes: dict mapping analyst type to ToolNode instance - selected_analysts: list of analyst types to run - """ - - async def parallel_analysts_node(state): - """Run all analysts concurrently and merge their reports.""" - - async def run_single(analyst_type): - """Run one analyst through its complete tool-calling loop.""" - fn = analyst_fns[analyst_type] - tn = tool_nodes[analyst_type] - - # Each analyst gets its own isolated message state - local_state = { - "messages": list(state["messages"]), - "trade_date": state["trade_date"], - "company_of_interest": state["company_of_interest"], - } - - result = {} - for _ in range(10): # safety limit on tool rounds - result = await asyncio.to_thread(fn, local_state) - ai_msg = result["messages"][0] - local_state["messages"] = local_state["messages"] + [ai_msg] - - if not ai_msg.tool_calls: - break - - # Process tool calls - tool_result = await asyncio.to_thread(tn.invoke, local_state) - local_state["messages"] = ( - local_state["messages"] + tool_result["messages"] - ) - - # Return only report fields (not messages) - return {k: v for k, v in result.items() if k != "messages"} - - # Run all analysts concurrently - tasks = [run_single(at) for at in selected_analysts if at in analyst_fns] - results = await asyncio.gather(*tasks) - - # Merge all report fields - merged = {} - for r in results: - merged.update(r) - - # Clear messages and add placeholder (same as Msg Clear nodes) - messages = state.get("messages", []) - removal_ops = [ - RemoveMessage(id=m.id) - for m in messages - if hasattr(m, "id") and m.id - ] - merged["messages"] = removal_ops + [HumanMessage(content="Continue")] - - return merged - - return parallel_analysts_node - - -def _snapshot_research_state(state): - """Extract research-relevant fields into a plain dict.""" - return { - "investment_debate_state": dict(state.get("investment_debate_state", {})), - "market_report": state.get("market_report", ""), - "sentiment_report": state.get("sentiment_report", ""), - "news_report": state.get("news_report", ""), - "fundamentals_report": state.get("fundamentals_report", ""), - } - - -def _snapshot_risk_state(state): - """Extract risk-relevant fields into a plain dict.""" - return { - "risk_debate_state": dict(state.get("risk_debate_state", {})), - "market_report": state.get("market_report", ""), - "sentiment_report": state.get("sentiment_report", ""), - "news_report": state.get("news_report", ""), - "fundamentals_report": state.get("fundamentals_report", ""), - "trader_investment_plan": state.get("trader_investment_plan", ""), - } - - -def create_parallel_research_node(bull_fn, bear_fn): - """Create a node that runs Bull and Bear researchers in parallel. - - Uses async + asyncio.to_thread + asyncio.gather — the same pattern - that works for create_parallel_analyst_node. - """ - - async def parallel_research_node(state): - import time - import sys - - state_snap = _snapshot_research_state(state) - t0 = time.time() - - async def run_bull(): - print(f"[PARALLEL] Bull starting at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) - result = await asyncio.to_thread(bull_fn, state_snap) - print(f"[PARALLEL] Bull done at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) - return result - - async def run_bear(): - print(f"[PARALLEL] Bear starting at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) - result = await asyncio.to_thread(bear_fn, state_snap) - print(f"[PARALLEL] Bear done at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) - return result - - bull_result, bear_result = await asyncio.gather(run_bull(), run_bear()) - - print(f"[PARALLEL] Research total: {time.time()-t0:.1f}s", file=sys.stderr, flush=True) - - bull_debate = bull_result["investment_debate_state"] - bear_debate = bear_result["investment_debate_state"] - - merged_debate = { - "bull_history": bull_debate.get("bull_history", ""), - "bear_history": bear_debate.get("bear_history", ""), - "history": bull_debate.get("bull_history", "") - + "\n" - + bear_debate.get("bear_history", ""), - "current_response": bear_debate.get("current_response", ""), - "judge_decision": "", - "count": 2, - } - return {"investment_debate_state": merged_debate} - - return parallel_research_node - - -def create_parallel_risk_node(aggressive_fn, conservative_fn, neutral_fn): - """Create a node that runs all 3 risk analysts in parallel. - - Uses async + asyncio.to_thread + asyncio.gather — the same pattern - that works for create_parallel_analyst_node. - """ - - async def parallel_risk_node(state): - import time - import sys - - state_snap = _snapshot_risk_state(state) - t0 = time.time() - - async def run_agg(): - print(f"[PARALLEL] Aggressive starting at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) - result = await asyncio.to_thread(aggressive_fn, state_snap) - print(f"[PARALLEL] Aggressive done at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) - return result - - async def run_con(): - print(f"[PARALLEL] Conservative starting at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) - result = await asyncio.to_thread(conservative_fn, state_snap) - print(f"[PARALLEL] Conservative done at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) - return result - - async def run_neu(): - print(f"[PARALLEL] Neutral starting at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) - result = await asyncio.to_thread(neutral_fn, state_snap) - print(f"[PARALLEL] Neutral done at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) - return result - - agg_result, con_result, neu_result = await asyncio.gather( - run_agg(), run_con(), run_neu() - ) - - print(f"[PARALLEL] Risk total: {time.time()-t0:.1f}s", file=sys.stderr, flush=True) - - agg_debate = agg_result["risk_debate_state"] - con_debate = con_result["risk_debate_state"] - neu_debate = neu_result["risk_debate_state"] - - merged_debate = { - "aggressive_history": agg_debate.get("aggressive_history", ""), - "conservative_history": con_debate.get("conservative_history", ""), - "neutral_history": neu_debate.get("neutral_history", ""), - "history": agg_debate.get("aggressive_history", "") - + "\n" - + con_debate.get("conservative_history", "") - + "\n" - + neu_debate.get("neutral_history", ""), - "latest_speaker": "Neutral", - "current_aggressive_response": agg_debate.get( - "current_aggressive_response", "" - ), - "current_conservative_response": con_debate.get( - "current_conservative_response", "" - ), - "current_neutral_response": neu_debate.get( - "current_neutral_response", "" - ), - "judge_decision": "", - "count": 3, - } - return {"risk_debate_state": merged_debate} - - return parallel_risk_node +"""Parallel execution nodes for TradingAgents. + +Provides parallel wrappers for: +- Analyst phase (Market, Social, News, Fundamentals) +- Research debate phase (Bull + Bear) +- Risk debate phase (Aggressive + Conservative + Neutral) +""" + +import asyncio +import logging + +from langchain_core.messages import HumanMessage, RemoveMessage + +logger = logging.getLogger(__name__) + + +def create_parallel_analyst_node(analyst_fns, tool_nodes, selected_analysts): + """Create a single LangGraph node that runs all analysts in parallel. + + Each analyst gets its own isolated message state and runs its complete + tool-calling loop independently. Results are merged at the end. + + Args: + analyst_fns: dict mapping analyst type (e.g. "market") to node function + tool_nodes: dict mapping analyst type to ToolNode instance + selected_analysts: list of analyst types to run + """ + + async def parallel_analysts_node(state): + """Run all analysts concurrently and merge their reports.""" + + async def run_single(analyst_type): + """Run one analyst through its complete tool-calling loop.""" + fn = analyst_fns[analyst_type] + tn = tool_nodes[analyst_type] + + # Each analyst gets its own isolated message state + local_state = { + "messages": list(state["messages"]), + "trade_date": state["trade_date"], + "company_of_interest": state["company_of_interest"], + } + + result = {} + for _ in range(10): # safety limit on tool rounds + result = await asyncio.to_thread(fn, local_state) + ai_msg = result["messages"][0] + local_state["messages"] = local_state["messages"] + [ai_msg] + + if not ai_msg.tool_calls: + break + + # Process tool calls + tool_result = await asyncio.to_thread(tn.invoke, local_state) + local_state["messages"] = ( + local_state["messages"] + tool_result["messages"] + ) + + # Return only report fields (not messages) + return {k: v for k, v in result.items() if k != "messages"} + + # Run all analysts concurrently + tasks = [run_single(at) for at in selected_analysts if at in analyst_fns] + results = await asyncio.gather(*tasks) + + # Merge all report fields + merged = {} + for r in results: + merged.update(r) + + # Clear messages and add placeholder (same as Msg Clear nodes) + messages = state.get("messages", []) + removal_ops = [ + RemoveMessage(id=m.id) + for m in messages + if hasattr(m, "id") and m.id + ] + merged["messages"] = removal_ops + [HumanMessage(content="Continue")] + + return merged + + return parallel_analysts_node + + +def _snapshot_research_state(state): + """Extract research-relevant fields into a plain dict.""" + return { + "investment_debate_state": dict(state.get("investment_debate_state", {})), + "market_report": state.get("market_report", ""), + "sentiment_report": state.get("sentiment_report", ""), + "news_report": state.get("news_report", ""), + "fundamentals_report": state.get("fundamentals_report", ""), + } + + +def _snapshot_risk_state(state): + """Extract risk-relevant fields into a plain dict.""" + return { + "risk_debate_state": dict(state.get("risk_debate_state", {})), + "market_report": state.get("market_report", ""), + "sentiment_report": state.get("sentiment_report", ""), + "news_report": state.get("news_report", ""), + "fundamentals_report": state.get("fundamentals_report", ""), + "trader_investment_plan": state.get("trader_investment_plan", ""), + } + + +def create_parallel_research_node(bull_fn, bear_fn): + """Create a node that runs Bull and Bear researchers in parallel. + + Uses async + asyncio.to_thread + asyncio.gather — the same pattern + that works for create_parallel_analyst_node. + """ + + async def parallel_research_node(state): + import time + import sys + + state_snap = _snapshot_research_state(state) + t0 = time.time() + + async def run_bull(): + print(f"[PARALLEL] Bull starting at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) + result = await asyncio.to_thread(bull_fn, state_snap) + print(f"[PARALLEL] Bull done at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) + return result + + async def run_bear(): + print(f"[PARALLEL] Bear starting at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) + result = await asyncio.to_thread(bear_fn, state_snap) + print(f"[PARALLEL] Bear done at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) + return result + + bull_result, bear_result = await asyncio.gather(run_bull(), run_bear()) + + print(f"[PARALLEL] Research total: {time.time()-t0:.1f}s", file=sys.stderr, flush=True) + + bull_debate = bull_result["investment_debate_state"] + bear_debate = bear_result["investment_debate_state"] + + merged_debate = { + "bull_history": bull_debate.get("bull_history", ""), + "bear_history": bear_debate.get("bear_history", ""), + "history": bull_debate.get("bull_history", "") + + "\n" + + bear_debate.get("bear_history", ""), + "current_response": bear_debate.get("current_response", ""), + "judge_decision": "", + "count": 2, + } + return {"investment_debate_state": merged_debate} + + return parallel_research_node + + +def create_parallel_risk_node(aggressive_fn, conservative_fn, neutral_fn): + """Create a node that runs all 3 risk analysts in parallel. + + Uses async + asyncio.to_thread + asyncio.gather — the same pattern + that works for create_parallel_analyst_node. + """ + + async def parallel_risk_node(state): + import time + import sys + + state_snap = _snapshot_risk_state(state) + t0 = time.time() + + async def run_agg(): + print(f"[PARALLEL] Aggressive starting at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) + result = await asyncio.to_thread(aggressive_fn, state_snap) + print(f"[PARALLEL] Aggressive done at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) + return result + + async def run_con(): + print(f"[PARALLEL] Conservative starting at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) + result = await asyncio.to_thread(conservative_fn, state_snap) + print(f"[PARALLEL] Conservative done at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) + return result + + async def run_neu(): + print(f"[PARALLEL] Neutral starting at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) + result = await asyncio.to_thread(neutral_fn, state_snap) + print(f"[PARALLEL] Neutral done at +{time.time()-t0:.1f}s", file=sys.stderr, flush=True) + return result + + agg_result, con_result, neu_result = await asyncio.gather( + run_agg(), run_con(), run_neu() + ) + + print(f"[PARALLEL] Risk total: {time.time()-t0:.1f}s", file=sys.stderr, flush=True) + + agg_debate = agg_result["risk_debate_state"] + con_debate = con_result["risk_debate_state"] + neu_debate = neu_result["risk_debate_state"] + + merged_debate = { + "aggressive_history": agg_debate.get("aggressive_history", ""), + "conservative_history": con_debate.get("conservative_history", ""), + "neutral_history": neu_debate.get("neutral_history", ""), + "history": agg_debate.get("aggressive_history", "") + + "\n" + + con_debate.get("conservative_history", "") + + "\n" + + neu_debate.get("neutral_history", ""), + "latest_speaker": "Neutral", + "current_aggressive_response": agg_debate.get( + "current_aggressive_response", "" + ), + "current_conservative_response": con_debate.get( + "current_conservative_response", "" + ), + "current_neutral_response": neu_debate.get( + "current_neutral_response", "" + ), + "judge_decision": "", + "count": 3, + } + return {"risk_debate_state": merged_debate} + + return parallel_risk_node diff --git a/tradingagents/graph/propagation.py b/tradingagents/graph/propagation.py index 7aba5258..78a1f253 100644 --- a/tradingagents/graph/propagation.py +++ b/tradingagents/graph/propagation.py @@ -1,57 +1,57 @@ -# TradingAgents/graph/propagation.py - -from typing import Dict, Any, List, Optional -from tradingagents.agents.utils.agent_states import ( - AgentState, - InvestDebateState, - RiskDebateState, -) - - -class Propagator: - """Handles state initialization and propagation through the graph.""" - - def __init__(self, max_recur_limit=100): - """Initialize with configuration parameters.""" - self.max_recur_limit = max_recur_limit - - def create_initial_state( - self, company_name: str, trade_date: str - ) -> Dict[str, Any]: - """Create the initial state for the agent graph.""" - return { - "messages": [("human", company_name)], - "company_of_interest": company_name, - "trade_date": str(trade_date), - "investment_debate_state": InvestDebateState( - {"history": "", "current_response": "", "count": 0} - ), - "risk_debate_state": RiskDebateState( - { - "history": "", - "current_aggressive_response": "", - "current_conservative_response": "", - "current_neutral_response": "", - "count": 0, - } - ), - "market_report": "", - "fundamentals_report": "", - "sentiment_report": "", - "news_report": "", - } - - def get_graph_args(self, callbacks: Optional[List] = None) -> Dict[str, Any]: - """Get arguments for the graph invocation. - - Args: - callbacks: Optional list of callback handlers for tool execution tracking. - Note: LLM callbacks are handled separately via LLM constructor. - """ - config = {"recursion_limit": self.max_recur_limit} - if callbacks: - config["callbacks"] = callbacks - return { - "stream_mode": "values", - "config": config, - } +# TradingAgents/graph/propagation.py + +from typing import Dict, Any, List, Optional +from tradingagents.agents.utils.agent_states import ( + AgentState, + InvestDebateState, + RiskDebateState, +) + + +class Propagator: + """Handles state initialization and propagation through the graph.""" + + def __init__(self, max_recur_limit=100): + """Initialize with configuration parameters.""" + self.max_recur_limit = max_recur_limit + + def create_initial_state( + self, company_name: str, trade_date: str + ) -> Dict[str, Any]: + """Create the initial state for the agent graph.""" + return { + "messages": [("human", company_name)], + "company_of_interest": company_name, + "trade_date": str(trade_date), + "investment_debate_state": InvestDebateState( + {"history": "", "current_response": "", "count": 0} + ), + "risk_debate_state": RiskDebateState( + { + "history": "", + "current_aggressive_response": "", + "current_conservative_response": "", + "current_neutral_response": "", + "count": 0, + } + ), + "market_report": "", + "fundamentals_report": "", + "sentiment_report": "", + "news_report": "", + } + + def get_graph_args(self, callbacks: Optional[List] = None) -> Dict[str, Any]: + """Get arguments for the graph invocation. + + Args: + callbacks: Optional list of callback handlers for tool execution tracking. + Note: LLM callbacks are handled separately via LLM constructor. + """ + config = {"recursion_limit": self.max_recur_limit} + if callbacks: + config["callbacks"] = callbacks + return { + "stream_mode": "values", + "config": config, + } diff --git a/tradingagents/graph/reflection.py b/tradingagents/graph/reflection.py index 33303231..57a876dc 100644 --- a/tradingagents/graph/reflection.py +++ b/tradingagents/graph/reflection.py @@ -1,121 +1,121 @@ -# TradingAgents/graph/reflection.py - -from typing import Dict, Any -from langchain_openai import ChatOpenAI - - -class Reflector: - """Handles reflection on decisions and updating memory.""" - - def __init__(self, quick_thinking_llm: ChatOpenAI): - """Initialize the reflector with an LLM.""" - self.quick_thinking_llm = quick_thinking_llm - self.reflection_system_prompt = self._get_reflection_prompt() - - def _get_reflection_prompt(self) -> str: - """Get the system prompt for reflection.""" - return """ -You are an expert financial analyst tasked with reviewing trading decisions/analysis and providing a comprehensive, step-by-step analysis. -Your goal is to deliver detailed insights into investment decisions and highlight opportunities for improvement, adhering strictly to the following guidelines: - -1. Reasoning: - - For each trading decision, determine whether it was correct or incorrect. A correct decision results in an increase in returns, while an incorrect decision does the opposite. - - Analyze the contributing factors to each success or mistake. Consider: - - Market intelligence. - - Technical indicators. - - Technical signals. - - Price movement analysis. - - Overall market data analysis - - News analysis. - - Social media and sentiment analysis. - - Fundamental data analysis. - - Weight the importance of each factor in the decision-making process. - -2. Improvement: - - For any incorrect decisions, propose revisions to maximize returns. - - Provide a detailed list of corrective actions or improvements, including specific recommendations (e.g., changing a decision from HOLD to BUY on a particular date). - -3. Summary: - - Summarize the lessons learned from the successes and mistakes. - - Highlight how these lessons can be adapted for future trading scenarios and draw connections between similar situations to apply the knowledge gained. - -4. Query: - - Extract key insights from the summary into a concise sentence of no more than 1000 tokens. - - Ensure the condensed sentence captures the essence of the lessons and reasoning for easy reference. - -Adhere strictly to these instructions, and ensure your output is detailed, accurate, and actionable. You will also be given objective descriptions of the market from a price movements, technical indicator, news, and sentiment perspective to provide more context for your analysis. -""" - - def _extract_current_situation(self, current_state: Dict[str, Any]) -> str: - """Extract the current market situation from the state.""" - curr_market_report = current_state["market_report"] - curr_sentiment_report = current_state["sentiment_report"] - curr_news_report = current_state["news_report"] - curr_fundamentals_report = current_state["fundamentals_report"] - - return f"{curr_market_report}\n\n{curr_sentiment_report}\n\n{curr_news_report}\n\n{curr_fundamentals_report}" - - def _reflect_on_component( - self, component_type: str, report: str, situation: str, returns_losses - ) -> str: - """Generate reflection for a component.""" - messages = [ - ("system", self.reflection_system_prompt), - ( - "human", - f"Returns: {returns_losses}\n\nAnalysis/Decision: {report}\n\nObjective Market Reports for Reference: {situation}", - ), - ] - - result = self.quick_thinking_llm.invoke(messages).content - return result - - def reflect_bull_researcher(self, current_state, returns_losses, bull_memory): - """Reflect on bull researcher's analysis and update memory.""" - situation = self._extract_current_situation(current_state) - bull_debate_history = current_state["investment_debate_state"]["bull_history"] - - result = self._reflect_on_component( - "BULL", bull_debate_history, situation, returns_losses - ) - bull_memory.add_situations([(situation, result)]) - - def reflect_bear_researcher(self, current_state, returns_losses, bear_memory): - """Reflect on bear researcher's analysis and update memory.""" - situation = self._extract_current_situation(current_state) - bear_debate_history = current_state["investment_debate_state"]["bear_history"] - - result = self._reflect_on_component( - "BEAR", bear_debate_history, situation, returns_losses - ) - bear_memory.add_situations([(situation, result)]) - - def reflect_trader(self, current_state, returns_losses, trader_memory): - """Reflect on trader's decision and update memory.""" - situation = self._extract_current_situation(current_state) - trader_decision = current_state["trader_investment_plan"] - - result = self._reflect_on_component( - "TRADER", trader_decision, situation, returns_losses - ) - trader_memory.add_situations([(situation, result)]) - - def reflect_invest_judge(self, current_state, returns_losses, invest_judge_memory): - """Reflect on investment judge's decision and update memory.""" - situation = self._extract_current_situation(current_state) - judge_decision = current_state["investment_debate_state"]["judge_decision"] - - result = self._reflect_on_component( - "INVEST JUDGE", judge_decision, situation, returns_losses - ) - invest_judge_memory.add_situations([(situation, result)]) - - def reflect_risk_manager(self, current_state, returns_losses, risk_manager_memory): - """Reflect on risk manager's decision and update memory.""" - situation = self._extract_current_situation(current_state) - judge_decision = current_state["risk_debate_state"]["judge_decision"] - - result = self._reflect_on_component( - "RISK JUDGE", judge_decision, situation, returns_losses - ) - risk_manager_memory.add_situations([(situation, result)]) +# TradingAgents/graph/reflection.py + +from typing import Dict, Any +from langchain_openai import ChatOpenAI + + +class Reflector: + """Handles reflection on decisions and updating memory.""" + + def __init__(self, quick_thinking_llm: ChatOpenAI): + """Initialize the reflector with an LLM.""" + self.quick_thinking_llm = quick_thinking_llm + self.reflection_system_prompt = self._get_reflection_prompt() + + def _get_reflection_prompt(self) -> str: + """Get the system prompt for reflection.""" + return """ +You are an expert financial analyst tasked with reviewing trading decisions/analysis and providing a comprehensive, step-by-step analysis. +Your goal is to deliver detailed insights into investment decisions and highlight opportunities for improvement, adhering strictly to the following guidelines: + +1. Reasoning: + - For each trading decision, determine whether it was correct or incorrect. A correct decision results in an increase in returns, while an incorrect decision does the opposite. + - Analyze the contributing factors to each success or mistake. Consider: + - Market intelligence. + - Technical indicators. + - Technical signals. + - Price movement analysis. + - Overall market data analysis + - News analysis. + - Social media and sentiment analysis. + - Fundamental data analysis. + - Weight the importance of each factor in the decision-making process. + +2. Improvement: + - For any incorrect decisions, propose revisions to maximize returns. + - Provide a detailed list of corrective actions or improvements, including specific recommendations (e.g., changing a decision from HOLD to BUY on a particular date). + +3. Summary: + - Summarize the lessons learned from the successes and mistakes. + - Highlight how these lessons can be adapted for future trading scenarios and draw connections between similar situations to apply the knowledge gained. + +4. Query: + - Extract key insights from the summary into a concise sentence of no more than 1000 tokens. + - Ensure the condensed sentence captures the essence of the lessons and reasoning for easy reference. + +Adhere strictly to these instructions, and ensure your output is detailed, accurate, and actionable. You will also be given objective descriptions of the market from a price movements, technical indicator, news, and sentiment perspective to provide more context for your analysis. +""" + + def _extract_current_situation(self, current_state: Dict[str, Any]) -> str: + """Extract the current market situation from the state.""" + curr_market_report = current_state["market_report"] + curr_sentiment_report = current_state["sentiment_report"] + curr_news_report = current_state["news_report"] + curr_fundamentals_report = current_state["fundamentals_report"] + + return f"{curr_market_report}\n\n{curr_sentiment_report}\n\n{curr_news_report}\n\n{curr_fundamentals_report}" + + def _reflect_on_component( + self, component_type: str, report: str, situation: str, returns_losses + ) -> str: + """Generate reflection for a component.""" + messages = [ + ("system", self.reflection_system_prompt), + ( + "human", + f"Returns: {returns_losses}\n\nAnalysis/Decision: {report}\n\nObjective Market Reports for Reference: {situation}", + ), + ] + + result = self.quick_thinking_llm.invoke(messages).content + return result + + def reflect_bull_researcher(self, current_state, returns_losses, bull_memory): + """Reflect on bull researcher's analysis and update memory.""" + situation = self._extract_current_situation(current_state) + bull_debate_history = current_state["investment_debate_state"]["bull_history"] + + result = self._reflect_on_component( + "BULL", bull_debate_history, situation, returns_losses + ) + bull_memory.add_situations([(situation, result)]) + + def reflect_bear_researcher(self, current_state, returns_losses, bear_memory): + """Reflect on bear researcher's analysis and update memory.""" + situation = self._extract_current_situation(current_state) + bear_debate_history = current_state["investment_debate_state"]["bear_history"] + + result = self._reflect_on_component( + "BEAR", bear_debate_history, situation, returns_losses + ) + bear_memory.add_situations([(situation, result)]) + + def reflect_trader(self, current_state, returns_losses, trader_memory): + """Reflect on trader's decision and update memory.""" + situation = self._extract_current_situation(current_state) + trader_decision = current_state["trader_investment_plan"] + + result = self._reflect_on_component( + "TRADER", trader_decision, situation, returns_losses + ) + trader_memory.add_situations([(situation, result)]) + + def reflect_invest_judge(self, current_state, returns_losses, invest_judge_memory): + """Reflect on investment judge's decision and update memory.""" + situation = self._extract_current_situation(current_state) + judge_decision = current_state["investment_debate_state"]["judge_decision"] + + result = self._reflect_on_component( + "INVEST JUDGE", judge_decision, situation, returns_losses + ) + invest_judge_memory.add_situations([(situation, result)]) + + def reflect_risk_manager(self, current_state, returns_losses, risk_manager_memory): + """Reflect on risk manager's decision and update memory.""" + situation = self._extract_current_situation(current_state) + judge_decision = current_state["risk_debate_state"]["judge_decision"] + + result = self._reflect_on_component( + "RISK JUDGE", judge_decision, situation, returns_losses + ) + risk_manager_memory.add_situations([(situation, result)]) diff --git a/tradingagents/graph/setup.py b/tradingagents/graph/setup.py index aa539f1a..a8b5217d 100644 --- a/tradingagents/graph/setup.py +++ b/tradingagents/graph/setup.py @@ -1,208 +1,208 @@ -"""Graph setup for the structured equity ranking pipeline. - -Pipeline stages: - START → Validation → [veto gate] → Tier 1 (Macro+Liquidity parallel) - → Tier 2 (8 agents parallel) → Scoring (Archetype+MasterScore) - → Tier 3 (Bull+Bear parallel → Debate → Risk → FinalDecision) - → END -""" - -from __future__ import annotations - -import asyncio -import logging -import time -from typing import Any, Dict, List - -from langgraph.graph import END, START, StateGraph - -from tradingagents.agents.utils.agent_states import PipelineState - -logger = logging.getLogger(__name__) - - -class StructuredGraphSetup: - """Builds the structured equity ranking LangGraph.""" - - def __init__(self, quick_llm, deep_llm): - self.quick_llm = quick_llm - self.deep_llm = deep_llm - - def setup_graph(self): - """Build and compile the structured pipeline graph.""" - from tradingagents.agents.structured import ( - create_archetype_node, - create_backlog_node, - create_bear_case_node, - create_bull_case_node, - create_business_quality_node, - create_crowding_node, - create_debate_node, - create_earnings_revisions_node, - create_entry_timing_node, - create_final_decision_node, - create_institutional_flow_node, - create_liquidity_node, - create_macro_node, - create_position_replacement_node, - create_risk_node, - create_scoring_node, - create_sector_rotation_node, - create_theme_substitution_node, - create_validation_node, - create_valuation_node, - ) - - # Create node functions - # Tier 1: cheap model (or no LLM for validation) - validation_fn = create_validation_node() - macro_fn = create_macro_node(self.quick_llm) - liquidity_fn = create_liquidity_node(self.quick_llm) - - # Tier 2: cheap model for analysis - bq_fn = create_business_quality_node(self.quick_llm) - inst_fn = create_institutional_flow_node(self.quick_llm) - val_fn = create_valuation_node(self.quick_llm) - et_fn = create_entry_timing_node(self.quick_llm) - er_fn = create_earnings_revisions_node(self.quick_llm) - sr_fn = create_sector_rotation_node(self.quick_llm) - bl_fn = create_backlog_node(self.quick_llm) - cr_fn = create_crowding_node(self.quick_llm) - arch_fn = create_archetype_node(self.quick_llm) - score_fn = create_scoring_node() - - # Portfolio-level: deep model for theme/replacement analysis - theme_fn = create_theme_substitution_node(self.deep_llm) - replace_fn = create_position_replacement_node(self.deep_llm) - - # Tier 3: deep model for reasoning/debate - bull_fn = create_bull_case_node(self.deep_llm) - bear_fn = create_bear_case_node(self.deep_llm) - debate_fn = create_debate_node(self.deep_llm) - risk_fn = create_risk_node(self.deep_llm) - final_fn = create_final_decision_node(self.deep_llm) - - # Build parallel wrapper nodes - parallel_tier1 = _create_parallel_node( - [("macro", macro_fn), ("liquidity", liquidity_fn)], - "Tier 1", - ) - parallel_tier2 = _create_parallel_node( - [ - ("business_quality", bq_fn), - ("institutional_flow", inst_fn), - ("valuation", val_fn), - ("entry_timing", et_fn), - ("earnings_revisions", er_fn), - ("sector_rotation", sr_fn), - ("backlog", bl_fn), - ("crowding", cr_fn), - ], - "Tier 2", - ) - parallel_bull_bear = _create_parallel_node( - [("bull_case", bull_fn), ("bear_case", bear_fn)], - "Bull/Bear", - ) - - # Archetype + Score combined node - def archetype_and_score(state): - arch_result = arch_fn(state) - merged = {**state, **arch_result} - score_result = score_fn(merged) - return {**arch_result, **score_result} - - # Theme + Replacement combined node (sequential: theme feeds replacement) - def theme_and_replacement(state): - theme_result = theme_fn(state) - merged = {**state, **theme_result} - replace_result = replace_fn(merged) - return {**theme_result, **replace_result} - - # Risk + Final Decision combined node - def risk_and_decision(state): - risk_result = risk_fn(state) - merged = {**state, **risk_result} - final_result = final_fn(merged) - return {**risk_result, **final_result} - - # Build graph - workflow = StateGraph(PipelineState) - - workflow.add_node("Validation", validation_fn) - workflow.add_node("Tier 1 Analysis", parallel_tier1) - workflow.add_node("Tier 2 Analysis", parallel_tier2) - workflow.add_node("Scoring", archetype_and_score) - workflow.add_node("Portfolio Analysis", theme_and_replacement) - workflow.add_node("Debate", parallel_bull_bear) - workflow.add_node("Debate Referee", debate_fn) - workflow.add_node("Decision", risk_and_decision) - - # Edges - workflow.add_edge(START, "Validation") - workflow.add_conditional_edges( - "Validation", - _veto_gate, - {END: END, "continue": "Tier 1 Analysis"}, - ) - workflow.add_edge("Tier 1 Analysis", "Tier 2 Analysis") - workflow.add_edge("Tier 2 Analysis", "Scoring") - workflow.add_edge("Scoring", "Portfolio Analysis") - workflow.add_edge("Portfolio Analysis", "Debate") - workflow.add_edge("Debate", "Debate Referee") - workflow.add_edge("Debate Referee", "Decision") - workflow.add_edge("Decision", END) - - return workflow.compile() - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - -def _veto_gate(state: Dict[str, Any]) -> str: - """Check if validation resulted in a hard veto.""" - if state.get("hard_veto"): - return END - validation = state.get("validation") or {} - if validation.get("veto"): - return END - return "continue" - - -def _create_parallel_node(agent_fns: List[tuple], label: str): - """Create an async node that runs multiple agent functions in parallel. - - Args: - agent_fns: List of (name, fn) tuples. - label: Label for logging. - """ - - async def parallel_node(state): - t0 = time.time() - - async def run_one(name, fn): - logger.debug("[%s] %s starting", label, name) - result = await asyncio.to_thread(fn, state) - logger.debug("[%s] %s done (%.1fs)", label, name, time.time() - t0) - return result - - tasks = [run_one(name, fn) for name, fn in agent_fns] - results = await asyncio.gather(*tasks, return_exceptions=True) - - merged: Dict[str, Any] = {} - all_flags: list = [] - for (name, _), result in zip(agent_fns, results): - if isinstance(result, Exception): - logger.error("[%s] %s failed: %s", label, name, result) - continue - flags = result.pop("global_flags", []) - all_flags.extend(flags) - merged.update(result) - if all_flags: - merged["global_flags"] = all_flags - - logger.info("[%s] completed in %.1fs", label, time.time() - t0) - return merged - - return parallel_node +"""Graph setup for the structured equity ranking pipeline. + +Pipeline stages: + START → Validation → [veto gate] → Tier 1 (Macro+Liquidity parallel) + → Tier 2 (8 agents parallel) → Scoring (Archetype+MasterScore) + → Tier 3 (Bull+Bear parallel → Debate → Risk → FinalDecision) + → END +""" + +from __future__ import annotations + +import asyncio +import logging +import time +from typing import Any, Dict, List + +from langgraph.graph import END, START, StateGraph + +from tradingagents.agents.utils.agent_states import PipelineState + +logger = logging.getLogger(__name__) + + +class StructuredGraphSetup: + """Builds the structured equity ranking LangGraph.""" + + def __init__(self, quick_llm, deep_llm): + self.quick_llm = quick_llm + self.deep_llm = deep_llm + + def setup_graph(self): + """Build and compile the structured pipeline graph.""" + from tradingagents.agents.structured import ( + create_archetype_node, + create_backlog_node, + create_bear_case_node, + create_bull_case_node, + create_business_quality_node, + create_crowding_node, + create_debate_node, + create_earnings_revisions_node, + create_entry_timing_node, + create_final_decision_node, + create_institutional_flow_node, + create_liquidity_node, + create_macro_node, + create_position_replacement_node, + create_risk_node, + create_scoring_node, + create_sector_rotation_node, + create_theme_substitution_node, + create_validation_node, + create_valuation_node, + ) + + # Create node functions + # Tier 1: cheap model (or no LLM for validation) + validation_fn = create_validation_node() + macro_fn = create_macro_node(self.quick_llm) + liquidity_fn = create_liquidity_node(self.quick_llm) + + # Tier 2: cheap model for analysis + bq_fn = create_business_quality_node(self.quick_llm) + inst_fn = create_institutional_flow_node(self.quick_llm) + val_fn = create_valuation_node(self.quick_llm) + et_fn = create_entry_timing_node(self.quick_llm) + er_fn = create_earnings_revisions_node(self.quick_llm) + sr_fn = create_sector_rotation_node(self.quick_llm) + bl_fn = create_backlog_node(self.quick_llm) + cr_fn = create_crowding_node(self.quick_llm) + arch_fn = create_archetype_node(self.quick_llm) + score_fn = create_scoring_node() + + # Portfolio-level: deep model for theme/replacement analysis + theme_fn = create_theme_substitution_node(self.deep_llm) + replace_fn = create_position_replacement_node(self.deep_llm) + + # Tier 3: deep model for reasoning/debate + bull_fn = create_bull_case_node(self.deep_llm) + bear_fn = create_bear_case_node(self.deep_llm) + debate_fn = create_debate_node(self.deep_llm) + risk_fn = create_risk_node(self.deep_llm) + final_fn = create_final_decision_node(self.deep_llm) + + # Build parallel wrapper nodes + parallel_tier1 = _create_parallel_node( + [("macro", macro_fn), ("liquidity", liquidity_fn)], + "Tier 1", + ) + parallel_tier2 = _create_parallel_node( + [ + ("business_quality", bq_fn), + ("institutional_flow", inst_fn), + ("valuation", val_fn), + ("entry_timing", et_fn), + ("earnings_revisions", er_fn), + ("sector_rotation", sr_fn), + ("backlog", bl_fn), + ("crowding", cr_fn), + ], + "Tier 2", + ) + parallel_bull_bear = _create_parallel_node( + [("bull_case", bull_fn), ("bear_case", bear_fn)], + "Bull/Bear", + ) + + # Archetype + Score combined node + def archetype_and_score(state): + arch_result = arch_fn(state) + merged = {**state, **arch_result} + score_result = score_fn(merged) + return {**arch_result, **score_result} + + # Theme + Replacement combined node (sequential: theme feeds replacement) + def theme_and_replacement(state): + theme_result = theme_fn(state) + merged = {**state, **theme_result} + replace_result = replace_fn(merged) + return {**theme_result, **replace_result} + + # Risk + Final Decision combined node + def risk_and_decision(state): + risk_result = risk_fn(state) + merged = {**state, **risk_result} + final_result = final_fn(merged) + return {**risk_result, **final_result} + + # Build graph + workflow = StateGraph(PipelineState) + + workflow.add_node("Validation", validation_fn) + workflow.add_node("Tier 1 Analysis", parallel_tier1) + workflow.add_node("Tier 2 Analysis", parallel_tier2) + workflow.add_node("Scoring", archetype_and_score) + workflow.add_node("Portfolio Analysis", theme_and_replacement) + workflow.add_node("Debate", parallel_bull_bear) + workflow.add_node("Debate Referee", debate_fn) + workflow.add_node("Decision", risk_and_decision) + + # Edges + workflow.add_edge(START, "Validation") + workflow.add_conditional_edges( + "Validation", + _veto_gate, + {END: END, "continue": "Tier 1 Analysis"}, + ) + workflow.add_edge("Tier 1 Analysis", "Tier 2 Analysis") + workflow.add_edge("Tier 2 Analysis", "Scoring") + workflow.add_edge("Scoring", "Portfolio Analysis") + workflow.add_edge("Portfolio Analysis", "Debate") + workflow.add_edge("Debate", "Debate Referee") + workflow.add_edge("Debate Referee", "Decision") + workflow.add_edge("Decision", END) + + return workflow.compile() + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def _veto_gate(state: Dict[str, Any]) -> str: + """Check if validation resulted in a hard veto.""" + if state.get("hard_veto"): + return END + validation = state.get("validation") or {} + if validation.get("veto"): + return END + return "continue" + + +def _create_parallel_node(agent_fns: List[tuple], label: str): + """Create an async node that runs multiple agent functions in parallel. + + Args: + agent_fns: List of (name, fn) tuples. + label: Label for logging. + """ + + async def parallel_node(state): + t0 = time.time() + + async def run_one(name, fn): + logger.debug("[%s] %s starting", label, name) + result = await asyncio.to_thread(fn, state) + logger.debug("[%s] %s done (%.1fs)", label, name, time.time() - t0) + return result + + tasks = [run_one(name, fn) for name, fn in agent_fns] + results = await asyncio.gather(*tasks, return_exceptions=True) + + merged: Dict[str, Any] = {} + all_flags: list = [] + for (name, _), result in zip(agent_fns, results): + if isinstance(result, Exception): + logger.error("[%s] %s failed: %s", label, name, result) + continue + flags = result.pop("global_flags", []) + all_flags.extend(flags) + merged.update(result) + if all_flags: + merged["global_flags"] = all_flags + + logger.info("[%s] completed in %.1fs", label, time.time() - t0) + return merged + + return parallel_node diff --git a/tradingagents/graph/signal_processing.py b/tradingagents/graph/signal_processing.py index 903e8529..d4b843e5 100644 --- a/tradingagents/graph/signal_processing.py +++ b/tradingagents/graph/signal_processing.py @@ -1,31 +1,31 @@ -# TradingAgents/graph/signal_processing.py - -from langchain_openai import ChatOpenAI - - -class SignalProcessor: - """Processes trading signals to extract actionable decisions.""" - - def __init__(self, quick_thinking_llm: ChatOpenAI): - """Initialize with an LLM for processing.""" - self.quick_thinking_llm = quick_thinking_llm - - def process_signal(self, full_signal: str) -> str: - """ - Process a full trading signal to extract the core decision. - - Args: - full_signal: Complete trading signal text - - Returns: - Extracted decision (BUY, SELL, or HOLD) - """ - messages = [ - ( - "system", - "You are an efficient assistant designed to analyze paragraphs or financial reports provided by a group of analysts. Your task is to extract the investment decision: SELL, BUY, or HOLD. Provide only the extracted decision (SELL, BUY, or HOLD) as your output, without adding any additional text or information.", - ), - ("human", full_signal), - ] - - return self.quick_thinking_llm.invoke(messages).content +# TradingAgents/graph/signal_processing.py + +from langchain_openai import ChatOpenAI + + +class SignalProcessor: + """Processes trading signals to extract actionable decisions.""" + + def __init__(self, quick_thinking_llm: ChatOpenAI): + """Initialize with an LLM for processing.""" + self.quick_thinking_llm = quick_thinking_llm + + def process_signal(self, full_signal: str) -> str: + """ + Process a full trading signal to extract the core decision. + + Args: + full_signal: Complete trading signal text + + Returns: + Extracted decision (BUY, SELL, or HOLD) + """ + messages = [ + ( + "system", + "You are an efficient assistant designed to analyze paragraphs or financial reports provided by a group of analysts. Your task is to extract the investment decision: SELL, BUY, or HOLD. Provide only the extracted decision (SELL, BUY, or HOLD) as your output, without adding any additional text or information.", + ), + ("human", full_signal), + ] + + return self.quick_thinking_llm.invoke(messages).content diff --git a/tradingagents/graph/trading_graph.py b/tradingagents/graph/trading_graph.py index 56329f86..3d23b0ee 100644 --- a/tradingagents/graph/trading_graph.py +++ b/tradingagents/graph/trading_graph.py @@ -1,198 +1,198 @@ -"""Main orchestrator for the structured equity ranking engine. - -Replaces the old TradingAgentsGraph with a tiered Pydantic-based pipeline. -""" - -from __future__ import annotations - -import json -import os -import logging -from datetime import date -from pathlib import Path -from typing import Any, Dict, List, Optional - -from tradingagents.default_config import DEFAULT_CONFIG -from tradingagents.llm_clients import create_llm_client -from tradingagents.dataflows.config import set_config - -from .setup import StructuredGraphSetup - -logger = logging.getLogger(__name__) - - -class TradingAgentsGraph: - """Structured equity ranking engine built on LangGraph.""" - - def __init__( - self, - selected_analysts=None, # ignored — all agents run in structured pipeline - debug=False, - config: Optional[Dict[str, Any]] = None, - callbacks: Optional[List] = None, - ): - self.debug = debug - self.config = config or DEFAULT_CONFIG - self.callbacks = callbacks or [] - - set_config(self.config) - - os.makedirs( - os.path.join(self.config["project_dir"], "dataflows/data_cache"), - exist_ok=True, - ) - - # Initialize LLMs - llm_kwargs = self._get_provider_kwargs() - if self.callbacks: - llm_kwargs["callbacks"] = self.callbacks - - deep_client = create_llm_client( - provider=self.config["llm_provider"], - model=self.config["deep_think_llm"], - base_url=self.config.get("backend_url"), - **llm_kwargs, - ) - quick_client = create_llm_client( - provider=self.config["llm_provider"], - model=self.config["quick_think_llm"], - base_url=self.config.get("backend_url"), - **llm_kwargs, - ) - - self.deep_thinking_llm = deep_client.get_llm() - self.quick_thinking_llm = quick_client.get_llm() - - # Build the structured pipeline graph - graph_setup = StructuredGraphSetup( - self.quick_thinking_llm, self.deep_thinking_llm - ) - self.graph = graph_setup.setup_graph() - - # State tracking - self.curr_state = None - self.ticker = None - - def _get_provider_kwargs(self) -> Dict[str, Any]: - kwargs = {} - provider = self.config.get("llm_provider", "").lower() - if provider == "google": - thinking_level = self.config.get("google_thinking_level") - if thinking_level: - kwargs["thinking_level"] = thinking_level - elif provider == "openai": - reasoning_effort = self.config.get("openai_reasoning_effort") - if reasoning_effort: - kwargs["reasoning_effort"] = reasoning_effort - return kwargs - - async def propagate(self, company_name: str, trade_date: str): - """Run the structured pipeline for a company (async — parallel nodes).""" - import asyncio - - self.ticker = company_name - init_state = self._create_initial_state(company_name, trade_date) - args = {"config": {"recursion_limit": 50}} - - if self.debug: - trace = [] - async for chunk in self.graph.astream(init_state, stream_mode="values", **args): - trace.append(chunk) - final_state = trace[-1] if trace else init_state - else: - final_state = await self.graph.ainvoke(init_state, **args) - - self.curr_state = final_state - self._log_state(trade_date, final_state) - - decision = final_state.get("final_decision") or {} - signal = decision.get("action", "AVOID") - return final_state, signal - - def _create_initial_state(self, ticker: str, trade_date: str) -> Dict[str, Any]: - return { - "ticker": ticker.upper(), - "trade_date": str(trade_date), - "validation": None, - "company_card": None, - "macro": None, - "liquidity": None, - "sector_rotation": None, - "business_quality": None, - "institutional_flow": None, - "valuation": None, - "entry_timing": None, - "earnings_revisions": None, - "backlog": None, - "crowding": None, - "archetype": None, - "master_score": None, - "adjusted_score": None, - "position_role": None, - "theme_substitution": None, - "position_replacement": None, - "bull_case": None, - "bear_case": None, - "debate": None, - "risk": None, - "final_decision": None, - "hard_veto": False, - "hard_veto_reason": None, - "global_flags": [], - } - - def _log_state(self, trade_date: str, state: Dict[str, Any]): - """Log the final state to JSON.""" - log_data = { - "ticker": state.get("ticker"), - "trade_date": str(trade_date), - "master_score": state.get("master_score"), - "adjusted_score": state.get("adjusted_score"), - "position_role": state.get("position_role"), - "hard_veto": state.get("hard_veto"), - "validation": state.get("validation"), - "company_card": state.get("company_card"), - "macro": state.get("macro"), - "liquidity": state.get("liquidity"), - "business_quality": state.get("business_quality"), - "institutional_flow": state.get("institutional_flow"), - "valuation": state.get("valuation"), - "entry_timing": state.get("entry_timing"), - "earnings_revisions": state.get("earnings_revisions"), - "sector_rotation": state.get("sector_rotation"), - "backlog": state.get("backlog"), - "crowding": state.get("crowding"), - "archetype": state.get("archetype"), - "theme_substitution": state.get("theme_substitution"), - "position_replacement": state.get("position_replacement"), - "bull_case": state.get("bull_case"), - "bear_case": state.get("bear_case"), - "debate": state.get("debate"), - "risk": state.get("risk"), - "final_decision": state.get("final_decision"), - } - - directory = Path(f"eval_results/{self.ticker}/StructuredPipeline_logs/") - directory.mkdir(parents=True, exist_ok=True) - - filepath = directory / f"analysis_{trade_date}.json" - with open(filepath, "w") as f: - json.dump(log_data, f, indent=2, default=str) - logger.info("State logged to %s", filepath) - - def process_signal(self, decision_text: str) -> str: - """Extract signal from decision text (legacy compatibility).""" - if isinstance(decision_text, dict): - return decision_text.get("action", "AVOID") - text = str(decision_text).upper() - if "BUY" in text: - return "BUY" - if "SELL" in text: - return "SELL" - if "HOLD" in text: - return "HOLD" - return "AVOID" - - def reflect_and_remember(self, returns_losses): - """No-op for structured pipeline (no BM25 memory).""" - pass +"""Main orchestrator for the structured equity ranking engine. + +Replaces the old TradingAgentsGraph with a tiered Pydantic-based pipeline. +""" + +from __future__ import annotations + +import json +import os +import logging +from datetime import date +from pathlib import Path +from typing import Any, Dict, List, Optional + +from tradingagents.default_config import DEFAULT_CONFIG +from tradingagents.llm_clients import create_llm_client +from tradingagents.dataflows.config import set_config + +from .setup import StructuredGraphSetup + +logger = logging.getLogger(__name__) + + +class TradingAgentsGraph: + """Structured equity ranking engine built on LangGraph.""" + + def __init__( + self, + selected_analysts=None, # ignored — all agents run in structured pipeline + debug=False, + config: Optional[Dict[str, Any]] = None, + callbacks: Optional[List] = None, + ): + self.debug = debug + self.config = config or DEFAULT_CONFIG + self.callbacks = callbacks or [] + + set_config(self.config) + + os.makedirs( + os.path.join(self.config["project_dir"], "dataflows/data_cache"), + exist_ok=True, + ) + + # Initialize LLMs + llm_kwargs = self._get_provider_kwargs() + if self.callbacks: + llm_kwargs["callbacks"] = self.callbacks + + deep_client = create_llm_client( + provider=self.config["llm_provider"], + model=self.config["deep_think_llm"], + base_url=self.config.get("backend_url"), + **llm_kwargs, + ) + quick_client = create_llm_client( + provider=self.config["llm_provider"], + model=self.config["quick_think_llm"], + base_url=self.config.get("backend_url"), + **llm_kwargs, + ) + + self.deep_thinking_llm = deep_client.get_llm() + self.quick_thinking_llm = quick_client.get_llm() + + # Build the structured pipeline graph + graph_setup = StructuredGraphSetup( + self.quick_thinking_llm, self.deep_thinking_llm + ) + self.graph = graph_setup.setup_graph() + + # State tracking + self.curr_state = None + self.ticker = None + + def _get_provider_kwargs(self) -> Dict[str, Any]: + kwargs = {} + provider = self.config.get("llm_provider", "").lower() + if provider == "google": + thinking_level = self.config.get("google_thinking_level") + if thinking_level: + kwargs["thinking_level"] = thinking_level + elif provider == "openai": + reasoning_effort = self.config.get("openai_reasoning_effort") + if reasoning_effort: + kwargs["reasoning_effort"] = reasoning_effort + return kwargs + + async def propagate(self, company_name: str, trade_date: str): + """Run the structured pipeline for a company (async — parallel nodes).""" + import asyncio + + self.ticker = company_name + init_state = self._create_initial_state(company_name, trade_date) + args = {"config": {"recursion_limit": 50}} + + if self.debug: + trace = [] + async for chunk in self.graph.astream(init_state, stream_mode="values", **args): + trace.append(chunk) + final_state = trace[-1] if trace else init_state + else: + final_state = await self.graph.ainvoke(init_state, **args) + + self.curr_state = final_state + self._log_state(trade_date, final_state) + + decision = final_state.get("final_decision") or {} + signal = decision.get("action", "AVOID") + return final_state, signal + + def _create_initial_state(self, ticker: str, trade_date: str) -> Dict[str, Any]: + return { + "ticker": ticker.upper(), + "trade_date": str(trade_date), + "validation": None, + "company_card": None, + "macro": None, + "liquidity": None, + "sector_rotation": None, + "business_quality": None, + "institutional_flow": None, + "valuation": None, + "entry_timing": None, + "earnings_revisions": None, + "backlog": None, + "crowding": None, + "archetype": None, + "master_score": None, + "adjusted_score": None, + "position_role": None, + "theme_substitution": None, + "position_replacement": None, + "bull_case": None, + "bear_case": None, + "debate": None, + "risk": None, + "final_decision": None, + "hard_veto": False, + "hard_veto_reason": None, + "global_flags": [], + } + + def _log_state(self, trade_date: str, state: Dict[str, Any]): + """Log the final state to JSON.""" + log_data = { + "ticker": state.get("ticker"), + "trade_date": str(trade_date), + "master_score": state.get("master_score"), + "adjusted_score": state.get("adjusted_score"), + "position_role": state.get("position_role"), + "hard_veto": state.get("hard_veto"), + "validation": state.get("validation"), + "company_card": state.get("company_card"), + "macro": state.get("macro"), + "liquidity": state.get("liquidity"), + "business_quality": state.get("business_quality"), + "institutional_flow": state.get("institutional_flow"), + "valuation": state.get("valuation"), + "entry_timing": state.get("entry_timing"), + "earnings_revisions": state.get("earnings_revisions"), + "sector_rotation": state.get("sector_rotation"), + "backlog": state.get("backlog"), + "crowding": state.get("crowding"), + "archetype": state.get("archetype"), + "theme_substitution": state.get("theme_substitution"), + "position_replacement": state.get("position_replacement"), + "bull_case": state.get("bull_case"), + "bear_case": state.get("bear_case"), + "debate": state.get("debate"), + "risk": state.get("risk"), + "final_decision": state.get("final_decision"), + } + + directory = Path(f"eval_results/{self.ticker}/StructuredPipeline_logs/") + directory.mkdir(parents=True, exist_ok=True) + + filepath = directory / f"analysis_{trade_date}.json" + with open(filepath, "w") as f: + json.dump(log_data, f, indent=2, default=str) + logger.info("State logged to %s", filepath) + + def process_signal(self, decision_text: str) -> str: + """Extract signal from decision text (legacy compatibility).""" + if isinstance(decision_text, dict): + return decision_text.get("action", "AVOID") + text = str(decision_text).upper() + if "BUY" in text: + return "BUY" + if "SELL" in text: + return "SELL" + if "HOLD" in text: + return "HOLD" + return "AVOID" + + def reflect_and_remember(self, returns_losses): + """No-op for structured pipeline (no BM25 memory).""" + pass diff --git a/tradingagents/llm_clients/TODO.md b/tradingagents/llm_clients/TODO.md index d5b5ac9c..835bdc8d 100644 --- a/tradingagents/llm_clients/TODO.md +++ b/tradingagents/llm_clients/TODO.md @@ -1,24 +1,24 @@ -# LLM Clients - Consistency Improvements - -## Issues to Fix - -### 1. `validate_model()` is never called -- Add validation call in `get_llm()` with warning (not error) for unknown models - -### 2. Inconsistent parameter handling -| Client | API Key Param | Special Params | -|--------|---------------|----------------| -| OpenAI | `api_key` | `reasoning_effort` | -| Anthropic | `api_key` | `thinking_config` → `thinking` | -| Google | `google_api_key` | `thinking_budget` | - -**Fix:** Standardize with unified `api_key` that maps to provider-specific keys - -### 3. `base_url` accepted but ignored -- `AnthropicClient`: accepts `base_url` but never uses it -- `GoogleClient`: accepts `base_url` but never uses it (correct - Google doesn't support it) - -**Fix:** Remove unused `base_url` from clients that don't support it - -### 4. Update validators.py with models from CLI -- Sync `VALID_MODELS` dict with CLI model options after Feature 2 is complete +# LLM Clients - Consistency Improvements + +## Issues to Fix + +### 1. `validate_model()` is never called +- Add validation call in `get_llm()` with warning (not error) for unknown models + +### 2. Inconsistent parameter handling +| Client | API Key Param | Special Params | +|--------|---------------|----------------| +| OpenAI | `api_key` | `reasoning_effort` | +| Anthropic | `api_key` | `thinking_config` → `thinking` | +| Google | `google_api_key` | `thinking_budget` | + +**Fix:** Standardize with unified `api_key` that maps to provider-specific keys + +### 3. `base_url` accepted but ignored +- `AnthropicClient`: accepts `base_url` but never uses it +- `GoogleClient`: accepts `base_url` but never uses it (correct - Google doesn't support it) + +**Fix:** Remove unused `base_url` from clients that don't support it + +### 4. Update validators.py with models from CLI +- Sync `VALID_MODELS` dict with CLI model options after Feature 2 is complete diff --git a/tradingagents/llm_clients/__init__.py b/tradingagents/llm_clients/__init__.py index e528eabe..5c42f2e6 100644 --- a/tradingagents/llm_clients/__init__.py +++ b/tradingagents/llm_clients/__init__.py @@ -1,4 +1,4 @@ -from .base_client import BaseLLMClient -from .factory import create_llm_client - -__all__ = ["BaseLLMClient", "create_llm_client"] +from .base_client import BaseLLMClient +from .factory import create_llm_client + +__all__ = ["BaseLLMClient", "create_llm_client"] diff --git a/tradingagents/llm_clients/anthropic_client.py b/tradingagents/llm_clients/anthropic_client.py index e2f1abba..4336fda4 100644 --- a/tradingagents/llm_clients/anthropic_client.py +++ b/tradingagents/llm_clients/anthropic_client.py @@ -1,27 +1,27 @@ -from typing import Any, Optional - -from langchain_anthropic import ChatAnthropic - -from .base_client import BaseLLMClient -from .validators import validate_model - - -class AnthropicClient(BaseLLMClient): - """Client for Anthropic Claude models.""" - - def __init__(self, model: str, base_url: Optional[str] = None, **kwargs): - super().__init__(model, base_url, **kwargs) - - def get_llm(self) -> Any: - """Return configured ChatAnthropic instance.""" - llm_kwargs = {"model": self.model} - - for key in ("timeout", "max_retries", "api_key", "max_tokens", "callbacks"): - if key in self.kwargs: - llm_kwargs[key] = self.kwargs[key] - - return ChatAnthropic(**llm_kwargs) - - def validate_model(self) -> bool: - """Validate model for Anthropic.""" - return validate_model("anthropic", self.model) +from typing import Any, Optional + +from langchain_anthropic import ChatAnthropic + +from .base_client import BaseLLMClient +from .validators import validate_model + + +class AnthropicClient(BaseLLMClient): + """Client for Anthropic Claude models.""" + + def __init__(self, model: str, base_url: Optional[str] = None, **kwargs): + super().__init__(model, base_url, **kwargs) + + def get_llm(self) -> Any: + """Return configured ChatAnthropic instance.""" + llm_kwargs = {"model": self.model} + + for key in ("timeout", "max_retries", "api_key", "max_tokens", "callbacks"): + if key in self.kwargs: + llm_kwargs[key] = self.kwargs[key] + + return ChatAnthropic(**llm_kwargs) + + def validate_model(self) -> bool: + """Validate model for Anthropic.""" + return validate_model("anthropic", self.model) diff --git a/tradingagents/llm_clients/base_client.py b/tradingagents/llm_clients/base_client.py index 43845575..8074ab11 100644 --- a/tradingagents/llm_clients/base_client.py +++ b/tradingagents/llm_clients/base_client.py @@ -1,21 +1,21 @@ -from abc import ABC, abstractmethod -from typing import Any, Optional - - -class BaseLLMClient(ABC): - """Abstract base class for LLM clients.""" - - def __init__(self, model: str, base_url: Optional[str] = None, **kwargs): - self.model = model - self.base_url = base_url - self.kwargs = kwargs - - @abstractmethod - def get_llm(self) -> Any: - """Return the configured LLM instance.""" - pass - - @abstractmethod - def validate_model(self) -> bool: - """Validate that the model is supported by this client.""" - pass +from abc import ABC, abstractmethod +from typing import Any, Optional + + +class BaseLLMClient(ABC): + """Abstract base class for LLM clients.""" + + def __init__(self, model: str, base_url: Optional[str] = None, **kwargs): + self.model = model + self.base_url = base_url + self.kwargs = kwargs + + @abstractmethod + def get_llm(self) -> Any: + """Return the configured LLM instance.""" + pass + + @abstractmethod + def validate_model(self) -> bool: + """Validate that the model is supported by this client.""" + pass diff --git a/tradingagents/llm_clients/factory.py b/tradingagents/llm_clients/factory.py index 028c88a2..155c4408 100644 --- a/tradingagents/llm_clients/factory.py +++ b/tradingagents/llm_clients/factory.py @@ -1,43 +1,43 @@ -from typing import Optional - -from .base_client import BaseLLMClient -from .openai_client import OpenAIClient -from .anthropic_client import AnthropicClient -from .google_client import GoogleClient - - -def create_llm_client( - provider: str, - model: str, - base_url: Optional[str] = None, - **kwargs, -) -> BaseLLMClient: - """Create an LLM client for the specified provider. - - Args: - provider: LLM provider (openai, anthropic, google, xai, ollama, openrouter) - model: Model name/identifier - base_url: Optional base URL for API endpoint - **kwargs: Additional provider-specific arguments - - Returns: - Configured BaseLLMClient instance - - Raises: - ValueError: If provider is not supported - """ - provider_lower = provider.lower() - - if provider_lower in ("openai", "ollama", "openrouter"): - return OpenAIClient(model, base_url, provider=provider_lower, **kwargs) - - if provider_lower == "xai": - return OpenAIClient(model, base_url, provider="xai", **kwargs) - - if provider_lower == "anthropic": - return AnthropicClient(model, base_url, **kwargs) - - if provider_lower == "google": - return GoogleClient(model, base_url, **kwargs) - - raise ValueError(f"Unsupported LLM provider: {provider}") +from typing import Optional + +from .base_client import BaseLLMClient +from .openai_client import OpenAIClient +from .anthropic_client import AnthropicClient +from .google_client import GoogleClient + + +def create_llm_client( + provider: str, + model: str, + base_url: Optional[str] = None, + **kwargs, +) -> BaseLLMClient: + """Create an LLM client for the specified provider. + + Args: + provider: LLM provider (openai, anthropic, google, xai, ollama, openrouter) + model: Model name/identifier + base_url: Optional base URL for API endpoint + **kwargs: Additional provider-specific arguments + + Returns: + Configured BaseLLMClient instance + + Raises: + ValueError: If provider is not supported + """ + provider_lower = provider.lower() + + if provider_lower in ("openai", "ollama", "openrouter"): + return OpenAIClient(model, base_url, provider=provider_lower, **kwargs) + + if provider_lower == "xai": + return OpenAIClient(model, base_url, provider="xai", **kwargs) + + if provider_lower == "anthropic": + return AnthropicClient(model, base_url, **kwargs) + + if provider_lower == "google": + return GoogleClient(model, base_url, **kwargs) + + raise ValueError(f"Unsupported LLM provider: {provider}") diff --git a/tradingagents/llm_clients/google_client.py b/tradingagents/llm_clients/google_client.py index a1bd386b..4a05ad51 100644 --- a/tradingagents/llm_clients/google_client.py +++ b/tradingagents/llm_clients/google_client.py @@ -1,65 +1,65 @@ -from typing import Any, Optional - -from langchain_google_genai import ChatGoogleGenerativeAI - -from .base_client import BaseLLMClient -from .validators import validate_model - - -class NormalizedChatGoogleGenerativeAI(ChatGoogleGenerativeAI): - """ChatGoogleGenerativeAI with normalized content output. - - Gemini 3 models return content as list: [{'type': 'text', 'text': '...'}] - This normalizes to string for consistent downstream handling. - """ - - def _normalize_content(self, response): - content = response.content - if isinstance(content, list): - texts = [ - item.get("text", "") if isinstance(item, dict) and item.get("type") == "text" - else item if isinstance(item, str) else "" - for item in content - ] - response.content = "\n".join(t for t in texts if t) - return response - - def invoke(self, input, config=None, **kwargs): - return self._normalize_content(super().invoke(input, config, **kwargs)) - - -class GoogleClient(BaseLLMClient): - """Client for Google Gemini models.""" - - def __init__(self, model: str, base_url: Optional[str] = None, **kwargs): - super().__init__(model, base_url, **kwargs) - - def get_llm(self) -> Any: - """Return configured ChatGoogleGenerativeAI instance.""" - llm_kwargs = {"model": self.model} - - for key in ("timeout", "max_retries", "google_api_key", "callbacks"): - if key in self.kwargs: - llm_kwargs[key] = self.kwargs[key] - - # Map thinking_level to appropriate API param based on model - # Gemini 3 Pro: low, high - # Gemini 3 Flash: minimal, low, medium, high - # Gemini 2.5: thinking_budget (0=disable, -1=dynamic) - thinking_level = self.kwargs.get("thinking_level") - if thinking_level: - model_lower = self.model.lower() - if "gemini-3" in model_lower: - # Gemini 3 Pro doesn't support "minimal", use "low" instead - if "pro" in model_lower and thinking_level == "minimal": - thinking_level = "low" - llm_kwargs["thinking_level"] = thinking_level - else: - # Gemini 2.5: map to thinking_budget - llm_kwargs["thinking_budget"] = -1 if thinking_level == "high" else 0 - - return NormalizedChatGoogleGenerativeAI(**llm_kwargs) - - def validate_model(self) -> bool: - """Validate model for Google.""" - return validate_model("google", self.model) +from typing import Any, Optional + +from langchain_google_genai import ChatGoogleGenerativeAI + +from .base_client import BaseLLMClient +from .validators import validate_model + + +class NormalizedChatGoogleGenerativeAI(ChatGoogleGenerativeAI): + """ChatGoogleGenerativeAI with normalized content output. + + Gemini 3 models return content as list: [{'type': 'text', 'text': '...'}] + This normalizes to string for consistent downstream handling. + """ + + def _normalize_content(self, response): + content = response.content + if isinstance(content, list): + texts = [ + item.get("text", "") if isinstance(item, dict) and item.get("type") == "text" + else item if isinstance(item, str) else "" + for item in content + ] + response.content = "\n".join(t for t in texts if t) + return response + + def invoke(self, input, config=None, **kwargs): + return self._normalize_content(super().invoke(input, config, **kwargs)) + + +class GoogleClient(BaseLLMClient): + """Client for Google Gemini models.""" + + def __init__(self, model: str, base_url: Optional[str] = None, **kwargs): + super().__init__(model, base_url, **kwargs) + + def get_llm(self) -> Any: + """Return configured ChatGoogleGenerativeAI instance.""" + llm_kwargs = {"model": self.model} + + for key in ("timeout", "max_retries", "google_api_key", "callbacks"): + if key in self.kwargs: + llm_kwargs[key] = self.kwargs[key] + + # Map thinking_level to appropriate API param based on model + # Gemini 3 Pro: low, high + # Gemini 3 Flash: minimal, low, medium, high + # Gemini 2.5: thinking_budget (0=disable, -1=dynamic) + thinking_level = self.kwargs.get("thinking_level") + if thinking_level: + model_lower = self.model.lower() + if "gemini-3" in model_lower: + # Gemini 3 Pro doesn't support "minimal", use "low" instead + if "pro" in model_lower and thinking_level == "minimal": + thinking_level = "low" + llm_kwargs["thinking_level"] = thinking_level + else: + # Gemini 2.5: map to thinking_budget + llm_kwargs["thinking_budget"] = -1 if thinking_level == "high" else 0 + + return NormalizedChatGoogleGenerativeAI(**llm_kwargs) + + def validate_model(self) -> bool: + """Validate model for Google.""" + return validate_model("google", self.model) diff --git a/tradingagents/llm_clients/openai_client.py b/tradingagents/llm_clients/openai_client.py index 96ceb66b..6d371ca0 100644 --- a/tradingagents/llm_clients/openai_client.py +++ b/tradingagents/llm_clients/openai_client.py @@ -1,75 +1,75 @@ -import os -from typing import Any, Optional - -from langchain_openai import ChatOpenAI - -from .base_client import BaseLLMClient -from .validators import validate_model - - -class UnifiedChatOpenAI(ChatOpenAI): - """ChatOpenAI subclass that strips incompatible params for certain models.""" - - def __init__(self, **kwargs): - model = kwargs.get("model", "") - if self._is_reasoning_model(model): - kwargs.pop("temperature", None) - kwargs.pop("top_p", None) - super().__init__(**kwargs) - - @staticmethod - def _is_reasoning_model(model: str) -> bool: - """Check if model is a reasoning model that doesn't support temperature.""" - model_lower = model.lower() - return ( - model_lower.startswith("o1") - or model_lower.startswith("o3") - or "gpt-5" in model_lower - ) - - -class OpenAIClient(BaseLLMClient): - """Client for OpenAI, Ollama, OpenRouter, and xAI providers.""" - - def __init__( - self, - model: str, - base_url: Optional[str] = None, - provider: str = "openai", - **kwargs, - ): - super().__init__(model, base_url, **kwargs) - self.provider = provider.lower() - - def get_llm(self) -> Any: - """Return configured ChatOpenAI instance.""" - llm_kwargs = {"model": self.model} - - if self.provider == "xai": - llm_kwargs["base_url"] = "https://api.x.ai/v1" - api_key = os.environ.get("XAI_API_KEY") - if api_key: - llm_kwargs["api_key"] = api_key - elif self.provider == "openrouter": - llm_kwargs["base_url"] = "https://openrouter.ai/api/v1" - api_key = os.environ.get("OPENROUTER_API_KEY") - if api_key: - llm_kwargs["api_key"] = api_key - elif self.provider == "ollama": - llm_kwargs["base_url"] = "http://localhost:11434/v1" - llm_kwargs["api_key"] = "ollama" # Ollama doesn't require auth - elif self.base_url: - llm_kwargs["base_url"] = self.base_url - api_key = os.environ.get("OPENAI_API_KEY") - if api_key: - llm_kwargs["api_key"] = api_key - - for key in ("timeout", "max_retries", "reasoning_effort", "api_key", "callbacks"): - if key in self.kwargs: - llm_kwargs[key] = self.kwargs[key] - - return UnifiedChatOpenAI(**llm_kwargs) - - def validate_model(self) -> bool: - """Validate model for the provider.""" - return validate_model(self.provider, self.model) +import os +from typing import Any, Optional + +from langchain_openai import ChatOpenAI + +from .base_client import BaseLLMClient +from .validators import validate_model + + +class UnifiedChatOpenAI(ChatOpenAI): + """ChatOpenAI subclass that strips incompatible params for certain models.""" + + def __init__(self, **kwargs): + model = kwargs.get("model", "") + if self._is_reasoning_model(model): + kwargs.pop("temperature", None) + kwargs.pop("top_p", None) + super().__init__(**kwargs) + + @staticmethod + def _is_reasoning_model(model: str) -> bool: + """Check if model is a reasoning model that doesn't support temperature.""" + model_lower = model.lower() + return ( + model_lower.startswith("o1") + or model_lower.startswith("o3") + or "gpt-5" in model_lower + ) + + +class OpenAIClient(BaseLLMClient): + """Client for OpenAI, Ollama, OpenRouter, and xAI providers.""" + + def __init__( + self, + model: str, + base_url: Optional[str] = None, + provider: str = "openai", + **kwargs, + ): + super().__init__(model, base_url, **kwargs) + self.provider = provider.lower() + + def get_llm(self) -> Any: + """Return configured ChatOpenAI instance.""" + llm_kwargs = {"model": self.model} + + if self.provider == "xai": + llm_kwargs["base_url"] = "https://api.x.ai/v1" + api_key = os.environ.get("XAI_API_KEY") + if api_key: + llm_kwargs["api_key"] = api_key + elif self.provider == "openrouter": + llm_kwargs["base_url"] = "https://openrouter.ai/api/v1" + api_key = os.environ.get("OPENROUTER_API_KEY") + if api_key: + llm_kwargs["api_key"] = api_key + elif self.provider == "ollama": + llm_kwargs["base_url"] = "http://localhost:11434/v1" + llm_kwargs["api_key"] = "ollama" # Ollama doesn't require auth + elif self.base_url: + llm_kwargs["base_url"] = self.base_url + api_key = os.environ.get("OPENAI_API_KEY") + if api_key: + llm_kwargs["api_key"] = api_key + + for key in ("timeout", "max_retries", "reasoning_effort", "api_key", "callbacks"): + if key in self.kwargs: + llm_kwargs[key] = self.kwargs[key] + + return UnifiedChatOpenAI(**llm_kwargs) + + def validate_model(self) -> bool: + """Validate model for the provider.""" + return validate_model(self.provider, self.model) diff --git a/tradingagents/llm_clients/validators.py b/tradingagents/llm_clients/validators.py index 3c0f2290..8e32a49c 100644 --- a/tradingagents/llm_clients/validators.py +++ b/tradingagents/llm_clients/validators.py @@ -1,82 +1,82 @@ -"""Model name validators for each provider. - -Only validates model names - does NOT enforce limits. -Let LLM providers use their own defaults for unspecified params. -""" - -VALID_MODELS = { - "openai": [ - # GPT-5 series (2025) - "gpt-5.2", - "gpt-5.1", - "gpt-5", - "gpt-5-mini", - "gpt-5-nano", - # GPT-4.1 series (2025) - "gpt-4.1", - "gpt-4.1-mini", - "gpt-4.1-nano", - # o-series reasoning models - "o4-mini", - "o3", - "o3-mini", - "o1", - "o1-preview", - # GPT-4o series (legacy but still supported) - "gpt-4o", - "gpt-4o-mini", - ], - "anthropic": [ - # Claude 4.5 series (2025) - "claude-opus-4-5", - "claude-sonnet-4-5", - "claude-haiku-4-5", - # Claude 4.x series - "claude-opus-4-1-20250805", - "claude-sonnet-4-20250514", - # Claude 3.7 series - "claude-3-7-sonnet-20250219", - # Claude 3.5 series (legacy) - "claude-3-5-haiku-20241022", - "claude-3-5-sonnet-20241022", - ], - "google": [ - # Gemini 3 series (preview) - "gemini-3-pro-preview", - "gemini-3-flash-preview", - # Gemini 2.5 series - "gemini-2.5-pro", - "gemini-2.5-flash", - "gemini-2.5-flash-lite", - # Gemini 2.0 series - "gemini-2.0-flash", - "gemini-2.0-flash-lite", - ], - "xai": [ - # Grok 4.1 series - "grok-4-1-fast", - "grok-4-1-fast-reasoning", - "grok-4-1-fast-non-reasoning", - # Grok 4 series - "grok-4", - "grok-4-0709", - "grok-4-fast-reasoning", - "grok-4-fast-non-reasoning", - ], -} - - -def validate_model(provider: str, model: str) -> bool: - """Check if model name is valid for the given provider. - - For ollama, openrouter - any model is accepted. - """ - provider_lower = provider.lower() - - if provider_lower in ("ollama", "openrouter"): - return True - - if provider_lower not in VALID_MODELS: - return True - - return model in VALID_MODELS[provider_lower] +"""Model name validators for each provider. + +Only validates model names - does NOT enforce limits. +Let LLM providers use their own defaults for unspecified params. +""" + +VALID_MODELS = { + "openai": [ + # GPT-5 series (2025) + "gpt-5.2", + "gpt-5.1", + "gpt-5", + "gpt-5-mini", + "gpt-5-nano", + # GPT-4.1 series (2025) + "gpt-4.1", + "gpt-4.1-mini", + "gpt-4.1-nano", + # o-series reasoning models + "o4-mini", + "o3", + "o3-mini", + "o1", + "o1-preview", + # GPT-4o series (legacy but still supported) + "gpt-4o", + "gpt-4o-mini", + ], + "anthropic": [ + # Claude 4.5 series (2025) + "claude-opus-4-5", + "claude-sonnet-4-5", + "claude-haiku-4-5", + # Claude 4.x series + "claude-opus-4-1-20250805", + "claude-sonnet-4-20250514", + # Claude 3.7 series + "claude-3-7-sonnet-20250219", + # Claude 3.5 series (legacy) + "claude-3-5-haiku-20241022", + "claude-3-5-sonnet-20241022", + ], + "google": [ + # Gemini 3 series (preview) + "gemini-3-pro-preview", + "gemini-3-flash-preview", + # Gemini 2.5 series + "gemini-2.5-pro", + "gemini-2.5-flash", + "gemini-2.5-flash-lite", + # Gemini 2.0 series + "gemini-2.0-flash", + "gemini-2.0-flash-lite", + ], + "xai": [ + # 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