Merge branch 'temp' into gemini
This commit is contained in:
kimheesu
commit
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env/
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__pycache__/
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.DS_Store
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||||
*.csv
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src/
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||||
eval_results/
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||||
eval_data/
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||||
*.egg-info/
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||||
results/
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||||
.env
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||||
env/
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||||
__pycache__/
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||||
.DS_Store
|
||||
*.csv
|
||||
src/
|
||||
eval_results/
|
||||
eval_data/
|
||||
*.egg-info/
|
||||
results/
|
||||
.env
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||||
tradingagents/dataflows/data_cache/
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CLAUDE.md
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|||
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@ -1 +1 @@
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3.10
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||||
3.10
|
||||
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|||
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@ -0,0 +1,15 @@
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|||
{
|
||||
// Use IntelliSense to learn about possible attributes.
|
||||
// Hover to view descriptions of existing attributes.
|
||||
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
|
||||
"version": "0.2.0",
|
||||
"configurations": [
|
||||
{
|
||||
"name": "Python Debugger: main.py",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"program": "${workspaceFolder}/main.py",
|
||||
"console": "integratedTerminal"
|
||||
}
|
||||
]
|
||||
}
|
||||
402
LICENSE
402
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. Definitions.
|
||||
|
||||
"License" shall mean the terms and conditions for use, reproduction,
|
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and distribution as defined by Sections 1 through 9 of this document.
|
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|
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|
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"Legal Entity" shall mean the union of the acting entity and all
|
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|
||||
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|
||||
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|
||||
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|
||||
otherwise, or (ii) ownership of fifty percent (50%) or more of the
|
||||
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|
||||
|
||||
"You" (or "Your") shall mean an individual or Legal Entity
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
Apache License
|
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|
||||
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|
||||
|
||||
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
|
||||
|
||||
1. Definitions.
|
||||
|
||||
"License" shall mean the terms and conditions for use, reproduction,
|
||||
and distribution as defined by Sections 1 through 9 of this document.
|
||||
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"Licensor" shall mean the copyright owner or entity authorized by
|
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"Legal Entity" shall mean the union of the acting entity and all
|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
"You" (or "Your") shall mean an individual or Legal Entity
|
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||||
"Source" form shall mean the preferred form for making modifications,
|
||||
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|
||||
source, and configuration files.
|
||||
|
||||
"Object" form shall mean any form resulting from mechanical
|
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|
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|
||||
and conversions to other media types.
|
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|
||||
"Work" shall mean the work of authorship, whether in Source or
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|
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|
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|
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
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||||
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Notwithstanding the above, nothing herein shall supersede or modify
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||||
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|
||||
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|
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||||
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|
||||
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|
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|
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|
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|
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defend, and hold each Contributor harmless for any liability
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|
||||
|
||||
END OF TERMS AND CONDITIONS
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||||
|
||||
APPENDIX: How to apply the Apache License to your work.
|
||||
|
||||
To apply the Apache License to your work, attach the following
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|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
|
|
|
|||
430
README.md
430
README.md
|
|
@ -1,215 +1,215 @@
|
|||
<p align="center">
|
||||
<img src="assets/TauricResearch.png" style="width: 60%; height: auto;">
|
||||
</p>
|
||||
|
||||
<div align="center" style="line-height: 1;">
|
||||
<a href="https://arxiv.org/abs/2412.20138" target="_blank"><img alt="arXiv" src="https://img.shields.io/badge/arXiv-2412.20138-B31B1B?logo=arxiv"/></a>
|
||||
<a href="https://discord.com/invite/hk9PGKShPK" target="_blank"><img alt="Discord" src="https://img.shields.io/badge/Discord-TradingResearch-7289da?logo=discord&logoColor=white&color=7289da"/></a>
|
||||
<a href="./assets/wechat.png" target="_blank"><img alt="WeChat" src="https://img.shields.io/badge/WeChat-TauricResearch-brightgreen?logo=wechat&logoColor=white"/></a>
|
||||
<a href="https://x.com/TauricResearch" target="_blank"><img alt="X Follow" src="https://img.shields.io/badge/X-TauricResearch-white?logo=x&logoColor=white"/></a>
|
||||
<br>
|
||||
<a href="https://github.com/TauricResearch/" target="_blank"><img alt="Community" src="https://img.shields.io/badge/Join_GitHub_Community-TauricResearch-14C290?logo=discourse"/></a>
|
||||
</div>
|
||||
|
||||
<div align="center">
|
||||
<!-- Keep these links. Translations will automatically update with the README. -->
|
||||
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=de">Deutsch</a> |
|
||||
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=es">Español</a> |
|
||||
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=fr">français</a> |
|
||||
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ja">日本語</a> |
|
||||
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ko">한국어</a> |
|
||||
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=pt">Português</a> |
|
||||
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ru">Русский</a> |
|
||||
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=zh">中文</a>
|
||||
</div>
|
||||
|
||||
---
|
||||
|
||||
# TradingAgents: Multi-Agents LLM Financial Trading Framework
|
||||
|
||||
> 🎉 **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!
|
||||
|
||||
<div align="center">
|
||||
<a href="https://www.star-history.com/#TauricResearch/TradingAgents&Date">
|
||||
<picture>
|
||||
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date&theme=dark" />
|
||||
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date" />
|
||||
<img alt="TradingAgents Star History" src="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date" style="width: 80%; height: auto;" />
|
||||
</picture>
|
||||
</a>
|
||||
</div>
|
||||
|
||||
<div align="center">
|
||||
|
||||
🚀 [TradingAgents](#tradingagents-framework) | ⚡ [Installation & CLI](#installation-and-cli) | 🎬 [Demo](https://www.youtube.com/watch?v=90gr5lwjIho) | 📦 [Package Usage](#tradingagents-package) | 🤝 [Contributing](#contributing) | 📄 [Citation](#citation)
|
||||
|
||||
</div>
|
||||
|
||||
## 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.
|
||||
|
||||
<p align="center">
|
||||
<img src="assets/schema.png" style="width: 100%; height: auto;">
|
||||
</p>
|
||||
|
||||
> 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.
|
||||
|
||||
<p align="center">
|
||||
<img src="assets/analyst.png" width="100%" style="display: inline-block; margin: 0 2%;">
|
||||
</p>
|
||||
|
||||
### 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.
|
||||
|
||||
<p align="center">
|
||||
<img src="assets/researcher.png" width="70%" style="display: inline-block; margin: 0 2%;">
|
||||
</p>
|
||||
|
||||
### 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.
|
||||
|
||||
<p align="center">
|
||||
<img src="assets/trader.png" width="70%" style="display: inline-block; margin: 0 2%;">
|
||||
</p>
|
||||
|
||||
### 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.
|
||||
|
||||
<p align="center">
|
||||
<img src="assets/risk.png" width="70%" style="display: inline-block; margin: 0 2%;">
|
||||
</p>
|
||||
|
||||
## 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
|
||||
|
||||
You will also need the FinnHub API for financial data. All of our code is implemented with the free tier.
|
||||
```bash
|
||||
export FINNHUB_API_KEY=$YOUR_FINNHUB_API_KEY
|
||||
```
|
||||
|
||||
You will need the OpenAI API or GEMINI API for all the agents.
|
||||
```bash
|
||||
export OPENAI_API_KEY=$YOUR_OPENAI_API_KEY
|
||||
export GEMINI_API_KEY=$YOUR_GEMINI_API_KEY
|
||||
export GOOGLE_API_KEY=$YOUR_GEMINI_API_KEY
|
||||
```
|
||||
|
||||
### 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.
|
||||
|
||||
<p align="center">
|
||||
<img src="assets/cli/cli_init.png" width="100%" style="display: inline-block; margin: 0 2%;">
|
||||
</p>
|
||||
|
||||
An interface will appear showing results as they load, letting you track the agent's progress as it runs.
|
||||
|
||||
<p align="center">
|
||||
<img src="assets/cli/cli_news.png" width="100%" style="display: inline-block; margin: 0 2%;">
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<img src="assets/cli/cli_transaction.png" width="100%" style="display: inline-block; margin: 0 2%;">
|
||||
</p>
|
||||
|
||||
## TradingAgents Package
|
||||
|
||||
### Implementation Details
|
||||
|
||||
We built TradingAgents with LangGraph to ensure flexibility and modularity. We utilize `o1-preview` and `gpt-4o` as our deep thinking and fast thinking LLMs for our experiments. However, for testing purposes, we recommend you use `o4-mini` and `gpt-4.1-mini` to save on costs as our framework makes **lots of** API calls.
|
||||
|
||||
### 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", "2024-05-10")
|
||||
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
|
||||
|
||||
# Create a custom config
|
||||
config = DEFAULT_CONFIG.copy()
|
||||
config["deep_think_llm"] = "gpt-4.1-nano" # Use a different model
|
||||
config["quick_think_llm"] = "gpt-4.1-nano" # Use a different model
|
||||
config["max_debate_rounds"] = 1 # Increase debate rounds
|
||||
config["online_tools"] = True # Use online tools or cached data
|
||||
|
||||
# Initialize with custom config
|
||||
ta = TradingAgentsGraph(debug=True, config=config)
|
||||
|
||||
# forward propagate
|
||||
_, decision = ta.propagate("NVDA", "2024-05-10")
|
||||
print(decision)
|
||||
```
|
||||
|
||||
> For `online_tools`, we recommend enabling them for experimentation, as they provide access to real-time data. The agents' offline tools rely on cached data from our **Tauric TradingDB**, a curated dataset we use for backtesting. We're currently in the process of refining this dataset, and we plan to release it soon alongside our upcoming projects. Stay tuned!
|
||||
|
||||
You can view the full list of configurations in `tradingagents/default_config.py`.
|
||||
|
||||
## 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},
|
||||
}
|
||||
```
|
||||
<p align="center">
|
||||
<img src="assets/TauricResearch.png" style="width: 60%; height: auto;">
|
||||
</p>
|
||||
|
||||
<div align="center" style="line-height: 1;">
|
||||
<a href="https://arxiv.org/abs/2412.20138" target="_blank"><img alt="arXiv" src="https://img.shields.io/badge/arXiv-2412.20138-B31B1B?logo=arxiv"/></a>
|
||||
<a href="https://discord.com/invite/hk9PGKShPK" target="_blank"><img alt="Discord" src="https://img.shields.io/badge/Discord-TradingResearch-7289da?logo=discord&logoColor=white&color=7289da"/></a>
|
||||
<a href="./assets/wechat.png" target="_blank"><img alt="WeChat" src="https://img.shields.io/badge/WeChat-TauricResearch-brightgreen?logo=wechat&logoColor=white"/></a>
|
||||
<a href="https://x.com/TauricResearch" target="_blank"><img alt="X Follow" src="https://img.shields.io/badge/X-TauricResearch-white?logo=x&logoColor=white"/></a>
|
||||
<br>
|
||||
<a href="https://github.com/TauricResearch/" target="_blank"><img alt="Community" src="https://img.shields.io/badge/Join_GitHub_Community-TauricResearch-14C290?logo=discourse"/></a>
|
||||
</div>
|
||||
|
||||
<div align="center">
|
||||
<!-- Keep these links. Translations will automatically update with the README. -->
|
||||
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=de">Deutsch</a> |
|
||||
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=es">Español</a> |
|
||||
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=fr">français</a> |
|
||||
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ja">日本語</a> |
|
||||
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ko">한국어</a> |
|
||||
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=pt">Português</a> |
|
||||
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ru">Русский</a> |
|
||||
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=zh">中文</a>
|
||||
</div>
|
||||
|
||||
---
|
||||
|
||||
# TradingAgents: Multi-Agents LLM Financial Trading Framework
|
||||
|
||||
> 🎉 **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!
|
||||
|
||||
<div align="center">
|
||||
<a href="https://www.star-history.com/#TauricResearch/TradingAgents&Date">
|
||||
<picture>
|
||||
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date&theme=dark" />
|
||||
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date" />
|
||||
<img alt="TradingAgents Star History" src="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date" style="width: 80%; height: auto;" />
|
||||
</picture>
|
||||
</a>
|
||||
</div>
|
||||
|
||||
<div align="center">
|
||||
|
||||
🚀 [TradingAgents](#tradingagents-framework) | ⚡ [Installation & CLI](#installation-and-cli) | 🎬 [Demo](https://www.youtube.com/watch?v=90gr5lwjIho) | 📦 [Package Usage](#tradingagents-package) | 🤝 [Contributing](#contributing) | 📄 [Citation](#citation)
|
||||
|
||||
</div>
|
||||
|
||||
## 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.
|
||||
|
||||
<p align="center">
|
||||
<img src="assets/schema.png" style="width: 100%; height: auto;">
|
||||
</p>
|
||||
|
||||
> 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.
|
||||
|
||||
<p align="center">
|
||||
<img src="assets/analyst.png" width="100%" style="display: inline-block; margin: 0 2%;">
|
||||
</p>
|
||||
|
||||
### 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.
|
||||
|
||||
<p align="center">
|
||||
<img src="assets/researcher.png" width="70%" style="display: inline-block; margin: 0 2%;">
|
||||
</p>
|
||||
|
||||
### 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.
|
||||
|
||||
<p align="center">
|
||||
<img src="assets/trader.png" width="70%" style="display: inline-block; margin: 0 2%;">
|
||||
</p>
|
||||
|
||||
### 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.
|
||||
|
||||
<p align="center">
|
||||
<img src="assets/risk.png" width="70%" style="display: inline-block; margin: 0 2%;">
|
||||
</p>
|
||||
|
||||
## 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
|
||||
|
||||
You will also need the FinnHub API for financial data. All of our code is implemented with the free tier.
|
||||
```bash
|
||||
export FINNHUB_API_KEY=$YOUR_FINNHUB_API_KEY
|
||||
```
|
||||
|
||||
You will need the OpenAI API or GEMINI API for all the agents.
|
||||
```bash
|
||||
export OPENAI_API_KEY=$YOUR_OPENAI_API_KEY
|
||||
export GEMINI_API_KEY=$YOUR_GEMINI_API_KEY
|
||||
export GOOGLE_API_KEY=$YOUR_GEMINI_API_KEY
|
||||
```
|
||||
|
||||
### 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.
|
||||
|
||||
<p align="center">
|
||||
<img src="assets/cli/cli_init.png" width="100%" style="display: inline-block; margin: 0 2%;">
|
||||
</p>
|
||||
|
||||
An interface will appear showing results as they load, letting you track the agent's progress as it runs.
|
||||
|
||||
<p align="center">
|
||||
<img src="assets/cli/cli_news.png" width="100%" style="display: inline-block; margin: 0 2%;">
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<img src="assets/cli/cli_transaction.png" width="100%" style="display: inline-block; margin: 0 2%;">
|
||||
</p>
|
||||
|
||||
## TradingAgents Package
|
||||
|
||||
### Implementation Details
|
||||
|
||||
We built TradingAgents with LangGraph to ensure flexibility and modularity. We utilize `o1-preview` and `gpt-4o` as our deep thinking and fast thinking LLMs for our experiments. However, for testing purposes, we recommend you use `o4-mini` and `gpt-4.1-mini` to save on costs as our framework makes **lots of** API calls.
|
||||
|
||||
### 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", "2024-05-10")
|
||||
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
|
||||
|
||||
# Create a custom config
|
||||
config = DEFAULT_CONFIG.copy()
|
||||
config["deep_think_llm"] = "gpt-4.1-nano" # Use a different model
|
||||
config["quick_think_llm"] = "gpt-4.1-nano" # Use a different model
|
||||
config["max_debate_rounds"] = 1 # Increase debate rounds
|
||||
config["online_tools"] = True # Use online tools or cached data
|
||||
|
||||
# Initialize with custom config
|
||||
ta = TradingAgentsGraph(debug=True, config=config)
|
||||
|
||||
# forward propagate
|
||||
_, decision = ta.propagate("NVDA", "2024-05-10")
|
||||
print(decision)
|
||||
```
|
||||
|
||||
> For `online_tools`, we recommend enabling them for experimentation, as they provide access to real-time data. The agents' offline tools rely on cached data from our **Tauric TradingDB**, a curated dataset we use for backtesting. We're currently in the process of refining this dataset, and we plan to release it soon alongside our upcoming projects. Stay tuned!
|
||||
|
||||
You can view the full list of configurations in `tradingagents/default_config.py`.
|
||||
|
||||
## 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},
|
||||
}
|
||||
```
|
||||
|
|
|
|||
|
|
@ -132,6 +132,8 @@ def select_shallow_thinking_agent(provider) -> str:
|
|||
("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"),
|
||||
("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"),
|
||||
("GPT-4o - Standard model with solid capabilities", "gpt-4o"),
|
||||
("o4-mini - Specialized reasoning model (compact)", "o4-mini"),
|
||||
("o3 - Full advanced reasoning model", "o3"),
|
||||
],
|
||||
"anthropic": [
|
||||
("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"),
|
||||
|
|
|
|||
|
|
@ -0,0 +1,59 @@
|
|||
version: '3.8'
|
||||
|
||||
services:
|
||||
mysql:
|
||||
image: mysql:8.0
|
||||
container_name: tradingagents_mysql
|
||||
restart: unless-stopped
|
||||
environment:
|
||||
MYSQL_ROOT_PASSWORD: ${DB_PASSWORD:-password}
|
||||
MYSQL_DATABASE: ${DB_NAME:-tradingagents_db}
|
||||
MYSQL_USER: ${DB_USER:-tradinguser}
|
||||
MYSQL_PASSWORD: ${DB_PASSWORD:-password}
|
||||
ports:
|
||||
- "3306:3306"
|
||||
volumes:
|
||||
- /home/hskim/mysql_data:/var/lib/mysql
|
||||
- /home/hskim/docker/mysql/init:/docker-entrypoint-initdb.d
|
||||
command: --default-authentication-plugin=mysql_native_password --character-set-server=utf8mb4 --collation-server=utf8mb4_unicode_ci
|
||||
networks:
|
||||
- tradingagents_network
|
||||
|
||||
redis:
|
||||
image: redis:7-alpine
|
||||
container_name: tradingagents_redis
|
||||
restart: unless-stopped
|
||||
ports:
|
||||
- "6379:6379"
|
||||
volumes:
|
||||
- redis_data:/data
|
||||
command: redis-server --appendonly yes
|
||||
networks:
|
||||
- tradingagents_network
|
||||
|
||||
# 개발용 phpMyAdmin (선택사항)
|
||||
# phpmyadmin:
|
||||
# image: phpmyadmin/phpmyadmin
|
||||
# container_name: tradingagents_phpmyadmin
|
||||
# restart: unless-stopped
|
||||
# environment:
|
||||
# PMA_HOST: mysql
|
||||
# PMA_PORT: 3306
|
||||
# PMA_USER: root
|
||||
# PMA_PASSWORD: ${DB_PASSWORD:-password}
|
||||
# ports:
|
||||
# - "8080:80"
|
||||
# depends_on:
|
||||
# - mysql
|
||||
# networks:
|
||||
# - tradingagents_network
|
||||
|
||||
volumes:
|
||||
mysql_data:
|
||||
driver: local
|
||||
redis_data:
|
||||
driver: local
|
||||
|
||||
networks:
|
||||
tradingagents_network:
|
||||
driver: bridge
|
||||
21
main.py
21
main.py
|
|
@ -1,21 +0,0 @@
|
|||
from tradingagents.graph.trading_graph import TradingAgentsGraph
|
||||
from tradingagents.default_config import DEFAULT_CONFIG
|
||||
|
||||
# Create a custom config
|
||||
config = DEFAULT_CONFIG.copy()
|
||||
config["llm_provider"] = "google" # Use a different model
|
||||
config["backend_url"] = "https://generativelanguage.googleapis.com/v1" # Use a different backend
|
||||
config["deep_think_llm"] = "gemini-2.5-pro" # Use a different model
|
||||
config["quick_think_llm"] = "gemini-2.5-flash-lite-preview-06-17" # Use a different model
|
||||
config["max_debate_rounds"] = 1 # Increase debate rounds
|
||||
config["online_tools"] = True # Increase debate rounds
|
||||
|
||||
# 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
|
||||
|
|
@ -1,34 +1,34 @@
|
|||
[project]
|
||||
name = "tradingagents"
|
||||
version = "0.1.0"
|
||||
description = "Add your description here"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10"
|
||||
dependencies = [
|
||||
"akshare>=1.16.98",
|
||||
"backtrader>=1.9.78.123",
|
||||
"chainlit>=2.5.5",
|
||||
"chromadb>=1.0.12",
|
||||
"eodhd>=1.0.32",
|
||||
"feedparser>=6.0.11",
|
||||
"finnhub-python>=2.4.23",
|
||||
"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",
|
||||
"parsel>=1.10.0",
|
||||
"praw>=7.8.1",
|
||||
"pytz>=2025.2",
|
||||
"questionary>=2.1.0",
|
||||
"redis>=6.2.0",
|
||||
"requests>=2.32.4",
|
||||
"rich>=14.0.0",
|
||||
"setuptools>=80.9.0",
|
||||
"stockstats>=0.6.5",
|
||||
"tqdm>=4.67.1",
|
||||
"tushare>=1.4.21",
|
||||
"typing-extensions>=4.14.0",
|
||||
"yfinance>=0.2.63",
|
||||
]
|
||||
[project]
|
||||
name = "tradingagents"
|
||||
version = "0.1.0"
|
||||
description = "Add your description here"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10"
|
||||
dependencies = [
|
||||
"akshare>=1.16.98",
|
||||
"backtrader>=1.9.78.123",
|
||||
"chainlit>=2.5.5",
|
||||
"chromadb>=1.0.12",
|
||||
"eodhd>=1.0.32",
|
||||
"feedparser>=6.0.11",
|
||||
"finnhub-python>=2.4.23",
|
||||
"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",
|
||||
"parsel>=1.10.0",
|
||||
"praw>=7.8.1",
|
||||
"pytz>=2025.2",
|
||||
"questionary>=2.1.0",
|
||||
"redis>=6.2.0",
|
||||
"requests>=2.32.4",
|
||||
"rich>=14.0.0",
|
||||
"setuptools>=80.9.0",
|
||||
"stockstats>=0.6.5",
|
||||
"tqdm>=4.67.1",
|
||||
"tushare>=1.4.21",
|
||||
"typing-extensions>=4.14.0",
|
||||
"yfinance>=0.2.63",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -1,27 +1,27 @@
|
|||
typing-extensions
|
||||
langchain-openai
|
||||
langchain-experimental
|
||||
pandas
|
||||
yfinance
|
||||
praw
|
||||
feedparser
|
||||
stockstats
|
||||
eodhd
|
||||
langgraph
|
||||
chromadb
|
||||
setuptools
|
||||
backtrader
|
||||
akshare
|
||||
tushare
|
||||
finnhub-python
|
||||
parsel
|
||||
requests
|
||||
tqdm
|
||||
pytz
|
||||
redis
|
||||
chainlit
|
||||
rich
|
||||
questionary
|
||||
langchain_anthropic
|
||||
langchain-google-genai
|
||||
google-genai
|
||||
typing-extensions
|
||||
langchain-openai
|
||||
langchain-experimental
|
||||
pandas
|
||||
yfinance
|
||||
praw
|
||||
feedparser
|
||||
stockstats
|
||||
eodhd
|
||||
langgraph
|
||||
chromadb
|
||||
setuptools
|
||||
backtrader
|
||||
akshare
|
||||
tushare
|
||||
finnhub-python
|
||||
parsel
|
||||
requests
|
||||
tqdm
|
||||
pytz
|
||||
redis
|
||||
chainlit
|
||||
rich
|
||||
questionary
|
||||
langchain_anthropic
|
||||
langchain-google-genai
|
||||
google-genai
|
||||
|
|
|
|||
86
setup.py
86
setup.py
|
|
@ -1,43 +1,43 @@
|
|||
"""
|
||||
Setup script for the TradingAgents package.
|
||||
"""
|
||||
|
||||
from setuptools import setup, find_packages
|
||||
|
||||
setup(
|
||||
name="tradingagents",
|
||||
version="0.1.0",
|
||||
description="Multi-Agents LLM Financial Trading Framework",
|
||||
author="TradingAgents Team",
|
||||
author_email="yijia.xiao@cs.ucla.edu",
|
||||
url="https://github.com/TauricResearch",
|
||||
packages=find_packages(),
|
||||
install_requires=[
|
||||
"langchain>=0.1.0",
|
||||
"langchain-openai>=0.0.2",
|
||||
"langchain-experimental>=0.0.40",
|
||||
"langgraph>=0.0.20",
|
||||
"numpy>=1.24.0",
|
||||
"pandas>=2.0.0",
|
||||
"praw>=7.7.0",
|
||||
"stockstats>=0.5.4",
|
||||
"yfinance>=0.2.31",
|
||||
"typer>=0.9.0",
|
||||
"rich>=13.0.0",
|
||||
"questionary>=2.0.1",
|
||||
],
|
||||
python_requires=">=3.10",
|
||||
entry_points={
|
||||
"console_scripts": [
|
||||
"tradingagents=cli.main:app",
|
||||
],
|
||||
},
|
||||
classifiers=[
|
||||
"Development Status :: 3 - Alpha",
|
||||
"Intended Audience :: Financial and Trading Industry",
|
||||
"License :: OSI Approved :: Apache Software License",
|
||||
"Programming Language :: Python :: 3",
|
||||
"Programming Language :: Python :: 3.10",
|
||||
"Topic :: Office/Business :: Financial :: Investment",
|
||||
],
|
||||
)
|
||||
"""
|
||||
Setup script for the TradingAgents package.
|
||||
"""
|
||||
|
||||
from setuptools import setup, find_packages
|
||||
|
||||
setup(
|
||||
name="tradingagents",
|
||||
version="0.1.0",
|
||||
description="Multi-Agents LLM Financial Trading Framework",
|
||||
author="TradingAgents Team",
|
||||
author_email="yijia.xiao@cs.ucla.edu",
|
||||
url="https://github.com/TauricResearch",
|
||||
packages=find_packages(),
|
||||
install_requires=[
|
||||
"langchain>=0.1.0",
|
||||
"langchain-openai>=0.0.2",
|
||||
"langchain-experimental>=0.0.40",
|
||||
"langgraph>=0.0.20",
|
||||
"numpy>=1.24.0",
|
||||
"pandas>=2.0.0",
|
||||
"praw>=7.7.0",
|
||||
"stockstats>=0.5.4",
|
||||
"yfinance>=0.2.31",
|
||||
"typer>=0.9.0",
|
||||
"rich>=13.0.0",
|
||||
"questionary>=2.0.1",
|
||||
],
|
||||
python_requires=">=3.10",
|
||||
entry_points={
|
||||
"console_scripts": [
|
||||
"tradingagents=cli.main:app",
|
||||
],
|
||||
},
|
||||
classifiers=[
|
||||
"Development Status :: 3 - Alpha",
|
||||
"Intended Audience :: Financial and Trading Industry",
|
||||
"License :: OSI Approved :: Apache Software License",
|
||||
"Programming Language :: Python :: 3",
|
||||
"Programming Language :: Python :: 3.10",
|
||||
"Topic :: Office/Business :: Financial :: Investment",
|
||||
],
|
||||
)
|
||||
|
|
|
|||
|
|
@ -4,11 +4,11 @@ from .embedding_providers import (
|
|||
GeminiEmbeddingProvider,
|
||||
OllamaEmbeddingProvider
|
||||
)
|
||||
|
||||
from typing import Any
|
||||
|
||||
class EmbeddingProviderFactory:
|
||||
@staticmethod
|
||||
def create_provider(config : dict[str, any])->EmbeddingProvider:
|
||||
def create_provider(config : dict[str, Any])->EmbeddingProvider:
|
||||
backend_url = config["backend_url"]
|
||||
|
||||
if "generativelanguage.googleapis.com" in backend_url:
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from typing import Dict, Optional
|
|||
|
||||
# Use default config but allow it to be overridden
|
||||
_config: Optional[Dict] = None
|
||||
DATA_DIR: Optional[str] = None
|
||||
DATA_DIR: str = ""
|
||||
|
||||
|
||||
def initialize_config():
|
||||
|
|
|
|||
|
|
@ -74,10 +74,14 @@ def getNewsData(query, start_date, end_date):
|
|||
for el in results_on_page:
|
||||
try:
|
||||
link = el.find("a")["href"]
|
||||
title = el.select_one("div.MBeuO").get_text()
|
||||
snippet = el.select_one(".GI74Re").get_text()
|
||||
date = el.select_one(".LfVVr").get_text()
|
||||
source = el.select_one(".NUnG9d span").get_text()
|
||||
title_el = el.select_one("div.MBeuO")
|
||||
title = title_el.get_text() if title_el else ""
|
||||
snippet_el = el.select_one(".GI74Re")
|
||||
snippet = snippet_el.get_text() if snippet_el else ""
|
||||
date_el = el.select_one(".LfVVr")
|
||||
date = date_el.get_text() if date_el else ""
|
||||
source_el = el.select_one(".NUnG9d span")
|
||||
source = source_el.get_text() if source_el else ""
|
||||
news_results.append(
|
||||
{
|
||||
"link": link,
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from typing import Annotated, Dict
|
||||
from typing import Annotated, Dict, Tuple
|
||||
from .reddit_utils import fetch_top_from_category
|
||||
from .yfin_utils import *
|
||||
from .stockstats_utils import *
|
||||
|
|
@ -14,7 +14,24 @@ from tqdm import tqdm
|
|||
import yfinance as yf
|
||||
from openai import OpenAI
|
||||
from .config import get_config, set_config, DATA_DIR
|
||||
from .search_provider_factory import SearchProviderFactory
|
||||
from .search_provider_factory import SearchProviderFactory, create_search_provider_factory
|
||||
|
||||
|
||||
def parse_date_range(curr_date: str, look_back_days: int) -> Tuple[str, str]:
|
||||
"""
|
||||
Parse date range and return start and end dates.
|
||||
|
||||
Args:
|
||||
curr_date: Current date in yyyy-mm-dd format
|
||||
look_back_days: Number of days to look back
|
||||
|
||||
Returns:
|
||||
Tuple of (start_date, end_date) as strings
|
||||
"""
|
||||
end_date = curr_date
|
||||
start_date_obj = datetime.strptime(curr_date, "%Y-%m-%d")
|
||||
before = start_date_obj - relativedelta(days=look_back_days)
|
||||
return before.strftime("%Y-%m-%d"), end_date
|
||||
|
||||
|
||||
def get_finnhub_news(
|
||||
|
|
@ -37,9 +54,7 @@ def get_finnhub_news(
|
|||
|
||||
"""
|
||||
|
||||
start_date = datetime.strptime(curr_date, "%Y-%m-%d")
|
||||
before = start_date - relativedelta(days=look_back_days)
|
||||
before = before.strftime("%Y-%m-%d")
|
||||
before, _ = parse_date_range(curr_date, look_back_days)
|
||||
|
||||
result = get_data_in_range(ticker, before, curr_date, "news_data", DATA_DIR)
|
||||
|
||||
|
|
@ -76,9 +91,7 @@ def get_finnhub_company_insider_sentiment(
|
|||
str: a report of the sentiment in the past 15 days starting at curr_date
|
||||
"""
|
||||
|
||||
date_obj = datetime.strptime(curr_date, "%Y-%m-%d")
|
||||
before = date_obj - relativedelta(days=look_back_days)
|
||||
before = before.strftime("%Y-%m-%d")
|
||||
before, _ = parse_date_range(curr_date, look_back_days)
|
||||
|
||||
data = get_data_in_range(ticker, before, curr_date, "insider_senti", DATA_DIR)
|
||||
|
||||
|
|
@ -117,9 +130,7 @@ def get_finnhub_company_insider_transactions(
|
|||
str: a report of the company's insider transaction/trading informtaion in the past 15 days
|
||||
"""
|
||||
|
||||
date_obj = datetime.strptime(curr_date, "%Y-%m-%d")
|
||||
before = date_obj - relativedelta(days=look_back_days)
|
||||
before = before.strftime("%Y-%m-%d")
|
||||
before, _ = parse_date_range(curr_date, look_back_days)
|
||||
|
||||
data = get_data_in_range(ticker, before, curr_date, "insider_trans", DATA_DIR)
|
||||
|
||||
|
|
@ -290,9 +301,7 @@ def get_google_news(
|
|||
) -> str:
|
||||
query = query.replace(" ", "+")
|
||||
|
||||
start_date = datetime.strptime(curr_date, "%Y-%m-%d")
|
||||
before = start_date - relativedelta(days=look_back_days)
|
||||
before = before.strftime("%Y-%m-%d")
|
||||
before, _ = parse_date_range(curr_date, look_back_days)
|
||||
|
||||
news_results = getNewsData(query, before, curr_date)
|
||||
|
||||
|
|
@ -323,18 +332,17 @@ def get_reddit_global_news(
|
|||
str: A formatted dataframe containing the latest news articles posts on reddit and meta information in these columns: "created_utc", "id", "title", "selftext", "score", "num_comments", "url"
|
||||
"""
|
||||
|
||||
start_date = datetime.strptime(start_date, "%Y-%m-%d")
|
||||
before = start_date - relativedelta(days=look_back_days)
|
||||
before = before.strftime("%Y-%m-%d")
|
||||
before, start_date_str = parse_date_range(start_date, look_back_days)
|
||||
start_date_dt = datetime.strptime(start_date_str, "%Y-%m-%d")
|
||||
|
||||
posts = []
|
||||
# iterate from start_date to end_date
|
||||
curr_date = datetime.strptime(before, "%Y-%m-%d")
|
||||
|
||||
total_iterations = (start_date - curr_date).days + 1
|
||||
pbar = tqdm(desc=f"Getting Global News on {start_date}", total=total_iterations)
|
||||
total_iterations = (start_date_dt - curr_date).days + 1
|
||||
pbar = tqdm(desc=f"Getting Global News on {start_date_dt}", total=total_iterations)
|
||||
|
||||
while curr_date <= start_date:
|
||||
while curr_date <= start_date_dt:
|
||||
curr_date_str = curr_date.strftime("%Y-%m-%d")
|
||||
fetch_result = fetch_top_from_category(
|
||||
"global_news",
|
||||
|
|
@ -377,21 +385,20 @@ def get_reddit_company_news(
|
|||
str: A formatted dataframe containing the latest news articles posts on reddit and meta information in these columns: "created_utc", "id", "title", "selftext", "score", "num_comments", "url"
|
||||
"""
|
||||
|
||||
start_date = datetime.strptime(start_date, "%Y-%m-%d")
|
||||
before = start_date - relativedelta(days=look_back_days)
|
||||
before = before.strftime("%Y-%m-%d")
|
||||
before, start_date_str = parse_date_range(start_date, look_back_days)
|
||||
start_date_dt = datetime.strptime(start_date_str, "%Y-%m-%d")
|
||||
|
||||
posts = []
|
||||
# iterate from start_date to end_date
|
||||
curr_date = datetime.strptime(before, "%Y-%m-%d")
|
||||
|
||||
total_iterations = (start_date - curr_date).days + 1
|
||||
total_iterations = (start_date_dt - curr_date).days + 1
|
||||
pbar = tqdm(
|
||||
desc=f"Getting Company News for {ticker} on {start_date}",
|
||||
desc=f"Getting Company News for {ticker} on {start_date_dt}",
|
||||
total=total_iterations,
|
||||
)
|
||||
|
||||
while curr_date <= start_date:
|
||||
while curr_date <= start_date_dt:
|
||||
curr_date_str = curr_date.strftime("%Y-%m-%d")
|
||||
fetch_result = fetch_top_from_category(
|
||||
"company_news",
|
||||
|
|
@ -509,8 +516,9 @@ def get_stock_stats_indicators_window(
|
|||
)
|
||||
|
||||
end_date = curr_date
|
||||
curr_date = datetime.strptime(curr_date, "%Y-%m-%d")
|
||||
before = curr_date - relativedelta(days=look_back_days)
|
||||
before_str, _ = parse_date_range(curr_date, look_back_days)
|
||||
before_dt = datetime.strptime(before_str, "%Y-%m-%d")
|
||||
curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
|
||||
|
||||
if not online:
|
||||
# read from YFin data
|
||||
|
|
@ -524,30 +532,30 @@ def get_stock_stats_indicators_window(
|
|||
dates_in_df = data["Date"].astype(str).str[:10]
|
||||
|
||||
ind_string = ""
|
||||
while curr_date >= before:
|
||||
while curr_date_dt >= before_dt:
|
||||
# only do the trading dates
|
||||
if curr_date.strftime("%Y-%m-%d") in dates_in_df.values:
|
||||
if curr_date_dt.strftime("%Y-%m-%d") in dates_in_df.values:
|
||||
indicator_value = get_stockstats_indicator(
|
||||
symbol, indicator, curr_date.strftime("%Y-%m-%d"), online
|
||||
symbol, indicator, curr_date_dt.strftime("%Y-%m-%d"), online
|
||||
)
|
||||
|
||||
ind_string += f"{curr_date.strftime('%Y-%m-%d')}: {indicator_value}\n"
|
||||
ind_string += f"{curr_date_dt.strftime('%Y-%m-%d')}: {indicator_value}\n"
|
||||
|
||||
curr_date = curr_date - relativedelta(days=1)
|
||||
curr_date_dt = curr_date_dt - relativedelta(days=1)
|
||||
else:
|
||||
# online gathering
|
||||
ind_string = ""
|
||||
while curr_date >= before:
|
||||
while curr_date_dt >= before_dt:
|
||||
indicator_value = get_stockstats_indicator(
|
||||
symbol, indicator, curr_date.strftime("%Y-%m-%d"), online
|
||||
symbol, indicator, curr_date_dt.strftime("%Y-%m-%d"), online
|
||||
)
|
||||
|
||||
ind_string += f"{curr_date.strftime('%Y-%m-%d')}: {indicator_value}\n"
|
||||
ind_string += f"{curr_date_dt.strftime('%Y-%m-%d')}: {indicator_value}\n"
|
||||
|
||||
curr_date = curr_date - relativedelta(days=1)
|
||||
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"
|
||||
f"## {indicator} values from {before_dt.strftime('%Y-%m-%d')} to {end_date}:\n\n"
|
||||
+ ind_string
|
||||
+ "\n\n"
|
||||
+ best_ind_params.get(indicator, "No description available.")
|
||||
|
|
@ -565,20 +573,20 @@ def get_stockstats_indicator(
|
|||
online: Annotated[bool, "to fetch data online or offline"],
|
||||
) -> str:
|
||||
|
||||
curr_date = datetime.strptime(curr_date, "%Y-%m-%d")
|
||||
curr_date = curr_date.strftime("%Y-%m-%d")
|
||||
curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
|
||||
curr_date_str = curr_date_dt.strftime("%Y-%m-%d")
|
||||
|
||||
try:
|
||||
indicator_value = StockstatsUtils.get_stock_stats(
|
||||
symbol,
|
||||
indicator,
|
||||
curr_date,
|
||||
curr_date_str,
|
||||
os.path.join(DATA_DIR, "market_data", "price_data"),
|
||||
online=online,
|
||||
)
|
||||
except Exception as e:
|
||||
print(
|
||||
f"Error getting stockstats indicator data for indicator {indicator} on {curr_date}: {e}"
|
||||
f"Error getting stockstats indicator data for indicator {indicator} on {curr_date_str}: {e}"
|
||||
)
|
||||
return ""
|
||||
|
||||
|
|
@ -591,9 +599,7 @@ def get_YFin_data_window(
|
|||
look_back_days: Annotated[int, "how many days to look back"],
|
||||
) -> str:
|
||||
# calculate past days
|
||||
date_obj = datetime.strptime(curr_date, "%Y-%m-%d")
|
||||
before = date_obj - relativedelta(days=look_back_days)
|
||||
start_date = before.strftime("%Y-%m-%d")
|
||||
start_date, _ = parse_date_range(curr_date, look_back_days)
|
||||
|
||||
# read in data
|
||||
data = pd.read_csv(
|
||||
|
|
@ -703,9 +709,13 @@ def get_YFin_data(
|
|||
return filtered_data
|
||||
|
||||
|
||||
# Enhanced search provider factory instance (singleton)
|
||||
_search_factory = create_search_provider_factory()
|
||||
|
||||
|
||||
def get_stock_news(ticker, curr_date):
|
||||
config = get_config()
|
||||
search_provider = SearchProviderFactory.create_provider(config)
|
||||
search_provider = _search_factory.create_provider(config)
|
||||
query = f"Can you search Social Media for {ticker} from 7 days before {curr_date} to {curr_date}? Make sure you only get the data posted during that period."
|
||||
return search_provider.search(query)
|
||||
|
||||
|
|
@ -713,7 +723,7 @@ def get_stock_news(ticker, curr_date):
|
|||
|
||||
def get_global_news(curr_date):
|
||||
config = get_config()
|
||||
search_provider = SearchProviderFactory.create_provider(config)
|
||||
search_provider = _search_factory.create_provider(config)
|
||||
query = f"Search for global macroeconomic news and financial market updates from 7 days before {curr_date} to {curr_date}. Focus on central bank decisions, economic indicators, geopolitical events, and market-moving news that would be important for trading decisions."
|
||||
return search_provider.search(query)
|
||||
|
||||
|
|
@ -721,7 +731,7 @@ def get_global_news(curr_date):
|
|||
|
||||
def get_fundamentals(ticker, curr_date):
|
||||
config = get_config()
|
||||
search_provider = SearchProviderFactory.create_provider(config)
|
||||
search_provider = _search_factory.create_provider(config)
|
||||
query = f"Search for fundamental analysis data and financial metrics for {ticker} stock from the month before {curr_date} to the month of {curr_date}. Look for earnings reports, financial ratios like PE, PS, cash flow, revenue growth, analyst ratings, and any fundamental analysis discussions. Please present key metrics in a structured format."
|
||||
return search_provider.search(query)
|
||||
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ from abc import ABC, abstractmethod
|
|||
|
||||
class SearchProvider(ABC):
|
||||
@abstractmethod
|
||||
def search(self, query: str, ticker: str, curr_date: str) -> str:
|
||||
def search(self, query: str) -> str:
|
||||
pass
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,47 +1,133 @@
|
|||
from .search_provider import (
|
||||
SearchProvider,
|
||||
GoogleSearchProvider,
|
||||
OpenAISearchProvider
|
||||
)
|
||||
from .search_provider import SearchProvider
|
||||
import hashlib
|
||||
import json
|
||||
from typing import Dict, Callable, Any
|
||||
from abc import ABC, abstractmethod
|
||||
|
||||
|
||||
class SearchProviderFactory:
|
||||
_cache = {} # 클래스 레벨 캐시
|
||||
class ProviderSelector(ABC):
|
||||
"""Abstract base class for provider selection strategies."""
|
||||
|
||||
@staticmethod
|
||||
def create_provider(config: dict[str, any]) -> SearchProvider:
|
||||
@abstractmethod
|
||||
def select_provider_type(self, config: Dict[str, Any]) -> str:
|
||||
"""Select provider type based on configuration."""
|
||||
pass
|
||||
|
||||
|
||||
class MappingBasedProviderSelector(ProviderSelector):
|
||||
"""Selects provider based on URL pattern mapping table."""
|
||||
|
||||
def __init__(self, mappings: Dict[str, str], default_provider: str = "openai"):
|
||||
self._mappings = mappings
|
||||
self._default_provider = default_provider
|
||||
|
||||
def select_provider_type(self, config: Dict[str, Any]) -> str:
|
||||
backend_url = config.get("backend_url", "")
|
||||
for pattern, provider_type in self._mappings.items():
|
||||
if pattern in backend_url:
|
||||
return provider_type
|
||||
return self._default_provider
|
||||
|
||||
|
||||
class SearchProviderRegistry:
|
||||
"""Registry for search provider creation functions."""
|
||||
|
||||
def __init__(self):
|
||||
self._providers: Dict[str, Callable[[Dict[str, Any]], SearchProvider]] = {}
|
||||
|
||||
def register(self, provider_type: str, creator: Callable[[Dict[str, Any]], SearchProvider]):
|
||||
"""Register a provider creator function."""
|
||||
self._providers[provider_type] = creator
|
||||
|
||||
def create(self, provider_type: str, config: Dict[str, Any]) -> SearchProvider:
|
||||
"""Create a provider instance using registered creator."""
|
||||
if provider_type not in self._providers:
|
||||
raise ValueError(f"Unknown provider type: {provider_type}")
|
||||
return self._providers[provider_type](config)
|
||||
|
||||
def get_available_types(self) -> list[str]:
|
||||
"""Get list of available provider types."""
|
||||
return list(self._providers.keys())
|
||||
|
||||
|
||||
class SearchProviderFactoryImpl:
|
||||
"""Enhanced factory for creating SearchProvider instances with caching and extensibility."""
|
||||
|
||||
def __init__(self, registry: SearchProviderRegistry, selector: ProviderSelector):
|
||||
self._registry = registry
|
||||
self._selector = selector
|
||||
self._cache: Dict[str, SearchProvider] = {}
|
||||
|
||||
def create_provider(self, config: Dict[str, Any]) -> SearchProvider:
|
||||
"""
|
||||
Create a SearchProvider with caching to avoid creating new instances.
|
||||
Uses config hash as cache key for efficient reuse.
|
||||
"""
|
||||
# Create cache key from relevant config values
|
||||
cache_key_data = {
|
||||
"backend_url": config["backend_url"],
|
||||
"model": config["quick_think_llm"]
|
||||
"backend_url": config.get("backend_url", ""),
|
||||
"model": config.get("quick_think_llm", "")
|
||||
}
|
||||
cache_key = hashlib.md5(json.dumps(cache_key_data, sort_keys=True).encode()).hexdigest()
|
||||
|
||||
# Return cached instance if exists
|
||||
if cache_key in SearchProviderFactory._cache:
|
||||
return SearchProviderFactory._cache[cache_key]
|
||||
if cache_key in self._cache:
|
||||
return self._cache[cache_key]
|
||||
|
||||
# Create new instance
|
||||
backend_url = config["backend_url"]
|
||||
model = config["quick_think_llm"]
|
||||
|
||||
if "generativelanguage.googleapis.com" in backend_url:
|
||||
provider = GoogleSearchProvider(model)
|
||||
else:
|
||||
provider = OpenAISearchProvider(model, backend_url)
|
||||
# Select and create provider
|
||||
provider_type = self._selector.select_provider_type(config)
|
||||
provider = self._registry.create(provider_type, config)
|
||||
|
||||
# Cache and return
|
||||
SearchProviderFactory._cache[cache_key] = provider
|
||||
self._cache[cache_key] = provider
|
||||
return provider
|
||||
|
||||
def clear_cache(self):
|
||||
"""Clear the provider cache (useful for testing or config changes)."""
|
||||
self._cache.clear()
|
||||
|
||||
def get_available_provider_types(self) -> list[str]:
|
||||
"""Get list of available provider types."""
|
||||
return self._registry.get_available_types()
|
||||
|
||||
|
||||
def create_search_provider_factory() -> SearchProviderFactoryImpl:
|
||||
"""Create a configured SearchProviderFactory with default providers."""
|
||||
registry = SearchProviderRegistry()
|
||||
|
||||
# Register default providers
|
||||
def create_google_provider(config: Dict[str, Any]) -> SearchProvider:
|
||||
from .search_provider import GoogleSearchProvider
|
||||
return GoogleSearchProvider(config["quick_think_llm"])
|
||||
|
||||
def create_openai_provider(config: Dict[str, Any]) -> SearchProvider:
|
||||
from .search_provider import OpenAISearchProvider
|
||||
return OpenAISearchProvider(config["quick_think_llm"], config["backend_url"])
|
||||
|
||||
registry.register("google", create_google_provider)
|
||||
registry.register("openai", create_openai_provider)
|
||||
|
||||
# Create URL pattern mappings (easily extensible)
|
||||
url_mappings = {
|
||||
"generativelanguage.googleapis.com": "google",
|
||||
"api.openai.com": "openai",
|
||||
}
|
||||
|
||||
selector = MappingBasedProviderSelector(url_mappings, default_provider="openai")
|
||||
return SearchProviderFactoryImpl(registry, selector)
|
||||
|
||||
|
||||
# Backward compatibility - singleton instance
|
||||
_default_factory = create_search_provider_factory()
|
||||
|
||||
|
||||
class SearchProviderFactory:
|
||||
"""Backward compatibility wrapper for the old static factory."""
|
||||
|
||||
@staticmethod
|
||||
def create_provider(config: Dict[str, Any]) -> SearchProvider:
|
||||
return _default_factory.create_provider(config)
|
||||
|
||||
@staticmethod
|
||||
def clear_cache():
|
||||
"""Clear the provider cache (useful for testing or config changes)."""
|
||||
SearchProviderFactory._cache.clear()
|
||||
|
||||
_default_factory.clear_cache()
|
||||
|
|
@ -3,7 +3,7 @@ import os
|
|||
DEFAULT_CONFIG = {
|
||||
"project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
|
||||
"results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", "./results"),
|
||||
"data_dir": "/Users/yluo/Documents/Code/ScAI/FR1-data",
|
||||
"data_dir": os.getenv("TRADINGAGENTS_DATA_DIR", "./data"),
|
||||
"data_cache_dir": os.path.join(
|
||||
os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
|
||||
"dataflows/data_cache",
|
||||
|
|
|
|||
|
|
@ -127,7 +127,7 @@ class TradingAgentsGraph:
|
|||
# online tools
|
||||
self.toolkit.get_stock_news,
|
||||
# offline tools
|
||||
self.toolkit.get_reddit_stock_info,
|
||||
# self.toolkit.get_reddit_stock_info,
|
||||
]
|
||||
),
|
||||
"news": ToolNode(
|
||||
|
|
@ -136,8 +136,8 @@ class TradingAgentsGraph:
|
|||
self.toolkit.get_global_news,
|
||||
self.toolkit.get_google_news,
|
||||
# offline tools
|
||||
self.toolkit.get_finnhub_news,
|
||||
self.toolkit.get_reddit_news,
|
||||
# self.toolkit.get_finnhub_news,
|
||||
# self.toolkit.get_reddit_news,
|
||||
]
|
||||
),
|
||||
"fundamentals": ToolNode(
|
||||
|
|
@ -145,11 +145,11 @@ class TradingAgentsGraph:
|
|||
# online tools
|
||||
self.toolkit.get_fundamentals,
|
||||
# offline tools
|
||||
self.toolkit.get_finnhub_company_insider_sentiment,
|
||||
self.toolkit.get_finnhub_company_insider_transactions,
|
||||
self.toolkit.get_simfin_balance_sheet,
|
||||
self.toolkit.get_simfin_cashflow,
|
||||
self.toolkit.get_simfin_income_stmt,
|
||||
# self.toolkit.get_finnhub_company_insider_sentiment,
|
||||
# self.toolkit.get_finnhub_company_insider_transactions,
|
||||
# self.toolkit.get_simfin_balance_sheet,
|
||||
# self.toolkit.get_simfin_cashflow,
|
||||
# self.toolkit.get_simfin_income_stmt,
|
||||
]
|
||||
),
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue