7c95188bf0
- Created new files for industry performance, market indices, market movers, sector performance, and topic news.
- Implemented end-to-end tests for scanner functionality, ensuring all tools return expected data formats and can save results to files.
- Added integration tests to verify scanner tools work seamlessly with the CLI scan command.
- Enhanced test coverage for individual scanner tools, validating output structure and content.
## Summary
The changes refactor the scanner tool invocation to use LangChain's StructuredTool `.invoke()` method consistently across the codebase. This includes updating the CLI scan command, rewriting tests to use the new invocation pattern, and correcting yfinance screener key mappings. The changes also add comprehensive end-to-end test suites for scanner functionality.
## Issues Found
| Severity | File:Line | Issue |
|----------|-----------|-------|
| WARNING | cli/main.py:1193-1218 | Inconsistent error handling - some tools check for "Error" prefix while others check for "No data" prefix, but the actual error messages from yfinance_scanner.py use different formats |
| WARNING | tradingagents/dataflows/yfinance_scanner.py:34 | The condition `if not data or 'quotes' not in data:` may not catch all error cases - yfinance screener can return empty data structures that evaluate to False but don't contain 'quotes' key |
| SUGGESTION | tests/test_scanner_tools.py:38-46 | Test could be more robust by checking for actual data content rather than just headers |
| SUGGESTION | cli/main.py:1193-1218 | Consider extracting the scanner tool invocation pattern into a helper function to reduce duplication |
## Detailed Findings
### File: cli/main.py:1193-1218
- **Confidence:** 85%
- **Problem:** The error handling checks for different prefixes ("Error" vs "No data") but the actual functions in yfinance_scanner.py return error messages with different formats (e.g., "Error fetching market movers for..."). This inconsistency could lead to improper error handling where error results are still saved to files.
- **Suggestion:** Standardize error checking by creating a helper function that checks if a result indicates an error, or modify the yfinance_scanner functions to return consistent error prefixes.
### File: tradingagents/dataflows/yfinance_scanner.py:34
- **Confidence:** 80%
- **Problem:** The condition `if not data or 'quotes' not in data:` assumes that if data exists, it will contain a 'quotes' key. However, yfinance screener might return data in different formats or empty objects that don't contain this key, leading to potential KeyError exceptions.
- **Suggestion:** Add more robust checking: `if not data or not isinstance(data, dict) or 'quotes' not in data:` to prevent attribute errors.
### File: tests/test_scanner_tools.py:38-46
- **Confidence:** 75%
- **Problem:** The test for market movers only checks that the result contains the expected header but doesn't verify that actual financial data is present in the table rows.
- **Suggestion:** Enhance the test to verify that data rows are present (e.g., check for table rows with actual data, not just headers).
### File: cli/main.py:1193-1218
- **Confidence:** 70%
- **Problem:** The scanner tool invocation pattern is repeated 5 times with only minor variations in arguments, violating the DRY principle.
- **Suggestion:** Extract this pattern into a helper function like `invoke_scanner_tool(tool, args, filename)` to reduce code duplication and improve maintainability.
## Recommendation
**APPROVE WITH SUGGESTIONS**
The changes are fundamentally sound and improve code consistency by standardizing on the StructuredTool `.invoke()` interface. The added test coverage is excellent. Addressing the minor issues noted above would further improve robustness and maintainability.
|
||
|---|---|---|
| agents | ||
| assets | ||
| cli | ||
| plans | ||
| results/macro_scan/2026-03-15 | ||
| tests | ||
| tradingagents | ||
| .env.example | ||
| .gitignore | ||
| CLAUDE.md | ||
| LICENSE | ||
| README.md | ||
| main.py | ||
| pyproject.toml | ||
| requirements.txt | ||
| test.py | ||
| uv.lock | ||
README.md
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 released, with Terminal expected to land soon.
🎉 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 | ⚡ Installation & CLI | 🎬 Demo | 📦 Package Usage | 🤝 Contributing | 📄 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.
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:
git clone https://github.com/TauricResearch/TradingAgents.git
cd TradingAgents
Create a virtual environment in any of your favorite environment managers:
conda create -n tradingagents python=3.13
conda activate tradingagents
Install dependencies:
pip install -r requirements.txt
Required APIs
TradingAgents supports multiple LLM providers. Set the API key for your chosen provider:
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:
cp .env.example .env
CLI Usage
You can also try out the CLI directly by running:
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:
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.
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.
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},
}