TradingAgents
ActiveDescription
TradingAgents is a multi-agent trading framework built with LangGraph that mirrors real-world trading firm dynamics with specialized LLM-powered agents for fundamental analysis, sentiment analysis, risk management, and more.
Key Features
- Multi-role agent collaboration - Mirrors real trading firms with fundamental analyst, sentiment analyst, technical analyst, trader, and risk management agents
- Built on LangGraph - Uses LangGraph for dynamic inter-agent discussions and structured decision-making workflows
- Bull-bear researcher debate - Bullish and bearish researchers debate analyst insights to balance potential gains against risks
- Multi-LLM provider support - Compatible with GPT-5.x, Gemini 3.x, Claude 4.x, Grok 4.x, DeepSeek, Qwen, and more
- Structured-output agents - Research Manager, Trader, and Portfolio Manager use structured outputs for consistent decision formatting
- Docker and Ollama support - One-command Docker deployment with Ollama integration for fully offline local model inference
Use Cases
💡 Quantitative trading strategy research using multi-agent collaboration to analyze markets and generate trading signals
💡 Financial market sentiment analysis by aggregating news, StockTwits, and Reddit data for short-term mood assessment
💡 Portfolio risk assessment through continuous evaluation of market volatility and liquidity by risk management team
💡 Trading decision backtesting and simulation by executing strategies in a simulated exchange environment
💡 AI multi-agent architecture research for learning complex role-based collaboration and debate patterns
Strengths & Limitations
✅ Strengths
- • Actively maintained, recent updates
- • High community interest (102.3k stars)
- • Permissive open-source license (Apache-2.0)
- • Responsive to issues, low backlog
Categories
Quick Start
# Clone the repository
git clone https://github.com/TauricResearch/TradingAgents.git
cd TradingAgents
# Create and activate virtual environment
conda create -n tradingagents python=3.13
conda activate tradingagents
# Install dependencies
pip install .
# Set API key (OpenAI example)
export OPENAI_API_KEY=your-key
# Run trading analysis
python main.py --ticker AAPL