TradingAgents
TradingAgents is a multi-agent LLM framework that simulates a trading firm with analyst, researcher, trader, and risk-management roles to support collaborative market analysis and trading decisions.
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TradingAgents is a stock trading framework inspired by trading firms. It uses LLM-powered agents in specialized roles, including fundamental, sentiment, news, and technical analysts, bullish and bearish researchers, traders, and a risk management team. Agents communicate through structured reports and focused debate to assess market conditions and make trading decisions.
Key Features
- LLM-powered specialized agents cover fundamental, sentiment, news, and technical analysis.
- Bullish and bearish researcher agents debate market conditions and evaluate risk/reward.
- Trader agents synthesize analyst and researcher insights to decide on trades.
- A risk management team monitors exposure and adjusts trading plans within risk constraints.
- A structured communication protocol combines reports with natural language debate to reduce information loss.
- Backbone models can be exchanged for locally hosted or API-accessible alternatives.
Use Cases
- Simulating collaborative trading-firm workflows for research and decision-making.
- Backtesting trading strategies on historical financial data and comparing against baseline strategies.
- Analyzing market signals from news, social media sentiment, fundamentals, and technical indicators.
- Researching multi-agent LLM coordination, debate, and risk-management processes in finance.
Deployment & Requirements
- No GPU is required.
- API credits are needed for model access.
Before You Adopt
- License: Apache-2.0. Review its terms before using, modifying, or distributing the project.
- Deployment does not require a GPU but depends on API credits for model access.
