# TradingAgents — Multi-Agent LLM Financial Trading Framework

> An open-source multi-agent framework that simulates a trading firm with specialized LLM agents for market analysis, risk management, and trade execution.

## Install

Save as a script file and run:

# TradingAgents — Multi-Agent LLM Financial Trading Framework

## Quick Use
```bash
git clone https://github.com/TauricResearch/TradingAgents.git
cd TradingAgents
pip install -r requirements.txt
python main.py --ticker AAPL --date 2025-01-15
```

## Introduction
TradingAgents is an open-source framework that models a trading firm as a team of specialized LLM agents. Each agent plays a distinct role — analyst, researcher, risk manager, trader — collaborating through structured workflows to make informed trading decisions.

## What TradingAgents Does
- Simulates a multi-agent trading firm with role-based LLM agents
- Provides fundamental, technical, and sentiment analysis through specialized analyst agents
- Includes a risk management agent that evaluates portfolio exposure before trades
- Aggregates signals from multiple agents into a unified trading decision
- Supports backtesting against historical market data for strategy evaluation

## Architecture Overview
TradingAgents uses a LangGraph-based orchestration layer to coordinate agents. A market data pipeline feeds real-time and historical data to analyst agents, whose outputs flow to a portfolio manager agent for aggregation. A risk management agent applies constraints before the final trade execution decision. All agent interactions are logged for auditability.

## Self-Hosting & Configuration
- Requires Python 3.9+ with LangChain and LangGraph dependencies
- Configure API keys for market data providers and LLM services via environment variables
- Customize agent prompts and risk parameters through configuration files
- Supports multiple LLM backends including OpenAI, Anthropic, and local models
- Historical market data can be sourced from free APIs or local CSV files

## Key Features
- Role-based agent architecture mirrors real trading firm workflows
- Built-in backtesting engine for evaluating strategies on historical data
- Multi-source analysis combining fundamental, technical, and news sentiment signals
- Risk management layer with configurable position limits and drawdown thresholds
- Transparent decision logs showing each agent's contribution to the final trade

## Comparison with Similar Tools
- **QuantConnect / Backtrader** — traditional algorithmic trading; TradingAgents uses LLM reasoning
- **CrewAI** — general multi-agent framework; TradingAgents is purpose-built for financial markets
- **AutoGen** — multi-agent conversations; TradingAgents provides domain-specific trading roles
- **ai-hedge-fund** — similar concept; TradingAgents offers more structured role separation and risk controls

## FAQ
**Q: Can TradingAgents execute real trades?**
A: The default setup is for analysis and backtesting only. Broker integration requires additional configuration.

**Q: What market data sources are supported?**
A: Yahoo Finance, Alpha Vantage, and custom CSV data sources out of the box.

**Q: Does it work with local LLMs?**
A: Yes. Any LangChain-compatible model can be used, including Ollama-hosted models.

**Q: Is this financial advice?**
A: No. TradingAgents is a research and educational tool, not a financial advisory service.

## Sources
- https://github.com/TauricResearch/TradingAgents
- https://arxiv.org/abs/2412.20138

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Source: https://tokrepo.com/en/workflows/asset-488cac73
Author: Script Depot