An AI-powered hedge fund that uses multiple agents to make trading decisions. The system employs several specialized agents working together:

1. Market Data Agent - Gathers and preprocesses market data
2. Quantitative Agent - Analyzes technical indicators and generates trading signals
3. Fundamentals Agent - Analyzes fundamental data and generates trading signals
4. Sentiment Agent - Analyzes market sentiment and generates trading signals
5. Risk Management Agent - Evaluates portfolio risk and sets position limits
6. Portfolio Management Agent - Makes final trading decisions and generates orders

This project is for educational and research purposes only.

• Not intended for real trading or investment
• No warranties or guarantees provided
• Past performance does not indicate future results
• Creator assumes no liability for financial losses
• Consult a financial advisor for investment decisions

By using this software, you agree to use it solely for learning purposes.

• Features
• Setup
• Usage
  • Running the Hedge Fund
  • Running the Hedge Fund (with Reasoning)
  • Running the Backtester
• Project Structure
• Contributing
• License

• Multi-agent architecture for trading decisions
• Technical analysis using MACD, RSI, Bollinger Bands, and OBV
• Fundamental analysis using financial metrics
• Sentiment analysis using web search
• Risk management with position sizing recommendations
• Portfolio management with automated trading decisions
• Backtesting capabilities with performance analytics

Clone the repository:

git clone https://github.com/your-repo/ai-hedge-fund.git
cd ai-hedge-fund

1. Install Poetry (if not already installed):

curl -sSL https://install.python-poetry.org | python3 -

2. Install dependencies:

poetry install

3. Set up your environment variables:

# Create .env file for your API keys
cp .env.example .env

export OPENAI_API_KEY='your-api-key-here' # For using LLM
export FINANCIAL_DATASETS_API_KEY='your-api-key-here' # For fetching stock market data
export TAVILY_API_KEY='your-api-key-here' # For web search
export ALPACA_API_KEY='your-api-key-here' # For order execution
export ALPACA_API_SECRET='your-api-secret-here' # For order execution

poetry run python src/agents.py --ticker AAPL

You can optionally specify the start and end dates to make decisions for a specific time period.

poetry run python src/agents.py --ticker AAPL --start-date 2024-01-01 --end-date 2024-03-01

This will print the reasoning of each agent to the console.

poetry run python src/agents.py --ticker AAPL --show-reasoning

poetry run python src/backtester.py --ticker AAPL

Example Output:

Starting backtest...
Date         Ticker Action Quantity    Price         Cash    Stock  Total Value
----------------------------------------------------------------------
2024-01-01   AAPL   buy       519.0   192.53        76.93    519.0    100000.00
2024-01-02   AAPL   hold          0   185.64        76.93    519.0     96424.09
2024-01-03   AAPL   hold          0   184.25        76.93    519.0     95702.68
2024-01-04   AAPL   hold          0   181.91        76.93    519.0     94488.22
2024-01-05   AAPL   hold          0   181.18        76.93    519.0     94109.35
2024-01-08   AAPL   sell        519   185.56     96382.57      0.0     96382.57
2024-01-09   AAPL   buy       520.0   185.14       109.77    520.0     96382.57

You can optionally specify the start and end dates to backtest over a specific time period.

poetry run python src/backtester.py --ticker AAPL --start-date 2024-01-01 --end-date 2024-03-01

ai-hedge-fund/
├── src/
│   ├── agents.py # Main agent definitions and workflow
│   ├── backtester.py # Backtesting functionality
│   ├── tools.py # Agent tools
├── pyproject.toml # Poetry configuration
├── .env.example # Environment variables
└── README.md # Documentation

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

This project is licensed under the MIT License - see the LICENSE file for details.