AI Hedge Fund

This is a proof of concept for an AI-powered hedge fund. The goal of this project is to explore the use of AI to make trading decisions. This project is for educational purposes only and is not intended for real trading or investment.

This system employs several agents working together:

1. Ben Graham Agent - The godfather of value investing, only buys hidden gems with a margin of safety
2. Bill Ackman Agent - An activist investors, takes bold positions and pushes for change
3. Cathie Wood Agent - The queen of growth investing, believes in the power of innovation and disruption
4. Warren Buffett Agent - The oracle of Omaha, seeks wonderful companies at a fair price
5. Charlie Munger Agent - Warren Buffett's partner, only buys wonderful businesses at fair prices
6. Valuation Agent - Calculates the intrinsic value of a stock and generates trading signals
7. Sentiment Agent - Analyzes market sentiment and generates trading signals
8. Fundamentals Agent - Analyzes fundamental data and generates trading signals
9. Technicals Agent - Analyzes technical indicators and generates trading signals
10. Risk Manager - Calculates risk metrics and sets position limits
11. Portfolio Manager - Makes final trading decisions and generates orders

Note: the system simulates trading decisions, it does not actually trade.

Disclaimer

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.

Table of Contents

• Setup
• Usage
  • Running the Hedge Fund
  • Running the Backtester
• Project Structure
• Contributing
• Feature Requests
• License

Setup

Clone the repository:

git clone https://github.com/virattt/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

4. Set your API keys:

# For running LLMs hosted by openai (gpt-4o, gpt-4o-mini, etc.)
# Get your OpenAI API key from https://platform.openai.com/
OPENAI_API_KEY=your-openai-api-key

# For running LLMs hosted by groq (deepseek, llama3, etc.)
# Get your Groq API key from https://groq.com/
GROQ_API_KEY=your-groq-api-key

# For getting financial data to power the hedge fund
# Get your Financial Datasets API key from https://financialdatasets.ai/
FINANCIAL_DATASETS_API_KEY=your-financial-datasets-api-key

Important: You must set OPENAI_API_KEY, GROQ_API_KEY, or ANTHROPIC_API_KEY for the hedge fund to work. If you want to use LLMs from all providers, you will need to set all API keys.

Financial data for AAPL, GOOGL, MSFT, NVDA, and TSLA is free and does not require an API key.

For any other ticker, you will need to set the FINANCIAL_DATASETS_API_KEY in the .env file.

Usage

Running the Hedge Fund

poetry run python src/main.py --ticker AAPL,MSFT,NVDA

Example Output:

You can also specify a --show-reasoning flag to print the reasoning of each agent to the console.

poetry run python src/main.py --ticker AAPL,MSFT,NVDA --show-reasoning

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

poetry run python src/main.py --ticker AAPL,MSFT,NVDA --start-date 2024-01-01 --end-date 2024-03-01 

Running the Backtester

poetry run python src/backtester.py --ticker AAPL,MSFT,NVDA

Example Output:

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

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

Project Structure

ai-hedge-fund/
├── src/
│   ├── agents/                   # Agent definitions and workflow
│   │   ├── bill_ackman.py        # Bill Ackman agent
│   │   ├── fundamentals.py       # Fundamental analysis agent
│   │   ├── portfolio_manager.py  # Portfolio management agent
│   │   ├── risk_manager.py       # Risk management agent
│   │   ├── sentiment.py          # Sentiment analysis agent
│   │   ├── technicals.py         # Technical analysis agent
│   │   ├── valuation.py          # Valuation analysis agent
│   │   ├── warren_buffett.py     # Warren Buffett agent
│   ├── tools/                    # Agent tools
│   │   ├── api.py                # API tools
│   ├── backtester.py             # Backtesting tools
│   ├── main.py # Main entry point
├── pyproject.toml
├── ...

Contributing

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

Important: Please keep your pull requests small and focused. This will make it easier to review and merge.

Feature Requests

If you have a feature request, please open an issue and make sure it is tagged with enhancement.

License

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