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AI Hedge Fund

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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. Aswath Damodaran Agent - The Dean of Valuation, focuses on story, numbers, and disciplined valuation
  2. Ben Graham Agent - The godfather of value investing, only buys hidden gems with a margin of safety
  3. Bill Ackman Agent - An activist investor, takes bold positions and pushes for change
  4. Cathie Wood Agent - The queen of growth investing, believes in the power of innovation and disruption
  5. Charlie Munger Agent - Warren Buffett's partner, only buys wonderful businesses at fair prices
  6. Michael Burry Agent - The Big Short contrarian who hunts for deep value
  7. Mohnish Pabrai Agent - The Dhandho investor, who looks for doubles at low risk
  8. Peter Lynch Agent - Practical investor who seeks "ten-baggers" in everyday businesses
  9. Phil Fisher Agent - Meticulous growth investor who uses deep "scuttlebutt" research
  10. Rakesh Jhunjhunwala Agent - The Big Bull of India
  11. Stanley Druckenmiller Agent - Macro legend who hunts for asymmetric opportunities with growth potential
  12. Warren Buffett Agent - The oracle of Omaha, seeks wonderful companies at a fair price
  13. Valuation Agent - Calculates the intrinsic value of a stock and generates trading signals
  14. Sentiment Agent - Analyzes market sentiment and generates trading signals
  15. Fundamentals Agent - Analyzes fundamental data and generates trading signals
  16. Technicals Agent - Analyzes technical indicators and generates trading signals
  17. Risk Manager - Calculates risk metrics and sets position limits
  18. Portfolio Manager - Makes final trading decisions and generates orders

Note: the system does not actually make any trades.

Disclaimer

This project is for educational and research purposes only.

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

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

Table of Contents

How to Install

Before you can run the AI Hedge Fund, you'll need to install it and set up your API keys. These steps are common to both the full-stack web application and command line interface.

1. Clone the Repository

bash
git clone https://github.com/virattt/ai-hedge-fund.git
cd ai-hedge-fund

2. Set Up Your API Keys

Create a .env file for your API keys:

bash
# Create .env file for your API keys (in the root directory)
cp .env.example .env

Open and edit the .env file to add your API keys:

bash
# For running LLMs hosted by openai (gpt-4o, gpt-4o-mini, etc.)
OPENAI_API_KEY=your-openai-api-key

# For getting financial data to power the hedge fund
FINANCIAL_DATASETS_API_KEY=your-financial-datasets-api-key

Important: You must set at least one LLM API key (e.g. OPENAI_API_KEY, GROQ_API_KEY, ANTHROPIC_API_KEY, or DEEPSEEK_API_KEY) for the hedge fund to work.

Financial Data: 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.

How to Run

⌨️ Command Line Interface

For users who prefer working with command line tools, you can run the AI Hedge Fund directly via terminal. This approach offers more granular control and is useful for automation, scripting, and integration purposes.

Using Docker

  1. Make sure you have Docker installed on your system. If not, you can download it from Docker's official website.

  2. Navigate to the docker directory:

bash
cd docker
  1. Build the Docker image:
bash
# On Linux/Mac:
./run.sh build

# On Windows:
run.bat build

Running the AI Hedge Fund (with Docker)

bash
# Navigate to the docker directory first
cd docker

# On Linux/Mac:
./run.sh --ticker AAPL,MSFT,NVDA main

# On Windows:
run.bat --ticker AAPL,MSFT,NVDA main

You can also specify a --ollama flag to run the AI hedge fund using local LLMs.

bash
# With Docker (from docker/ directory):
# On Linux/Mac:
./run.sh --ticker AAPL,MSFT,NVDA --ollama main

# On Windows:
run.bat --ticker AAPL,MSFT,NVDA --ollama main

If you already have an Ollama server running (locally or on your network), point the containers at it instead of starting the bundled instance. You can either pass an explicit base URL or export OLLAMA_BASE_URL before running the scripts.

bash
# Linux / macOS
./run.sh --ticker AAPL,MSFT,NVDA --ollama --ollama-base-url http://localhost:11434 main

# Windows
run.bat --ticker AAPL,MSFT,NVDA --ollama --ollama-base-url http://localhost:11434 main

When OLLAMA_BASE_URL is provided, the Docker compose services reuse that endpoint and the Ollama container is not started. To launch the embedded Ollama service manually with Docker Compose, add the embedded-ollama profile (e.g. docker compose --profile embedded-ollama up).

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

bash
# With Docker (from docker/ directory):
# On Linux/Mac:
./run.sh --ticker AAPL,MSFT,NVDA --start-date 2024-01-01 --end-date 2024-03-01 main

# On Windows:
run.bat --ticker AAPL,MSFT,NVDA --start-date 2024-01-01 --end-date 2024-03-01 main

Running the Backtester (with Docker)

bash
# Navigate to the docker directory first
cd docker

# On Linux/Mac:
./run.sh --ticker AAPL,MSFT,NVDA backtest

# On Windows:
run.bat --ticker AAPL,MSFT,NVDA backtest

Example Output:

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

bash
# With Docker (from docker/ directory):
# On Linux/Mac:
./run.sh --ticker AAPL,MSFT,NVDA --start-date 2024-01-01 --end-date 2024-03-01 backtest

# On Windows:
run.bat --ticker AAPL,MSFT,NVDA --start-date 2024-01-01 --end-date 2024-03-01 backtest

You can also specify a --ollama flag to run the backtester using local LLMs.

bash
# With Docker (from docker/ directory):
# On Linux/Mac:
./run.sh --ticker AAPL,MSFT,NVDA --ollama backtest

# On Windows:
run.bat --ticker AAPL,MSFT,NVDA --ollama backtest

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.