AI Hedge Fund

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.

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.

How to Install

pipx install aihf

(or uv tool install aihf, or pip install aihf into an environment of your choice)

Then run it from anywhere:

aihf

API keys

The app asks for keys the first time it needs them and saves them to ~/.hedge-fund/.env — nothing to configure up front. It needs:

• A Financial Datasets API key, for prices, fundamentals, and earnings.
• One model API key for the investor agents. Supported providers: Anthropic, OpenAI, DeepSeek, Google, xAI, Kimi, TypeSafe (Jev).

Keys exported in your shell always win over the saved file.

How to Run

Interactive app

aihf

With no arguments, this launches the interactive terminal app. Build a fund — pick stocks, strategies, rebalance cadence — or backtest a saved fund and watch its equity curve draw against its benchmark. Funds you build are saved as mandate files in ~/.hedge-fund/mandates/.

Non-interactive

Run one fund cycle from a mandate file. The full cycle record prints to stdout as JSON; a short human summary goes to stderr:

aihf ~/.hedge-fund/mandates/example.yaml --tickers AAPL,MSFT

Run the same mandate with Jev after configuring TYPESAFE_API_KEY:

aihf ~/.hedge-fund/mandates/example.yaml --tickers AAPL,MSFT --model jev-1.13.0

Backtest the mandate over history at its rebalance cadence:

aihf ~/.hedge-fund/mandates/example.yaml --tickers AAPL,MSFT --backtest

An LLM trained after your backtest window may remember how those companies did, and that memory would score as skill. So a backtest withholds the ticker, industry and calendar dates from the investor agents' prompts; the personas see the fundamentals with periods labelled t-0, t-1, ... instead. This reduces the recall without removing it (distinctive numbers can still give a large company away), and a window after the model's training cutoff is the cleanest read. Live runs are unaffected and name the company.

A mandate is the desk — strategies, staff, risk, capital, cadence — and never names tickers; --tickers says what to point it at for this run.

Development

git clone https://github.com/virattt/ai-hedge-fund.git
cd ai-hedge-fund
poetry install
poetry run aihf
poetry run pytest hedge_fund

How to Contribute

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.