How to Build & Backtest an AI Trading Agent with ai-hedge-fund

What ai-hedge-fund is

Built by Virat Singh (virattt), ai-hedge-fund runs a panel of agents modeled on famous investors plus dedicated valuation, sentiment and risk agents. They each weigh in, and a portfolio-manager agent produces a final buy/sell/hold call. Because the code is clean and well-organised, it's the best repo to read first if you want to understand multi-agent reasoning end to end.

1. Install it

It uses Poetry. Clone, install, and add your keys to a .env file (an LLM key, and a financial-data key for fundamentals):

setup — terminal

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

cp .env.example .env
# then edit .env and set, for example:
#   ANTHROPIC_API_KEY=sk-ant-...
#   FINANCIAL_DATASETS_API_KEY=...

2. Run the agents on some tickers

Point it at one or more symbols and read the decision. Add the reasoning flag to watch each agent argue its case — invaluable for learning:

run the panel — terminal

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

3. Backtest it (the part that matters)

This is where ai-hedge-fund shines for our purposes: a built-in backtester runs the same agents day by day over a historical window and tracks the resulting portfolio. No look-ahead bias to hand-roll — it's handled for you:

backtest — terminal

poetry run python src/backtester.py --ticker AAPL,MSFT \
  --start-date 2024-01-01 --end-date 2024-06-01

Read the output the way a professional would: compare the agents' equity curve to a plain buy-and-hold of the same tickers, and weigh the drawdown you'd have had to stomach, not just the final number. A strategy that beats buy-and-hold only by taking wild swings isn't a win.

Honest caveats

• Educational by design: it produces decisions and a simulated portfolio — it does not connect to a broker or trade real money.
• Data dependency: results hinge on the financial-data provider; free tiers have limits and gaps.
• LLM variance & cost: longer backtests mean many model calls — pin the model and start with a short range.