Backtesting Engine: Test Your AI Strategies Against History

This section explains the AI Hedge Fund platform’s powerful Backtesting Engine. This core component allows you to rigorously test your AI-driven investment strategies against historical market data, providing a clear picture of how they would have performed in the past—all without risking any real capital.

The Core Value: From Strategy to Performance Report

The Backtesting Engine transforms your visually designed strategy into a detailed performance analysis. It bridges the gap between an investment idea and a quantitative assessment of its viability. By simulating trades over a specified historical period, it answers the critical question: “Would this strategy actually have worked?”

How It Works: A Day-by-Day Market Simulation

The backtesting process is a meticulous, step-by-step simulation of your strategy’s execution against past market conditions.

1. Define Your Test Environment: You begin by setting the parameters for the simulation. This includes:

   • The AI Strategy: The exact visual workflow you designed, with all your chosen agents and their connections.
   • The Assets: The specific stocks (tickers) you want the strategy to trade.
   • The Time Period: A defined start and end date for the historical analysis.

2. Iterating Through History: The engine retrieves the historical market data (prices, volume, etc.) for your chosen assets and time period. It then moves forward one day at a time.

3. Daily AI Decision-Making: For each day in the simulation, the engine feeds that day’s market data into your AI strategy. Your entire multi-agent workflow runs, from data analysis to risk management, culminating in a final decision from the Portfolio Manager agent (e.g., “BUY 10 shares of AAPL,” “SELL 5 shares of MSFT,” or “HOLD”).

4. Simulating Trades and Managing a Virtual Portfolio: The engine acts on the AI’s decision. It executes the simulated trade based on that day’s closing price and updates a virtual portfolio. This includes:

   • Adjusting the virtual cash balance.
   • Updating the number of shares held for each asset.
   • Continuously tracking the total value of the virtual portfolio.

5. Generating the Final Report: After simulating every day in the specified period, the engine aggregates all the data and generates a comprehensive performance report.

Key Performance Metrics: Understanding the Results

The final output is not just a simple profit or loss number. The engine provides a suite of standard financial metrics to give you a deep understanding of your strategy’s performance, risk, and efficiency. These include:

• Final Portfolio Value: The total value of your assets and cash at the end of the simulation.
• Total Return (%): The overall percentage gain or loss of the portfolio over the entire period.
• Annualized Return (%): The average yearly return generated by the strategy.
• Sharpe Ratio: A crucial measure of risk-adjusted return. A higher Sharpe Ratio indicates better performance for the amount of risk taken.
• Max Drawdown (%): The largest peak-to-trough decline in portfolio value, indicating the biggest loss experienced during the period. This is a key measure of risk.
• Volatility (%): Measures how much the portfolio’s value fluctuates. Higher volatility means higher risk.

These metrics allow you to objectively compare different strategies and understand their true historical behavior.

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