Backtesting is the single most important step between a clever idea and a strategy you can trust — but it is also the easiest place to fool yourself. In The Backtesting Handbook, Nathan Cole Reeve strips away the marketing hype to deliver a practical, no-nonsense guide to designing, running, and interpreting historical simulations of AI-driven trading systems. You will learn how to source and clean market data, avoid the silent traps of look‑ahead bias and overfitting, and use performance metrics that actually matter. The book walks you through building backtesting frameworks that incorporate machine learning models, handling multiple timeframes and market regimes, and realistically accounting for transaction costs and slippage. Every chapter emphasizes the uncomfortable truths: most strategies fail forward, past results never guarantee future returns, and even the best backtest is just a starting point. With clear explanations, code‑free conceptual outlines, and honest risk checks, you will finish this book knowing how to separate signal from noise — and when to walk away. Part of The AI Trading Library.