backtesting-frameworks

name backtesting-frameworks description Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure. Backtesting Frameworks Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates. When to Use This Skill Developing trading strategy backtests Building backtesting infrastructure Validating strategy performance Avoiding common backtesting biases Implementing walk-forward analysis Comparing strategy alternatives Core Concepts 1. Backtesting Biases Bias Description Mitigation Look-ahead Using future information Point-in-time data Survivorship Only testing on survivors Use delisted securities Overfitting Curve-fitting to history Out-of-sample testing Selection Cherry-picking strategies Pre-registration Transaction Ignoring trading costs Realistic cost models 2. Proper Backtest Structure Historical Data │ ▼ ┌─────────────────────────────────────────┐ │ Training Set │ │ (Strategy Development & Optimization) │ └─────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────┐ │ Validation Set │ │ (Parameter Selection, No Peeking) │ └─────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────┐ │ Test Set │ │ (Final Performance Evaluation) │ └─────────────────────────────────────────┘ 3. Walk-Forward Analysis Window 1: [Train──────][Test] Window 2: [Train──────][Test] Window 3: [Train──────][Test] Window 4: [Train──────][Test] ─────▶ Time Detailed worked examples and patterns Detailed sections (starting with ## Implementation Patterns ) live in references/details.md . Read that file when the navigation summary above is insufficient. Best Practices Do's Use point-in-time data - Avoid look-ahead bias Include transaction costs - Realistic estimates Test out-of-sample - Always reserve data Use walk-forward - Not just train/test Monte Carlo analysis - Understand uncertainty Don'ts Don't overfit - Limit parameters Don't ignore survivorship - Include delisted Don't use adjusted data carelessly - Understand adjustments Don't optimize on full history - Reserve test set Don't ignore capacity - Market impact matters