backtesting-frameworks

Robust backtesting systems that avoid look-ahead bias, survivorship bias, and overfitting. Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates. - Event-driven and vectorized backtester implementations with realistic transaction cost modeling, slippage, and commission handling - Walk-forward optimization and Monte Carlo simulation for strategy robustness testing across multiple time windows - Comprehensive performance metrics including Sharpe, Sortino, Calmar ratios, drawdown analysis, and win-rate calculations - Point-in-time data handling, out-of-sample validation, and parameter grid search to prevent curve-fitting and selection bias - Developing trading strategy backtests - Building backtesting infrastructure - Validating strategy performance - Avoiding common backtesting biases - Implementing walk-forward analysis - Comparing strategy alternatives - 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'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 -...