walk-forward-validation

Walk-forward validation prevents lookahead bias by strictly separating training and test sets in chronological order, ensuring future data never leaks into historical model training periods.

Overfitting detection in time-series models uses metrics like Deflated Sharpe Ratio and Probability of Backtest Overfitting to evaluate if historical performance is statistically reliable or inflated.

Rolling and expanding windows for time-series validation are configured by defining fixed training and test sizes, such as a 90-day training window and a 14-day test window, to simulate sequential trading periods.

Walk-forward validation includes purging and embargo periods to eliminate autocorrelation issues by removing overlapping data points between training and test sets, ensuring strictly independent performance evaluation.

Walk-forward backtesting fully supports pandas and numpy, utilizing these libraries to handle time-series data structures, rolling windows, and statistical calculations required for rigorous financial model validation.