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walk-forward-validation
Walk-forward validation framework for trading strategies and ML models with time-series-aware splits, overfit detection, and regime-aware validation
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# Walk-Forward Validation
Walk-forward validation framework for trading strategies and ML models. Standard cross-validation (k-fold, random splits) fails catastrophically for financial time series because it introduces lookahead bias and ignores autocorrelation. This skill covers proper time-series validation techniques including rolling and expanding windows, purged cross-validation, combinatorial purged cross-validation (CPCV), and overfit detection metrics.
## Why Standard Cross-Validation Fails
Standard k-fold CV assumes data points are independent and identically distributed (IID). Financial time series violate both assumptions:
1. **Lookahead bias** — Random splits let the model train on future data and predict past data, artificially inflating performance.
2. **Autocorrelation** — Adjacent observations are correlated. A random split that puts Monday in test and Tuesday in train leaks information.
3. **Regime dependence** — Markets shift between regimes. A model trained on a bull market and tested on a bull market tells you nothing about bear market performance.
4. **Label overlap** — If labels are computed over windows (e.g., 24h forward return), adjacent train/test samples share label computation periods, leaking information.
## Walk-Forward Framework