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

Rolling Window (Fixed Train Size)

The train window has a fixed size and slides forward in time. This is preferred when you believe older data is less relevant (common in crypto).

Window 1: [===TRAIN===][=TEST=]
Window 2:    [===TRAIN===][=TEST=]
Window 3:       [===TRAIN===][=TEST=]

Parameters:

• train_size: Number of bars/days in the training window
• test_size: Number of bars/days in the test window
• step_size: How far to advance between folds (often equals test_size)

Expanding Window (Growing Train)

The train window starts at the beginning and expands forward. This uses all available historical data, which helps when data is scarce.