Walk-Forward Cross-Validation

A single train/test split tells you nothing about how a model adapts over time. Walk-forward CV slides a window through the data, training and testing sequentially, revealing how performance evolves across market regimes.

The Problem

A single 80/20 train/test split produces one score from one market period. The model may excel in bull markets but fail in drawdowns - you cannot tell. Standard k-fold shuffles time, leaking future data. You need sequential evaluation that mirrors live deployment: train on the past, predict the future, advance, repeat. This exposes regime sensitivity and stationarity failures.

The Pattern

WRONG

from sklearn.model_selection import train_test_split