What is walk-forward analysis?

It is a testing method that repeatedly optimises a strategy on one window of data and tests it on the next unseen window, rolling forward through history. Performance is only ever measured on data not used to choose parameters, exposing overfitting.

How is walk-forward better than a backtest?

A normal backtest tunes and tests on the same data, so an overfit strategy looks great. Walk-forward always tests on unseen data, giving a far more honest estimate of how the strategy will perform live.

What is the difference between rolling and anchored walk-forward?

In rolling walk-forward, the in-sample window is a fixed length that slides forward and drops old data. In anchored walk-forward, the window starts at the beginning and grows over time, using ever more history. Rolling adapts faster; anchored uses more data.

How do I know a strategy passed walk-forward?

If the stitched out-of-sample performance is strong and consistent, and the optimal parameters stay stable across windows, the edge is likely real. Wildly jumping parameters or weak out-of-sample results signal overfitting.

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