Walk-forward: does the edge survive data it never saw?

Walk-forward re-tunes the strategy on one slice of history, then tests it on the following slice it had never seen — repeated across the whole data set. Only the unseen slices count toward the result.

The combined out-of-sample result

Expect this number to be smaller than the optimizer's — that's what an honest test looks like. (In the walkthrough example: profit factor 1.407 in the search, 1.227 pooled out-of-sample, still profitable → worth taking to the audit.) It still overstates live performance, because each window used freshly re-fitted settings; the audit gives the deployable answer.

Setting stability across windows

If the best settings jump around wildly from window to window, the optimizer is chasing noise — the "edge" needed a different strategy every few months. Settings that stay in a tight band (graded stable) are a sign the edge is repeatable.