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Permutation test

A significance test that scrambles the link between signal and outcome many times to see how often chance alone produces your result.

Concretely: keep your returns series but randomly shuffle your signal series, then re-run the strategy. Do this 1,000 times and collect 1,000 profit figures from a strategy with no real relationship to the market. If your actual profit exceeds 970 of them, your empirical p-value is 0.03.

This is more honest than a t-test on trade returns because it preserves the market's own properties: the same volatility clustering, the same fat tails, the same drift. Only the alignment between your rule and the market is destroyed.

Variants worth running: shuffle the signal, shuffle the trade entry dates, or shuffle bar returns within blocks. If your result survives all three, you have a genuine dependency rather than an accounting artefact.

Related: bootstrap, monte-carlo-simulation

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