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Statistic stability

Whether a performance measure holds up across sub-periods, which separates a durable edge from one that worked in a single regime.

Split the record into halves or thirds and recompute expectancy-per-trade, win-rate and payoff-ratio in each. Stable figures across periods are evidence of something real; figures that were strong in one stretch and flat afterwards describe a regime, not an edge.

Do the same across instruments and conditions. A strategy that works only in high-volatility environments is not broken - it is conditional, and knowing the condition lets you trade it deliberately rather than wondering why it stopped. That is a question about regime filters rather than a failure.

Beware of over-reading the split, though: each half has half the sample, so differences of 5-10 percentage points in win rate between halves are entirely expected. Compare the difference against the standard-error-of-expectancy before concluding anything decayed.

Related: performance-attribution, standard-error-of-expectancy, rolling-performance-window, serial-correlation-of-returns

See it drawn

Original diagrams for the ideas on this page. Illustrative, not real market data.

The spread of outcomes behind an expectancyA histogram of forty trades: a tall block of small losses on the left, a low spread of larger wins on the right, and a line marking the average outcome.NUMBER OF TRADES051024 LOSSES, AVG −$20016 WINS, AVG +$600EXPECTANCY +$120−$400−$200$0+$200+$400+$600+$800PROFIT OR LOSS PER TRADEexpectancy = (40% × $600) − (60% × $200) = +$120 per trade
Expectancy: the average trade. Forty trades sorted by outcome: 24 small losses and 16 larger wins. Weighting each side by how often it happens gives the average result per trade, marked here by the dashed line at +$120.

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