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Outlier dependence

How much of a strategy's profit comes from a handful of trades, which determines how repeatable the record really is.

Rank every trade by profit, remove the top 5%, and recompute expectancy. If a plus 0.30R edge becomes minus 0.02R, the strategy is an outlier-harvesting machine and should be understood as such.

That is not automatically bad. Trend following, venture-style position building and long-option strategies are designed to be outlier-dependent, and their practitioners accept long flat stretches as the cost. The failure is mismatching behaviour to design: taking profits at plus 1R in a system whose edge lives past plus 5R converts a profitable strategy into a losing one while every individual decision feels prudent.

It also changes the evidence standard. Outlier-dependent strategies need far more trades before their statistics mean anything, because the mean is dominated by rare events that a short sample may contain zero or three of, purely by luck.

Related: r-distribution, largest-win, return-skew, sample-size-for-edge

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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