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Luck versus skill

The problem of telling a real edge from a fortunate sequence, which cannot be settled by results alone over short horizons.

In activities with high outcome variance, short-run results carry very little information about ability. Trading sits at the extreme end of that scale: a year is a small sample, and thousands of participants guarantee that some will post excellent records through chance alone.

Three tests help. Sample size, via standard-error-of-expectancy, establishes whether the result could plausibly be zero. Process compliance asks whether the profitable trades were the planned ones - a winning month of improvised trades is luck even when it pays. And mechanism asks whether there is a reason the edge should exist: who is on the other side, and why do they keep taking it?

Held properly, this is not defeatism. It is the reason to judge yourself on process while results accumulate, and the reason to stay sceptical of anyone whose evidence is a screenshot. See process-over-outcome, and be wary of judging a decision by its result.

Related: sample-size-for-edge, sharpe-inflation, process-over-outcome, track-record-selection

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.

Educational only, not advice. Spotted an error? Post in Site Feedback.