The range your true win rate plausibly falls in given your sample, which is embarrassingly wide until you have hundreds of trades.
The standard error of an observed win rate p over n trades is sqrt(p(1-p)/n), and a rough 95% interval is p plus or minus twice that.
Worked: 30 trades with 18 winners gives p = 0.60 and a standard error of sqrt(0.24/30) = 0.089, so the interval runs from roughly 42% to 78%. At 100 trades the same 60% narrows to 50-70%. At 500 trades it is 55.6-64.4%. The width shrinks with the square root of n, which is why doubling confidence costs four times the data.
Apply this before concluding a strategy works. A 60% win rate on 30 trades is compatible with a true rate below the breakeven-win-rate of most payoff structures - meaning the evidence does not yet exclude a losing system. See sample-size-for-edge.
Original diagrams for the ideas on this page. Illustrative, not real market data.
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.The win rate needed to break even. How often a method must win just to stay level, for each reward-to-risk ratio. At 1:1 half the trades must win, at 1:2 a third, and at 1:3 a quarter, because each win covers more losses.
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