A trader runs 200 trades with a 60% win rate and feels confident. Then the account statement arrives: down 18%. The problem wasn't the win rate — it was a 1.2R average win against a 2.8R average loss. High win rate, negative expectancy. The system was mathematically guaranteed to lose money from the first trade.
Expectancy is the average amount a trading system earns per dollar risked, calculated across a statistically significant sample. The formula is: E = (Win% × Avg Win) − (Loss% × Avg Loss). Plug in the numbers above: (0.60 × 1.2R) − (0.40 × 2.8R) = 0.72R − 1.12R = −0.40R per trade. Every trade, on average, destroys 40 cents of every dollar risked.
To build a system with positive expectancy, traders typically target one of two structures. Structure A: 40% win rate, 2.5R average win, 1R average loss — expectancy = (0.40 × 2.5) − (0.60 × 1.0) = +0.40R. Structure B: 65% win rate, 1.5R average win, 1R average loss — expectancy = (0.65 × 1.5) − (0.35 × 1.0) = +0.625R. Both work. The arithmetic doesn't care which you prefer.
Once expectancy is confirmed positive, position sizing determines how aggressively to deploy it. A common rules-based approach: risk no more than 1–2% of account equity per trade. On a $50,000 account risking 1%, each trade risks $500. If expectancy is +0.40R, the mathematical expectation per trade is $200 — before variance. Sizing above 2% per trade accelerates both gains and ruin with equal enthusiasm. For deeper reading on the mechanics, traders reference expected value theory on Investopedia, the statistical foundations explained on Wikipedia's expected value page, and position sizing models detailed under the Kelly Criterion on Investopedia.
Calculate expectancy across your last 50 trades before adding a single dollar to any system. The number either earns its place or it doesn't.
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