Expectancy is the average dollar amount a trading system can be expected to make or lose per trade over a large sample, calculated from its win rate, average win size, and average loss size. A positive expectancy means the system makes money on average; a negative expectancy means it bleeds capital on average no matter how many trades you take. It is the single most important number for judging whether a strategy has edge, because unlike win rate or risk-reward in isolation, expectancy combines both.
Formula
Expectancy = (Win Rate × Average Win) − ((1 − Win Rate) × Average Loss)
Where Win Rate is the fraction of trades closed in profit (e.g. 0.55 for 55%), Average Win is the mean dollar profit on winning trades, and Average Loss is the mean dollar loss on losing trades expressed as a positive number.
Worked example
You take 100 trades. 40 win for an average of $300; 60 lose for an average of $150.
Win Rate = 0.40 Average Win = $300 Average Loss = $150
Expectancy = (0.40 × $300) − (0.60 × $150) = $120 − $90 = $30 per trade
Across 100 trades, the system produces roughly $3,000 in net profit. Notice the win rate is only 40% — losing more often than winning — yet the system is profitable because winners are twice the size of losers.
Why it matters
Expectancy answers the only question that matters about a strategy: should I keep trading it? Two systems with identical win rates can have wildly different expectancies depending on payoff structure. Use the expectancy calculator to evaluate a journal sample, and pair it with the AI coach to see which trade clusters drag expectancy down.
Common pitfalls
Expectancy is unreliable on small samples. Below 30-50 trades the average win and average loss are too noisy to trust. A single outlier winner can flip a losing system into apparent positive expectancy — strip the top and bottom 5% and recompute to sanity-check.