Expectancy answers the question that win rate and risk/reward ratio each answer only halfway: on average, what does one trade earn? A positive expectancy means the strategy should make money over many trades, assuming the past sample is representative; a negative one means it should lose, however good individual trades look.
How it’s calculated
Expectancy = (win rate × average win) − (loss rate × average loss)
where loss rate = 1 − win rate. Subtract average costs per trade if they are not already included.
Expressed in R (multiples of the amount risked), it becomes:
Expectancy (R) = (win rate × average win in R) − (loss rate × average loss in R)
Working in R makes strategies with different position sizes comparable.
Example
Over 50 trades, a trader wins 20 and loses 30.
- Win rate = 40%, loss rate = 60%
- Average win = $240; average loss = $100
- Expectancy = (0.40 × 240) − (0.60 × 100) = 96 − 60 = $36 per trade
The trader risks $100 per trade, so the average win is 2.4R and the average loss 1R:
- Expectancy = (0.40 × 2.4) − (0.60 × 1) = 0.36R
Over 100 similar trades, that would suggest roughly 36R before costs, though real results will vary. If fees and slippage average $10 per trade, the net figure falls to $26.
Common mistakes
- Judging by win rate alone. A strategy that wins 80% of the time can still have negative expectancy if its losses are much larger than its wins.
- Using a tiny sample. A handful of trades can show strong expectancy purely by chance.
- Leaving out costs. Spread and commissions matter most for strategies with small average wins.
- Assuming it is stable. Expectancy changes with market conditions; a breakout method can work in trending markets and fail in ranges.
- Ignoring the path. Positive expectancy can still produce deep drawdowns; keep risk per trade modest with the position size calculator.
To track the inputs reliably, see trading journal mistakes.
Educational content — not financial advice.
Updated