R-multiples and expectancy
Measure every outcome in units of initial risk (R). Expectancy tells you what a method earns per unit of risk — if your sample is honest.
What R means
1R is the amount you planned to lose on a trade. If you risked $200 and made $500, the result is +2.5R. If you lost $260 because of slippage, it is −1.3R. R lets you compare trades of different sizes and instruments.
Expectancy
Expectancy (in R) = (win rate × average win in R) − (loss rate × average loss in R). A positive number means the sample made money per unit of risk; it does not guarantee future results.
Breakeven win rate for a fixed reward:risk of k:1 is 1 ÷ (1 + k). At 2:1 you need to win more than 33.3% of the time before costs; at 1:1, more than 50%.
Small samples lie. Twenty trades can show a spectacular expectancy by chance. Treat any figure from fewer than ~100 consistently executed trades as a rough guess.
Worked example
Computing expectancy from a journal (synthetic)
- 50 trades: 20 winners averaging +2.5R, 30 losers averaging −1.0R.
- Win rate = 40%; loss rate = 60%.
- Expectancy = 0.40 × 2.5 − 0.60 × 1.0 = 1.00 − 0.60 = +0.40R per trade.
- At 1R = $100 that is +$40 per trade on average — before commissions, and only if the next 50 trades behave like the last 50.
Win rate alone says nothing. Combine it with the size of wins and losses.
Common misconceptions
“A 70% win rate means a profitable strategy.”
If average losses are 3R and wins 1R: 0.7 × 1 − 0.3 × 3 = −0.2R. Frequent small wins can hide rare big losses.
“Positive backtest expectancy is proof of an edge.”
Backtests suffer from overfitting, hindsight and missing costs. It is evidence to test further, not proof.
Checkpoint