Expectancy summarizes what a strategy tends to make per trade over a large sample, combining win rate with average win and average loss. Two traders can have different win rates yet similar expectancy if their payoff profiles differ.
Breakeven intuition
If your average win and average loss are stable, you can reason about the breakeven win rate implied by your typical R-multiple. Small changes in payoff often matter more than small changes in win rate.
Keep it honest with data
Track outcomes from a journal or export rather than memory. Memory biases win rate upward.
Explore combinations on the win rate vs risk/reward matrix page.
A toy example you can do in a spreadsheet
Assume 100 trades, 45% wins, average win 2R, average loss 1R. Expectancy per trade is roughly 0.45×2 − 0.55×1 in R terms—positive here, but fragile if win rate slips a few points. Sensitivity tables show why traders obsess over both sides of the equation.
Why sample size still matters
Twenty trades can look brilliant or broken purely from variance. Before you rewrite rules, ask whether the sample is large enough for the claim you are making.
- Separate results by setup type, not only globally.
- Track consecutive loss streaks, not only averages.
- Review expectancy when market regime shifts (trend vs range).
Grounding in execution
Expectancy lives in executed prices. Slippage that hurts losers more than winners drags real expectancy below the spreadsheet—model conservatively.