Two strategies with different win rates
Strategy A wins four out of five trades and still has negative gross expectancy. Strategy B loses more often and has positive gross expectancy. Costs would reduce both figures.
These simplified averages do not show variance, tail losses, or estimation uncertainty, but they demonstrate why win rate cannot be evaluated alone.
Metrics to pair with win rate
- average and median win;
- average and largest loss;
- expectancy after costs;
- standard deviation or other dispersion measure;
- maximum drawdown and time under water;
- length and clustering of losing streaks;
- skew and tail behavior;
- sample size and market regime;
- execution quality and missed trades.
Sample quality matters
Win rate is an estimate. It can change when the sample is small, rules are modified, discretionary trades are excluded, or market conditions shift. Break down results by setup and use out-of-sample or live data where possible.Prop-account implications
A strategy with frequent small wins and rare large losses may appear well suited to a profit target until one tail event violates the daily or maximum-loss rule. Conversely, a lower-win-rate strategy may be difficult to operate if normal losing sequences are too large for the available buffer. The relevant question is not “How often does it win?” It is “What is the net distribution, and can the account survive it?”Trading expectancy
Calculate the average outcome after costs.
Risk-reward ratio
Connect payoff size with break-even probability.
High-accuracy strategies
Examine rare large-loss structures.
40% win-rate example
Work through the payoff requirement.