Hit Rate vs Payoff Asymmetry
Why a strategy's win rate alone says nothing about profitability — a low hit rate can be highly profitable if winners are much bigger than losers, and a high hit rate can still lose money if the reverse holds.
Prerequisites: Information Coefficient
A strategy's hit rate — the fraction of trades that make money — is one of the most misleading numbers in quant trading when looked at alone, because expected profit depends just as much on how much winners and losers pay off, not just how often each occurs. A trend-following system that's right only 35% of the time can still be highly profitable if its average winner is four or five times the size of its average loser, because trends run far while stop-losses cap the damage on the 65% of trades that fail. Conversely, a mean-reversion or option-selling strategy that wins 80% of the time can lose money overall if the rare losing trade wipes out many winners' worth of gains — the classic "picking up pennies in front of a steamroller" pattern.
The number that actually matters is expectancy: hit rate times average win, minus miss rate times average loss. Reporting hit rate alone, without the payoff ratio alongside it, tells you almost nothing about whether a strategy makes money.
Worked example. Strategy A wins 35% of trades, averaging +$400 on winners and -$100 on losers: expectancy is 0.35 \times 400 - 0.65 \times 100 = 140 - 65 = \7550 on winners and -$300 on losers: expectancy is 0.80 \times 50 - 0.20 \times 300 = 40 - 60 = -\20$ per trade. The lower hit-rate strategy is the profitable one.
Expectancy, not hit rate, determines profitability: a strategy that wins rarely can be strongly profitable if winners are large relative to losers, and a strategy that wins often can still lose money if the occasional loss dwarfs many wins — always ask for the payoff ratio alongside any hit rate.
Related concepts
Practice in interviews
Further reading
- Common trend-following industry commentary on low hit-rate, positive-expectancy systems