Traders say a 75% win rate is better than a 50% win rate.
In our hedge fund, we ran a 2,500-strategy correlation study to test that assumption.
The data disagreed completely.
Win percentage has only 11% correlation between backtest and live results.
You can spend weeks adding filters and tightening parameters until your backtest shows 80% winners. The data says that work has virtually no predictive value for what happens when you go live.
Worse: the harder you push for a higher win rate in-sample, the lower your average out-of-sample win rate becomes.
That relationship is not a nuance. It is the central finding.
Here is why it happens.
Win percentage measures one dimension of performance. It says nothing about how big your wins are, how big your losses are, how many trades the strategy takes, or whether the edge is real or manufactured.
When a trader adds filters to push win rate from 60% to 80%, they are eliminating historical losing trades. But those trades lost for random reasons specific to that historical period. The strategy does not know that. It now treats those conditions as permanently unfavorable and skips every trade that matches them, including future trades that would have been winners.
The strategy memorized noise and called it an edge.
Net profit, by contrast, showed 0.63 correlation in the same study.
Why the difference?
Net profit is not easily inflated by filter accumulation. When you add a filter that eliminates a losing trade, you also eliminate all the winning trades that share that same characteristic. If the filter is genuine, net profit rises. If it is noise-fitting, net profit stays flat or drops.
Net profit rewards real edges. Win percentage rewards backtest cosmetics.
Breakout strategies naturally produce a profile where win rate is modest but winners are substantially larger than losers. That asymmetry tends to persist across datasets because it reflects how markets actually move: most breakout attempts fail, but when a real breakout runs, it runs far.
Optimizing for win rate fights against that natural profile. It produces strategies that take smaller, more frequent wins and tighten stops to avoid letting losses breathe, which is the exact opposite of what breakout trading requires.
The instinct to want a high win rate is completely understandable. Losing trades feel bad. Losing streaks feel unbearable.
But that instinct, when applied to strategy optimization, destroys the strategies you are trying to build.
11% vs. 63%. That gap is the cost of optimizing for the wrong metric.
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