It sounds contradictory: you can lose more trades than you win and still make good money. But a low win rate paired with a high enough reward-to-risk is a genuinely profitable model — the one trend-followers and many great traders run. The math is clear; the psychology is brutal.
The math
Say you win 40% of trades, but your average winner is 3R and your average loser is 1R. Expectancy = (0.40 × 3) − (0.60 × 1) = 1.2 − 0.6 = +0.6R per trade. You lose 6 of every 10 trades and make solid money, because the 4 winners each pay 3× what the 6 losers cost. Push the reward-to-risk higher and the required win rate drops further — at 4R winners you're profitable winning barely a third of the time.
Why it's psychologically hard
Being wrong 60% of the time is miserable to live through. You'll face frequent losses, long losing streaks (normal variance at a low win rate produces ugly runs), and constant temptation to “improve” the win rate by cutting winners early — which destroys the exact 3R winners the whole model depends on. The strategy works only if you hold your winners and take your many small losses without flinching.
The low-win-rate trader is wrong most of the time and profitable overall — but only if he lets the rare big winner run. Cut it short for comfort and the math collapses.
Making it livable
Judge yourself on process, not hit rate, keep losers small and uniform, and protect the big winners religiously — a runner in your exit ladder is the mechanism. Track expectancy over a real sample so a losing streak doesn't shake you off a working edge. Automated exits help enormously here: they let winners run and losers cut by rule, immune to the discomfort that makes low-win-rate trading so hard to execute by hand.