A trader who is wrong 60 times out of 100 can outearn one who is right 60 times out of 100. That reads like a riddle and is actually just multiplication. What decides it is the ratio between what a winner pays and what a loser costs, and it is the single most misunderstood lever in retail trading, because everything in human wiring wants to maximize the feeling of being right instead.
Think in R, not dollars
Define 1R as the amount on the line at your stop. On a $10,000 account risking 1 percent, 1R is $100. From then on, every result is a multiple: a $250 winner is +2.5R, a full stop-out is exactly 1R, a scratched trade is 0.2R. This normalization matters because dollar results are distorted by position size and instrument, while R tells the truth about trade quality. A journal kept in R makes a month of EURUSD, gold and Nasdaq trades directly comparable, and it makes the next section's arithmetic something you can compute on your own numbers in minutes. The habit also reframes losing. A stop-out is minus 1R, the planned price of finding out, and a month is judged by net R against trades taken. Traders who think in dollars take losses personally and protect open profit badly; traders who think in R run the tally like a business ledger.
The breakeven table
For any average reward-to-risk ratio, there is a win rate below which you lose money. The table is short enough to memorize:
- Winners of 1R: you need better than 50 percent winners to profit.
- Winners of 1.5R: better than 40 percent.
- Winners of 2R: better than 33.3 percent.
- Winners of 3R: better than 25 percent.
Those are frictionless numbers. Spread and slippage typically cost an intraday trader 0.1R or so per trade, which pushes each threshold up a few points: a 2R trader realistically needs about 37 percent winners, not 33. The table's real lesson is the direction of the trade-off. Every pip of extra target lowers the win rate you need, and every pip of unnecessary stop width raises it.
Forty percent winners, compounding anyway
Now the promised math. One hundred trades, 40 percent winners, average winner 2R, average loser 1R: the wins collect 80R, the losses give back 60R, and the net is +20R. At $100 per R that is $2,000 on the $10,000 account, a 20 percent return across the sample, while losing more often than winning. The same 40 percent win rate at 1R targets produces minus 20R, a $2,000 loss. Identical entries, identical accuracy, opposite outcomes; the payoff structure did all the work. This is why the ratio forgives mistakes. A mistimed entry, a choppy week, a few outright errors: at 2R or better, the winners keep absorbing them. At 1R, every mistake lands on bare skin. Variance still bites, of course: at 40 percent, losing streaks arrive often, which is why this arithmetic shares a chapter with the 1% rule. The ratio makes the system profitable; the sizing makes the streaks survivable.
Where good ratios actually come from
The trap is treating reward-to-risk as a setting. Dragging your target from 2R to 4R does not create a 4R trade; it usually creates a 2R trade that gives itself back on the way to a target price never reached. Real ratios come from two places: stops placed tight against genuine invalidation (structure or ATR, covered in the stop placement article), and targets at levels the market plausibly reaches, prior highs, range extremes, measured moves. Then audit the realized number: if your journal shows planned 3R trades actually resolving at 25 percent winners and 1.4R average, that is your true ratio, and the breakeven table must be read against it. Setup type matters too: mean-reversion fades tend to top out near 1.5R because the target is the middle of a range, while trend continuation can reach 2R and beyond because the target is open air. Ask each setup what it can realistically pay, demand at least 1.5R after costs, and let the ones that cannot deliver go untraded.