Win Rate vs Risk-Reward: Where a Strategy Breaks Even
A win rate means nothing without its reward-to-risk. Two computed grids show the break-even line and what each pairing is worth in R.
3 Nov 2026 · 5 min read
The win rate a strategy needs to break even is 1 ÷ (1 + reward-to-risk). Risk 1R to make 2R and you need to win one trade in three; risk 1R to make 0.5R and you need to win two in three. A win rate means nothing until it is set against the reward-to-risk it comes with. This is arithmetic on made-up inputs, not advice.
The break-even line
With every loss at 1R and every win at RR, expectancy is zero when win rate × RR = (1 − win rate), which solves to win rate = 1 ÷ (1 + RR). Costs move the line: if each trade also costs 0.10R in spread and commission, the line becomes (1 + 0.10) ÷ (1 + RR).
| Reward-to-risk | Break-even win rate | With 0.10R cost per trade |
|---|---|---|
| 0.5:1 | 66.7% | 73.3% |
| 0.75:1 | 57.1% | 62.9% |
| 1:1 | 50.0% | 55.0% |
| 1.5:1 | 40.0% | 44.0% |
| 2:1 | 33.3% | 36.7% |
| 3:1 | 25.0% | 27.5% |
| 4:1 | 20.0% | 22.0% |
| 5:1 | 16.7% | 18.3% |
The same cost moves the line further at a low reward-to-risk: at 1:1 a 0.10R cost lifts the line from 50.0% to 55.0%, and at 3:1 from 25.0% to 27.5%. Spread is a bigger share of a small stop, so the same pip cost is more R on a tight stop.
Expectancy for every pairing
Each cell is the expectancy per trade, in R, before costs: win rate × RR − (1 − win rate). Zero is the break-even line from above; positive cells are the pairings where the average trade gains.
| Win rate | 0.5:1 | 1:1 | 1.5:1 | 2:1 | 3:1 |
|---|---|---|---|---|---|
| 30% | −0.55R | −0.40R | −0.25R | −0.10R | +0.20R |
| 40% | −0.40R | −0.20R | 0.00R | +0.20R | +0.60R |
| 50% | −0.25R | 0.00R | +0.25R | +0.50R | +1.00R |
| 60% | −0.10R | +0.20R | +0.50R | +0.80R | +1.40R |
| 70% | +0.05R | +0.40R | +0.75R | +1.10R | +1.80R |
- Read across a row to see what a larger target does when the win rate stays put: at 40% it moves the average trade from −0.40R at 0.5:1 to +0.60R at 3:1.
- Read down a column to see what a better hit rate is worth at a fixed target: at 1:1 each ten points of win rate is worth +0.20R a trade.
- A high win rate does not rescue a small target. 70% at 0.5:1 gives +0.05R, below the +0.20R of 40% at 2:1.
What the grid cannot tell you
- The two axes are not independent in real trading. A farther target is usually hit less often, and the grid holds the win rate fixed as RR changes. The table says what each pairing is worth, not which pairing your method actually produces. Only your own records show that.
- It assumes every win and every loss is the same size. Real results scatter, partial closes and stop moves included; use expectancy on your actual trades, which averages them.
- Planned reward-to-risk is not realised. A 2:1 target that is closed early half the time is not 2:1. Measure the R you actually took, as in the R-multiple guide.
- A short sample hides all of it. Whether a measured win rate has cleared its line is a sample-size question; see how many trades are enough.
How to check your own
- Work out your average win and average loss in R, and your win rate, from your closed trades.
- Divide average win by average loss for your realised reward-to-risk.
- Compute 1 ÷ (1 + that figure) and add your cost per trade in R to the numerator, as above.
- Compare it with your win rate, and note how many trades the comparison rests on.
We're working on a free journal and a backtester, with no date yet. Today Trepeat is a trade copier for MT4, MT5 and cTrader, currently on demo accounts, and you can add notes, a grade and screenshots to any copied trade once it closes, with a calendar of your results.
The journal's calendar shows each day's win rate for trades with an entry. It exports to Excel (.xlsx) and does not calculate break-even win rate, expectancy or any other metric today; see the journal documentation. The lot size calculator sets the size that makes 1R an amount you chose, and the metrics overview shows where this sits beside the others. Terms are in the glossary.
Written by the Trepeat team.