R-Multiple
The risk-normalised unit for measuring trade outcomes. Why expressing wins and losses as multiples of risk makes strategies comparable across account sizes, position sizes, and time.
An R-multiple is a trade outcome expressed as a multiple of the amount risked on that trade. 1R is the planned dollar risk: the difference between your entry and your stop, multiplied by your position size. Every trade outcome can be expressed as a multiple of that unit.
A trade where you risked $100 and made $250 is a +2.5R trade. A trade where you risked $100 and lost $100 is a −1R trade. A trade where you risked $100 and lost $150 (because of slippage beyond the stop) is a −1.5R trade. A trade where you closed manually at break-even is a 0R trade.
Why the normalisation matters
Dollars scale with account size and position size. A $250 win on a $5,000 account is meaningful; the same $250 win on a $250,000 account is a rounding error. R-multiples remove this scaling, so the same trade record can be evaluated across very different account sizes, position sizes, and time periods.
Two practical consequences:
- Strategies are comparable. A strategy that averages +0.3R per trade is unambiguously better than one that averages +0.1R per trade, regardless of the dollar amounts they were tested with.
- The trader’s own progress is measurable. Tracking the R distribution of your closed trades shows whether the strategy is working independently of how much you happened to risk per trade in any given month.
Computing it for a single trade
The arithmetic is simple. For any closed trade:
R-multiple = (exit price − entry price) × direction × position size ÷ planned risk
For a long EUR/USD trade entered at 1.0850 with a stop at 1.0820 (planned risk = 30 pips × position size × pip value), exited at 1.0910:
- Move in favour = (1.0910 − 1.0850) = 60 pips
- Planned risk = 30 pips
- R-multiple = 60 / 30 = +2R
If the same trade had been exited at the stop, the R-multiple would have been −1R. If exited at 1.0865 (a partial winner), it would have been +0.5R.
The slippage case: if the stop fired at 1.0820 but the actual fill was 1.0808, the realised risk was 42 pips against the planned 30 pips, so the R-multiple is −1.4R. This is the relevant number, not the planned 1R.
Why win rate is the wrong number
R-multiples expose the trap that win-rate-only marketing exploits. Consider two strategies:
| Strategy | Win rate | Avg win | Avg loss | Per-trade R |
|---|---|---|---|---|
| A | 70% | +1R | −3R | 0.7 × 1 − 0.3 × 3 = −0.2R |
| B | 40% | +3R | −1R | 0.4 × 3 − 0.6 × 1 = +0.6R |
Strategy A wins more than two-thirds of the time and loses money. Strategy B is wrong on most of its calls and is the better strategy by a wide margin. The win rate alone tells you nothing about whether the strategy makes money. The R distribution tells you everything.
Any source that advertises a trading method by its win rate, without R-multiples, is either confused or selling something. Most signal-selling marketing exploits exactly this gap. See What Are Forex Signals for the long-form version of the argument.
R, expectancy, and the strategy view
R-multiples combine with win rate to produce expectancy, the average R per trade. A strategy’s expectancy is the right single number to evaluate it on. See expectancy for the companion glossary entry, and Risk Management Basics for the framework that ties R, sizing, and expectancy together.