What Are R-Multiples and How Do I Calculate Mine?
What you will find out: What R-multiples are, why they matter more than dollar P&L for analyzing your strategy, and how to calculate your own stats from your trading history.
The Problem With Dollar P&L
If you made $500 on a trade, was that a good trade? It depends. If you risked $100 to make $500, that’s excellent. If you risked $2,000 to make $500, that’s a different story.
Dollar amounts don’t tell you how well your strategy is performing because they change with position size. A trader risking $50 per trade and a trader risking $500 per trade can have the exact same edge, but their dollar P&L looks completely different. You need a way to measure performance that strips out position size. That’s what R does.
What is R?
R is simply your risk on a trade. It’s the dollar amount you would lose if the trade hits your stop loss.
If you enter a trade and your stop loss represents a $200 loss, then 1R = $200 for that trade.
From there, every trade result is expressed as a multiple of that risk:
- You risked $200 and made $400 → that’s a 2R win
- You risked $200 and made $100 → that’s a 0.5R win
- You risked $200 and lost $200 → that’s a 1R loss
- You risked $200 and lost $150 → that’s a 0.75R loss
That’s it. R-multiples just express your trade results relative to what you risked.
Why This Matters
When you measure in R, you can compare any two strategies regardless of account size or position size. A strategy that averages 1.5R wins and 1R losses has the same edge whether the trader is risking $50 or $5,000 per trade.
This is also exactly what Edge Engine needs as input. The simulator works in R-multiples because it doesn’t care what dollar amount you’re risking. It cares about the shape of your edge. Your win rate, your average win in R, and your average loss in R are the three numbers that define your strategy’s performance.
How to Calculate Your Stats
You need a list of your trades with the dollar P&L for each one, and you need to know what you risked on each trade.
Step 1: Find your risk per trade
If you use a fixed dollar risk on every trade (say $200), this is straightforward. Every trade’s R-value is just its dollar P&L divided by $200.
If your risk varies per trade, you need the risk for each individual trade. Some platforms include this in their export. If yours doesn’t, you’ll need the entry price, stop loss price, and position size to calculate it: risk = (entry price − stop loss price) × position size.
Step 2: Convert each trade to R
For each trade, divide the dollar P&L by the risk amount:
R-multiple = Dollar P&L ÷ Risk
A trade that made $300 on $200 risk = 1.5R. A trade that lost $180 on $200 risk = −0.9R.
Step 3: Calculate your three stats
Once every trade is expressed in R:
- Win Rate — count wins, divide by total trades, multiply by 100. 120 wins out of 230 = 52.2%.
- Average Win (in R) — add up R-values of all winners, divide by number of winners. Wins of 1.2R, 0.8R, 2.1R, 1.5R = 1.4R average.
- Average Loss (in R) — same for losing trades, ignoring the negative sign. Losses of −0.9R, −1.0R, −1.1R = 1.0R average.
Those three numbers are everything you need to run a simulation.
A Quick Example
Say you have 10 trades with a fixed $200 risk:
| Trade | Dollar P&L | R-Multiple |
|---|---|---|
| 1 | +$310 | +1.55R |
| 2 | −$200 | −1.00R |
| 3 | +$180 | +0.90R |
| 4 | −$160 | −0.80R |
| 5 | +$420 | +2.10R |
| 6 | +$140 | +0.70R |
| 7 | −$200 | −1.00R |
| 8 | +$260 | +1.30R |
| 9 | −$190 | −0.95R |
| 10 | +$300 | +1.50R |
Win Rate: 6 wins out of 10 = 60%
Average Win: (1.55 + 0.90 + 2.10 + 0.70 + 1.30 + 1.50) ÷ 6 = 1.34R
Average Loss: (1.00 + 0.80 + 1.00 + 0.95) ÷ 4 = 0.94R
Plug those into Edge Engine and you can see exactly what that edge looks like over hundreds or thousands of trades.
What If My Risk Varies Per Trade?
If you don’t risk the same amount every trade, you have two options.
The more accurate approach is to calculate the R-multiple for each trade individually using that trade’s specific risk amount. This gives you the truest picture of your strategy.
The simpler approach is to use your average risk per trade as the divisor for all trades. This is less precise but gets you close enough to run a meaningful simulation, especially if your risk doesn’t vary dramatically from trade to trade.
Either way, the goal is the same: get your results into R so the simulator can work with them.
Next Steps
- Launch the simulator and enter your stats to see what your edge looks like across thousands of paths.
- What is a Good Win Rate? — understand how your win rate interacts with your R-multiples.
- What is a Monte Carlo Simulation? — learn why running thousands of simulations matters more than looking at a single backtest.