R-multiple

An R-multiple is a trade result expressed in units of the initial risk you took on entry, where 1R equals the dollar distance from your entry to your stop. A trade that makes twice what you risked is +2R; one that hits your stop is −1R. Because every result is scaled by its own risk, a +2R on a micro futures scalp and a +2R on a swing stock position mean exactly the same thing to your account math. That normalization is what makes R the common currency of a serious trading journal.

How it's calculated

R is defined the moment you enter, not the moment you exit. Your initial risk (1R) is the per-trade dollar amount you stand to lose if price runs to your stop. The result in R is simply your realized profit or loss divided by that number.

Worked example. You buy 200 shares at $50.00 with a stop at $48.50. Your initial risk is $1.50 per share × 200 = $300. That $300 is your 1R. Now run three exits:

• Exit at $54.50 → +$900 profit → +3R (900 ÷ 300).
• Exit at $51.50 → +$300 profit → +1R.
• Stopped out at $48.50 → −$300 → −1R.

Note that share count, entry price, and instrument have all dropped out. A crude oil trade that risks $300 and books $900 is also +3R. Once you fix R at entry, every trade collapses onto the same axis regardless of size, price, or market. A quick way to pre-set your 1R distance before you take the trade is a risk/reward calculator.

Why it matters

Raw P&L in dollars is almost useless for judging trade quality, because it blends two things: how good the trade was and how big you sized it. R strips the sizing out. A +$500 winner where you risked $2,000 (+0.25R) was a worse trade than a +$300 winner where you risked $100 (+3R), even though the dollar figure looks better. Grade decisions in R and size decisions in dollars, and you stop confusing the two.

The payoff shows up in expectancy. Average the R-multiple across a sample of trades and you get your edge per unit risked. If your last 100 trades average +0.35R, you make 35 cents for every dollar you put at risk, on average — independent of whether you were trading one contract or ten. That single number is what trading expectancy is built on, and it's what tells you whether the system is worth scaling at all. It also feeds directly into position sizing: once you know your expectancy in R and your win distribution, you can back out how much risk per trade your account can carry.

Two common misreads. First, don't confuse average R with reliability — a +0.5R average driven by rare +8R outliers behaves nothing like a +0.5R average from steady +1R winners, even though the mean is identical. Look at the distribution, not just the mean. Second, R is only honest if your stop is real. If you widen or remove stops mid-trade, your effective risk was never the 1R you logged, and every downstream R figure is fiction.

Related concepts

Expectancy in R only means survival if your risk-per-trade keeps risk of ruin low enough to reach the long run, so R-multiples and sizing are two halves of the same problem. The R framing also sharpens exit analysis: comparing the R you captured against your maximum favorable excursion shows how much of each trade's potential you actually banked. And once you know your average win in R and average loss in R, you can solve for the breakeven win rate — the hit rate below which even a good-looking system quietly bleeds out.