R is your planned risk on a trade, and an R-multiple states the outcome as a multiple of that risk. Risk $100, make $200: +2R. Risk $100, get stopped: −1R. The unit strips out position size and account size, which is exactly why honest track records use it — a percentage return can be inflated by sizing; an R-multiple cannot.
R-multiple = (exit − entry) ÷ (entry − stop), sign-adjusted for shorts. The denominator is the planned risk — which means the number is only meaningful if a stop existed before entry. No stop, no R, no comparability; that is not pedantry, it is the entire point.
Distinguish planned R:R (the ratio a setup offers before entry: target distance over stop distance) from the realised R-multiple (what actually happened). A "3R setup" that gets stopped is a −1R trade. Confusing the promise with the result is how bad records are accidentally — or deliberately — written.
Expectancy per trade = (win rate × average win) − (loss rate × average loss), all in R. Real numbers from the published record: a 53% win rate, average winner +1.85R, average loser −0.88R gives (0.52 × 1.74) − (0.48 × 0.90) ≈ +0.57R per closed trade — which is precisely how 79 trades containing 37 losers still finished +34R.
The same line explains why loss containment beats win-picking: hold the average loser near −1R and a mid-40s win rate still compounds at a 2:1 payoff. Let losers drift to −2R and a 53% win rate quietly bleeds. The stop-loss guide is the practice behind that arithmetic.
Percent returns answer "how did the account change" — a function of sizing, leverage and luck as much as skill. R answers "how good was the trading". A 40% year at 10% risk per trade and a 15% year at 1% risk are not close, and only R exposes the difference. Percent also invites the oldest flattery in the business: quoting returns on the best account, the best month, the survivor. R on a complete, dated list of trades — losers included — is the format that resists the flattery, which is why it is the only format the record on this site uses.
Theory is cheap, so here is the arithmetic on a real published trade. Microsoft, long from $385, stop at $352, target $481 — called in the Sunday plan, opened 2 August 2026, target hit 8 August. The planned risk (1R) was $385 − $352 = $33. The exit banked $481 − $385 = $96. So: 96 ÷ 33 = +2.91R — the trade paid nearly three times what it risked.
Notice what the R-multiple did there: it graded the trade without knowing the position size. Whether that was a $500 account risking $5 or a $200,000 prop account risking $2,000, the quality of the trade is identical — and that is exactly why the whole record is kept in R. To size a position from a stop in seconds, use the free position size calculator.
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Get this week’s setups →The question is really about expectancy. Anything reliably positive after costs compounds — the record behind this site runs at about +0.57R per closed trade. Chasing a high average R by holding for home runs usually just fattens the losers.
No single ratio is sacred: viability is payoff × win rate. A 1.5R average winner works at a 53% win rate; a 3R target that rarely fills works for nobody. What is close to mandatory is capping losers at −1R, because every combination dies once the average loss grows.
They should — compute R on net results or your expectancy is fiction by exactly your cost rate. High-frequency approaches feel this hardest, one of the quiet arguments for lower-frequency swing trading.
Record entry, stop and exit for every trade — the R math falls out automatically. Any spreadsheet works; a purpose-built journal automates it (RB Trading’s journal at rbtrading.site computes R per trade natively, with a free tier).