R-Multiple and Expectancy Calculator
R-multiple expresses a result as a multiple of what you risked, so trades of wildly different sizes become comparable. Calculate a single trade's R below, then use the expectancy section to find out whether a strategy has an edge at all.
R-multiple = (Exit − Entry) ÷ (Entry − Stop) · Expectancy = (Win rate × Avg win in R) − (Loss rate × Avg loss in R)
Single trade R-multiple
- R-multiple
- +3.00R
Measure against your original stop, not a stop you moved. The risk you committed to at entry is what makes this trade comparable with the rest of your history.
Strategy expectancy
- Expectancy per trade
- +0.40R
- Over 100 trades
- +40.0R
Positive expectancy: this strategy makes money over a large enough sample, even if the win rate feels low.
Free to use, no sign-up. Nothing you type here is sent anywhere — the calculation runs entirely in your browser.
Why R-multiple beats dollar P&L
Dollar P&L is distorted by position size. A trade sized at three times your normal risk that makes $300 looks better on paper than a correctly sized trade that made $100 — even when the oversized trade was a rule violation that happened to work out.
R-multiple removes size from the comparison. A trade risking $200 that makes $600 is a +3R trade whether you were trading a $10,000 account or a $500,000 one. Once every trade is recorded in R, your win rate, expectancy and best and worst setups all become directly comparable.
Expectancy is the number that decides
Expectancy is the average R you can expect per trade over a large enough sample. A strategy with a 40% win rate, an average win of 2.5R and an average loss of 1R has an expectancy of (0.40 × 2.5) − (0.60 × 1.0) = +0.4R per trade.
That is positive despite losing 60% of the time. Over 100 trades at consistent risk it is worth roughly +40R before compounding. It also explains why win rate alone is close to meaningless: a 75% win rate can lose money, and a 35% win rate can be highly profitable.
How many trades before this means anything
Around 30 to 50 trades with consistent R data is the practical minimum, and even that is a preliminary read. Ten trades is an anecdote — random variance dominates at that sample size, and a mediocre strategy can easily produce a great-looking short sequence.
Calculate expectancy per setup tag rather than across all trades. A blended figure routinely hides the fact that one setup carries the entire edge while another is quietly losing money.
Frequently asked questions
How do you calculate R-multiple?
R-multiple = (exit price − entry price) ÷ (entry price − stop price), inverted for short trades. A trade risking $200 that makes $600 is +3R; one risking $200 that loses $200 is −1R.
How do you calculate expectancy in trading?
Expectancy = (win rate × average win in R) − (loss rate × average loss in R). A 40% win rate with a 2.5R average win and a 1R average loss gives (0.40 × 2.5) − (0.60 × 1.0) = +0.4R per trade.
What is a good expectancy?
Anything above 0R is a real edge. The absolute value matters less than whether it stays positive across a large enough sample and holds up when broken down by setup tag rather than viewed as one blended number.
What if I moved my stop?
Then the trade's R is measured against your original stop, because that is the risk you actually committed to when you entered. This is precisely why moving stops corrupts a journal — the result is no longer comparable with anything else in your history.