R-Multiple Calculation
// formula · worked_examples · stop_priority · why_R
An R-multiple expresses a trade's outcome in units of the risk you took on entry. R is the unit that lets a trader compare a $50 stock setup to a $400 stock setup honestly, because both are denominated in their own risk.
What R is and why every serious journal tracks it
An R-multiple expresses a trade's outcome in units of the risk you took on entry, not in dollars and not in percent. A +2R trade made twice what it risked. A −1R trade lost exactly what it risked. R is the unit that lets a trader compare a $50 stock setup to a $400 stock setup honestly, because both are denominated in their own risk.
Formula
For a long trade:
R = (exit - entry) / (entry - stop)
For a short trade, the sign of both the numerator and denominator flips, which gives the same R sign convention (positive = profitable, negative = loss):
R_short = (entry - exit) / (stop - entry)
Stop priority
The stop used in the denominator is selected by priority: (1) the stop attached to the trade's bracket order if one was attached on entry, (2) a manually entered stop field on the journal row, (3) null if no stop was ever set. Trades with a null stop are excluded from R-based analytics.
Worked examples
Winner
Long entry $50, stop $48, exit $54.
R = (54 - 50) / (50 - 48) = 4 / 2 = +2R
The trader risked $2/share and made $4/share, a 2-to-1 reward-to-risk outcome regardless of position size.
Loser
Long entry $50, stop $48, exit $47.50 (stop blew through; exited at a slightly worse price).
R = (47.50 - 50) / (50 - 48) = -2.50 / 2 = -1.25R
The trader risked $2/share but the actual exit was worse than planned, so the realized loss is 1.25 units of risk, not the 1.00 units originally planned.
Why R, not percent or dollars
A 20% gain on a $10 stock and a 0.5% gain on a $400 stock can be the same R-multiple if the risk was sized to match. Percent is a function of the stock's price; R is a function of the trader's plan. Comparing setups across position sizes and price tiers requires a unit normalized by risk, and that unit is R.
Dollars are worse than percent for the same reason — a $200 win on one trade and a $200 win on another tell you nothing about the quality of the setup if the positions were different size. R strips out position-sizing and stock-price noise and leaves the question of whether the setup itself worked.
Where R appears in the Tapeboard journal
The Analytics tab in the trade journal surfaces R-based metrics in three forms: average R per setup tag (so the trader can see which named setups produce positive R on average), win rate by R bucket (how often each R outcome occurs), and an R-distribution histogram across all closed trades. These three views together answer the question "which of my setups have positive expectancy and which are noise."
Source: workers-api journal analytics routes. See also: /methodology (full methodology index), /methodology/journal-autolog (capture path), and /trade-journal (feature page). Last reviewed 2026-05-11.