R-Multiple
Category
Performance-Analyse & Journaling
Sub-category
Performance-Kennzahlen
Curated by
Last reviewed
An R-multiple expresses a trade's result as a multiple of the amount originally risked (1R). Risking $1,000 and winning $2,300 books +2.3R; a full stop-out is −1R. The normalisation makes trades of different sizes and accounts comparable and is the basis for expectancy and distribution analyses in the trading journal.
Context & Mechanics
Definition
The R-multiple – popularised by Van K. Tharp – normalises every trade result to the initial risk: R-multiple = result ÷ 1R. Here 1R is the amount standing between entry price and stop-loss at entry – $1,000 with $1,000 risk per trade on a $100,000 account. A $2,300 win is +2.3R, a full stop −1R, an exit at entry 0R. Losses larger than −1R are a warning signal: they only occur when the stop was moved, jumped (gap, slippage) or ignored.
Why think in R?
Currency results are meaningless without context: a $2,300 win can be an excellent trade with $1,000 risk or a miserable one with $10,000 risk. R-normalisation separates decision quality from position size and account size – the same evaluation works for a $10,000 account as for a $100,000 one. The central journal metrics are built on R: expectancy in R (+0.375R per trade in the example journal), the R-distribution (histogram of all trades) and planned versus realised risk-reward ratio. The distribution reveals patterns that averages hide: cut-off winners (many +0.5R instead of fewer +2R), moved stops (outliers at −1.4R) or revenge trades.
R-multiples in prop trading
Loss limits can be thought of elegantly in R: at 1 % risk per trade, a 5 % daily loss limit equals five full losing trades – a clear, operational daily boundary (e.g. stop after three losers).
Why it matters for traders
Thinking in R means assessing processes instead of amounts. The GlanWick journal records every trade automatically as an R-multiple and visualises it as a distribution – GlanWick is a training and simulation tool and not a prop firm itself.
Execution Example
A trader risks $1,000 per trade (1R) on a $100,000 account. Three trades from the journal: +$2,300, −$1,000 and −$1,400 (stop jumped in a gap).
- Trade 1: +$2,300 ÷ $1,000 = +2.3R – planned RRR achieved.
- Trade 2: −$1,000 ÷ $1,000 = −1R – the normal case of a disciplined loss.
- Trade 3: −$1,400 ÷ $1,000 = −1.4R – warning signal: check gap or slippage, otherwise stop discipline.
- Journal sum: +2.3 − 1 − 1.4 = −0.1R across three trades – calculated in R it is immediately comparable whether the account is $10,000 or $100,000.
Execution Risk & Errors
Comparing results in currency instead of R and overlooking position-size effects
Accepting losses beyond −1R instead of investigating stop discipline and slippage
Redefining 1R retroactively when the stop was moved
Looking only at the R average and ignoring the distribution
Calculating R-multiples without a consistent initial risk
Frequently Asked
What exactly is 1R?
The amount standing between entry price and stop-loss at entry – the trade's planned maximum risk. All results are normalised to this amount.
Why are losses beyond −1R a warning signal?
Because they only occur when the stop was moved, ignored or jumped (gap, slippage). If they accumulate, there is a discipline or execution problem.
What does the R-distribution offer over the average?
It reveals patterns the mean hides: cut-off winners, breached stops or outliers. Two journals with the same expectancy can have completely different distributions.
How are R-multiples and expectancy related?
Expectancy is the average of all R-multiples in a journal – +0.375R per trade in the example. R-multiples are the raw data, expectancy the condensation.