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R-Multiples, The Universal Language of Trade Results

Imagine comparing two traders. One says, "I made $500 today." The other says, "I made $50." Who did better? Without knowing the account size or risk, you can't tell.

R-multiples solve that problem. One R equals the amount you risk on a trade. If you risk $100, then +$300 is +3R and -$100 is -1R.

Why R is powerful:

- It standardizes results across account sizes and position sizes
- It lets you compare setups, sessions, and strategies fairly
- It separates performance from money emotions
- It makes expectancy calculations simple

How to use it:

1. Before entering, define 1R as the distance from entry to stop (in dollars or pips).
2. After exiting, calculate the result as a multiple: profit or loss divided by the risk.
3. Record every trade in R in your journal.
4. Calculate your average R per trade (expectancy), and total R per month.

Example month: +2R, -1R, -1R, +3R, -1R, +1.5R. Total: +3.5R. If you risk 1% per trade, that's about +3.5% for the month.

Insights from R:

- Which setups produce the highest average R?
- How large are your winners relative to your losers?
- How many Rs do you lose to rule-breaking?

Thinking in R reduces dollar-driven emotion and sharpens your focus on process.

Convert your last 30 trades to R this weekend and see what you learn.

Your example month adds up: +2, -1, -1, +3, -1, +1.5 is +3.5R over six trades, so expectancy is a little under 0.6R per trade.

One refinement that matters for anyone who trades around events: record the R you actually got, not the R you planned. A stop that slips on a release turns -1R into -1.3R or worse, and an entry filled 2 pips late changes the size of the winner too. If the journal uses planned R, the slippage disappears from the statistics and the strategy looks better than it is.

When I split my own records into "event" and "non-event" trades, the average loss on event trades was noticeably larger than 1R, purely from execution. The setups themselves were fine. That one column changed how much size I was willing to put on during releases.

A quick worked example since a few people asked how to count R. Risk 40 dollars on a trade with a 20 pip stop. If it closes at +30 pips, that's +1.5R. If you got out early at +10, that's +0.5R. Losses that hit the stop are -1R, and any slippage past your stop shows up as something like -1.2R, which is worth tracking on its own.

Something I like about R is how it handles different account sizes. If you grow the account, your dollar figures change but your R results stay comparable. That's what lets you compare this quarter to last year honestly. It also helps when you change risk. If you trade at 0.5 percent during a drawdown and 1 percent normally, the dollar results look very different, but the R results show whether the strategy itself performed the same. Most journal spreadsheets just need two columns for this, the dollar risk at entry and the dollar result. Divide one by the other and you have R.

One more use for R that people miss: set your daily and weekly limits in R instead of dollars. Stop at minus 3R for the day works the same whether the account is 500 or 50,000, and it doesn't need updating every time the balance changes. The same goes for targets. A weekly review that says plus 4R this week is far more useful than a dollar figure that depends on what risk you happened to use. If you run a funded challenge alongside your own money, R lets you compare the two directly too. Keep the dollars for tax and the R for decisions.