Two traders take the identical setup. One makes $200, the other $2,000. Same trade, same skill, same decision — the only difference is account size and position sizing. If your journal is in dollars, you’d swear the second trader is ten times better. In R, they’re identical.
Dollars measure your account. R-multiples measure your trading. For anyone running multiple prop accounts at different sizes, that distinction is the whole game.
What an R-multiple is and why dollars distort your review
One R is the amount you risked on a trade — the distance from entry to stop, times your position size. Everything else is expressed as a multiple of that unit:
- Risked $100, made $250 → +2.5R
- Risked $100, lost the full stop → −1R
- Risked $100, scratched at −$40 → −0.4R
The power is that R strips out account size. A dollar-based journal mixes together the quality of your decisions with the size of your account, and you can’t separate them afterward. Your best month in dollars might just be the month you traded a bigger account, not the month you traded well.
For a fuller walkthrough of the mechanics, the R-multiple explainer is worth ten minutes. The practical upshot for your journal: record R and your trades become comparable to each other and to everyone else’s, forever.
Recording planned R and realized R on every trade
Log two R values per trade, and the journal starts diagnosing itself.
Planned R — your target reward-to-risk at entry. If your stop is 10 points away and your target is 25 points, that’s a planned 2.5R. Decide this before you enter; a risk/reward calculator turns your entry, stop, and target prices into the number in one step.
Realized R — what the trade actually returned, in R:
Realized R = (exit − entry) ÷ (entry − stop) [for a long]…or, most reliably, profit or loss ÷ dollars risked. A +1.8R means you made 1.8× your risk; a −1R means you took the full stop as planned.
The gap between planned and realized R is one of the most honest signals in your whole log. Plan 3R, realize 1R on average, and you don’t have a strategy problem — you have an exit problem. No dollar figure ever tells you that this cleanly.
Building an R-distribution from your journal
Once every trade carries a realized R, plot them as a distribution — a simple histogram of how often each R bucket shows up:
- A cluster of −1R losses (clean stops) and a long right tail of winners is a healthy trend-following shape.
- Lots of small winners and a few large −2R and −3R losses is the signature of cutting winners and letting losers run — the exact opposite of what you want, and invisible in a dollar P&L that happens to be green.
- A wall of −0.3R scratches means you’re bailing before trades resolve.
The shape tells you how you make money, not just whether you do. Two traders with the same net R can have completely different distributions — and completely different futures, because one is one bad day from a drawdown the other will never see.
Reading expectancy straight off an R-based log
Here’s the payoff that makes R worth the small effort: expectancy falls straight out of an R-based journal. It’s just the average realized R across your sample.
- Average +0.3R per trade → you make about a third of your risk every time you enter, on average. Over hundreds of trades, that compounds.
- Average −0.1R → you have a losing system, no matter how many green days you can point to.
Drop your realized-R column into an expectancy calculator and you get one number that answers the only question that matters: is this worth scaling? The full definition and the win-rate-versus-R tradeoff are covered in the expectancy guide, but the headline is that a dollar journal buries expectancy while an R journal hands it to you.
Shibiki computes this continuously. Because it captures every fill from the broker automatically, each strategy carries a live expectancy in R — and wraps it in a Wilson confidence interval so you know whether +0.3R reflects a real edge or just a small, lucky sample. That’s the difference between “I’m up this month” and “this edge is statistically real.”
Why R makes prop and personal accounts comparable
This is where R stops being a nicety and becomes essential. Prop-firm traders rarely run one account:
| Personal account | $50k evaluation | $150k funded | |
|---|---|---|---|
| Same setup, dollar P&L | $80 | $150 | $450 |
| In R | +1.5R | +1.5R | +1.5R |
In dollars, these look like three different trades and three different traders. In R, they’re one repeatable edge executed across three accounts — which is exactly what it is. Only R lets you pool them into a single, honest sample and ask whether your strategy works, independent of which account it ran on.
That’s also why R is the native unit for copying a setup across accounts. When Shibiki mirrors a strategy across your prop accounts, it’s the same trade at the same risk-per-trade, and your journal reads it as one edge — not a confusing spread of dollar figures you can never reconcile. Log in R, and your review finally measures the trader instead of the balance.
Related: R-Multiple explained · Trading Expectancy · Expectancy Calculator