A $200 win and a $200 loss look equal in your P&L, but if the first risked $400 and the second risked $100, they’re nothing alike. Dollars hide that. R-multiples don’t.
What 1R is and why it standardizes every trade
1R is the amount you put at risk on a trade — the distance from entry to stop, in money. If your stop being hit costs $150, then 1R is $150 for that trade, full stop. Everything else is measured against it: a trade that makes $300 is +2R, one that loses the full stop is -1R, one you exit early for a $75 gain is +0.5R.
The power is in the normalisation. Across a hundred trades you’ll risk different dollar amounts on different instruments with different stop distances — a tight index scalp and a wide swing on gold have nothing in common in dollars. Converted to R, they land on the same scale, and you can finally compare a Tuesday scalp to a Friday swing as if they were the same bet. For the full definition and edge cases, see what an R-multiple is.
Recording wins and losses as +2R, -1R, etc.
Logging in R is a one-column habit. For every closed trade, divide the profit or loss by the risk you took:
- Risked $150, made $450 → +3R
- Risked $150, lost $150 → -1R
- Risked $150, lost $90 because you cut it early → -0.6R
- Risked $150, scratched at break-even → 0R
Two rules keep the column honest. Record R against the risk you actually took, not the risk you meant to take — if you oversized, the denominator is bigger and the R is smaller. And log the realized R, not the planned one, so a 2R setup you bailed on at +0.7R goes in the book as +0.7R. That gap between planned and realized R is one of the most useful things a journal can show you.
Building an expectancy figure straight from R data
Once trades are in R, expectancy falls out with almost no work. Expectancy is just the average R across all your trades — the mean of that column. An expectancy of +0.3R means every trade you take is worth, on average, three-tenths of one unit of risk. Positive is an edge; zero is a coin flip that pays your broker; negative is a leak.
That single number answers the only question that matters: does this system make money when repeated? And it scales trivially — if you’re risking 1% per trade, a +0.3R expectancy is roughly +0.3% of account per trade before variance. Drop your average R and win rate into an expectancy calculator to see it, or size the R itself with a risk/reward calculator before the trade.
Spotting sizing inconsistency hidden by dollar P&L
Here’s what dollar journals bury. Suppose your P&L is up for the month but your win rate is under 40%. In dollars that looks fine. In R it might tell a darker story: your wins are +1.2R and your losses are -1.8R, meaning you’re routinely risking more on losers than you make on winners. One oversized loss is quietly eating three disciplined wins.
You will almost never catch that in a dollar column, because the biggest loss and the biggest win can be the same dollar figure while representing wildly different R. R-multiples put every trade on one axis, so an outlier -3R loss screams off the page instead of blending in. Inconsistent risk is the most common reason a technically-sound trader still loses — and it’s invisible until you measure in R.
Using an R distribution to see your real edge
Don’t stop at the average. Lay your trades out as an R distribution — a simple histogram of how often each R outcome occurs — and the shape tells you what kind of trader you actually are:
- A wall of -1R losses with a scattered tail of big winners → a trend-follower who lives on rare runners. Cutting winners early would be fatal here.
- A cluster of small positive R with a few ugly -2R and -3R losses → you’re not respecting your stop. Those outliers are the whole problem.
- A tidy pile just above zero → a thin but real edge that variance can easily mask over short samples.
The distribution also shows whether one lucky +8R trade is carrying an otherwise break-even system — a fragility you’d never see in a P&L line.
Why R-based journals beat raw dollar journals
Dollar journals answer “did I make money?” R journals answer “do I have an edge, and how consistent is my risk?” — which is the question that actually predicts next month. This is where a manual spreadsheet or a P&L-first tool like Edgewonk leaves work on the table: the numbers are only as good as your discipline in entering them.
Shibiki computes R for every trade automatically from your fills and stop, so the column is never forgotten or fudged. It then rolls those R-multiples into a live edge-health score wrapped in a Wilson confidence interval — telling you not just your average R, but whether your sample is large enough to trust it yet. That last part matters: a +0.4R expectancy over 12 trades and over 200 trades mean very different things, and the confidence interval is what keeps you from betting the account on noise.
Related: Understanding R-multiples · Expectancy calculator · Risk/reward calculator