What does an average R of 0.3 actually mean?

It means that across all the trades under review you earned just under a third of your committed risk on average. At 1 percent of account risk per trade, that corresponds to roughly 0.3 percent account growth per trade before tax and regardless of sequence. The figure is an average, not a forecast: it says nothing about how your next ten trades will go, only what the distribution so far produced. Sample size matters. Below roughly thirty to fifty trades, average R swings so much that it is barely interpretable. It is also worth checking whether the value rests on a few large winners, because if it does, it is less stable than the number suggests.

Why can I not use the trailed stop in the calculation?

Because doing so shrinks the denominator after the fact and inflates your result. An R multiple is meant to answer how much return you got for the risk you accepted when you made the decision, and that risk was fixed at entry. Trailing the stop later reduces your live exposure but changes nothing about the original decision. Using the tighter level anyway means trades where you trailed early score systematically better than others, despite not having been planned any better. For managing an open position the current stop is obviously the relevant number. For review it is the original one.

How do I calculate R with partial exits?

Weight the partial results by their share of the total position. For example: you sell half at plus 1R and the other half at plus 4R. The overall result is half of 1R plus half of 4R, so plus 2.5R. Recording the final partial exit as the result instead overstates your trades considerably, and it does so precisely for the strategy that uses scaling out systematically. In practice this means every partial exit needs quantity and price in the journal. The denominator stays the original total risk of the position throughout, not the proportional risk of each tranche.

What should I do with trades that closed worse than minus 1R?

Record the cause first, because there are three very different ones. First, a price gap or a jump straight through the stop, typically around news. Second, poor execution in thin trading, where the stop triggers but fills far lower. Third, a stop you did not honour, which is a rule break. The first two are market conditions you can respond to with smaller positions, more liquid instruments, or avoiding scheduled events. The third is an execution problem and needs a different answer entirely. So review the share of trades beyond minus 1R split by those three causes. If the third share is rising, your strategy is not the issue.

Does R work for leveraged positions?

Yes, because the formula does not change: the distance between entry and original stop, multiplied by position size, gives you 1R. Leverage affects how much capital you must commit to the position, not how much you lose when the stop triggers. That is exactly why R is particularly useful with derivatives, since it makes two trades at different leverage directly comparable. Two additions apply. First, funding payments belong in the trade result before you divide by 1R, otherwise a position held for days scores too well. Second, know your distance to the liquidation price: if the original stop sits close to it, the position is too large no matter how tidy the R calculation looks.

R Multiples in Trading: Measuring Risk Properly

An R multiple expresses a trade's result in units of the risk you took at entry. One R is the amount you would have lost if your original stop had been hit. So a trade returning plus 3R made three times the risk you committed, whether that risk was 90 or 9,000 in account currency.

What is an R multiple?

R stands for risk. The unit is deliberately relative: it normalises every result against what you were willing to lose before you entered.

That solves something currency amounts cannot. A gain of 400 tells you nothing on its own. If the position was small and the stop tight, it was an excellent trade. If the position was large and the stop wide, the same amount may be a weak result for a lot of risk. Only the conversion into R makes the two comparable.

This is why R is the standard unit whenever traders discuss setups. It works across accounts of different sizes, across equities and crypto, and across periods in which your capital changed.

How do you calculate R?

You need exactly two values, and both are fixed before the order goes in:

1R = (entry price minus original stop price) x position size

R multiple = trade result / 1R

A worked example with illustrative figures. You buy 100 units at 50 and place the stop at 47:

risk per unit: 3
1R = 3 x 100 = 300

Exit at 59 and the gain is 900. In R terms that is 900 divided by 300, so plus 3R. If the trade hits the stop instead, the result is minus 1R.

The value of this shows in a second example. A different trade with 20 units, a stop 4 away and 80 of risk, returning 240, is also plus 3R. In currency the two trades are worlds apart. In R they are identical, and that is exactly what an honest review needs.

Which stop counts for the calculation?

Always the original one, meaning the stop that was in place at entry. This is the single most common error with R multiples. Using the new, tighter level after trailing your stop inflates R artificially, because the denominator shrinks. The result looks better while saying nothing about the quality of the original decision.

Why are currency amounts or percentages not enough?

Currency amounts are tied to your capital and therefore not comparable over time. A gain of 200 on a small account and the same amount two years later on a larger one represent completely different achievements.

Percentages of the entry price fail too, because they ignore stop distance. Plus 6 percent on an instrument with a 2 percent stop is plus 3R. The same 6 percent on an instrument with a 12 percent stop is half an R, which is a weak trade. The percentage is identical, the quality is not.

R resolves both, because it uses the only variable you actually control: the risk you chose to take. How to set that risk in the first place is covered in the guide to position sizing.

How do you read an R distribution?

The individual value is not very interesting. R becomes informative once you look at all trades as a distribution. Three numbers are enough to start.

The average. Mean R across all trades is your expectancy in risk units. At plus 0.2R you earn a fifth of your risk per trade on average. At 100 trades a year and 1 percent risk per trade, that is a magnitude you can plan around.

The largest loss. It should sit at roughly minus 1R. Anything beyond that means something failed: a price gap, poor execution, or a stop you did not honour. Those three causes are different, and all three deserve to be visible.

The share of trades beyond minus 1R. That is not really a market metric, it is a discipline metric. When it rises, either you are not honouring your own stops or you are trading instruments whose liquidity does not support your risk.

It is also worth looking at your largest winners. If your entire positive expectancy rests on two trades at plus 8R, that is not a flaw, but you should know it. It means the next fifty trades without such an outlier will probably come out negative.

How does R relate to win rate and expectancy?

Expectancy in R follows directly from two inputs:

Expectancy = (win rate x average win in R) minus ((1 minus win rate) x average loss in R)

A practical orientation falls out of that. If your winners average plus 2R and your losers cost minus 1R, you need roughly a third of trades to work just to break even. With winners averaging plus 1R you need half.

This is why the question "what is your win rate" means nothing without the average R alongside it. The two numbers only form a statement together.

What goes wrong when calculating R?

Four errors recur, and all of them distort the review in the same direction: too optimistic.

Using the trailed stop. As above. It has to be the original stop or the number is meaningless.
Assigning a plausible stop to trades that had none. That invents the denominator. Such trades belong in their own category rather than mixed into the R statistics.
Leaving out costs. Fees and spread belong in the trade result before you divide by 1R. With tight stops they move the number noticeably.
Ignoring partial exits. Selling half at plus 1R and the rest at plus 4R produces plus 2.5R, not plus 4R. Every partial exit needs recording with quantity and price.

How do you record R in your journal?

Three extra fields per trade are enough: the original stop price, the resulting risk amount in money, and the result in R net of costs. Everything else derives from those.

Timing of the entry matters. The original stop has to be in the journal before you enter, not afterwards. Recorded later, you are not logging your decision but your memory of it, and memory adjusts itself to the outcome.

That separation between planned and actual is why a structured trading journal does more than a list of results. It shows whether your R distribution came from the market or from not honouring your own parameters.

What is R not suitable for?

R has no sense of time. A plus 2R trade over three days and one over eight months look identical, even though capital was committed for wildly different periods. For that you need holding time per trade as well.

R also has no sense of correlation. Five simultaneously open positions at 1R each are not 5R of total risk if all five express the same theme. On a bad day they behave more like one larger position.

And R says nothing about your equity curve along the way. Ten consecutive losses at minus 1R produce the same average as ten scattered ones, but they are far harder to sit through. For that you need maximum drawdown.

Conclusion

R multiples normalise every trade against the risk taken, making results comparable across position sizes, markets and time periods. What matters is discipline in the calculation: always the original stop, always net of costs, partial exits weighted. And look at the distribution rather than the individual value, particularly your largest loss and the share of trades beyond minus 1R.

Disclaimer: this article is for informational purposes only and does not constitute investment advice. The example figures are illustrative and are not a statement about achievable results. Trading securities and crypto assets carries the risk of loss, up to and including total loss of capital.

R Multiples in Trading: Measuring Risk and Reward Correctly