Trade journaling · Intermediate

Calculating Win Rate Versus Risk-Reward

Neither figure means anything alone. Expectancy is the one number in your journal that says whether the process pays.

Last reviewed 15 September 2026 by the MarketPro research desk · Editorial policy

In short

Win rate is the share of trades closing in profit, and reward-to-risk compares your average winner with your average loser. Neither decides profitability alone. Expectancy joins them: (win rate x average win) minus (loss rate x average loss). The break-even win rate for a given reward multiple R is 1 divided by (1 + R), so 2R needs 33.3% and 0.5R needs 66.7%.

What to remember

The break-even win rate is 1 divided by (1 + R). At 2R you need 33.3%, at 0.5R you need 66.7%.
Expectancy in R ignores costs. A 1.4 pip spread on a 25 pip stop is 0.056R per trade, a quarter of a +0.22R edge.
Put the median win beside the mean. A far lower median means the result leans on rare large winners.
Under 30 trades in a group, treat expectancy as provisional rather than as a measurement.

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Win rate answers how often you win. Reward-to-risk answers how much you win when you do. Neither is a result on its own, and either one quoted alone can describe a method that loses money. The figure that joins them is expectancy: (win rate x average win) minus (loss rate x average loss). It is the only number in your journal that says whether the process pays.

The break-even line for every reward multiple

For a fixed reward-to-risk ratio R, the win rate needed just to break even is 1 divided by (1 + R). That one formula settles most arguments about which style is better.

Average reward per 1 risked	Break-even win rate	Result at a 50% win rate
0.5R	66.7%	-0.25R per trade
1.0R	50.0%	0.00R per trade
1.5R	40.0%	+0.25R per trade
2.0R	33.3%	+0.50R per trade
3.0R	25.0%	+1.00R per trade

A scalper winning 70% of the time at 0.5R is barely above the line. A swing trader winning 35% at 3R is comfortably above it. A 60% win rate at 1:1 is +0.20R per trade before costs, a real edge but a thin one. The two figures have to be read together or the comparison means nothing.

Ninety trades, worked through

Straight out of a journal that logs the size of every result.

90 trades closed, 47 winners and 43 losers, so a 52.2% win rate.
Average winner +1.36R. Average loser -1.02R, slightly worse than 1R because stops slip.
Expectancy: (0.522 x 1.36) minus (0.478 x 1.02), which is 0.710 minus 0.488, or +0.222R per trade.
Across 90 trades that is +20.0R. At 1% risk on a 3,000 USD account, roughly 600 USD before costs.

Now subtract what the R figure leaves out. If the average stop is 25 pips and the round-trip spread is 1.4 pips, every trade pays about 0.056R to the broker, which is 5.0R across the 90, a quarter of the profit. Trade a pair at 2.5 pips with 18-pip stops instead and the cost becomes 0.139R per trade, or 12.5R, and most of the edge has gone. The pip calculator turns your own spread into account currency in a few seconds.

Split the same calculation by setup tag and it usually stops being one number. The 90 trades above might hold a pullback tag at +0.45R across 50 trades and a breakout tag at -0.06R across 40, which is a different situation entirely from an even +0.222R spread across both. One blended expectancy figure can hide a losing setup subsidised by a working one for a very long time.

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The outlier that flatters the average

Average win is a mean, and means are fragile when one result dwarfs the rest. If one of those 47 winners ran +9R because a release went your way while you were away from the screen, it supplied 0.19R of the 1.36R average by itself. Remove it and expectancy drops from +0.222R to roughly +0.122R, still positive and half the size.

So put the median win next to the mean. A median far below the mean means your result depends on rare large winners, and that dependence has a consequence worth planning for: those winners arrive irregularly, so the flat stretches between them are longer and deeper than the average suggests.

The same fragility applies to the loss side, in reverse. One 4R loss from a gap over a weekend drags the average loser from 1.02R to something closer to 1.09R on a 43-loss sample, which by itself removes about 0.033R per trade from expectancy. Outsized losses are rarer than outsized wins in a stopped-out method, and they are worth flagging in their own column rather than blended into an average.

What expectancy cannot promise

Past expectancy measures trades that already closed. It does not carry forward on its own. A 90-trade sample whose true expectancy is zero prints a measured +0.2R often enough that no single quarter should be treated as settled, and conditions shift underneath any sample long enough to look convincing.

It is also blind to the path. Two methods with identical expectancy can have very different worst runs, and the one with longer losing streaks can empty an account before the average asserts itself. Expectancy describes the destination, not the road, which is why it is read beside the streak and drawdown columns rather than instead of them. The risk of ruin calculator is the other half of that picture.

Running the numbers on your own columns

Three columns are enough: result in R, a win or loss flag, and the setup tag. Everything else follows from those.

Compute win rate, average win in R and average loss in R for the whole log, then again for each tag.
Subtract your measured cost per trade in R, which is spread plus commission divided by your average stop distance.
Compare each tag against the whole-log figure, treating anything under 30 trades as provisional rather than as a measurement.

Recompute monthly and keep the history. One expectancy figure is a snapshot; six monthly figures show whether the number is stable, drifting, or simply bouncing around zero, and that distinction is what decides whether to leave the plan alone. The rules that keep you trading long enough to find out are in the risk management guide.

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Written and reviewed by the MarketPro research desk

MarketPro is a trading-signal and trading-education app operated by Harajuku Holdings LTD (Cyprus). Our signal desk publishes and tracks every trade idea in-app, and every page here is checked against the app's live behaviour before publishing. Last reviewed 15 September 2026. How we produce signals and content · About MarketPro

FAQ

Calculating Win Rate Versus Risk-Reward: questions

What win rate do I need to break even?

It depends entirely on your reward-to-risk ratio. The break-even win rate is 1 divided by (1 plus your average reward per unit risked), so 1:1 needs 50%, 2:1 needs 33.3% and 0.5:1 needs 66.7%. Costs push every one of those figures a little higher, because the spread is paid on every trade whether it wins or loses.

How do you calculate expectancy from a trading journal?

Take the win rate, the average winner and the average loser, all measured in R. Expectancy is (win rate x average win) minus (loss rate x average loss). A log with 52.2% winners averaging 1.36R against losers averaging 1.02R gives (0.522 x 1.36) minus (0.478 x 1.02), which works out at +0.222R per trade before costs.

Is a high win rate better than a high risk-reward ratio?

Neither is better in itself, because they trade off against each other. What matters is whether the pair clears the break-even line and by how much. High win rate methods tend to have shorter losing streaks and are easier to sit through, while high reward methods tolerate long barren stretches, so the choice is partly about which path you can actually follow.

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