Expectancy and R-multiple in trading, explained

Win rate is the number traders quote most and the one that explains least. Expectancy measured in R tells you what a strategy earns per trade on average, and it fits on one line.

R: measuring every trade in units of risk

R is the amount you planned to lose on a trade if the stop was hit. A trade's R-multiple is its net result divided by that initial risk. A worked example: you risk $200. If you close for +$500, the trade is +2.5R. If the stop is hit cleanly, it is -1R. If the stop slips and you lose $260, it is -1.3R, and that extra 0.3R is worth noticing, because it shows execution costs that a planned 1R never captures. Expressing results in R strips out position size. A +$500 trade on a large account and a +$50 trade on a small one can be exactly the same trade in R terms. That is what makes trades comparable across months, accounts and risk levels, and it is why every calculation below is done in R rather than money.

The expectancy formula

Expectancy is the average result per trade. In R terms: E = win rate × average win in R − loss rate × average loss in R. A worked example: a strategy wins 40% of the time, the average winner is 2.2R and the average loser is 1R. Then E = 0.4 × 2.2 − 0.6 × 1 = 0.88 − 0.6 = +0.28R per trade. If you risk $100 per trade, that is $28 per trade on average, so over a hundred trades the expected total is about +28R, or $2,800, before you account for the fact that real results scatter around that figure. An even simpler way to get the same number from a journal is to add up the R of all trades and divide by the number of trades. Both methods give the same answer; the formula is useful because it shows which lever moves the result.

Why win rate alone misleads

Consider two illustrative traders. Trader A wins 70% of trades, but the average winner is 0.6R and the average loser is 1.5R, because losers are held and winners are taken early. Expectancy: 0.7 × 0.6 − 0.3 × 1.5 = 0.42 − 0.45 = -0.03R. Trader B wins only 35% of trades, with an average winner of 2.5R and an average loser of 1R. Expectancy: 0.35 × 2.5 − 0.65 × 1 = 0.875 − 0.65 = +0.225R. Trader A feels right most days and slowly loses money; Trader B is wrong most days and makes it. A related number is the breakeven win rate: with an average win of W in R and an average loss of 1R, you need a win rate of 1 / (1 + W) to break even. At W = 2, that is 33.3%; at W = 0.6, it is 62.5%.

Planned R:R versus realised R

The reward to risk ratio you set at entry is a plan, not a result. Expectancy has to be calculated from realised R, the outcome you actually got. The gap between the two is one of the most informative numbers in a journal. Suppose you plan every trade at 3:1, yet your realised average winner is 1.6R. The formula immediately shows what that costs: at a 40% win rate, planned 3R winners give 0.4 × 3 − 0.6 × 1 = +0.6R, while realised 1.6R winners give 0.4 × 1.6 − 0.6 × 1 = +0.04R. The edge did not disappear in the market; it disappeared in the exits. The same applies to losers. If your average loss is 1.2R rather than 1R, slippage, wide spreads or moving the stop are taxing every trade, and expectancy makes the size of that tax visible.

Sample size and the one big winner

Expectancy calculated from twenty trades is a rough estimate. A single outlier can carry the whole figure. As an illustration: twenty trades total +6R, which looks like +0.3R per trade. One of them was a +8R runner. Remove it and the other nineteen trades total -2R. Neither number is wrong, but they tell different stories, and only more trades will show which is closer to the truth. Two habits help. First, look at expectancy with and without the largest win, and check whether the result depends on rare events you cannot plan for. Second, calculate expectancy per model rather than for everything combined, because a blended figure can hide one model that carries the account and another that drains it. Treat any conclusion from a small sample as provisional.

Putting it to work in your journal

To calculate expectancy, a journal needs only two numbers per trade: the planned risk in money and the net P/L. Divide one by the other and you have R; average the R values and you have expectancy. Then split it. Expectancy by model tells you which ideas deserve more of your risk budget. By session it shows when you trade well. By account it shows whether you behave differently under prop firm rules than with your own money. A model with clearly negative expectancy over a reasonable sample is a candidate to drop or rework, not to trade harder. Haunted calculates expectancy automatically and breaks it down by session, pair, model and account, but the logic is the same in any spreadsheet: consistent risk, honest R, enough trades.

What is a good expectancy in trading?

Any expectancy that stays positive after costs over a meaningful sample is a working edge. The number alone is not the whole story: a small positive expectancy with many trades can outperform a larger one with very few. Compare expectancy with how many trades the model produces and how deep its drawdowns get.

Should commissions be included when calculating R?

Yes. Use net P/L after commission and swap, divided by the planned risk. Otherwise expectancy looks better than what reaches your account, and for frequent short-term trading the difference can decide whether a model is profitable at all.

How should breakeven trades be counted?

Record them at their real result, which is usually slightly negative after costs, for example -0.05R, and include them in the average. Leaving them out inflates the win rate or the loss rate depending on how you classify them, and distorts expectancy.

What is the difference between R:R and R-multiple?

R:R, reward to risk, is the plan at entry: how far the target is compared to the stop. The R-multiple is the outcome: what the trade actually returned, measured in units of the initial risk. Expectancy is built from R-multiples, not from planned R:R.