Expectancy is the average amount you win or lose per trade, measured in units of the risk you took. It is the single number that tells you whether a strategy makes money over many trades — and it is the reason a strategy that loses more often than it wins can still be excellent.
Most beginners judge a strategy by how often it wins. This feels obvious and is actively misleading. A strategy that wins 90% of the time can bankrupt you, if the 10% of losses are enormous. A strategy that wins 35% of the time can compound beautifully, if the winners are much bigger than the losers.
What actually determines whether you make money is the combination of how often you win and how much you win when you do, versus how much you lose when you do not. Expectancy is the number that combines all of that into one figure.
Expectancy is the average R-multiple across your closed trades. An R-multiple expresses each result in units of the risk you originally put at stake: if you risked $200 on a trade (the distance from your entry to your stop, times your position size) and made $400, that trade was +2R. If you were stopped out for the full $200, it was −1R.
Measuring in R rather than dollars is what makes trades comparable. A +2R result on a small position and a +2R result on a large one represent the same quality of decision, even though the dollar figures differ. Averaging those R-multiples gives you expectancy: the typical return per unit of risk.
Equivalently, and often more intuitively: expectancy = (win rate × average win in R) − (loss rate × average loss in R).
Example (illustrative — invented numbers to show the arithmetic). Strategy A wins 40% of the time. Winners average +3R; losers average −1R (because a stop-loss caps them). Expectancy = (0.40 × 3) − (0.60 × 1) = 1.20 − 0.60 = +0.60R. Over many trades, this strategy earns about 0.6 times the risked amount per trade, despite losing 6 times out of 10.
Strategy B wins 80% of the time — twice as often. But winners average only +0.5R while losers average −3R, because there is no stop and losses are allowed to run. Expectancy = (0.80 × 0.5) − (0.20 × 3) = 0.40 − 0.60 = −0.20R. Strategy B wins constantly and loses money. This is not a contrived case; it is the exact shape of the most common way retail traders lose — taking small profits quickly and letting losses run, which feels wonderful and is fatal.
The lesson to take away: any expectancy above 0 is profitable over enough trades, and any expectancy below 0 is not, no matter how good the win rate looks.
You cannot directly control your win rate — the market decides that. You can, however, directly control the size of your losses (with a stop) and the ratio you are willing to accept between reward and risk before entering. That is why we insist on a risk/reward ratio before a signal is published, and why every entry plan carries a stop level. Both are levers on expectancy that do not require predicting anything better.
The dominant failure is sample size. Expectancy computed over a dozen trades is nearly meaningless — the number will swing wildly as a few more trades close. This is exactly why our track record shows a t-statistic next to expectancy: it is the check on whether the expectancy you are reading is distinguishable from luck. A handsome expectancy with a t-statistic below 2 is not yet evidence of anything.
The second failure is survivorship in your own records. If a trade is still open because it is deeply underwater and you are "giving it time", it is not in the closed-trade average — and your expectancy is flattered by exactly the trades that are hurting you most.
The third is that expectancy assumes you will actually take every signal, including the ones after four losses in a row. A strategy with +0.6R expectancy that you abandon during its inevitable drawdown delivers you the drawdown and none of the expectancy.
The average profit or loss per trade, measured in units of the risk you took (R) — the number that determines whether a strategy makes money over many trades.
Yes, and many good ones are. If winners are much larger than losers, a win rate well below 50% can still produce positive expectancy. Winning often and winning overall are different things.
Anything above 0R is profitable over enough trades. In practice, a sustained expectancy of +0.2R to +0.5R across a large sample is a genuinely strong result — but the sample size and the t-statistic matter as much as the figure itself.
Because R normalises for position size. Measuring in R lets you compare a result on a small position with one on a large position, and judge the quality of the decision rather than the size of the bet.
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Educational research only — not investment advice.