Expectancy is a simple way of asking whether a trading approach makes money over a run of trades, not just on the last one. It blends how often you win with how big the wins and losses tend to be. Once you see it clearly, win rate stops looking like the whole story.
The basic idea
Expectancy answers: if I took this kind of trade many times, what would I expect to make or lose on average per trade? A positive expectancy means the maths of the approach leans in your favour over a large sample. A negative expectancy means the opposite, even if you sometimes string together a few winners.
It is a forward-looking estimate built from past results or from a clear plan. It is not a promise that the next trade will pay.
A plain example
Suppose you win 40 per cent of the time and lose 60 per cent. That sounds poor until you look at size. If winners average £150 and losers average £50, each block of ten trades might look roughly like this: four wins at £150 (£600) and six losses at £50 (£300). Net £300 across ten trades, or about £30 per trade on average.
That approach has positive expectancy despite a win rate below half. Flip the sizes and the same 40 per cent win rate can destroy an account. This is why judging a strategy by win rate alone misleads so many beginners.
How traders work it out
A common form is:
Expectancy ≈ (win rate × average win) − (loss rate × average loss)
You can also express it in R-multiples, where 1R is the amount you risked on the trade. Then expectancy is the average R per trade. That makes different position sizes easier to compare.
The inputs matter. Use a fair sample, include costs and slippage, and separate market conditions if your edge only appears in certain regimes. Ten lucky trades are not enough to declare an edge.
Why it beats gut feel
Without expectancy, traders often over-weight recent results. A hot streak feels like proof. A cold streak feels like the method is broken. Expectancy forces you to look at the distribution: how often, how large, and at what cost.
It also disciplines position size. If average loss is large relative to average win, you need either a higher win rate or smaller risk per trade to stay solvent while the edge plays out.
Where expectancy can mislead
Past expectancy is not future expectancy. Markets change, spreads widen, and a setup that worked in a quiet range can fail in a trend. Costs rise when you trade thinner names. Overfitting a backtest can invent a beautiful expectancy that never appears live.
Expectancy also says nothing about the pain along the way. Two approaches can share the same average per trade while one suffers deeper drawdowns. Pair it with a clear risk plan and a realistic view of how long a cold patch can last.
Using it to shape risk
Expectancy becomes most useful when it guides how much you risk. If your average gain per trade is small relative to your typical loss, a run of losers can hurt badly before the edge has time to show. Sensible position sizing keeps each loss small enough that you can survive the normal swings of a positive-expectancy approach. Review your numbers every few months rather than after every trade, so you react to evidence instead of mood.
Bringing it together
Expectancy asks what you should make or lose on average per trade once wins, losses and their sizes are combined. It explains why a lower win rate can still be healthy, and why a high win rate with poor payoff can quietly bleed. Treat it as a design check on your plan, not as a forecast of the next click.
If you want a clear read on how you think about edge, risk and process, our free trader assessment is a useful starting point.
