What is trade expectancy,
and how do you calculate it?

What expectancy actually is

Trade expectancy is the average amount you can expect to win or lose per trade, measured over a large number of trades. It rolls your win rate and the size of your wins and losses into a single dollar figure. A positive expectancy means the system makes money over time, no matter how a single trade feels in the moment. A negative expectancy means it bleeds, slowly and surely, even on days when you feel like a genius.

That framing matters because trading is not decided one trade at a time. It is decided over hundreds. Expectancy tells you what the average of those hundreds looks like, so you can judge a system by its math instead of by the emotional memory of your last big winner or your last painful loss.

The expectancy formula

Every term in that equation is something you can pull from your own trade history: how often you win, how much you make when you win, and how much you give back when you lose. Nothing here requires a prediction about the future. It is a measurement of what your system has actually done.

A worked example

Say you win 40% of your trades. Your average winner is $300 and your average loser is $150. Plug it in: expectancy = (0.40 × 300) − (0.60 × 150) = 120 − 90 = $30 per trade. That system makes money, roughly $30 for every trade you take on average, even though it loses more often than it wins. Take 200 trades in a year and that is about $6,000 of expected edge, purely from the math working in your favor.

Sit with that result for a second. You are wrong 60% of the time and still profitable. The reason is that your winners are twice the size of your losers, so the smaller pile of wins outweighs the larger pile of losses. This is why professionals will happily run a strategy that feels like it loses constantly, because they have done this arithmetic and know the edge is real.

Why win rate alone lies to you

Win rate on its own is meaningless, and this is the single most expensive misunderstanding in retail trading. A 40% win rate with a 2:1 reward-to-risk beats a 60% win rate with a 1:2 reward-to-risk, and it is not close. You have to know both the hit rate and the size of wins versus losses before you can say a word about whether a system works.

Run the second one to see it: 60% win rate, but your average win is half your average loss. If you risk $200 to make $100, expectancy = (0.60 × 100) − (0.40 × 200) = 60 − 80 = −$20 per trade. You win more often than you lose and you are still handing the market $20 a trade. High win rate, negative edge. The number that felt reassuring was the number quietly draining the account.

Expectancy in R-multiples

There is a cleaner way to think about all of this that strips out the dollars entirely. Express every win and loss as a multiple of the amount you risked, called R. If you risk $100 on a trade, then a $200 winner is +2R and a $100 loss is −1R. Now your expectancy comes out in R instead of dollars, which lets you compare setups of any size on equal footing.

Take a system that risks 1R to make 2R, winning 45% of the time. Expectancy = (0.45 × 2) − (0.55 × 1) = 0.90 − 0.55 = 0.35R per trade. Every trade, on average, nets you a third of what you risked. Double your position size and the R stays 0.35; the dollars scale but the edge per unit of risk does not. That is the beauty of R: it measures the quality of the system separately from how big you bet.

Sample size is everything

One warning before you trust any expectancy number you calculate: it needs a large sample to mean anything. Over ten trades, randomness swamps the signal. A genuinely profitable system can post a losing streak that makes it look hopeless, and a losing system can string together enough lucky wins to look like a machine. Neither picture is real. You need dozens and ideally hundreds of trades before the average settles down to the truth. Judge your edge on the body of your record, never on the last handful.