Expectancy: The One Number That Tells You If a System Works

Ask most traders how good their approach is and they will tell you their win rate. "I win about 60% of my trades." It sounds like an answer. It is only half of one.

Win rate tells you how often you are right. It says nothing about how much you make when you are right or how much you lose when you are wrong. A trader who wins 60% of the time can still go broke, and a trader who wins 40% of the time can grind steadily upward. The number that settles the question is expectancy.

What expectancy actually is

Expectancy is the average result you can expect from a single trade, taken over many trades. It folds three things into one figure: how often you win, how much you win when you do, and how much you lose when you do not.

The plain-English version of the formula is this:

Expectancy = (Win rate × Average win) − (Loss rate × Average loss)

That is it. You weight your average win by how often you win, weight your average loss by how often you lose, and subtract the second from the first. If the answer is positive, the approach makes money on average. If it is negative, it loses money on average, no matter how good a single screenshot looks.

The cleanest way to measure the wins and losses is in R, where 1R is the amount you risk on a trade. Measuring in R strips out account size and position size so you can compare one approach to another fairly. A trade that makes twice what you risked is +2R. A trade that hits your stop is −1R.

Why win rate on its own is misleading

Here is the trap. A high win rate feels good because being right often is satisfying. But if your few losses are much larger than your many wins, a high win rate can still produce a negative expectancy. This is the classic shape of an account that looks great for weeks and then gives it all back in two trades.

The reverse is also true and far less intuitive. A trader can be wrong more often than right and still come out ahead, as long as the wins are big enough relative to the losses. Low win rate does not mean a bad approach. It means the math has to be carried by the size of the wins instead of their frequency.

The illustrative comparison below shows two hypothetical approaches. One wins more often. The other wins less often but lets its winners run further. Expectancy is what tells them apart.

A worked example in plain numbers

Take Approach B above. It wins 40% of the time, and a win is worth three times what a loss costs. Risking 1R per trade, the average win is +3R and the average loss is −1R. Run the formula:

(0.40 × 3R) − (0.60 × 1R) = 1.2R − 0.6R = +0.6R per trade.

So even though this approach is wrong more often than it is right, it earns about six-tenths of one unit of risk every time you trade it, on average. Compare that to Approach A, which wins 60% of the time but only makes what it risks: (0.60 × 1R) − (0.40 × 1R) = +0.2R. Both are positive. One is three times stronger. Win rate alone would have ranked them the other way around.

These are hypothetical figures chosen to illustrate the math, not numbers you should expect to produce. The point is the method, not the values.

Why this matters more than it sounds

Expectancy is the bridge between a single trade and a long run of them. One trade is mostly noise. You can do everything right and lose, or everything wrong and win. Over a hundred trades, expectancy is what actually shows up in the account. It is the difference between an approach that survives a bad week and one that does not.

It also changes how you read a losing stretch. If your expectancy is genuinely positive and you keep following the same process, a run of losses is uncomfortable but expected. It is variance doing what variance does. The mistake is concluding the approach is broken and abandoning a positive-expectancy method right before it would have paid off. Expectancy gives you a reason to keep your hands steady.

How to find your own number

You cannot calculate expectancy without a record, which is the quiet reason a trading journal matters so much. Log every trade in R: what you risked, what you made or lost, whether it was a win or a loss. After a meaningful sample, calculate your real win rate, your real average win, and your real average loss, then run the formula.

A few honest cautions. A small sample lies. Twenty trades is not enough to trust a number, because one outlier swings the average. Expectancy also drifts as conditions change, so it is something you recheck, not something you measure once and frame on the wall. And a positive expectancy on paper still has to survive contact with slippage, fees, and your own discipline in the moment.

The honest version

Expectancy does not predict your next trade and it does not promise a result. It is a way of asking whether the thing you are doing makes sense over time, instead of judging it by the last outcome you happened to get. A positive number is a reason to keep going. A negative one is a reason to change the approach before the account does it for you.

The useful place to learn this is where a bad sample costs you nothing. In a simulated environment you can build a real record, calculate your real expectancy, and find out whether your approach holds up, all before any of it touches your own capital.