Expectancy is the average amount you win or lose per trade, once wins and losses are weighted by how often each happens. A positive expectancy after real costs means the strategy has a mathematical reason to be traded. A negative one means it is bleeding money no matter how good a few individual trades looked.
The expectancy formula
The most common form of the formula is:
Expectancy = (Win rate x Average win) - (Loss rate x Average loss)
Every part of that comes straight out of a backtest or trade journal:
- Win rate - the percentage of trades that closed in profit.
- Loss rate - simply 1 minus the win rate (or 100% minus win rate).
- Average win - the mean profit of all winning trades.
- Average loss - the mean loss of all losing trades, entered as a positive number.
The result is a per-trade figure. If you multiply it by the number of trades you expect to take, you get a rough projection of net result before variance.
A worked example in dollars
Say a backtest of 100 trades produced a 45% win rate. The average winner made $210 and the average loser cost $120. Plug it in:
Expectancy = (0.45 x $210) - (0.55 x $120) = $94.50 - $66.00 = +$28.50 per trade
Over 100 trades that is roughly +$2,850 before spread and commission, and before the ups and downs of a real equity curve. The win rate here is below 50%, yet the strategy is clearly profitable because the winners are almost twice the size of the losers. This is exactly why risk-to-reward and win rate must always be read together.
The same example in R
Traders often prefer expectancy expressed in R, where 1R is the amount risked on a trade. R normalizes trades that had different stop sizes, so a wide-stop trade and a tight-stop trade can be compared fairly.
If the average winner is +1.8R and the average loser is -1.0R at a 45% win rate:
Expectancy = (0.45 x 1.8) - (0.55 x 1.0) = 0.81 - 0.55 = +0.26R per trade
An expectancy of +0.26R means that, on average, every trade returns just over a quarter of what you risked. That is a healthy edge if it holds across a large, mixed sample.
Strategy A wins far more often but risks 1R to make 0.4R, so its expectancy is negative. Strategy B loses more often but its winners are large. Expectancy exposes which one is actually tradeable.
Why win rate alone lies to you
A 70% win rate feels safe. But if those wins are small and the 30% of losses are large, the strategy can still be a net loser. The reverse is also true: a 35% win rate looks scary until you realize the winners run three or four times the size of the losers. Expectancy is the referee that settles the argument, because it accounts for both frequency and size at once.
Important: positive expectancy does not mean every week is green. It describes the average trade across the whole sample. You will still hit losing streaks, and you still need position sizing that lets you survive them. Expectancy tells you the edge exists; risk management keeps you in the game long enough to collect it.
Costs turn borderline strategies negative
Expectancy calculated on clean prices is optimistic. Real trades pay spread, and often commission and slippage. A setup with +0.05R of raw expectancy can flip to negative once a one-pip spread is subtracted from every entry. Always calculate expectancy on results that already include realistic costs, or subtract an estimate of your typical cost per trade before you trust the number.
How to read expectancy from a backtest
You do not need to compute this by hand. When you run a manual backtest in a simulator, the session report gives you win rate, average win, average loss, and expectancy directly, so you can see whether an edge is real before risking a cent. The practical workflow looks like this:
- Test the setup with fixed rules for at least 100 trades across trending and ranging conditions.
- Read expectancy beside sample size and drawdown - a great expectancy on 12 trades means nothing.
- Check whether one outlier trade is carrying the whole figure. Remove the best trade and see if expectancy survives.
- If expectancy is positive and stable, forward test it before scaling risk.
Turning expectancy into a decision
A number is only useful if it changes what you do next. Positive and stable expectancy is a green light to keep the rules and forward test. Marginally positive expectancy that collapses after costs is a signal to tighten entries or widen targets. Negative expectancy is a clear instruction: do not trade this setup as written, and find out which part - entries, exits, session, or risk-to-reward - is dragging it below zero.
Expectancy FAQ
What is a good expectancy in trading?
Anything above zero after realistic spread and commission costs means the strategy made money on average per trade in the tested sample. In R terms, a stable +0.1R to +0.3R per trade is common for durable strategies. Consistency across a large sample matters more than the exact figure.
Is expectancy the same as win rate?
No. Win rate is only how often you win. Expectancy combines win rate with the size of the average winner and loser, so a low win rate can still produce strong positive expectancy if winners dwarf losers.
How many trades do I need to trust an expectancy figure?
A rough read is possible after 30 trades, but a figure you can act on usually needs 100 or more trades that include winning streaks, losing streaks, and at least one uncomfortable drawdown. Small samples swing wildly with a single outlier.