Trading expectancy
Expectancy compresses a whole results table into one question: on average, what does one trade of this strategy earn me? Positive, and repetition works for you.
- Win rate
- 40%
- worked example
- Avg win / avg loss
- 2R / 1R
- Expectancy per trade
- +0.2R
- 0.4 × 2R − 0.6 × 1R
Trading expectancy, explained.
Expectancy is the average amount a strategy wins or loses per trade. The formula is (win rate × average win) − (loss rate × average loss). A positive expectancy means each trade earns money on average; repeated over many trades, the edge compounds. Negative expectancy compounds the other way.
Expectancy is where the win-rate illusion dies. A strategy that wins 90% of the time loses money if the rare losers are big enough; a strategy that wins 30% of the time prints money if its winners dwarf its losers. Expectancy forces the two halves — how often you win and how much — into one honest number.
It is often expressed per unit of risk (expectancy in R, where R is the amount risked per trade), which makes strategies with different position sizes comparable. Two caveats keep it honest: expectancy is an average that says nothing about the variance around it, and a backtest expectancy is an estimate from one historical sample — fees, slippage, and regime changes all take their cut live.
From idea to a running bot.
Computing expectancy from any backtest report takes three steps.
Split the trades
From the results, take the win rate, the average winning trade, and the average losing trade. Every backtest report carries these numbers.
Weigh both sides
Multiply win rate by average win, then subtract loss rate times average loss. The result is the expected value of one trade in currency or percent.
Scale by opportunity
Expectancy per trade times trade frequency gives expected growth per period — a small edge exercised often can outpace a large edge exercised rarely, within risk limits.
Built for the way you trade.
Expectancy thinking separates system traders from streak chasers.
Strategy evaluators
Expectancy is the cleanest single answer to 'does this strategy have an edge?' — independent of how flattering or ugly the win rate looks.
Risk-reward tuners
Because the formula exposes both levers, it shows exactly whether a tweak should raise the win rate or fatten the average winner — and what each costs the other.
VolatiCloud backtesters
VolatiCloud backtest reports include expectancy alongside win rate and profit factor, so the per-trade edge is visible before a strategy ever trades live.
- (Win rate × avg win) − (loss rate × avg loss)
- Positive = the strategy earns on average per trade
- Exposes the win-rate illusion
- Often expressed in R (per unit risked)
- Reported in every VolatiCloud backtest
Frequently asked questions.
What is a good expectancy?
Any reliably positive expectancy can be traded profitably with enough opportunities and controlled risk. Expressed in R, seasoned system traders often consider 0.2R-0.5R per trade solid. The reliability matters more than the size — validate it out-of-sample.
How is expectancy different from profit factor?
Profit factor is gross wins divided by gross losses over the whole sample; expectancy is the average result of one trade. They usually agree in direction, but expectancy translates directly into 'what happens if I take one more trade', which makes it more intuitive for sizing decisions.
Can a high win rate strategy have negative expectancy?
Easily. Win 95% of the time collecting small gains, and one uncapped loss can erase months. That profile — common in strategies without stop-losses — is exactly what expectancy exposes and what risk management exists to prevent.
Where do I find expectancy on VolatiCloud?
In every backtest report, next to win rate, profit factor, and the risk metrics. It is computed from the simulated trades on real historical data for the exact strategy configuration you tested.
Related capabilities.
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