Trading Expectancy
A framework for combining win rate, average win and average loss to evaluate a trading process over many trades.
Expectancy asks what the process earns or loses on average
Trading expectancy combines how often trades win with the average size of wins and losses. It is a framework for evaluating a series of trades, not predicting the outcome of the next trade.
(Win rate × average win) − (Loss rate × average loss)
Example
If a hypothetical process wins 40% of the time, averages $200 on winners and loses $100 on losing trades, its arithmetic expectancy is $20 per trade before fees and execution effects: (0.40 × $200) − (0.60 × $100).
Why a high win rate can still lose
A strategy can win frequently but give back more on its occasional losses than it earns on winners. Conversely, a lower-win-rate approach can have positive expectancy if winners are sufficiently larger than losses.
Use meaningful samples
Expectancy estimates are unstable with very small samples and can change as market conditions change. Track actual results after costs, separate strategies where possible, and avoid treating historical expectancy as a guarantee.
Sample size and market regime
Ten trades are usually too little to say much about a noisy process. As the sample grows, estimates become more informative, but changes in volatility, competition or execution can still make old data less representative of the future.
Costs can erase a small edge
If the example above has $20 gross expectancy but average round-trip trading friction is $7, the simplified net expectancy falls to $13. If average friction rises to $22, the process becomes negative even though the win rate and gross trade outcomes did not change.
Trading friction can include commissions, regulatory fees, spread cost and slippage. The more frequently a strategy trades, the more important these costs become.
Using R-multiples
Some traders record results in R, where 1R is the amount planned to be at risk on a trade. If 1R is $100, a +1.5R result is +$150 and a −1R result is −$100. This makes it easier to compare trades taken at different dollar sizes.
R-multiples do not remove execution risk. A gap or fast move can produce a loss larger than the planned 1R.
Positive expectancy can still lose for long periods
Expectancy is an average, not a schedule. A positive historical expectancy does not prevent losing streaks, drawdowns or future strategy degradation. Variance determines how unevenly outcomes arrive.
The core equation
A common simplified model is: expectancy = (win rate × average win) − (loss rate × average loss). The units can be dollars, percentage return or R-multiples. The calculation describes the historical average per trade under the measured assumptions.