Trading math / 03
How Do You Compare Average Win and Average Loss in Trading?
Divide your average winning trade by the positive size of your average losing trade to get your payoff ratio. Then weight those averages by how often each outcome occurs to calculate expectancy, the average result per trade. A high win rate can still produce a loss when the losers are much larger than the winners.
Work from closed trades
For cash expectancy, use the same currency throughout and use net results after any trading costs. Divide total winning P&L by the number of winners. Divide the absolute value of total losing P&L by the number of losers. Count net-flat trades in the overall trade total and assign them a zero contribution.
Expectancy = (win rate × average net win) − (loss rate × average net loss)
Both rates use the total count of closed trades as their denominator. You can check your result by dividing total net P&L by that same count. To compare trades in R, divide each trade’s net P&L by its own planned cash risk first, then group and average those R results separately. A payoff ratio requires both wins and losses in the sample.
Compare two trade journals
| Journal | Wins and average win | Losses and average loss | Total net P&L | Net expectancy |
|---|---|---|---|---|
| A | 4 at $70 | 6 at $35 | +$70 | +$7 per trade |
| B | 7 at $25 | 3 at $70 | −$35 | −$3.50 per trade |
Journal A wins 40% of the time and has a payoff ratio of 2.0. Journal B wins 70% of the time and has a payoff ratio of about 0.36. These ten-trade examples show the arithmetic. A small sample can change materially after a single unusually large trade, so review the period, the trade count, and the largest losses alongside the averages.
For the win rate needed to bring the average to zero, see break-even win rate after costs.
Watch the short
Can a 40% Win Rate Be Fine? Check the R Math
Sources
Discuss it with RealTradeMaster
You can discuss what goes into a trade journal and how to read the public results.