Trading Expectancy Calculator
Estimate average result per trade from win rate, average win and average loss, with unit, cost, sample and classification checks.
Define the calculation
Replace the example values with the values relevant to the decision being evaluated.
Calculation output
Interpret the result together with the assumptions used to produce it.
Enter valid inputs to calculate.
Educational calculation only. The result depends on the supplied inputs and assumptions and is not investment advice, a recommendation, or a promise of performance.
Understand the tool before relying on the output.
The existing published material remains part of the resource and provides the assumptions, examples and context.
A trading-expectancy calculator estimates the average result per trade from the observed win rate, average win and average loss. The result describes the supplied sample and assumptions; it does not promise the next trade or future performance.
Calculate expectancy
Formula and units
Expectancy = (win probability × average win) − (loss probability × average loss magnitude). All payoff values must use the same unit: money, percentage of capital, points after conversion, or units of initial risk such as R. Mixing units produces a meaningless output.
Use net outcomes consistently
If commissions, spread, slippage and financing are already included in each recorded trade, use net average wins and losses. If the inputs are gross, subtract a separate cost estimate consistently. Do not deduct some costs twice while omitting others.
| Input | Review question |
|---|---|
| Win rate | How were break-even trades and partial exits classified? |
| Average win | Are rare large wins dominating the estimate? |
| Average loss | Are gaps and unusually large losses included? |
| Sample | Does it represent the rule, market and period being evaluated? |
| Cost treatment | Are all outcomes gross or all outcomes net? |
A positive estimate can still be uncertain
Expectancy is a sample mean assembled from estimated probabilities and payoffs. A small sample, dependent trades or a heavy-tailed outcome distribution can make it unstable. Use Trading Strategy Sample Size to assess evidence sufficiency and intervals rather than treating the sign alone as proof.
Break-even trades and multiple outcomes need explicit rules
The simple form uses winners and losers. If the process has break-even outcomes, partial exits or several payoff categories, calculate the weighted average across all mutually exclusive outcomes or use the average of the complete net-return series. State the classification rule before computing the metric.
Compare planned and realized payoff
A planned risk-reward ratio uses entry, stop and target geometry. Realized expectancy uses actual completed outcomes. Differences can reveal execution costs, early exits, stop movement or a target that is rarely reached.
Review sequence
- Clean the trade record and define classifications.
- Choose one unit and use it for every outcome.
- Calculate win rate and average net win/loss.
- Inspect the distribution and influential trades.
- Measure uncertainty and segment only where sample size supports it.
- Compare later out-of-sample and forward results.
- Investigate rule changes instead of hiding them in the aggregate.
Common errors
- Entering average loss as a negative number and subtracting it again.
- Using a target ratio instead of realized average wins.
- Ignoring costs or break-even classifications.
- Calling a positive historical estimate a guarantee.
- Comparing expectancy values expressed in different units.
Expectancy calculator questions
Can expectancy be measured in R?
Yes. Express every completed trade as a multiple of its initial planned risk and use the net average win and net average loss in the calculator. The classification must stay consistent across the sample.
What should I do with break-even trades?
Define the classification before calculating. Either model break-even trades as a separate outcome with their observed net result or calculate the arithmetic mean of the complete net-return series.
Is positive expectancy enough to validate a strategy?
No. A positive sample estimate can be unstable. Review sample size, dependence, trading costs, tail losses, market regimes and out-of-sample behaviour before deciding whether the evidence is sufficient.
Trading Expectancy develops interpretation, while Strategy Validation places the metric inside a wider evidence process. Find related resources through MFXG tools.