Trading Expectancy Calculator
Combine entered win rate, average R-multiples, costs, and frequency into a weighted-average scenario.
Scenario inputs
Use your actual averages after fees and slippage whenever possible.
Input-based estimate
Weighted average per trade under the values entered.
The entered averages produce a positive estimate. Compare it with a reconciled historical sample and its uncertainty; it does not predict the next trade or month.
Free, no sign-up, nothing leaves your browser — all math runs locally.
Informational scenario only, not investment or risk advice. Outputs depend on your inputs and may not reflect fees, slippage, gaps, taxes, or provider-specific rules.
What the expectancy equation estimates
Trading expectancy is an input-based weighted-average estimate over a sample. It combines your win rate, the size of your average winner, the size of your average loser, and the friction that every trade pays. A high win rate alone is not enough to describe results; a sample with a 70% win rate can still be negative if its losses are much larger than its wins.
Expressing the calculation in R keeps it comparable across account sizes and instruments. If you risk $100 per trade, an expectancy of +0.25R means an expected +$25 per trade before position-size changes. At 40 trades per month, the same inputs produce a +10R monthly scenario. That multiplication is not a forecast: trade frequency, outcome distribution, costs, and market conditions can change.
Why win rate can mislead you
A 40% win-rate sample with 2R average winners and 1R average losses produces a positive estimate before costs. A 65% win-rate sample with 0.5R winners and 1.5R losses produces a negative estimate. The calculation therefore needs both frequency and magnitude.
Use realized results, not the trade plan
Planned risk/reward and realized data answer different questions. Reconciled history can include partial exits, changed stops, commissions, and slippage. Compare expectancy by available setup, market, session, and direction while retaining sample size and data-quality context.
Frequently asked questions
What is positive trading expectancy?
Positive expectancy means the entered win rate, win size, loss size, and costs produce a positive weighted-average estimate. It does not establish that the sample is representative or predict the next trade, week, or month.
How do you calculate trading expectancy?
Multiply your win rate by your average win in R, then subtract your loss rate times your average loss in R, and subtract costs per trade in R. For example: 45% wins × 2R minus 55% losses × 1R minus 0.05R of costs equals +0.30R expectancy per trade.
Can a low win-rate strategy be profitable?
A sample can have a positive estimate below a 50% win rate when average winners are large enough relative to average losses. For example, 40% wins at 2R and 60% losses at 1R produces a positive estimate before costs. Historical inputs do not guarantee future results.
Why include commissions and slippage in expectancy?
Costs are paid on every trade, so they reduce the edge even when a setup looks profitable before fees. Small costs matter especially for high-frequency or tight-target strategies. Entering them in R makes their effect comparable to your actual risk per trade.
How is expected performance different from actual performance?
Expectancy is a weighted-average estimate based on the values entered. Actual results vary because trades are not identical, samples are noisy, and market conditions and costs change. Preserve the sample size and limitations when comparing segments.
More free tools & resources
Inspect expectancy beside its evidence
TradeInsights calculates realized metrics from supported imported records and lets you compare available setup, session, market, and direction fields with sample context.
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