Trading Strategy Expectancy Calculator
Find out if your strategy actually has an edge, and whether your current results are meaningful or just early-sample variance.
Strategy verdict
+0.375R
per trade
Your strategy has a clear, measurable edge. Keep trading it consistently.
Profit Factor
1.83
Strong
Min. Win Rate to Break Even
40.0%
You're 15.0% above
Is your data reliable?
Your results become statistically reliable after 381 trades.
Where you stand vs. the break-even line
The dot is your strategy. Above the line = profitable. Below = not profitable.
The formula
Expectancy = (Win Rate × Avg Win) - (Loss Rate × Avg Loss)
Example: 55% win rate, average win 1.5R, average loss 1R. (0.55 × 1.5) - (0.45 × 1) = 0.825 - 0.45 = +0.375R per trade. On 100 trades risking $100 each, that is an expected profit of $3,750.
What is trading expectancy and why win rate alone doesn't tell you anything
Win rate is the most misunderstood metric in trading. A strategy that wins 70% of the time can still be unprofitable if the average loss is 3x larger than the average win. Conversely, a strategy winning only 35% of the time can generate strong returns if winners average 4R and losers average 1R.
Expectancy combines win rate and reward-to-risk into a single number that tells you what you can expect to earn per trade on average. It is the correct metric for evaluating whether a trading strategy has a genuine edge.
How to use this calculator
Enter your win rate, average win size, and average loss size in R multiples (or percentages using the toggle). The calculator instantly shows your expectancy, profit factor, and a plain-language verdict. If you enter trades per month and risk per trade, you will also see monthly and annual return projections.
The significance tracker is the most useful optional feature: enter how many trades you have taken so far to see whether your results have crossed the threshold for statistical reliability.
Win rate vs. reward-to-risk: what actually determines profitability
The table below shows how different combinations of win rate and R:R produce different expectancy values. A cell is green if the combination is profitable, amber if marginal, and red if it is losing money.
| Win Rate | 0.5R:1R | 1R:1R | 1.5R:1R | 2R:1R | 3R:1R |
|---|---|---|---|---|---|
| 35% | -0.48R | -0.30R | -0.12R | +0.05R | +0.40R |
| 40% | -0.40R | -0.20R | 0.00R | +0.20R | +0.60R |
| 45% | -0.33R | -0.10R | +0.13R | +0.35R | +0.80R |
| 50% | -0.25R | 0.00R | +0.25R | +0.50R | +1.00R |
| 55% | -0.18R | +0.10R | +0.38R | +0.65R | +1.20R |
| 60% | -0.10R | +0.20R | +0.50R | +0.80R | +1.40R |
| 65% | -0.03R | +0.30R | +0.63R | +0.95R | +1.60R |
Expectancy per trade in R multiples. Positive = profitable. Use the calculator for precise numbers at your exact inputs.
Understanding statistical significance in trading
Most traders evaluate their strategy after 20 to 30 trades. This is almost always too early. At 95% confidence with a 5% margin of error, a strategy with a 55% win rate requires approximately 384 trades before conclusions are reliable.
Until you reach that threshold, your results, whether positive or negative, could be explained by random variance. This does not mean you should ignore early results, but it does mean you should weight them appropriately and avoid making major system changes based on small samples.
What is a good profit factor?
Profit factor above 1.5 is generally considered good for a live trading strategy. Above 2.0 is excellent. Below 1.0 means the strategy is losing money in aggregate. A profit factor of exactly 1.0 is breakeven before costs, which means it is unprofitable once commissions and spreads are accounted for.
| Profit Factor | Rating | What it means |
|---|---|---|
| Below 1.0 | Not profitable | Losing money on net |
| 1.0 to 1.2 | Breakeven | Unprofitable after costs |
| 1.2 to 1.5 | Marginal | Profitable, narrow margin |
| 1.5 to 2.0 | Good | Healthy edge |
| 2.0 to 3.0 | Excellent | Strong, consistent edge |
| Above 3.0 | Exceptional | Verify sample size is sufficient |
When to stop trading a strategy
The decision to stop trading a strategy should not be based on a losing streak or a short run of bad results. It should be based on a systematic review against clear criteria.
The trade count has crossed the statistical significance threshold and expectancy is still negative.
You can identify a specific execution or process error that has changed your real-world results versus your system rules.
Market conditions have structurally changed in a way your edge specifically relied on.
Your drawdown has hit a pre-agreed circuit breaker set before you started trading.