Win rate measures how often trades finish positive. Profit factor compares the money made on winning trades with the money lost on losing trades. Neither metric is a prediction. Read them together, using the same set of closed trades and the same treatment of costs, before drawing a conclusion about your recorded performance.
The formulas
- Win rate = number of winning closed trades ÷ total closed trades × 100. State whether breakeven trades remain in the denominator.
- Profit factor = sum of positive trade results ÷ absolute sum of negative trade results. State whether results include fees.
- Average result per trade = sum of trade results ÷ number of trades. This is a descriptive sample average, sometimes called historical expectancy.
Formula reference: TradingView’s definition of profit factor
Free profit factor and win rate calculator
Start with either fictional example or enter your own closed-trade totals. Use one currency and include costs before grouping wins, losses and breakevens. Breakevens count toward the win-rate denominator. Calculations stay in this page; no account is needed.
Results for 10 closed trades
- Win rate
- 40.00%
- Profit factor
- 2.00
- Average result per trade
- 30.00
- Net sample result
- 300.00
Profit factor divides 600.00 won by 300.00 lost.
Amounts use your input currency; results are rounded to two decimal places. This describes the entered sample, not future performance. It does not measure drawdown, position sizing or the reliability of a strategy.
A worked example with ten fictional trades
| Result group | Trades | Net result each | Total |
|---|---|---|---|
| Wins | 4 | +$150 | +$600 |
| Losses | 6 | −$50 | −$300 |
| All closed trades | 10 | Average +$30 | +$300 |
This sample has a 40% win rate: four wins divided by ten trades. Its profit factor is 2.0: $600 divided by $300. The average result is $30 per trade: $300 divided by ten. A win rate below 50% is compatible with a positive result because the average win is larger than the average loss. These numbers describe this fictional sample only.
Why a high win rate can still lose money
Now consider another fictional set: eight wins of $20 and two losses of $100, after fees. The win rate is 80%, but winners add up to $160 while losses add up to $200. Profit factor is 0.8 and the average result is −$4. Looking only at the eight winning trades hides the effect of the two larger losses. This is why win rate alone is an incomplete scorecard.
Handle fees and edge cases explicitly
If your trade results are gross, subtract the applicable recorded costs before calculating a net version. Do not subtract costs again if the broker export already includes them. A small gross winner can become a net loser after costs, changing both win rate and profit factor. Currency conversion, partial closes and grouped executions can also make two tools disagree even when both calculations follow their own definitions.
With no losing trades, the profit-factor denominator is zero. Show ‘not defined’ or explain the missing losses rather than treating infinity as proof of a robust strategy. With no trades, win rate and average result are undefined. With only losses, profit factor is zero. Include breakeven trades consistently and keep unrealized positions out of a closed-trade sample.
What these numbers leave out
- Sample size: one large win can dominate a small history.
- Sequence: two samples can have identical profit factor but very different drawdowns.
- Exposure: dollar results change with position size and cannot always be compared across accounts.
- Selection: excluding mistakes or inconvenient trades can make any metric misleading.
- Conditions: a past sample does not establish how a process will behave in future markets.
Start by reconciling the underlying trades, then review profit factor alongside trade count, average win and loss, drawdown, and rule adherence. Compare groups only when their definitions match. There is no universal profit-factor threshold that establishes future profitability, and ten trades are not enough to validate an edge.