Profit factor
The profit factor is the ratio of a strategy's gross profits to its gross losses: above 1, the strategy is profitable.
The profit factor condenses a strategy's profitability into a single figure: the sum of all gains divided by the absolute value of the sum of all losses. A profit factor of 1 means gains and losses exactly offset each other; below it, the strategy destroys capital; above it, it creates capital. A profit factor of 1.8 reads as follows: for every euro lost on losing trades, the strategy earned 1.80 on winning trades.
Interpretation and pitfalls
In practice, a profit factor consistently between 1.5 and 2 is generally considered healthy; very high values in a backtest should raise suspicion: they often betray a too-small sample, an overly favourable period or overfitting. The profit factor complements the win rate well, since it captures the asymmetry between gains and losses that the win rate ignores. In Screener.Trading, every screener backtest displays the tested strategy's profit factor alongside its win rate and maximum drawdown.
Compare the profit factor across several periods and asset universes: a robust strategy keeps a profit factor above 1 outside its optimisation window.
Keep exploring
- FeatureThe backtesting tool built into your screenersBacktests built from your screeners: entries, exits, fees and full metrics to judge a strategy on numbers instead of gut feeling.
- GlossaryWin rateThe win rate is the percentage of winning trades out of all trades of a strategy, to be interpreted together with the average win / average loss ratio.
- GlossaryPnL (profit and loss)PnL (profit and loss) is the financial result of a position or portfolio, split into realised PnL on closed positions and unrealised PnL on open ones.
- GlossaryMaximum drawdownThe maximum drawdown is the largest decline suffered by the equity curve from a peak to the subsequent trough, expressed as a percentage of capital.
- GuideBacktesting a trading strategy, step by stepBefore risking a cent, replay your strategy on historical data: backtest setup, reading the key metrics, and the traps of over-optimization.