Its strength and its blind spot are the same thing
Its strength is that it folds win rate and trade size into one immediately readable number: 1.5 means a dollar fifty for every dollar lost. It needs no context and no comparison to a threshold.
Its blind spot is the same property: it is a sum, and sums hide distribution. A system whose net profit came entirely from one exceptional trade can show an excellent factor while the rest of its trades lose. So it is always read alongside the largest winner: if removing that trade drops the figure below 1, you do not have a system — you have one documented piece of luck.
The shared reference record
The same record used across the other performance pages: 100 trades, 40 winners averaging +2R and 60 losers averaging −1R, for an expectancy of +0.20R per trade.
Reusing one record across pages is deliberate: you can compare the measures directly instead of comparing different examples, and it becomes visible that each one views the same thing from an angle, and that none is sufficient alone.
The formula
Profit factor = gross profit ÷ |gross loss|
A worked example
In the reference record: 40 winners × 2R = 80R of profit and 60 losers × 1R = 60R of loss. Profit factor = 80 ÷ 60 = 1.33 — $1.33 for every dollar lost.
At 1% risk on a $1,000 account ($10 per R) that is $800 of profit against $600 of loss across a hundred trades, netting $200 before costs — the same figure expectancy produces from another angle. The two do not conflict; they measure the same thing on different scales.
Common mistakes with this term
- Reading it without checking the largest winner, when the whole system may hang on one exceptional result.
- Comparing profit factors computed over different periods or trade counts as if they were equivalent.
- Computing it before costs, when spread and swap lower the numerator and raise the denominator at once.