If you could keep only one statistic about your trading, keep expectancy: the average R you earn (or lose) per trade taken. It is your edge, quantified — and it predicts your account's future better than any screenshot of a winning week.
The calculation
Expectancy = (Win% × Average win in R) − (Loss% × Average loss in R). Example: 50% wins, +1.8R average winner, −1R average loser → (0.5×1.8) − (0.5×1.0) = +0.4R per trade. Take 20 such trades a month at 1% risk and the math compounds to roughly +8% monthly before the inevitable variance — which is exactly why the number matters more than any single outcome.
Sample size honesty
Ten trades tell you nothing; luck dominates. Thirty begins to whisper; a hundred speaks clearly. This is why we insist students journal from day one — expectancy only exists inside a maintained journal, and the app computes it automatically from your logged trades.
Using it as an instrument panel
- Grading changes: altered your entry trigger? Compare expectancy across the 30 trades before and after — feelings lie, the number doesn't.
- Diagnosing leaks: slice by session, pair, setup, even mood. A trader profitable everywhere except "Friday afternoons" just found free money in the app's AI-coach style breakdowns.
- Sizing courageously: proven positive expectancy is what earns the right to scale risk; scaling on hope is just bigger gambling.
Negative expectancy with great discipline still loses — the method needs work. Positive expectancy with poor discipline also loses — the trader does. The stat tells you which project you are.
Education only — not financial advice. Trading carries risk of loss; never trade money you cannot afford to lose.
