Expectancy

Definition and calculation

Expectancy answers the most important question about any strategy: what does the next trade earn on statistical average? Formula: (win rate × average win) − (loss rate × average loss). Example: (0.55 × $1,500) − (0.45 × $1,000) = $825 − $450 = $375 per trade. Normalised to risk per trade (1R = $1,000) that equals 0.375R – expressing it in R-multiples makes strategies with different account sizes comparable.

Interpretation

Positive expectancy is a necessary but not sufficient condition for success: it says nothing about the fluctuations along the way. A strategy with +0.375R expectancy can easily produce eight losses in a row – only across many trades does the result converge towards the expected value (law of large numbers). Sample size and maximum drawdown therefore always belong next to expectancy. Small shifts have large effects: if the average loss in the example rises from $1,000 to $1,400 (poor stop management), expectancy falls from $375 to $195 – almost halved.

Expectancy in prop trading

For challenge maths, expectancy is the central planning tool: at 0.375R per trade and 1 % risk, an 8 % profit target arithmetically takes about 21–22 trades – a realistic basis for scheduling and minimum trading days. It also shows whether a rulebook is reachable at all without dangerously raising risk per trade.

Why it matters for traders

Expectancy shifts the focus from the single trade to the process: what counts is not one trade's outcome but the quality of the distribution across hundreds of trades. The GlanWick journal calculates expectancy automatically from recorded trades – GlanWick is a training and simulation tool and not a prop firm itself.