Expectancy is the average profit (or loss) per trade over many trades. It combines win rate and average trade size into a single number: expectancy = (win rate × average win) – (loss rate × average loss). For example, if 100 automated trades net a total of $8,000 profit, the expectancy is $80 per trade. In journaling, expectancy tells whether an algorithm is mathematically sound in the long run. Trading journals like Edgewonk compute expectancy automatically for any set of trades. A positive expectancy means that, on average, each trade adds money after accounting for losses. Developers use this to validate strategies: even a moderate win rate can be lucrative if expectancy is high (e.g. large winners), while a high win rate can still lose money if losses are large. By monitoring expectancy over time, robot traders ensure that strategy tweaks (like adjusting take-profit levels) actually improve expected outcomes.