= (Probability of Win * Average Win) – (Probability of Loss * Average Loss)
As an example let’s say that a trader has a system that produces winning trades
30% of the time. That trader’s average winning trade nets 10% while losing trades lose 3%. So if he were trading
$10,000 positions his expectancy
(0.3 * $1,000) – (0.7 * $300) = $90
So even though that system produces losing trades 70% of the time the expectancy
is still positive and thus the trader can make money over time. You can also see how you could have a system that produces winning trades the majority of the time but would have a negative expectancy
if the average loss was larger than the average win:
(0.6 * $400) – (0.4 * $650) = -$20
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Link to Van Tharps introductory discussion of expectancy, where the concept of including the individual trade risk ('R') of each trade is introduced as a way of turning expectancy into a risk adjusted return metric comparable across systems.
Alternative calculation involves using 'average losing trade' as the denominator, which is usually easier to calculate, for a quick measure of risk adjusted returns.
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