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Expectancy = (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 would be:
(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


This article is a stub. Please edit the article to improve it and add additional details.
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.
[top]See Also
In mathematical and probability terms, this concept is known as "Expected Value" (EV).

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Contributors:  mikusha ,  Big Mike 

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