I’ve been looking at the concept of Positive Expectancy, its calculation, and what is/isn’t a robust benchmark for positive expectancy.
A hypothetical day trading system produces the following results:
81% are winners
19% are losers
av. win is $492
av. loss is $416
I’ve come across two formulas to calculate expectancy. Both generate the same end result. But the proponents of each formula conflict in terms of what is/isn’t a desirable expectancy benchmark.
[probability of win x av. win] – [prob. of loss x av. loss] = expectancy
using the above hypothetical:
[0.81 x 492] – [0.19 x 416] = 319 rounded
319/416 = 77
So our hypothetical trader with the above stats expects to make 77 cents for every dollar $1 risked.
My research shows that proponents of this formula argue that an expectancy of 60 cents or greater represents a “good” system.
ie., an expectancy of 60 cents is the benchmark.
[reward to risk ratio x win ratio] – loss ratio = expectancy ratio
[492/416 x 0.81] – 0.19 = 0.77 expectancy ratio
Each formula obviously gives the same answer. But the proponents of this second formula state that an expectancy ratio >1.0 is the benchmark.
So formula one advocates would be happy with their 77 cents, but formula two advocates would not.
Have I missed something in my attempt to understand expectancy and formulate a benchmark? Is it 60 cents or $1.00??
Thanks for feedback.