Probabilistic Sharpe Ratio

From @jrai in the forums, a nice description with code of probabilistic sharpe. Has anyone written a version of this for Numerai? Of course on Numerai, we don’t use returns but instead correlation with the target but perhaps this idea can be used as a way to choose models that generalize much better out of sample. I think things like skewness, kurtosis will matter for your distribution of era correlations for the same reasons.

https://quantdare.com/probabilistic-sharpe-ratio/

Can anyone show with cross validation whether it’s better to optimize for probabilistic sharpe than smart sharpe from @mdo?

here is some of the code (not by me):

May also be helpful in Numerai Quant

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Better confidence intervals around a flawed metric is still a…flawed metric. What this adjustment is trying to get at is to maximize the upside variance / downside variance, which partial moments already do.

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