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.
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
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.