If you are looking to avoid spurious correlations Algoman’s algorithm can do that while looking at correlations over a range of market conditions. Not just a point in time. Doing it with Pearsons correlation, Spearman’s Rank Correlation AND even using non-parametric hierarchical clustering. This last, at least, probably crosses into the area of “machine learning” for some while simply being a non-parametric way to look at correlations for others. IF the label of “machine learning” does not bother you, then you should check it out. It is really quite impressive and sophisticated.
And importantly, Marco is likely to make this even more accessible with Voila (opening up the platform to creative ideas from members essentially). You can just use it (without worrying about the label) if you want: Python program to find correlations and multicollinearity