Joakim, the error you are seeing is
UserWarning: MinMaxScaler assumes floating point values as input, got bool. That's the clue. You are passing boolean values to the custom factor. Probably unintentional. Here's the offending line of code
factor1_scaled = FactorScaler(inputs=[factor1.notnull()], mask=universe )
The input you are passing
factor1.notnull() is a filter (ie boolean values) it's not the factor stripped of nulls. What you want is this
factor1_scaled = FactorScaler(inputs=[factor1], mask=universe & factor1.isfinite() )
Pass the factor but then filter that factor with the mask. Notice too that it's probably wiser to use the
isfinite() method. This will not only catch the nans but also the infinite values. Infinite values don't play well with the
MinMaxScaler method. Best to avoid those too.
You may also want to set
copy=False (rather than True) to avoid the additional step of copying the input array.
This is actually a nice way to normalize factors and an alternate to the 'rank' or 'demean' approaches.
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