Factor preprocessing

I am developing an algo following the contest guideline with constraints and optimization in place. When producing a factor, do I have to worry about outliers adjustment, factor standardization and common factor (market & sector) neutralization other than a simple preprocessing as follows ?

def preprocess(a):
a = a.astype(np.float64)
a[np.isinf(a)] = np.nan
a = np.nan_to_num(a - np.nanmean(a))
return preprocessing.scale(a)