Hi Tom, that part is not so obvious. You would use a CustomFactor:

class CombineFactorsNanFill(CustomFactor):  
    def compute(self, today, assets, out, *factors):  
        for f in factors:  
            nanfill(f)  
        out[:] = np.sum(factors, axis=0)

def nanfill(_in):   # Forward Fill NaNs  
    '''  
    From https://stackoverflow.com/questions/41190852/most-efficient-way-to-forward-fill-nan-values-in-numpy-array  
    https://www.quantopian.com/posts/forward-filling-nans-in-pipeline  
    '''

    #return _in            # uncomment to not run the code below

    '''  
    nan_num = np.count_nonzero(np.isnan(_in))   # count nans  
    if nan_num:  
        log.info(nan_num)  
        #log.info(str(_in))  
    '''  
    mask = np.isnan(_in)  
    idx = np.where(~mask,np.arange(mask.shape[1]),0)  
    np.maximum.accumulate(idx,axis=1, out=idx)  
    _in[mask] = _in[np.nonzero(mask)[0], idx[mask]]  
    return _in  

And use it this way in pipeline definition:

factors = [sales, roa, cfinc]  
combined_factors = CombineFactorsNanFill(factors, window_length = 1, mask=universe).zscore(mask=universe)  

Hope that helps!

Just keep in mind that filling with zeros will have impact on your scoring (i.e. not sure you can rely on stddev when there are lots of fake 0 values in your factor). Filling with mean would be better I think. Something like this would work but is kinda slow:

class CombineFactorsNanFill(CustomFactor):  
    def compute(self, today, assets, out, *factors):  
        for f in factors:  
            mean = np.nanmean(f)  
            f[np.isnan(f)]=mean  
        out[:] = np.sum(factors, axis=0)  

Cheers

Thanks Charles!