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Pipeline Custom Factor help - how to specify window length for slow moving factors

Hi,

I want to get the past 12 earnings data. These are slow moving factors and don't change everyday. Unfortunately if I try this:

class Quality(CustomFactor):  
    inputs = [morningstar.income_statement.gross_profit, morningstar.balance_sheet.total_assets]  
    window_length = 500  
    def compute(self, today, assets, out, gross_profit, total_assets):  
        norm = gross_profit * 1.0 / total_assets  
        out[:] = (norm[-1,:] - np.mean(norm, axis=0)) / np.std(norm, axis=0)  

It runs out of memory. Ideally I had like to do something like this:

class Quality(CustomFactor):  
    inputs = [morningstar.income_statement.gross_profit, morningstar.balance_sheet.total_assets]  
    window_length = 12 # quarters  
    def compute(self, today, assets, out, gross_profit, total_assets):  
        norm = gross_profit * 1.0 / total_assets  
        out[:] = (norm[-1,:] - np.mean(norm, axis=0)) / np.std(norm, axis=0)  
2 responses

Hi Pravin,

Unfortunately there isn't a better way to the past 12 earnings data than the solution you tried. However, we are actively working on speeding up the fundamental data integration in pipeline. We are also planning to create an interface like the one you described (N quarters historical lookup, N years, etc.).

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We are also planning to create an interface like the one you described (N quarters historical lookup, N years, etc.).  

Dear, Jamie,
When this feature will be integrated?