Try this:

class OHLC4(CustomFactor):  
    inputs = [USEquityPricing.high, USEquityPricing.low, USEquityPricing.close, USEquityPricing.open]  
    window_safe=True  
    def compute(self, today, assets, out, high, low, close, open):  
        out[:] = np.mean(open + high + low + close, axis=0) / 4  
def custom_pipeline(context):

    m = QTradableStocksUS()

    bband = BollingerBands(inputs=[OHLC4(window_length=6, mask=m)], window_length=20, k=2)  
    low_bb = bband.lower  
    high_bb = bband.upper  
    alpha = low_bb / high_bb  

Hi Daniel,

thanks for the time spent. I believe that

out[:] = np.mean(open + high + low + close, axis=0) / 4  

has to be

out[:] = np.mean(open + high + low + close, axis=1)  

to perform (open + high + close + low)/4, am I right?

Nevertheless, I tried but failed to make the custom factor OHLC4 work... feel free to check the notebook attached

Hi Mihail,

Try this:

class OHLC4(CustomFactor):  
    inputs = [USEquityPricing.high, USEquityPricing.low, USEquityPricing.close, USEquityPricing.open]  
    window_length = 2  
    window_safe=True  
    def compute(self, today, assets, out, high, low, close, open):  
        out[:] = (open[-1] + high[-1] + low[-1] + close[-1]) / 4.0

def custom_pipeline(context):

    m = QTradableStocksUS()

    bband = BollingerBands(inputs=[OHLC4(mask=m)], window_length=20, k=2)  
    low_bb = bband.lower  
    high_bb = bband.upper  
    alpha = low_bb / high_bb  

Thanks, James,

But does not seem to work that way either....

Error is Custom_factor not defined..

Attach is NB. You need to add the modules that need to be imported into the NB. Hopes this helps.

Sir, you are THE man!

Thanks alot!