I'm having difficulties running the below code and run into error message: BadBinaryOperator: Can't compute Latest < list. What I'm trying to do is combined the factors in "make_factors" then rank them based on their industry mean pe ratio to start.

def make_factors():  
        class Intraday(CustomFactor):  
            inputs = [USEquityPricing.close, USEquityPricing.open]  
            window_length = 6  
            window_safe = True  
            def compute(self, today, assets, out, close, open):  
                x = np.cumprod(open[:] / close[:], axis=0)[-1] - 1  
                out[:] = preprocess(x)  
        class Reversion_Volume(CustomFactor):  
            inputs = [USEquityPricing.volume]  
            window_length = 14  
            window_safe = True  
            def compute(self, today, assets, out, volumes):  
                x = -volumes[-1] / np.mean(volumes, axis=0)  
                out[:] = preprocess(x)  
        class BullBearIntensity(CustomFactor):  
            inputs = [stocktwits.bull_minus_bear]  
            window_length = 4  
            window_safe= True  
            def compute(self, today, assets, out, bull_minus_bear):  
                x= np.nanmean(bull_minus_bear, axis = 0)  
                out[:] = preprocess(x)  
        class PsychSignalMessages(CustomFactor):  
            inputs = [stocktwits.bull_scored_messages,stocktwits.bear_scored_messages,stocktwits.total_scanned_messages]  
            window_length = 30  
            def compute(self, today, assets, out, bull_msgs, bear_msgs, ts):  
                x = np.nanmean(((bull_msgs + bear_msgs)/ts), axis = 0)  
                out[:] = preprocess(x)  


        factors = [  
            Intraday,  
            Reversion_Volume,  
            BullBearIntensity,  
            PsychSignalMessages,  
        ]  
        return factors

def IndustryAVG():  
        class IndustryMeanPE(CustomFactor):  
            inputs = [Fundamentals.pe_ratio, Fundamentals.morningstar_industry_group_code]  
            window_length = 252  
            def compute(self, today, assets, out, pe_ratio, industry_codes):  
                df = pd.DataFrame(index=assets, data={"pe_ratio": pe_ratio[-1], "industry_codes": industry_codes[-1]})  
                out[:] = df.groupby("industry_codes").transform(np.mean).values.flatten()  
        indavg = [  
            IndustryMeanPE,  
        ]  
        return indavg  
def make_pipeline():  
    universe = QTradableStocksUS()  
    factors = make_factors()  
    beta = SimpleBeta(target=sid(8554),regression_length=260,  
                      allowed_missing_percentage=1.0  
                     )  
    indavg = IndustryAVG()  
#____________________________Combined Alphas___________________________  
    combined_alpha = None  
    for f in factors:  
        if combined_alpha == None:  
            combined_alpha = f(mask=universe).rank(mask=indavg)  
        else:  
            combined_alpha += f(mask=universe).rank(mask=indavg)  
    #combined_alpha_sma = None  
    #for f in factors:  
        #if combined_alpha_sma == None:  
            #combined_alpha_sma = SimpleMA([f(mask=universe)],window_length=FACTOR_AVG_WINDOW)  
        #else:  
            #combined_alpha_sma += SimpleMA([f(mask=universe)],window_length=FACTOR_AVG_WINDOW)  
    pipe = Pipeline(columns = {  
        'combined_alpha':combined_alpha,  
        'beta':beta,  
    },  
    screen = universe)# & screens)  
    return pipe