Greetings,

I want to compare a particular equity's PE to its industry and sector PE. My theory is that if an equity's PE is greater than it's industry and sector, it is overvalued.

I was able to create this metric by manipulating the resulting DataFrame after running the Pipeline. I would like to create a factor (inheriting CustomFactor) for this instead. My question is how I might do this.

# Forward Price to Earnings Ratio (MORNINGSTAR)  
class Price_To_Forward_Earnings(CustomFactor):  
    ...


def filter_universe():  
    """  
    Modified from Nathan Wolfe's Algorithm  
    """  
    ...


universe = Q500US()


pipe = Pipeline(  
    columns={  
        'Price_To_Forward_Earnings': Price_To_Forward_Earnings(mask=universe),  
        'morningstar_industry_group_code': asset_classification.morningstar_industry_group_code.latest,  
        'morningstar_sector_code': asset_classification.morningstar_sector_code.latest  
    },  
    screen=filter_universe()  
)


results = run_pipeline(pipe, '2014-01-01', '2014-06-30')  
results = results.fillna(value=0.0)


sector_forward_pe = results.groupby(['morningstar_sector_code'])['Price_To_Forward_Earnings'].mean()  
results['sector_forward_pe'] = results['morningstar_sector_code'].map(sector_forward_pe)


industry_forward_pe = results.groupby(['morningstar_industry_group_code'])['Price_To_Forward_Earnings'].mean()  
results['industry_forward_pd'] = results['morningstar_industry_group_code'].map(industry_forward_pe)


results['ForwardPEvsSector'] = results['Price_To_Forward_Earnings']/results['sector_forward_pe']  
results['ForwardPEvsIndustry'] = results['Price_To_Forward_Earnings']/results['industry_forward_pd']

The result DataFrame now contains ForwardPEvsSector and ForwardPEvsIndustry.

How might I accomplish this within a custom factor?