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Sector average values

Hi,

I have posted this question earlier but was not able to figure out how to query for a sector average value of a fundamental ratio.

A code example would be great, thanks.

Suppose I have some stock that has been returned and I have updated my universe with the results of the get_fundamentals, now how do I query for the sector average of p/e ratio of the stock's sector?

4 responses

I just wrote up this example that will get you the average value of the p/e ratio for the AAPL stock and its sector. This should give you a good enough basis so that you can use it for other stocks! Let me know if you have questions.

Clone Algorithm
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Total Returns
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Alpha
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Beta
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Sharpe
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Sortino
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Max Drawdown
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Benchmark Returns
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Volatility
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Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
import datetime

def initialize(context):
    context.stock = sid(24)

def before_trading_start(context, data):
    # Query for specific security fundamentals (by symbol in this example).
    fundamental_df = get_fundamentals(
        # Retrieve sector code.
        query(
            fundamentals.asset_classification.morningstar_sector_code,
        )

        # Filter for specific stock (AAPL in this example)
        .filter(fundamentals.share_class_reference.symbol == 'AAPL')
    )
    
    # Get the sector code of our stock.
    sector_code = fundamental_df.at['morningstar_sector_code', sid(24)]
    print sector_code
    
    # Query for securities based on their economic sector
    fundamental_df2 = get_fundamentals(
        # Retrieve PE ratio based on economic sector code.
        query(
            fundamentals.valuation_ratios.pe_ratio,
            fundamentals.asset_classification.morningstar_sector_code,
        )

        # Filter where the Sector code matches the sector code of our specific stock.
        .filter(fundamentals.asset_classification.morningstar_sector_code == sector_code)
    )
    
    # Print the unweighted mean PE ratio of the sector
    print 'Mean PE ratio for sector %d: %.2f' % (sector_code, fundamental_df2[fundamental_df2.index == 'pe_ratio'].transpose().mean())
    
def handle_data(context, data):
    pass
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Thank you Jamie! Much appreciated! This does not average out the p/e ratios of the stocks in the same sector as returned by the query does it?

What I mean is if you run the get_fundamentals, you only get 500 stocks, I have noticed that second query that is run makes sure that all 500 are the stocks of the same sector, but we are still not getting the sector mean, are we? Only approximation, because the actual total number of stocks in the sector may be different than 500, is this correct?

This calculation is actually being performed on 779 securities. Since I'm not actually adding these securities to my universe, I'm not blocked by the 500 security limit. Since you didn't want to actually trade all of these securities, there's no need to add them to the universe and our calculation done in before_trading_start is on all 779 securities (you can print fundamental_df2.shape to confirm).

Does this make sense?

Yeap, it does. I suppose If I wanted to compute variability of a particular ratio, I would do the similar process as above but would "collect" the fundamentals each year and append it to my dataframe and then do the computations on that DF. Thanks