Multiple get_fundamentals or an 'OR' for adding fundamentals on individual securities

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posted Aug 20, 2015

The goal is to pull a list of securities and their fundamentals information normally, and also fundamental info on sets of securities that one would specify by their symbols, where they would be independent from the filters.

Is an 'OR' available?

Generic example calling twice:

    f = fundamentals  
    panel1 = get_fundamentals(    # Fundamentals by filter  
        query(  
            f.valuation_ratios.pe_ratio,  
        )  
        .filter(f.share_class_reference.is_primary_share == True)  
        .order_by(f.valuation.market_cap.desc())  
        .limit(20)  
    )  
    panel2 = get_fundamentals(    # Fundamentals for specific symbols  
        query(  
            f.valuation_ratios.pe_ratio,  
        )  
        .filter(f.company_reference.primary_symbol.in_(['AAPL', 'TSLA']))  
        .order_by(f.valuation.market_cap.desc())  
    )  
    panel3 = pd.concat([panel1, panel2], axis=1)

... however the concat failed with:

"Runtime exception: TypeError: cannot concatenate a non-NDFrame object"

In get_fundamentals help, the return is said to be a pandas.Panel rather than a DataFrame.
So I'm wondering if this can be done in one call with an 'OR' inside get_fundamentals or whether the two panels can be combined.

Thanks

PS Tip: For those who did not know, the query() section populates the panel with specific values. If only the filters and ordering are being utilized, for the resulting securities list, then query() can be empty, that's faster.

3 responses

Josh Payne

Aug 20, 2015

I didn't solve your direct problem, but I accomplished your goal simply by concatenating the arrays of securities from separate fundamentals queries:

import numpy as np

def initialize(context):  
    pass

def before_trading_start(context):

    df1 = get_fundamentals(  
        query(  
            fundamentals.valuation.market_cap  
        )  
        .filter(fundamentals.company_reference.primary_symbol == 'AAPL')  
        ,'2015-08-20')

    df2 = get_fundamentals(  
        query(  
            fundamentals.valuation.market_cap  
        )  
        .filter(fundamentals.company_reference.primary_symbol == 'GOOGL')  
        ,'2015-08-20')  
    all_securities = np.concatenate((df2.columns.values, df1.columns.values), axis=0)  
    update_universe(all_securities)

def handle_data(context, data):

    for stock in data:  
        log.info("Stock in universe: %s" % stock.symbol)

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Blue Seahawk

Aug 20, 2015

Could be useful for someone, although optimally for utilizing the fundamental data, all would be combined in one panel or dataframe.
all_securities is made up of security objects lacking the fundamental data.

Maybe http://stackoverflow.com/questions/11714768/concat-pandas-dataframe-along-timeseries-indexes

Or to_frame(). I don't understand this:
http://stackoverflow.com/questions/22648773/what-is-the-simplest-way-to-concatenate-all-of-the-dataframes-in-a-pandas-panel

If only it were as easy as df1.append(df2)

Josh Payne

Aug 20, 2015

An alternative using SQLAlchemy (i.e. getting that OR into the SQL query so you only need to query the DB once):

from sqlalchemy import or_  
fundamental_df_1 = get_fundamentals(  
        query(  
            fundamentals.valuation_ratios.pe_ratio)  
        .filter(or_(fundamentals.share_class_reference.is_primary_share == True,  
                    fundamentals.company_reference.primary_symbol.in_(['AAPL', 'TSLA'])))  
        .order_by(fundamentals.valuation.market_cap.desc())  
        .limit(2000)  
        , '2015-08-20'  
    )

(This is using get_fundamentals() in research)

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