Fundamental Basic EPS Example¶

import quantopian.pipeline.data.morningstar as mstar

from quantopian.pipeline import Pipeline, CustomFilter
from quantopian.research import run_pipeline

import numpy as np

class EPS_NaN(CustomFilter):  
    # Define inputs
    inputs = [mstar.valuation_ratios.pe_ratio]
    
    window_length = 252 
    
    def compute(self, today, assets, out, pe):
        # The input 'pe' is a Numpy array with data as axis 0 (total rows = window length)
        # and axis 1 are all the assets (ie stocks and securities).
        # Here we use the built in Numpy methods to check for all NaNs (ie all negative earnings). 
        is_nan = np.isnan(pe)
        out[:] = np.all(is_nan, axis=0)
        

# Get the basic EPS and some info about where it comes from
filed = mstar.financial_statement_filing.file_date.latest
as_of = mstar.financial_statement_filing.period_ending_date.latest
form = mstar.financial_statement_filing.form_type.latest

pe = mstar.valuation_ratios.pe_ratio.latest
neg_pe = EPS_NaN()

# Create a pipline and add a fundamental factor
pipe = Pipeline()
pipe.add(filed, 'filed' )
pipe.add(as_of, 'as_of' )
pipe.add(form, 'form' )
pipe.add(pe, 'pe' )
pipe.add(neg_pe, 'neg_pe' )


# Print out the pipeline results listing the data and the dates
results = run_pipeline(pipe, '2017-1-7', '2017-1-7')
results

results.query('neg_pe').sort_values('pe')