CAGR Custom Factor¶

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

from quantopian.pipeline.data.builtin import USEquityPricing
import quantopian.pipeline.data.morningstar as morningstar

class CAGR(CustomFactor):  
    # Define inputs and outputs
    inputs = [USEquityPricing.close]
    outputs = ['start', 'end', 'cagr']
    
    # Set default window_length
    # This can also be set/over-ridden whem the class is instantiated as shown below:
    # my_cagr = CAGR(window_length = 250)
    
    window_length = 750 # approx 3 years 
    
    def compute(self, today, assets, out, close):
        # The input 'close' is a Numpy array with close prices as axis 0 (total rows = window length)
        # and axis 1 are all the assets (ie stocks and securities).

        trade_days = len(close)
        years = trade_days / 250.0 # approximation to get years. decimal forces years to be real.
        
        start = close[0]
        end = close[-1]
        
        out.start[:] = start # start and end aren't required 
        out.end[:] = end     # these though can be used to manually check the output
        out.cagr[:] = ((end / start) ** (1/years)) - 1  # annualized CAGR formula 
        

def create_pipeline():
    """
    Function to create a pipeline with our CAGR custom factor and sample fundamental filter
    """
    
    # Instantiate a factor from the custom factor CAGR and set desired look-back window length
    factor = CAGR(window_length = 500)

    start = factor.start
    end = factor.end
    cagr = factor.cagr
    
    # Instantiate a sample fundamental factor and create a filter from it
    financial_health_grade = morningstar.asset_classification.financial_health_grade.latest
    grade_a = financial_health_grade.eq('A')
    
    # Create a pipeline and add the factors to it
    p = Pipeline()
    p.add(cagr, 'cagr')
    p.add(start, 'start')
    p.add(end, 'end')    
    p.add(financial_health_grade, 'financial_health_grade') 
    
    # Set a screen for our pipeline (in this case on Morningstar grade A equities)
    p.set_screen(grade_a)
    
    return p

# Run the pipeline with desired date(s) 
# The pipeline filters results by "financial_health_grade" equal to A (as an example fiter)
# Results (which is a Pandas dataset) is then sorted by cagr from high to low
# Note that Close can return NaaN which results in a NaaN cagr

results = run_pipeline(create_pipeline(), '10-13-2016', '10-13-2016')
results.sort('cagr', ascending = False)