Last EWMA Crossover Custom Factor¶

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

from quantopian.pipeline.data.builtin import USEquityPricing

import quantopian.pipeline.filters as Filters
import quantopian.pipeline.factors as Factors

import numpy as np
import pandas as pd

class Last_Crossover(CustomFactor):  
    # Define inputs
    inputs = [USEquityPricing.close]

    # Set window_length to the lookback days. 252 (a year) may be a good value.
    window_length = 252
    
    def compute(self, today, assets, out, close):
        # First lets turn the numpy array 'close' into a pandas dataframe
        # We can then use the pandas ewm method
        closes_df = pd.DataFrame(
                        data = close,
                        columns = assets)
        
        # Make two new dataframes with short and long ewma
        ma_short = closes_df.ewm(span=20).mean()  
        ma_long = closes_df.ewm(span=50).mean()
        
        # Find where the short ewma is greater than the long ewma
        # All we really care about is the sign (ie is the short ewma above or below the long)
        # Need to go back to numpy for an easy sign method
        delta = np.sign(ma_short - ma_long)
        
        # Almost there.. lets just find the changes or crossovers. This is where the diff is <> 0
        crossovers = delta.diff(axis = 0)
        
        # A switch from below (-1) to above (+1) will have a diff of +2 (+1 - -1)
        # Lets find last index where this occurs (ie find the last +2)
        # Use the fact that +2 is the max value so use the idxmax method
        # However, this finds the first occurance, so first reverse our data using a loc trick
        crossovers_rev = crossovers.loc[::-1].reset_index(drop=True)
        last_crossover_up_index = crossovers_rev.idxmax(axis=0)
        
        # Just for debug - remove when running
        print last_crossover_up_index
        print crossovers
        print crossovers_rev
        
        # Output the indexes. 0 will be yesterday. 1 will be the day before yesterday etc.
        # If you want yesterday to be 1 then simply add one below (out[:] = last_crossover_up_index+1)
        out[:] = last_crossover_up_index
        

def create_pipeline():
    # Create a Pipeline computing the previous open factor.
    my_mask = Filters.StaticAssets(symbols(["IBM", "AAPL"]))
    
    # limit the output for testing
    last_crossover = Last_Crossover(mask = my_mask)

    p = Pipeline()
    p.add(last_crossover, 'last_crossover')
    p.set_screen(my_mask)

    return p

# Run the pipeline with desired date(s). 
# In this case the we run the pipeline as if it were 11-23-2016
# The data is from the prevous day 11-22-2016

results = run_pipeline(create_pipeline(), '11-23-2016', '11-23-2016')
results