Combining Alpha Factors - Python Programming for Finance p.23





Hello and welcome to part 11 of the Python for Finance with Quantopian tutorials. In the previous tutorial, we covered analysis of a couple new factors, which we have deemed to be decent new alpha factors. What we want to do here now is combine all three alpha factors into a new factor, and then analyze that. To do this, we just need to slightly modify our previous alpha factor checker notebook:

from quantopian.pipeline import Pipeline
from quantopian.research import run_pipeline
from quantopian.pipeline.filters.morningstar import Q1500US
from quantopian.pipeline.data.sentdex import sentiment
from quantopian.pipeline.data.morningstar import operation_ratios

def make_pipeline():
    # Yes: operation_ratios.revenue_growth, operation_ratios.operation_margin, sentiment
    
    testing_factor1 = operation_ratios.operation_margin.latest
    testing_factor2 = operation_ratios.revenue_growth.latest
    testing_factor3 = sentiment.sentiment_signal.latest
    
    universe = (Q1500US() &
               testing_factor1.notnull() &
               testing_factor2.notnull() &
               testing_factor3.notnull())
    
    testing_factor1 = testing_factor1.rank(mask=universe, method='average')
    testing_factor2 = testing_factor2.rank(mask=universe, method='average')
    testing_factor3 = testing_factor3.rank(mask=universe, method='average')
    
    testing_factor = testing_factor1 + testing_factor2 + testing_factor3
    
    testing_quantiles = testing_factor.quantiles(2)
    
    pipe = Pipeline(columns={
            'testing_factor':testing_factor,
            'shorts':testing_quantiles.eq(0),
            'longs':testing_quantiles.eq(1)},
                    
                   screen=universe)
    return pipe

result = run_pipeline(make_pipeline(), start_date='2015-01-01', end_date='2016-01-01')
result.head()

Notice the changes made are adding 2 new testing_factors, then having the final testing_factor just as these three factors added together. This is a pretty crude way of doing things, but it should work just fine. From here, everything is actually the same.

assets = result.index.levels[1].unique()
pricing = get_pricing(assets, start_date='2014-12-01', end_date='2016-02-01', fields='open_price')
len(assets)
import alphalens

alphalens.tears.create_factor_tear_sheet(factor = result['testing_factor'],
                                        prices = pricing,
                                        quantiles = 2,
                                        periods = (3, 10, 30))

Combining everything and running it:

Ann. alpha	0.123	0.108	0.098
beta	-0.147	-0.186	-0.221
IC Mean	0.045	0.069	0.105

This along with all of the analysis in Alphalens is a major improvement overall. The alpha here is higher than any single alpha factor, same with the IC.

The next tutorial:






  • Intro and Getting Stock Price Data - Python Programming for Finance p.1
  • Handling Data and Graphing - Python Programming for Finance p.2
  • Basic stock data Manipulation - Python Programming for Finance p.3
  • More stock manipulations - Python Programming for Finance p.4
  • Automating getting the S&P 500 list - Python Programming for Finance p.5
  • Getting all company pricing data in the S&P 500 - Python Programming for Finance p.6
  • Combining all S&P 500 company prices into one DataFrame - Python Programming for Finance p.7
  • Creating massive S&P 500 company correlation table for Relationships - Python Programming for Finance p.8
  • Preprocessing data to prepare for Machine Learning with stock data - Python Programming for Finance p.9
  • Creating targets for machine learning labels - Python Programming for Finance p.10 and 11
  • Machine learning against S&P 500 company prices - Python Programming for Finance p.12
  • Testing trading strategies with Quantopian Introduction - Python Programming for Finance p.13
  • Placing a trade order with Quantopian - Python Programming for Finance p.14
  • Scheduling a function on Quantopian - Python Programming for Finance p.15
  • Quantopian Research Introduction - Python Programming for Finance p.16
  • Quantopian Pipeline - Python Programming for Finance p.17
  • Alphalens on Quantopian - Python Programming for Finance p.18
  • Back testing our Alpha Factor on Quantopian - Python Programming for Finance p.19
  • Analyzing Quantopian strategy back test results with Pyfolio - Python Programming for Finance p.20
  • Strategizing - Python Programming for Finance p.21
  • Finding more Alpha Factors - Python Programming for Finance p.22
  • Combining Alpha Factors - Python Programming for Finance p.23
  • Portfolio Optimization - Python Programming for Finance p.24