Using Pipeline with Alphalens¶

Once you have a pipeline, there are several things you can do with it:

Feed the pipeline results to Alphalens to analyze whether your factors are predictive of forward returns (this lesson);
Explore the pipeline results in the Data Browser (the next lesson); or
Use the pipeline in a Zipline strategy for the purpose of universe selection and/or trading signal generation.

Analyzing your pipeline output with Alphalens makes sense if your pipeline includes factors that might be predictive of forward returns; it doesn't make sense if you are only using your pipeline for universe selection.

In this notebook, we will create a pipeline with a moving average factor for the TradableStocksUS universe, then use Alphalens to analyze whether the factor is predictive.

In [1]:

from zipline.pipeline import Pipeline, EquityPricing, master
from zipline.pipeline.factors import SimpleMovingAverage
# import our InitialUniverse and TradableStocksUS functions
from codeload.pipeline_tutorial.tradable_stocks import InitialUniverse, TradableStocksUS

Let's create a factor that measures the percent difference between the 10-day and 30-day moving averages (close price). In other words, we are computing the degree to which the 10-day moving average is above the 30-day moving average.

As an added benefit, we will include each stock's sector in our pipeline output, which will allow us to view some additional Alphalens plots breaking down the factor's performance by sector.

In [2]:

def make_pipeline():

    initial_universe = InitialUniverse()
    universe = TradableStocksUS()

    # 10-day close price average.
    mean_10 = SimpleMovingAverage(inputs=EquityPricing.close, window_length=10, mask=universe)

    # 30-day close price average.
    mean_30 = SimpleMovingAverage(inputs=EquityPricing.close, window_length=30, mask=universe)

    # Percent difference factor.
    percent_difference = (mean_10 - mean_30) / mean_30

    return Pipeline(
        columns={
            'percent_difference': percent_difference,
            'sector': master.SecuritiesMaster.usstock_Sector.latest
        },
        initial_universe=initial_universe,
        screen=universe
    )

Instead of executing the pipeline with run_pipeline, we pass the pipeline to the Alphalens from_pipeline function. This function executes the pipeline, splits the data into quantiles based on a specified column in the pipeline output (in this example, the percent_difference column), and plots the relative forward performance of each quantile. In this example we analyze returns for the next day, next week (5 trading days), and next month (approx. 21 trading days), using the periods argument. We also use the groupby argument to tell Alphalens to group by the sector column in the pipeline output, which provides additional plots showing performance by sector.

In the resulting tear sheet, the "Mean Relative Return By Factor Quantile" plot reveals that forward returns generally increase from quantiles 1 to 5. Intuitively, this tells us that stocks tend to perform better when their 10-day moving average is above their 30-day moving average.

In [3]:

import alphalens as al

al.from_pipeline(
    make_pipeline(), 
    start_date='2010-01-01', 
    end_date='2011-01-01',
    factor="percent_difference",
    # analyze forward returns for the next day, next week, and next month
    periods=[1,5,21],
    groupby="sector"
)

Factor Distribution
	min	max	mean	std	count	avg daily count	count %
Percent Difference Quantile
1	-0.615	0.020	-0.045	0.044	95,036	375.6	20.0%
2	-0.085	0.039	-0.008	0.027	94,888	375.1	20.0%
3	-0.061	0.055	0.009	0.025	94,889	375.1	20.0%
4	-0.043	0.076	0.026	0.025	94,888	375.1	20.0%
5	-0.025	0.742	0.065	0.042	95,003	375.5	20.0%

Returns Analysis
	1D	5D	21D
Ann. alpha	-0.063	-0.060	-0.035
beta	-0.029	-0.035	-0.026
Mean Relative Return Top Quantile (bps)	-2.856	-2.493	-0.629
Mean Relative Return Bottom Quantile (bps)	3.651	3.311	1.321
Mean Spread (bps)	-6.507	-5.796	-1.945

Information Analysis
	1D	5D	21D
IC Mean	-0.010	-0.021	-0.012
IC Std.	0.119	0.102	0.089
Risk-Adjusted IC	-0.085	-0.210	-0.138
t-stat(IC)	-1.350	-3.342	-2.195
p-value(IC)	0.178	0.001	0.029
IC Skew	0.014	0.305	0.072
IC Kurtosis	0.529	0.553	0.059

Turnover Analysis
	1D	5D	21D
Quantile 1 Mean Turnover	0.070	0.307	0.757
Quantile 2 Mean Turnover	0.163	0.562	0.787
Quantile 3 Mean Turnover	0.190	0.614	0.784
Quantile 4 Mean Turnover	0.163	0.567	0.787
Quantile 5 Mean Turnover	0.070	0.310	0.771

	1D	5D	21D
Mean Factor Rank Autocorrelation	0.99	0.818	0.05

Out[3]:

		1D	5D	21D	factor	sector	factor_quantile
date	asset
2010-01-04	Equity(FIBBG000C2V3D6 [A])	0.007403	-0.008690	-0.050531	0.017276	Technology	2
	Equity(FIBBG000M7KQ09 [AAI])	-0.007663	-0.022989	-0.055556	0.074104	Industrials	5
	Equity(FIBBG000F7RCJ1 [AAP])	-0.002470	0.003953	0.017540	0.015796	Consumer Discretionary	2
	Equity(FIBBG000B9XRY4 [AAPL])	0.015555	0.005922	-0.070573	0.020323	Technology	3
	Equity(FIBBG000C5QZ62 [AAV])	0.047546	0.118098	0.013804	0.082847	Energy	5
...	...	...	...	...	...	...	...
2011-01-03	Equity(FIBBG000BGFWQ6 [ZOLT])	0.037165	0.050130	0.005186	0.091792	Industrials	5
	Equity(FIBBG000FTMSF7 [ZQK])	0.071006	0.033531	-0.078895	0.074539	Consumer Discretionary	5
	Equity(FIBBG000FRFNV2 [ZRAN])	-0.002273	0.040909	0.131818	0.076333	Technology	5
	Equity(FIBBG000PYX812 [ZUMZ])	0.010048	-0.045776	-0.097507	-0.033094	Consumer Discretionary	1
	Equity(FIBBG000C3CQP1 [ZVO])	-0.004211	-0.072632	-0.012105	0.091348	Technology	5

474704 rows × 6 columns