End-of-Day Analysis with Alphalens¶

Alphalens is an open-source performance analysis library which pairs well with the Pipeline API. In this notebook we will use Alphalens to analyze whether our momentum factor is predictive of forward returns.

Using Alphalens makes sense when you believe your end-of-day Pipeline rules have alpha. In contrast, if your Pipeline rules simply perform a basic screen and the alpha is entirely provided by your intraday trading rules, it might make more sense to omit this step.

Let's re-define our pipeline from the previous notebook:

In [1]:

from zipline.pipeline import Pipeline
from zipline.pipeline.factors import AverageDollarVolume, Returns

pipeline = Pipeline(
    columns={
        "returns": Returns(window_length=252),
    },
    screen=AverageDollarVolume(window_length=30) > 10e6
)

To see if our momentum factor is predictive of forward returns, we can use the Alphalens from_pipeline function. This function executes a pipeline for a specified date range, splits the data into quantiles based on one of the pipeline columns, and provides a tear sheet of forward returns by quantile. For a predictive factor, the higher quantiles should perform better than the lower quantiles.

In [2]:

import alphalens as al

al.from_pipeline(
    pipeline, 
    # the `factor` argument tells which Pipeline column to use to form the quantiles
    factor="returns",
    start_date="2018-01-01", 
    end_date="2020-01-01",
    # For a very small sample universe, you might only want 2 quantiles 
    quantiles=2
)

Factor Distribution
	min	max	mean	std	count	avg daily count	count %
Factor Quantile
1	-0.619	0.397	-0.022	0.198	2,016	4.0	55.4%
2	-0.107	0.963	0.285	0.177	1,620	3.2	44.6%

Long/Short Returns Analysis
	1D
Ann. alpha	0.137
beta	-0.108
Mean Relative Return Top Quantile (bps)	1.207
Mean Relative Return Bottom Quantile (bps)	-1.099
Mean Spread (bps)	2.306

Information Analysis
	1D
IC Mean	0.036
IC Std.	0.463
Risk-Adjusted IC	0.077
t-stat(IC)	1.726
p-value(IC)	0.085
IC Skew	-0.016
IC Kurtosis	-0.898

Turnover Analysis
	1D
Quantile 1 Mean Turnover	0.042
Quantile 2 Mean Turnover	0.056

	1D
Mean Factor Rank Autocorrelation	0.979

End-of-Day Analysis with Alphalens¶

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