Alphalens + Quantopian | How To¶
For more information on how to read and understand the plots look at:
- Example notebook from the repo.
- Alphalens Docs for an analysis of a professional alpha factor.
In [1]:
import numpy as np
from quantopian.research import run_pipeline
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.factors import CustomFactor, Returns, AverageDollarVolume
from quantopian.pipeline.classifiers.morningstar import Sector
from quantopian.pipeline.filters import Q500US
In [2]:
MORNINGSTAR_SECTOR_CODES = {
-1: 'Misc',
101: 'Basic Materials',
102: 'Consumer Cyclical',
103: 'Financial Services',
104: 'Real Estate',
205: 'Consumer Defensive',
206: 'Healthcare',
207: 'Utilities',
308: 'Communication Services',
309: 'Energy',
310: 'Industrials',
311: 'Technology' ,
}
Define our factor¶
In [3]:
class LowVol(CustomFactor):
inputs = [Returns(window_length=2)]
window_length = 25
def compute(self, today, assets, out, close):
out[:] = -np.nanstd(close, axis=0)
Define our universe¶
In [4]:
universe = Q500US()
Create the Pipeline¶
In [5]:
pipe_low_vol = Pipeline(
columns={
'LowVol' : LowVol(mask=universe),
'Sector': Sector(mask=universe),
},
screen=universe
)
Run the Pipeline¶
In [6]:
results = run_pipeline(pipe_low_vol, '2015-06-30', '2016-06-30')
In [7]:
results.head()
Out[7]:
Get pricing¶
In [8]:
assets = results.index.levels[1].unique()
# We need to get a little more pricing data than the
# length of our factor so we can compare forward returns.
# We'll tack on another month in this example.
pricing = get_pricing(assets, start_date='2015-06-30', end_date='2016-07-31', fields='open_price')
In [9]:
pricing.head()
Out[9]:
Use Alphalens to create a factor tear sheet¶
In [11]:
import alphalens
factor_data = alphalens.utils.get_clean_factor_and_forward_returns(results['LowVol'],
pricing,
quantiles=5,
groupby=results['Sector'],
periods=(1,5,10))
alphalens.tears.create_full_tear_sheet(factor_data)
In [ ]: