Alphalens Quickstart Template



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from quantopian.pipeline import Pipeline
from quantopian.research import run_pipeline
from quantopian.pipeline.filters import QTradableStocksUS
from quantopian.pipeline.data import factset, USEquityPricing
from quantopian.pipeline.classifiers.fundamentals import Sector
from quantopian.pipeline.factors import Returns, SimpleMovingAverage, CustomFactor, RSI

from alphalens.performance import mean_information_coefficient
from alphalens.utils import get_clean_factor_and_forward_returns
from alphalens.tears import create_information_tear_sheet, create_returns_tear_sheet

Define Your Alpha Factor Here

Spend your time in this cell, creating good factors. Then simply run the rest of the notebook to analyze factor_to_analyze!



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def make_pipeline():
    
    assets_moving_average = SimpleMovingAverage(inputs=[factset.Fundamentals.assets], window_length=252)
    current_assets = factset.Fundamentals.assets.latest
    
    factor_to_analyze = (current_assets - assets_moving_average)
    
    sector = Sector()
    
    return Pipeline(
        columns = {'factor_to_analyze': factor_to_analyze, 'sector': sector},
        screen = QTradableStocksUS() & factor_to_analyze.notnull() & sector.notnull()
    )

factor_data = run_pipeline(make_pipeline(), '2015-1-1', '2016-1-1')
pricing_data = get_pricing(factor_data.index.levels[1], '2015-1-1', '2016-6-1', fields='open_price')

Determine The Decay Rate Of Your Alpha Factor.



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longest_look_forward_period = 63 # week = 5, month = 21, quarter = 63, year = 252
range_step = 5

merged_data = get_clean_factor_and_forward_returns(
    factor = factor_data['factor_to_analyze'],
    prices = pricing_data,
    periods = range(1, longest_look_forward_period, range_step)
)

mean_information_coefficient(merged_data).plot(title="IC Decay")

Create Group Neutral Tear Sheets



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sector_labels, sector_labels[-1] = dict(Sector.SECTOR_NAMES), "Unknown"

merged_data = get_clean_factor_and_forward_returns(
    factor = factor_data['factor_to_analyze'],
    prices = pricing_data,
    groupby = factor_data['sector'],
    groupby_labels = sector_labels,
    binning_by_group = True,
    periods = (1,5,10)
)

create_information_tear_sheet(merged_data, by_group=True, group_neutral=True)
create_returns_tear_sheet(merged_data, by_group=True, group_neutral=True)