Alphalens Quickstart Template



In [ ]:

    
from quantopian.pipeline import Pipeline
from quantopian.research import run_pipeline
from quantopian.pipeline.data import factset, USEquityPricing
from quantopian.pipeline.factors import SimpleMovingAverage, AverageDollarVolume

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!



In [ ]:

    
def make_pipeline():
    # Filter out equities with low market capitalization
    market_cap_filter = factset.Fundamentals.mkt_val.latest > 500000000

    # Filter out equities with low volume
    volume_filter = AverageDollarVolume(window_length=200) > 2500000

    # Filter out equities with a close price below $5
    price_filter = USEquityPricing.close.latest > 5

    # Our final base universe
    base_universe = market_cap_filter & volume_filter & price_filter
    
    assets_moving_average = SimpleMovingAverage(inputs=[factset.Fundamentals.assets], window_length=252)
    current_assets = factset.Fundamentals.assets.latest
    
    # This is the factor that the rest of the notebook will analyze
    factor_to_analyze = (current_assets - assets_moving_average)
    
    # The following columns will help us group assets by market cap. This will allow us to analyze
    # whether our alpha factor's predictiveness varies among assets with different market caps.
    market_cap = factset.Fundamentals.mkt_val.latest
    cap_type = market_cap.quantiles(bins=3, mask=base_universe)

    return Pipeline(
        columns = {
            'factor_to_analyze': factor_to_analyze,
            'cap_type': cap_type
            
        },
        screen = (
            base_universe
            & factor_to_analyze.notnull()
            & market_cap.notnull()
        )
    )

pipeline_output = run_pipeline(make_pipeline(), '2015-1-1', '2016-1-1')
# rename the 1's, 2's and 3's for clarity
pipeline_output['cap_type'].replace([1, 2, 3], ['small_cap', 'mid_cap', 'large_cap'], inplace=True)

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

Create Group Neutral Tear Sheets



In [ ]:

    
factor_data = get_clean_factor_and_forward_returns(
    factor = pipeline_output['factor_to_analyze'],
    prices = pricing_data,
    groupby = pipeline_output['cap_type'],
    binning_by_group = True,
    periods = (1,5,10)
)

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

Determine The Decay Rate Of Your Alpha Factor



In [ ]:

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

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

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



In [ ]: