Companion notebook for Alphalens tutorial lesson 2

Creating Tear Sheets With Alphalens

In the previous lesson, you learned what Alphalens is. In this lesson, you will learn a four step process for how to use it:

Express an alpha factor and define a trading universe by creating and running a Pipeline over a certain time period.
Query pricing data for the assets in our universe during that same time period with get_pricing().
Align the alpha factor data with the pricing data with get_clean_factor_and_forward_returns().
Visualize how well our alpha factor predicts future price movements with create_full_tear_sheet().

Build And Run A Pipeline

The following code creates a trading universe and expresses an alpha factor within a pipeline, then runs it with run_pipeline()



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

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

    # Measures a company's asset growth rate.
    asset_growth = factset.Fundamentals.assets_gr_qf.latest

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

pipeline_output = run_pipeline(pipeline=make_pipeline(), start_date='2014-1-1', end_date='2016-1-1')

# Show the first 5 rows of factor data
pipeline_output.head(5)

Query Pricing Data

Now that we have factor data, let's get pricing data for the same time period. get_pricing() returns pricing data for a list of assets over a specified time period. It requires four arguments:

A list of assets for which we want pricing.
A start date
An end date
Whether to use open, high, low or close pricing.

Execute the following cell to get pricing data.



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pricing_data = get_pricing(
    symbols=pipeline_output.index.levels[1], # Finds all assets that appear at least once in "pipeline_output"  
    start_date='2014-1-1',
    end_date='2016-2-1', # must be after run_pipeline()'s end date. Explained more in lesson 4
    fields='open_price' # Generally, you should use open pricing. Explained more in lesson 4
)

# Show the first 5 rows of pricing_data
pricing_data.head(5)

Align Data

get_clean_factor_and_forward_returns() aligns the factor data created by run_pipeline() with the pricing data created by get_pricing(), and returns an object suitable for analysis with Alphalens' charting functions. It requires two arguments:

The factor data we created with run_pipeline().
The pricing data we created with get_pricing().

Execute the following cell to align the factor data with the pricing data.



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from alphalens.utils import get_clean_factor_and_forward_returns

factor_data = get_clean_factor_and_forward_returns(
    factor=pipeline_output, 
    prices=pricing_data
)

# Show the first 5 rows of merged_data
factor_data.head(5)

Visualize Results

Finally, execute the following cell to pass the output of get_clean_factor_and_forward_returns() to a function called create_full_tear_sheet(). This will create whats known as a tear sheet.



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from alphalens.tears import create_full_tear_sheet

create_full_tear_sheet(factor_data)

That's It!

In the next lesson, we will show you how to interpret the charts produced by create_full_tear_sheet().



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