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Fundamental Backests and the Pipeline

I am confused as to what is the best practice for implementing fundamental screens in QT2.

Here is what i want to do. Every N months, do a fundamental screen and get a list of stocks, invest in those and discard any that don't match the screen. Is there a simple example using pipeline? Should i be using pipeline for this?

1 response

Hi Chris,

Pipeline would indeed be recommended to write an algorithm as you describe. Here you can find some example algorithms using Pipeline.

If you want to set a screen using fundamental data, you would import BoundColumns from quantopian.pipeline.data.morningstar for use to construct a filter. Here is an example of a pipeline screening based on diluted EPS growth:

from quantopian.algorithm import attach_pipeline, pipeline_output  
from quantopian.pipeline import Pipeline  
from quantopian.pipeline.data.builtin import USEquityPricing  
from quantopian.pipeline.data import morningstar

def initialize(context):  
    attach_pipeline(my_pipeline(context), 'my_pipeline')  
def my_pipeline(context):  
    return Pipeline(  
        columns={  
            'close': USEquityPricing.close.latest  
        },  
        screen=(morningstar.earnings_ratios.diluted_eps_growth.latest > 0)  
    )  
def before_trading_start(context, data):  
    context.data = pipeline_output('my_pipeline')  
    log.info(context.data)  

The full list of fundamental data fields can be found here.

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