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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