Pipeline Example Using Market Cap and Sentiment¶

# Imports needed to create and run pipeline
from quantopian.pipeline import Pipeline  
from quantopian.research import run_pipeline  

# Import any PIPELINE data we want to use
from quantopian.pipeline.data import Fundamentals
from quantopian.pipeline.data.sentdex import sentiment_free as sentiment

# Import any INTERACTIVE data we want to use
from quantopian.interactive.data.sentdex import sentiment_free as sentiment_interactive

# Import any built in factors we want to use
from quantopian.pipeline.factors.fundamentals import MarketCap

# Import any built in filters we want to use
from quantopian.pipeline.filters.morningstar import Q1500US
# maybe should be
# from quantopian.pipeline.filters import Q1500US

# Lets first check the data type of our pipline datasets
type(sentiment.sentiment_signal)

# Now lets check the data type of our interactive data
type(sentiment_interactive.sentiment_signal)

# Make a function with our pipline datasets to return our pipeline

def make_pipeline():  
    # Create the factors we want use
    sentiment_factor = sentiment.sentiment_signal.latest  
    market_cap =  Fundamentals.market_cap.latest 

    # Create a filter to select our 'universe'
    # Our universe is made up of stocks that have a non-null sentiment signal that was updated in  
    # the last day, are not within 2 days of an earnings announcement, are not announced acquisition  
    # targets, and are in the Q1500US.

    universe = (Q1500US()  
                & sentiment_factor.notnull()  
                & (market_cap > 50000000) ) 

    # Create our pipeline columns and filter to just our universe
    # Note that the 'longs' and 'shorts' columns are boolean filters creted from factors
    pipe = Pipeline(  
        columns={  
            'sentiment': sentiment_factor,  
            'longs': (sentiment_factor >= 4),  
            'shorts': (sentiment_factor <= 2),  
        },  
        screen=universe  
    )  
    return pipe  

# Now lets see our pipeline in action
# Run our pipeline and print the results
results = run_pipeline(make_pipeline(), '2017-1-7', '2017-1-7')
results

# Now lets see what our interactive data is
sentiment_interactive

# The above dataframe can be manipulated interactively (hence the name)
# For instance find securities with a sentiment signal > 5
sentiment_interactive[sentiment_interactive.sentiment_signal > 5]