# Import statements
# Need the following to run pipeline and make custom factors
from quantopian.pipeline import CustomFactor, Pipeline
from quantopian.research import run_pipeline

# Get some data
from quantopian.pipeline.data.builtin import USEquityPricing

# Import a few built in filters
from quantopian.pipeline.filters import Q1500US, StaticAssets

# Define class for a CustomFactor outputting signed gaps as %

class SignedGapPct(CustomFactor):
    # Default input
    inputs = [USEquityPricing.close, USEquityPricing.open]
    
    # Need a window length of 2 because we want 2 days of data (yesterday and the day before that)
    window_length = 2
    def compute(self, today, asset_ids, out, close, open):
        # Calculates the column-wise gaps, how to ignore NaNs?
        # Take yesterdays open (-1) and divide by the previous days close (-2)
        out[:] = (open[-1] - close[-2]) / close[-2]

def make_pipeline():
    """
    Create our pipeline.
    """

    # Base universe set to the Q1500US.
    base_universe = Q1500US()

    # Factor to calculate gap up/down (signed) in percent normalised to previous day close
    signed_gap_pct = SignedGapPct(mask=base_universe)
    open_price = USEquityPricing.open.latest
    close_price = USEquityPricing.close.latest

    # filter out NaN
    # This isn't really necessary (though doesn't hurt) since the top and bottom
    # methods don't look at NaN values
    #filter_not_nan = signed_gap_pct.notnan()
    
    # filter for top 5 gap up
    filter_top_gap_up = signed_gap_pct.top(5)
    
    # filter for bottom 5 gap down
    filter_bottom_gap_down = signed_gap_pct.bottom(5)
    
    # aggregated filter for pipeline screen
    aggr_screen = filter_top_gap_up | filter_bottom_gap_down

    return Pipeline(
        columns={
            'signed_gap_pct': signed_gap_pct,
            'open_price': open_price,
            'close_price': close_price,

        },
        screen = (aggr_screen),
        )

my_pipe = make_pipeline()

result = run_pipeline(my_pipe, '2017-12-17', '2017-12-19')

result