def make_pipeline():
# Sector
sector = Sector()
# Equity Filters
mkt_cap_filter = morningstar.valuation.market_cap.latest >= 500000000
price_filter = USEquityPricing.close.latest >= 5
nan_filter = sentiment_free.sentiment_signal.latest.notnull()
# Universe
universe = Q1500US() & price_filter & mkt_cap_filter & nan_filter
# Rank
sentiment_signal = sentiment_free.sentiment_signal.latest
sma_30 = SimpleMovingAverage(inputs=[sentiment_free.sentiment_signal],
window_length=30,
mask=universe)
combined_rank = (sentiment_signal + sma_30.rank(mask=universe).zscore())
# Long and Short Positions
longs = combined_rank.top(NUM_LONG_POSITIONS)
shorts = combined_rank.bottom(NUM_SHORT_POSITIONS)
long_short_screen = (longs | shorts)
# Bloomberg Beta Implementation
beta = 0.66 * RollingLinearRegressionOfReturns(
target=sid(8554),
returns_length=5,
regression_length=260,
mask=long_short_screen).beta + 0.33 * 1.0
pipe = Pipeline()
pipe.add(longs, 'longs')
pipe.add(shorts, 'shorts')
pipe.add(combined_rank, 'combined_rank')
pipe.add(sentiment_free.sentiment_signal.latest, 'sentiment_signal')
pipe.add(sma_30, 'sma_30')
pipe.add(sector, 'sector')
pipe.add(beta, 'market_beta')
pipe.set_screen(universe)
return pipe
def make_pipeline():
# 5-day sentiment moving average factor.
sentiment_factor = SimpleMovingAverage(inputs=[sentiment.sentiment_signal],
window_length=5)
# Filter for stocks that are not within 2 days of an earnings announcement.
not_near_earnings_announcement = ~(
(BusinessDaysUntilNextEarnings() <= 2)
| (BusinessDaysSincePreviousEarnings() <= 2))
# Filter for stocks that are announced acquisition target.
not_announced_acq_target = ~IsAnnouncedAcqTarget()
# Filter for stocks that had their sentiment signal updated in the last day.
new_info = (BusinessDaysSincePreviousEvent(
inputs=[sentiment.asof_date.latest]) <= 1)
# 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()
& not_near_earnings_announcement
& not_announced_acq_target
& new_info)
# A classifier to separate the stocks into quantiles based on sentiment rank.
sentiment_quantiles = sentiment_factor.rank(mask=universe,
method='average').quantiles(3)
# Go short the stocks in the 0th quantile, and long the stocks in the 2nd quantile.
pipe = Pipeline(columns={
'sentiment': sentiment_quantiles,
'shorts': sentiment_quantiles.eq(0),
'longs': sentiment_quantiles.eq(2),
},
screen=universe)
return pipe
