def make_pipeline():
sentiment_score = SimpleMovingAverage(inputs=[stocktwits.bull_minus_bear],
window_length=3,
mask=QTradableStocksUS())
return Pipeline(columns={
'sentiment_score': sentiment_score,
},
screen=sentiment_score.notnull())
def make_pipeline():
sentiment_score = SimpleMovingAverage(inputs=[stocktwits.bull_minus_bear],
window_length=5,
mask=QTradableStocksUS())
close_price = USEquityPricing.close.latest
return Pipeline(columns={
'close_price': close_price,
'sentiment_score': sentiment_score,
},
screen=sentiment_score.notnull())
def make_pipeline():
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
# filtering
mask=QTradableStocksUS())
return Pipeline(
columns={
'sentiment_score': sentiment_score,
},
# つまり、sentiment_scoreが null ではない銘柄は全部投資対象に入れる
screen=sentiment_score.notnull())
def make_pipeline():
not_near_earnings = ~((BusinessDaysUntilNextEarnings() <= 2) |
(BusinessDaysSincePreviousEarnings() <= 2))
not_acq_tar = ~IsAnnouncedAcqTarget()
universe = (QTradableStocksUS() & not_near_earnings & not_acq_tar)
sentiment_score = SimpleMovingAverage(inputs=[stocktwits.bull_minus_bear],
window_length=5,
mask=universe)
return Pipeline(columns={
'sentiment_score': sentiment_score,
},
screen=sentiment_score.notnull())
def make_pipeline():
"""
A function to create our dynamic stock selector (pipeline). Documentation
on pipeline can be found here:
https://www.quantopian.com/help#pipeline-title
"""
# Base universe set to QTradableStocksUS()
base_universe = QTradableStocksUS()
# Sentiment score, based on a moving average of the bull minus bear factor
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
pipe = Pipeline(
columns={'sentiment_score': sentiment_score},
screen=(base_universe & sentiment_score.notnull()),
)
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
def make_pipeline():
"""
A function that creates and returns our pipeline.
We break this piece of logic out into its own function to make it easier to
test and modify in isolation. In particular, this function can be
copy/pasted into research and run by itself.
Returns
-------
pipe : Pipeline
Represents computation we would like to perform on the assets that make
it through the pipeline screen.
"""
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=7,
)
Asset_Growth_7d = Returns(window_length=8);
eveb = morningstar.valuation_ratios.ev_to_ebitda.latest
cash_return = morningstar.valuation_ratios.cash_return.latest
total_yield = morningstar.valuation_ratios.total_yield.latest
#universe = Q1500US()
universe = QTradableStocksUS()
# We winsorize our factor values in order to lessen the impact of outliers
# For more information on winsorization, please see
# https://en.wikipedia.org/wiki/Winsorizing
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05, max_percentile=0.95)
Asset_Growth_7d_winsorized = Asset_Growth_7d.winsorize(min_percentile=0.05, max_percentile=0.95)
eveb_winsorized = eveb.winsorize(min_percentile=0.05, max_percentile=0.95)
cash_return_winsorized = cash_return.winsorize(min_percentile=0.05, max_percentile=0.95)
total_yield_winsorized = total_yield.winsorize(min_percentile=0.05, max_percentile=0.95)
# Here we combine our winsorized factors, z-scoring them to equalize their influence
"""
combined_factor = -0.3*Asset_Growth_7d_winsorized.zscore() -0.25*sentiment_score_winsorized.zscore() +0.05*eveb_winsorized.zscore() +0.05*cash_return_winsorized.zscore() +0.35*total_yield_winsorized.zscore();
"""
combined_factor = -0.3*Asset_Growth_7d_winsorized.zscore() -0.2*sentiment_score_winsorized.zscore() +0.1*eveb_winsorized.zscore() +0.1*cash_return_winsorized.zscore() +0.3*total_yield_winsorized.zscore();
# Build Filters representing the top and bottom baskets of stocks by our
# combined ranking system. We'll use these as our tradeable universe each
# day.
longs = combined_factor.top(TOTAL_POSITIONS//2, mask=universe)
shorts = combined_factor.bottom(TOTAL_POSITIONS//2, mask=universe)
# The final output of our pipeline should only include
# the top/bottom 300 stocks by our criteria
long_short_screen = (longs | shorts)
# Create pipeline
pipe = Pipeline(
columns={
'longs': longs,
'shorts': shorts,
'combined_factor': combined_factor
},
screen=long_short_screen & sentiment_score.notnull() & Asset_Growth_7d.notnull(),
)
return pipe
