def make_pipeline():
universe = QTradableStocksUS()
# Variables Seleccionadas Del Dataframe de Fundamentals
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
working_capital = Fundamentals.working_capital.latest
restricted_cash = Fundamentals.restricted_cash.latest
cash_and_cash_equivalents = Fundamentals.cash_and_cash_equivalents.latest
goodwill = Fundamentals.goodwill.latest
capital_stock = Fundamentals.capital_stock.latest
total_assets = Fundamentals.total_assets.latest
common_stock = Fundamentals.common_stock.latest
free_cash_flow = Fundamentals.free_cash_flow.latest
recent_returns = Returns(window_length=RETURNS_LOOKBACK_DAYS,
mask=universe)
# Winsorized - Variables (SIN ATIPICOS)
value_winsorized = value.winsorize(min_percentile=0.05,
max_percentile=0.95)
working_capital = working_capital.winsorize(min_percentile=0.05,
max_percentile=0.95)
restricted_cash = restricted_cash.winsorize(min_percentile=0.05,
max_percentile=0.95)
cash_and_cash_equivalents = cash_and_cash_equivalents.winsorize(
min_percentile=0.05, max_percentile=0.95)
goodwill = goodwill.winsorize(min_percentile=0.05, max_percentile=0.95)
capital_stock = capital_stock.winsorize(min_percentile=0.05,
max_percentile=0.95)
total_assets = total_assets.winsorize(min_percentile=0.05,
max_percentile=0.95)
common_stock = common_stock.winsorize(min_percentile=0.05,
max_percentile=0.95)
free_cash_flow = free_cash_flow.winsorize(min_percentile=0.05,
max_percentile=0.95)
recent_returns = recent_returns.winsorize(min_percentile=0.05,
max_percentile=0.95)
# FACTOR COMBINADO
combined_factor = (
value_winsorized.zscore() * 0.05 + working_capital.zscore() * 0.55 +
restricted_cash.zscore() * 0.2 +
cash_and_cash_equivalents.zscore() * 0.01 + goodwill.zscore() * 0.01 +
capital_stock.zscore() * 0.1 + total_assets.zscore() * 0.01 +
common_stock.zscore() * 0.01 + free_cash_flow.zscore() * 0.01 +
recent_returns.zscore() * 0.05)
longs = combined_factor.top(TOTAL_POSITIONS // 2, mask=universe)
shorts = combined_factor.bottom(TOTAL_POSITIONS // 2, mask=universe)
long_short_screen = (longs | shorts)
pipe = Pipeline(columns={
'longs': longs,
'shorts': shorts,
'combined_factor': combined_factor
},
screen=long_short_screen)
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
def make_pipeline():
universe = QTradableStocksUS()
# Variables seleccionadas del dataframe de Fundamentals
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
quality = Fundamentals.roe.latest
working_capital = Fundamentals.working_capital.latest
restricted_cash = Fundamentals.restricted_cash.latest
accumulated_depreciation = Fundamentals.accumulated_depreciation.latest
dps_growth = Fundamentals.dps_growth.latest
capital_stock = Fundamentals.capital_stock.latest
mcap = Fundamentals.market_cap.latest
daily_returns = Returns(window_length=2)
# Variables (SIN ATIPICOS)
value_winsorized = value.winsorize(min_percentile=0.05,
max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05,
max_percentile=0.95)
working_capital = working_capital.winsorize(min_percentile=0.05,
max_percentile=0.95)
restricted_cash = restricted_cash.winsorize(min_percentile=0.05,
max_percentile=0.95)
accumulated_depreciation = accumulated_depreciation.winsorize(
min_percentile=0.05, max_percentile=0.95)
dps_growth = dps_growth.winsorize(min_percentile=0.05, max_percentile=0.95)
capital_stock = capital_stock.winsorize(min_percentile=0.05,
max_percentile=0.95)
mcap = mcap.winsorize(min_percentile=0.05, max_percentile=0.95)
daily_returns = daily_returns.winsorize(min_percentile=0.05,
max_percentile=0.95)
# FACTOR COMBINADO
combined_factor = (
quality_winsorized.zscore() * 0.01 +
accumulated_depreciation.zscore() * 0.03 +
working_capital.zscore() * 0.05 + dps_growth.zscore() * 0.85 +
value_winsorized.zscore() * 0.01 + mcap.zscore() * 0.01 +
capital_stock.zscore() * 0.01 +
sentiment_score_winsorized.zscore() * 0.01 +
restricted_cash.zscore() * 0.01 + daily_returns.zscore(
groupby=mstar.company_reference.country_id.latest) * 0.01)
longs = combined_factor.top(TOTAL_POSITIONS // 2, mask=universe)
shorts = combined_factor.bottom(TOTAL_POSITIONS // 2, mask=universe)
long_short_screen = (longs | shorts)
pipe = Pipeline(columns={
'longs': longs,
'shorts': shorts,
'combined_factor': combined_factor
},
screen=long_short_screen)
return pipe