def make_pipeline():
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(inputs=[stocktwits.bull_minus_bear],window_length=3,)
twitter_score = SimpleMovingAverage(inputs=[twitter_withretweets.bull_bear_msg_ratio],window_length=3,)
news_score = SimpleMovingAverage(inputs=[sentiment.sentiment_signal], window_length=5)
total_revenue = Fundamentals.total_revenue.latest
growth = Fundamentals.sustainable_growth_rate.latest
operation_margin = Fundamentals.operation_margin.latest
net_margin = Fundamentals.net_margin.latest
rec_corredor = SimpleMovingAverage( inputs = [ConsensusRecommendations.total], window_length=3, )
universe = QTradableStocksUS()
value_winsorized = value.winsorize(min_percentile=0.17, max_percentile=0.83)
quality_winsorized = quality.winsorize(min_percentile=0.17, max_percentile=0.83)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.17, max_percentile=0.83)
twitter_score_winsorized = twitter_score.winsorize(min_percentile=0.17, max_percentile=0.83)
news_score_winsorized = news_score.winsorize(min_percentile=0.17, max_percentile=0.83)
total_revenue_winsorized = total_revenue.winsorize(min_percentile=0.17, max_percentile=0.83)
growth_winsorized = growth.winsorize(min_percentile=0.17, max_percentile=0.83)
operation_margin_winsorized = operation_margin.winsorize(min_percentile=0.17, max_percentile=0.83)
net_margin_winsorized = net_margin.winsorize(min_percentile=0.17, max_percentile=0.83)
rec_corredor_winsorized = rec_corredor.winsorize(min_percentile=0.17, max_percentile=0.83)
combined_factor = (
1*value_winsorized.zscore()
+ 6*quality_winsorized.zscore()
+ 1*sentiment_score_winsorized.zscore()
+ 5*twitter_score_winsorized.zscore()
+ 1*news_score_winsorized.zscore()
+ 6*growth_winsorized.zscore()
+ 1*operation_margin_winsorized.zscore()
+ 1*net_margin_winsorized.zscore()
+ 6*total_revenue_winsorized.zscore()
+ 9*rec_corredor_winsorized.zscore()
)
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():
#Predefined Factors
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
value_winsorized = value.winsorize(min_percentile=0.05,
max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05,
max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
# New Factors
total_revenue = Fundamentals.total_revenue.latest
mean_sentiment_5day = SimpleMovingAverage(
inputs=[sentiment.sentiment_signal], window_length=5)
mean_sentiment_5day_winsorized = mean_sentiment_5day.winsorize(
min_percentile=0.05, max_percentile=0.95)
operation_ratios_latest = operation_ratios.roic.latest
valuation_ratios_dividend_yield_latest = valuation_ratios.dividend_yield.latest
valuation_ratios_cash_return_latest = valuation_ratios.cash_return.latest
universe = (QTradableStocksUS() & operation_ratios_latest.notnull()
& valuation_ratios_dividend_yield_latest.notnull()
& valuation_ratios_cash_return_latest.notnull())
operation_ratios_latest = operation_ratios_latest.rank(mask=universe,
method='average')
valuation_ratios_dividend_yield_latest = valuation_ratios_dividend_yield_latest.rank(
mask=universe, method='average')
valuation_ratios_cash_return_latest = valuation_ratios_cash_return_latest.rank(
mask=universe, method='average')
combined_factor = (
#Original
3 * value_winsorized.zscore() + quality_winsorized.zscore() +
sentiment_score_winsorized.zscore() +
#New
operation_ratios_latest + valuation_ratios_dividend_yield_latest +
3 * valuation_ratios_cash_return_latest +
0.5 * mean_sentiment_5day_winsorized + 3 * total_revenue)
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():
# The factors we create here are based on fundamentals data and a moving
# average of sentiment data
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(inputs=[stocktwits.bull_minus_bear],
window_length=3)
sma50 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=50)
sma200 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=200)
smaScore = sma50/sma200
sizeScore = SimpleMovingAverage(inputs=[Fundamentals.size_score],window_length=3)
growthScore = SimpleMovingAverage(inputs=[Fundamentals.growth_score],window_length=3)
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
value_winsorized = value.winsorize(min_percentile=0.05, max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05, max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05, max_percentile=0.95)
smaScore_winsorized =smaScore.winsorize(min_percentile=0.05, max_percentile=0.95)
sizeScore_winsorized = sizeScore.winsorize(min_percentile=0.05, max_percentile=0.95)
growthScore_winsorized = growthScore.winsorize(min_percentile=0.05, max_percentile=0.95)
# Here we combine our winsorized factors, z-scoring them to equalize their influence
combined_factor = (
value_winsorized.zscore() +
quality_winsorized.zscore() +
sentiment_score_winsorized.zscore() +
smaScore_winsorized.zscore() +
sizeScore_winsorized.zscore() +
growthScore_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
)
return pipe
def make_pipeline():
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
rec_corredor = SimpleMovingAverage(
inputs=[ConsensusRecommendations.total],
window_length=3,
)
add_insider_transaction = SimpleMovingAverage(
inputs=[
Form3AggregatedTrades.slice(False, 90).num_unique_filers.latest
],
window_length=1,
)
universe = QTradableStocksUS()
value_winsorized = value.winsorize(min_percentile=0.05,
max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05,
max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
rec_corredor_winsorized = rec_corredor.winsorize(min_percentile=0.05,
max_percentile=0.95)
add_insider_transaction_winsorized = add_insider_transaction.winsorize(
min_percentile=0.05, max_percentile=0.95)
combined_factor = (1 * value_winsorized.zscore() +
1 * quality_winsorized.zscore() +
1 * sentiment_score_winsorized.zscore() +
9 * rec_corredor_winsorized.zscore() +
1 * add_insider_transaction_winsorized.zscore())
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():
# The factors we create here are based on fundamentals data and a moving
# average of sentiment data
value = Fundamentals.ebitda.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
#Puntaje para Stocktwits
sentiment_score = SimpleMovingAverage(inputs=[stocktwits.bull_minus_bear],window_length=3,)
#Puntaje para twitter
twitter_score = SimpleMovingAverage(inputs=[twitter_withretweets.bull_bear_msg_ratio],window_length=3,)
#Puntaje noticias
news_score = SimpleMovingAverage(inputs=[sentiment.sentiment_signal], window_length=5)
universe = QTradableStocksUS()
# We winsorize our factor values in order to lessen the impact of outliers
value_winsorized = value.winsorize(min_percentile=0.05, max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05, max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05, max_percentile=0.95)
twitter_score_winsorized = twitter_score.winsorize(min_percentile=0.05, max_percentile=0.95)
news_score_winsorized = news_score.winsorize(min_percentile=0.05, max_percentile=0.95)
# Here we combine our winsorized factors, z-scoring them to equalize their influence
combined_factor = (
3*value_winsorized.zscore()
+ 3*quality_winsorized.zscore()
+ 2*sentiment_score_winsorized.zscore()
+ twitter_score_winsorized.zscore()
+ 2*news_score_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
)
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.
"""
# The factors we create here are based on fundamentals data and a moving
# average of sentiment data
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
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
value_winsorized = value.winsorize(min_percentile=0.05, max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05, max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05, max_percentile=0.95)
# Here we combine our winsorized factors, z-scoring them to equalize their influence
combined_factor = (
value_winsorized.zscore() +
quality_winsorized.zscore() +
sentiment_score_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
)
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.
"""
# The factors we create here are based on fundamentals data and a moving
# average of sentiment data
value = Fundamentals.ebit.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
total_revenue = Fundamentals.total_revenue.latest
positive_sentiment_pct = (
twitter_sentiment.bull_scored_messages.latest
/ twitter_sentiment.total_scanned_messages.latest
)
positive_sentiment_pctt = (
twitter_sentiment2.bull_scored_messages.latest
/ twitter_sentiment2.total_scanned_messages.latest
)
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
value_winsorized = value.winsorize(min_percentile=0.05, max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05, max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05, max_percentile=0.95)
total_revenue_winsorized = total_revenue.winsorize(min_percentile=0.05, max_percentile=0.95)
positive_sentiment_pct_winsorized = positive_sentiment_pct.winsorize(min_percentile=0.05, max_percentile=0.95)
positive_sentiment_pctt_winsorized = positive_sentiment_pctt.winsorize(min_percentile=0.05, max_percentile=0.95)
# Here we combine our winsorized factors, z-scoring them to equalize their influence
combined_factor = (
0.2*value_winsorized.zscore() +
0.2*quality_winsorized.zscore() +
0.2*sentiment_score_winsorized.zscore()+
0.2*total_revenue_winsorized.zscore()+
0.1*positive_sentiment_pct_winsorized+
0.1*positive_sentiment_pctt_winsorized
)
def make_pipeline():
#Example Variables
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
#Aditional Variables
fcf_ratio = Fundamentals.fcf_ratio.latest
roa = Fundamentals.roa.latest
accounts_receivable = Fundamentals.accounts_receivable.latest
gain_loss_on_sale_of_ppe = Fundamentals.gain_loss_on_sale_of_ppe.latest
universe = QTradableStocksUS()
#Eliminamos los valores atipicos de las distribuciones
value_winsorized = value.winsorize(min_percentile=0.05,
max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05,
max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
fcf_ratio_winsorized = fcf_ratio.winsorize(min_percentile=0.05,
max_percentile=0.95)
roa_winsorized = roa.winsorize(min_percentile=0.05, max_percentile=0.95)
accounts_receivable_winsorized = accounts_receivable.winsorize(
min_percentile=0.05, max_percentile=0.95)
gain_loss_on_sale_of_ppe_winsorized = gain_loss_on_sale_of_ppe.winsorize(
min_percentile=0.05, max_percentile=0.95)
# Aqui hacemos la combinacion y asignamos pesos
combined_factor = (value_winsorized.zscore() * 0.1 +
quality_winsorized.zscore() * 0.1 +
sentiment_score_winsorized.zscore() * 0.2 +
fcf_ratio_winsorized.zscore() * 0.1 +
roa_winsorized.zscore() * 0.1 +
accounts_receivable_winsorized.zscore() * 0.2 +
gain_loss_on_sale_of_ppe_winsorized.zscore() * 0.2)
#Vamos escoger en cuales vamos en largo y cuales vamos en corto
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)
return pipe
def make_pipeline():
global value
global quality
global sentiment_score
global test_sentiment
global total_revenue
global yesterday_close
global yesterday_volume
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
test_sentiment = (twitter_sentiment.bull_scored_messages.latest /
twitter_sentiment.total_scanned_messages.latest)
total_revenue = Fundamentals.total_revenue.latest
yesterday_close = EquityPricing.close.latest
yesterday_volume = EquityPricing.volume.latest
universe = QTradableStocksUS()
value_winsorized = value.winsorize(min_percentile=0.05,
max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05,
max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
combined_factor = (value_winsorized.zscore() +
quality_winsorized.zscore() +
sentiment_score_winsorized.zscore())
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():
returns = (-1) * Returns(window_length = 21)
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=21,
)
ty = Fundamentals.total_yield.latest
wps = Fundamentals.working_capital_per_share.latest
epst = Fundamentals.tangible_book_value_per_share.latest
eps = (epst-wps) * (-1)
universe = QTradableStocksUS()
value_winsorized = value.winsorize(min_percentile=0.05, max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05, max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05, max_percentile=0.95)
returns_winsorized = returns.winsorize(min_percentile=0.05, max_percentile=0.95)
eps_wisorized = eps.winsorize(min_percentile=0.05, max_percentile=0.95)
ty_winsorized = ty.winsorize(min_percentile=0.05, max_percentile=0.95)
combined_factor = (
value_winsorized.zscore()
+ quality_winsorized.zscore()
+ sentiment_score_winsorized.zscore()
+ returns_winsorized.zscore()
+ eps_wisorized.zscore()
+ ty_winsorized.zscore()
)
longs = combined_factor.top(TOTAL_POSITIONS//2, mask=universe)
shorts = combined_factor.bottom(TOTAL_POSITIONS//2, mask=universe)
long_short_screen = (longs | shorts)
# Create pipeline
pipe = Pipeline(
columns={
'longs': longs,
'shorts': shorts,
'combined_factor': combined_factor
},
screen=long_short_screen
)
return pipe
def make_pipeline():
returns = (-1) * Returns(window_length = 21)
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.total_scanned_messages],
window_length=21,
)
ty = Fundamentals.total_yield.latest #-7.77
wps = Fundamentals.working_capital_per_share.latest #-7.53
epst = Fundamentals.tangible_book_value_per_share.latest
gro = Fundamentals.growth_score.latest
dollar_volume = AverageDollarVolume(window_length=63)
universe = QTradableStocksUS() & (dollar_volume > 10**7)
value_winsorized = value.winsorize(min_percentile=0.15, max_percentile=0.85)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.15, max_percentile=0.85)
eps_wisorized = wps.winsorize(min_percentile=0.15, max_percentile=0.85)
ty_winsorized = ty.winsorize(min_percentile=0.15, max_percentile=0.85)
gro_winsorize = gro.winsorize(min_percentile=0.15, max_percentile=0.85)
returns_winsorize = returns.winsorize(min_percentile=0.05, max_percentile=0.85)
# Here we combine our winsorized factors, z-scoring them to equalize their influence
combined_factor = (
value_winsorized.zscore() *0.15
+ gro_winsorize.zscore() *0.25
+ sentiment_score_winsorized.zscore()* 0.2
+ eps_wisorized.zscore() *0.15
+ ty_winsorized.zscore() *0.15
+ returns_winsorize.zscore() *0.1
)
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():
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.total_scanned_messages],
window_length=21,
)
ty = Fundamentals.total_yield.latest
wps = Fundamentals.working_capital_per_share.latest
gro = Fundamentals.growth_score.latest
dollar_volume = AverageDollarVolume(window_length=63)
universe = QTradableStocksUS()
dollar_winsorize = dollar_volume.winsorize(min_percentile=0.2, max_percentile=0.8)
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)
eps_wisorized = wps.winsorize(min_percentile=0.05, max_percentile=0.95)
ty_winsorized = ty.winsorize(min_percentile=0.05, max_percentile=0.95)
gro_winsorize = gro.winsorize(min_percentile=0.05, max_percentile=0.95)
combined_factor = (
value_winsorized.zscore() *0.15
+ gro_winsorize.zscore() *0.25
+ sentiment_score_winsorized.zscore()* 0.15
+ eps_wisorized.zscore() *0.15
+ ty_winsorized.zscore() *0.15
+ dollar_winsorize.zscore() *0.15
)
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():
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
workingCapital = Fundamentals.working_capital_per_share.latest
sGrowRate = Fundamentals.sustainable_growth_rate.latest
dividendYield = Fundamentals.forward_dividend_yield.latest
universe = QTradableStocksUS()
value_winsorized = value.winsorize(min_percentile=0.05,
max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05,
max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
workingCapital_winsorized = workingCapital.winsorize(min_percentile=0.05,
max_percentile=0.95)
sGrowRate_winsorized = sGrowRate.winsorize(min_percentile=0.05,
max_percentile=0.95)
dividendYield_winsorized = dividendYield.winsorize(min_percentile=0.05,
max_percentile=0.95)
combined_factor = (value_winsorized.zscore() +
quality_winsorized.zscore() +
sentiment_score_winsorized.zscore() +
workingCapital_winsorized + sGrowRate_winsorized +
dividendYield_winsorized)
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():
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
positived_sentiment = (twitter_sentiment.bull_scored_messages.latest /
twitter_sentiment.total_scanned_messages.latest)
sentiment_3day = SimpleMovingAverage(inputs=[sentiment.sentiment_signal],
window_length=3)
total_revenue = Fundamentals.total_revenue.latest
yesterday_close = EquityPricing.close.latest
yesterday_volume = EquityPricing.volume.latest
universe = QTradableStocksUS()
value_winsorized = value.winsorize(min_percentile=0.05,
max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05,
max_percentile=0.95)
sentiment_winsorized = sentiment_3day.winsorize(min_percentile=0.05,
max_percentile=0.95)
revenue_winsorized = total_revenue.winsorize(min_percentile=0.05,
max_percentile=0.95)
close_winsorized = yesterday_close.winsorize(min_percentile=0.05,
max_percentile=0.95)
volume_winsorized = yesterday_volume.winsorize(min_percentile=0.05,
max_percentile=0.95)
combined_factor = (
value_winsorized.zscore() + quality_winsorized.zscore() +
((positived_sentiment.zscore() + sentiment_winsorized.zscore()) / 2) +
revenue_winsorized.zscore() + close_winsorized.zscore() +
volume_winsorized.zscore())
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():
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
total_revenue = Fundamentals.total_revenue.latest
yesterday_close = EquityPricing.close.latest
yesterday_volume = EquityPricing.volume.latest
working_capital_per_share = Fundamentals.working_capital_per_share.latest
forward_dividend_yield = Fundamentals.forward_dividend_yield.latest
peg_ratio = Fundamentals.peg_ratio.latest
trailing_dividend_yield = Fundamentals.trailing_dividend_yield.latest
sentiment_score = SimpleMovingAverage(inputs=[stocktwits.bull_minus_bear],
window_length=2)
test_sentiment = (twitter_sentiment.bull_scored_messages.latest /
twitter_sentiment.total_scanned_messages.latest)
universe = QTradableStocksUS()
#-----------------------------------------------------------------
recent_returns = Returns(window_length=RETURNS_LOOKBACK_DAYS,
mask=universe)
recent_returns_zscore = recent_returns.zscore()
#----------------------------------------------------------------
value_winsorized = value.winsorize(min_percentile=0.10,
max_percentile=0.90)
quality_winsorized = quality.winsorize(min_percentile=0.10,
max_percentile=0.90)
total_revenue_winsorized = total_revenue.winsorize(min_percentile=0.10,
max_percentile=0.90)
yesterday_close_winsorized = yesterday_close.winsorize(min_percentile=0.10,
max_percentile=0.90)
yesterday_volume_winsorized = yesterday_volume.winsorize(
min_percentile=0.10, max_percentile=0.90)
working_capital_per_share_winsorized = working_capital_per_share.winsorize(
min_percentile=0.10, max_percentile=0.90)
forward_dividend_yield_winsorized = forward_dividend_yield.winsorize(
min_percentile=0.10, max_percentile=0.90)
peg_ratio_winsorized = peg_ratio.winsorize(min_percentile=0.10,
max_percentile=0.90)
trailing_dividend_yield_winsorized = trailing_dividend_yield.winsorize(
min_percentile=0.10, max_percentile=0.90)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.10,
max_percentile=0.90)
#---------------------------------------------------------------
combined_factor = (
value_winsorized.zscore() + quality_winsorized.zscore() +
total_revenue_winsorized.zscore() +
yesterday_volume_winsorized.zscore() +
working_capital_per_share_winsorized.zscore() * 2 +
forward_dividend_yield_winsorized.zscore() * 2 +
peg_ratio_winsorized.zscore() * 2 +
trailing_dividend_yield_winsorized.zscore() +
((sentiment_score_winsorized.zscore() + test_sentiment.zscore()) / 2))
#---------------------------------------------------------------
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,
'recent_returns_zscore': recent_returns_zscore,
'combined_factor': combined_factor,
'total_revenue': total_revenue,
'close': yesterday_close,
'volume': yesterday_volume,
},
screen=long_short_screen)
return pipe
def make_pipeline():
# The factors we create here are based on fundamentals data and a moving
# average of sentiment data
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=50,
)
growth_score = Fundamentals.growth_score.latest
opt_income = Fundamentals.operation_income_growth.latest
market_cap = Fundamentals.market_cap.latest
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
value_winsorized = value.winsorize(min_percentile=0.05,
max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05,
max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
growth_score_winsorized = growth_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
opt_income_winsorized = opt_income.winsorize(min_percentile=0.05,
max_percentile=0.95)
market_cap_winsorized = market_cap.winsorize(min_percentile=0.05,
max_percentile=0.95)
combined_factor = ((1 * value_winsorized + 1 * quality_winsorized +
2 * sentiment_score_winsorized +
4 * growth_score_winsorized +
0.5 * opt_income_winsorized +
0.5 * market_cap_winsorized))
# 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)
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.
"""
# The factors we create here are based on fundamentals data and a moving
# average of sentiment data
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
growth = Fundamentals.growth_score.latest
accountsreceivable = Fundamentals.accounts_receivable.latest
additionalcapital = Fundamentals.additional_paid_in_capital.latest
capitalstock = Fundamentals.capital_stock.latest
currentassets = Fundamentals.total_assets.latest
currentdebt = Fundamentals.total_debt.latest
dilutedgrowth = Fundamentals.equity_per_share_growth.latest
dilutedepsgrowth = Fundamentals.diluted_eps_growth.latest
dilutedcontepsgrowth = Fundamentals.diluted_cont_eps_growth.latest
cashfinancingactivities = Fundamentals.cash_flow_from_continuing_financing_activities.latest
cashinvestingactivities = Fundamentals.cash_flow_from_continuing_investing_activities.latest
cashoperatingactivities = Fundamentals.cash_flow_from_continuing_operating_activities.latest
changeinventory = Fundamentals.change_in_inventory.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
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
value_winsorized = value.winsorize(min_percentile=0.05,
max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05,
max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
growth_winsorized = growth.winsorize(min_percentile=0.05,
max_percentile=0.95)
accounts_receivable_winsorized = accountsreceivable.winsorize(
min_percentile=0.05, max_percentile=0.95)
additional_capital_winsorized = additionalcapital.winsorize(
min_percentile=0.05, max_percentile=0.95)
capital_stock_winsorized = capitalstock.winsorize(min_percentile=0.05,
max_percentile=0.95)
current_assets_winsorized = currentassets.winsorize(min_percentile=0.05,
max_percentile=0.95)
current_debt_winsorized = currentdebt.winsorize(min_percentile=0.05,
max_percentile=0.95)
diluted_growth_winsorized = dilutedgrowth.winsorize(min_percentile=0.05,
max_percentile=0.95)
diluted_eps_growth_winsorized = dilutedepsgrowth.winsorize(
min_percentile=0.05, max_percentile=0.95)
diluted_cont_eps_growth_winsorized = dilutedcontepsgrowth.winsorize(
min_percentile=0.05, max_percentile=0.95)
cash_financing_activities_winsorized = cashfinancingactivities.winsorize(
min_percentile=0.05, max_percentile=0.95)
cash_investing_activities_winsorized = cashinvestingactivities.winsorize(
min_percentile=0.05, max_percentile=0.95)
cash_operating_activities_winsorized = cashoperatingactivities.winsorize(
min_percentile=0.05, max_percentile=0.95)
change_in_inventory_winsorized = changeinventory.winsorize(
min_percentile=0.05, max_percentile=0.95)
# Here we combine our winsorized factors, z-scoring them to equalize their influence
combined_factor = (
value_winsorized.zscore() + quality_winsorized.zscore() +
sentiment_score_winsorized.zscore() + growth_winsorized.zscore() +
accounts_receivable_winsorized.zscore() +
additional_capital_winsorized.zscore() +
capital_stock_winsorized.zscore() +
current_assets_winsorized.zscore() - current_debt_winsorized.zscore() +
diluted_growth_winsorized.zscore() -
diluted_eps_growth_winsorized.zscore() -
diluted_cont_eps_growth_winsorized.zscore() +
cash_financing_activities_winsorized.zscore() +
cash_investing_activities_winsorized.zscore() +
cash_operating_activities_winsorized.zscore() +
change_in_inventory_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)
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():
# The factors we create here are based on fundamentals data and a moving
# average of sentiment data
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
growthh = Fundamentals.growth_score.latest
value_score = Fundamentals.value_score.latest
profitability_grade = Fundamentals.profitability_grade.latest
basic_average_shares = Fundamentals.basic_average_shares_earnings_reports.latest
a = Fundamentals.current_ratio.latest
b = Fundamentals.accumulated_depreciation.latest
c = Fundamentals.ps_ratio.latest
d = Fundamentals.style_score.latest
e = Fundamentals.total_yield.latest
#f = Fundamentals.
#g = Fundamentals.
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
value_winsorized = value.winsorize(min_percentile=0.0, max_percentile=1)
quality_winsorized = quality.winsorize(min_percentile=0.00,
max_percentile=1)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
growthh_winsorized = growthh.winsorize(min_percentile=0.05,
max_percentile=0.95)
value_score_winsorized = value_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
basic_average_shares_winzorized = basic_average_shares.winsorize(
min_percentile=0.05, max_percentile=0.95)
a_winso = a.winsorize(min_percentile=0.05, max_percentile=0.95)
b_winso = b.winsorize(min_percentile=0.05, max_percentile=0.95)
c_winso = c.winsorize(min_percentile=0.05, max_percentile=0.95)
d_winso = d.winsorize(min_percentile=0.05, max_percentile=0.95)
e_winso = e.winsorize(min_percentile=0.05, max_percentile=0.95)
#f_winso = f.winsorize(min_percentile=0.05, max_percentile=0.95)
#g_winso = g.winsorize(min_percentile=0.05, max_percentile=0.95)
#profitability_grade_winsorized = profitability_grade.winsorize(min_percentile=0.05,max_percentile=0.95)
# Here we combine our winsorized factors, z-scoring them to equalize their influence
combined_factor = (
#(-0.1*value_winsorized.zscore() )+
#(-1.1*quality_winsorized.zscore() )+
#(1*sentiment_score_winsorized.zscore())
(1.4 * growthh_winsorized.zscore()) +
(-1.6 * value_score_winsorized.zscore()) + (0.6 * a_winso.zscore()) +
(0.8 * b_winso.zscore()) + (2.3 * c_winso.zscore()) +
(0.75 * d_winso.zscore()) + (-1 * e_winso.zscore())
#(f_winso.zscore())
#(-0.1*basic_average_shares_winzorized.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)
return pipe
def make_pipeline():
universe = QTradableStocksUS()
# Variables seleccionadas del dataframe de Fundamentals
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
buildings_and_improvements = Fundamentals.buildings_and_improvements.latest
account_payable = Fundamentals.accounts_payable.latest
current_assets = Fundamentals.current_assets.latest
total_equity = Fundamentals.total_equity.latest
accumulated_depreciation = Fundamentals.accumulated_depreciation.latest
diluted_eps_growth = Fundamentals.diluted_eps_growth.latest
dps_growth = Fundamentals.dps_growth.latest
# Variables (SIN ATIPICOS)
value_winsorized = value.winsorize(min_percentile=0.05,
max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05,
max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
buildings_and_improvements = buildings_and_improvements.winsorize(
min_percentile=0.05, max_percentile=0.95)
account_payable = account_payable.winsorize(min_percentile=0.05,
max_percentile=0.95)
current_assets = current_assets.winsorize(min_percentile=0.05,
max_percentile=0.95)
total_equity = total_equity.winsorize(min_percentile=0.05,
max_percentile=0.95)
accumulated_depreciation = accumulated_depreciation.winsorize(
min_percentile=0.05, max_percentile=0.95)
diluted_eps_growth = diluted_eps_growth.winsorize(min_percentile=0.05,
max_percentile=0.95)
dps_growth = dps_growth.winsorize(min_percentile=0.05, max_percentile=0.95)
# FACTOR COMBINADO
combined_factor = (
quality_winsorized.zscore() * 0.2 + value_winsorized.zscore() * 0.4 +
dps_growth.zscore() * 0.15 +
sentiment_score_winsorized.zscore() * 0.3 +
buildings_and_improvements.zscore() * 0.2 +
accumulated_depreciation.zscore() * 0.25 +
account_payable.zscore() * 0.01 + current_assets.zscore() * 0.01 +
total_equity.zscore() * 0.01 + diluted_eps_growth.zscore() * 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
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.
"""
# The factors we create here are based on fundamentals data and a moving
# average of sentiment data
universe = QTradableStocksUS()
fe_rec = fe.ConsensusRecommendations # 5
pe_ratio = Fundamentals.forward_pe_ratio.latest # 4
sentiment_score = SimpleMovingAverage( # 3
inputs=[stocktwits.bullish_intensity],
window_length=5,
)
mean_close_10 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=10)
mean_close_30 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=30)
percent_difference = (mean_close_10 - mean_close_30) / mean_close_30
# 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
pe_ratio_winsorized = pe_ratio.winsorize(min_percentile=0.05,
max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
percent_difference_winsorized = percent_difference.winsorize( # 2
min_percentile=0.1, max_percentile=0.9)
recent_returns = Returns(window_length=5) # 6
fq1_eps_cons = PeriodicConsensus.slice('EPS', 'qf', 1) # 1
fq2_eps_cons = PeriodicConsensus.slice('EPS', 'qf', 2)
fq1_eps_mean = fq1_eps_cons.mean.latest
fq2_eps_mean = fq2_eps_cons.mean.latest
estimated_growth_factor = (fq2_eps_mean - fq1_eps_mean) / fq1_eps_mean
estimated_growth_factor_windsorized = estimated_growth_factor.winsorize(
min_percentile=0.01, max_percentile=0.99)
# Here we combine our winsorized factors, z-scoring them to equalize their influence
combined_factor = (0.01 * fe_rec.total.latest +
pe_ratio_winsorized.zscore() +
sentiment_score_winsorized.zscore() +
0.005 * percent_difference_winsorized.zscore() +
0.01 * recent_returns.zscore() +
estimated_growth_factor_windsorized.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)
return pipe
def make_pipeline():
qtu = QTradableStocksUS()
# Aggregated Insider Transactions
insider_txns_form3_90d = Form3AggregatedTrades.slice(False, 90)
insider_txns_form4and5_90d = Form4and5AggregatedTrades.slice(False, 90)
unique_filers_form3_90d = insider_txns_form3_90d.num_unique_filers.latest
unique_buyers_form4and5_90d = insider_txns_form4and5_90d.num_unique_buyers.latest
unique_sellers_form4and5_90d = insider_txns_form4and5_90d.num_unique_sellers.latest
unique_buyers_90d = unique_filers_form3_90d + unique_buyers_form4and5_90d
unique_sellers_90d = unique_sellers_form4and5_90d
frac_insiders_buying_90d = unique_buyers_90d / (unique_buyers_90d +
unique_sellers_90d)
frac_insiders_selling_90d = unique_sellers_90d / (unique_buyers_90d +
unique_sellers_90d)
alpha_factor = frac_insiders_buying_90d - frac_insiders_selling_90d
screen = qtu & ~alpha_factor.isnull() & alpha_factor.isfinite()
alpha_winsorized = alpha_factor.winsorize(min_percentile=0.05,
max_percentile=0.95,
mask=screen)
alpha_rank = alpha_winsorized.zscore()
# FactSet Estimates – Consensus
fq1_eps_cons = PeriodicConsensus.slice('EPS', 'qf', 1)
fq2_eps_cons = PeriodicConsensus.slice('EPS', 'qf', 2)
fq1_eps_mean = fq1_eps_cons.mean.latest
fq2_eps_mean = fq2_eps_cons.mean.latest
estimated_growth_factor = (fq2_eps_mean - fq1_eps_mean) / fq1_eps_mean
estimated_growth_factor_rank = estimated_growth_factor.winsorize(
min_percentile=0.05, max_percentile=0.95).zscore()
# Sentdex News Sentiment
mean_sentiment_5day = SimpleMovingAverage(
inputs=[sentiment.sentiment_signal], window_length=5)
# The factors we create here are based on fundamentals data and a moving
# average of sentiment data
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
# 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
value_winsorized = value.winsorize(min_percentile=0.05,
max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05,
max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
# Here we combine our winsorized factors, z-scoring them to equalize their influence
combined_factor = (value_winsorized.zscore() +
quality_winsorized.zscore() +
sentiment_score_winsorized.zscore() + alpha_rank +
estimated_growth_factor_rank + mean_sentiment_5day)
# 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=screen)
shorts = combined_factor.bottom(TOTAL_POSITIONS // 2, mask=screen)
# 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)
return pipe
def make_pipeline():
# The factors we create here are based on fundamentals data and a moving
# average of sentiment data
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=50,
)
growth_score = Fundamentals.growth_score.latest
amortization = Fundamentals.amortization_of_securities.latest
dividends = Fundamentals.cash_dividends_paid.latest
asst_turnover = Fundamentals.assets_turnover.latest
dps_growth = Fundamentals.dps_growth.latest
sustainable = Fundamentals.sustainable_growth_rate.latest
enterprise_value = Fundamentals.enterprise_value.latest
gross_margin = Fundamentals.gross_margin.latest
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
value_winsorized = value.winsorize(min_percentile=0.05,
max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05,
max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
growth_score_winsorized = growth_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
amortization_winsorized = amortization.winsorize(min_percentile=0.05,
max_percentile=0.95)
dividends_winsorized = dividends.winsorize(min_percentile=0.05,
max_percentile=0.95)
asst_turnover_winsorized = asst_turnover.winsorize(min_percentile=0.05,
max_percentile=0.95)
dps_growth_winsorized = dps_growth.winsorize(min_percentile=0.05,
max_percentile=0.95)
sustainable_winsorized = sustainable.winsorize(min_percentile=0.05,
max_percentile=0.95)
enterprise_value_winsorized = enterprise_value.winsorize(
min_percentile=0.05, max_percentile=0.95)
gross_margin_winsorized = gross_margin.winsorize(min_percentile=0.05,
max_percentile=0.95)
combined_factor = ((0.1 * value_winsorized + 0.1 * quality_winsorized +
0.2 * sentiment_score_winsorized +
0.2 * growth_score_winsorized +
0.3 * gross_margin_winsorized +
0.1 * amortization_winsorized))
# 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)
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.
"""
positive_sentiment_pct = (twitter_sentiment.bull_scored_messages.latest /
twitter_sentiment.total_scanned_messages.latest)
# The factors we create here are based on fundamentals data and a moving
# average of sentiment data
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
universe = QTradableStocksUS()
yesterday_close = EquityPricing.close.latest
yesterday_volume = EquityPricing.volume.latest
v1 = Fundamentals.accounts_receivable.latest
v2 = Fundamentals.size_score.latest
v3 = Fundamentals.style_score.latest
# 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
positive_sentiment_pct_winsorized = positive_sentiment_pct.winsorize(
min_percentile=0.05, max_percentile=0.95)
value_winsorized = value.winsorize(min_percentile=0.05,
max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05,
max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
v1_winsorized = v1.winsorize(min_percentile=0.05, max_percentile=0.95)
v2_winsorized = v2.winsorize(min_percentile=0.05, max_percentile=0.95)
v3_winsorized = v3.winsorize(min_percentile=0.05, max_percentile=0.95)
yesterday_close_winsorized = yesterday_close.winsorize(min_percentile=0.05,
max_percentile=0.95)
yesterday_volume_winsorized = yesterday_volume.winsorize(
min_percentile=0.05, max_percentile=0.95)
# Here we combine our winsorized factors, z-scoring them to equalize their influence
combined_factor = (positive_sentiment_pct_winsorized.zscore() * 0.8 +
value_winsorized.zscore() * 0.5 +
quality_winsorized.zscore() * 0.5 +
sentiment_score_winsorized.zscore() * 0.5 +
v1_winsorized.zscore() * 0.5 +
v2_winsorized.zscore() * 0.5 +
v3_winsorized.zscore() * 0.5 +
yesterday_close_winsorized.zscore() * 0.5 +
yesterday_volume_winsorized.zscore() * 0.7)
# 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={
'positive_sentiment_pct': positive_sentiment_pct,
'longs': longs,
'shorts': shorts,
'combined_factor': combined_factor,
'close': yesterday_close,
'volume': yesterday_volume
},
screen=long_short_screen,
domain=US_EQUITIES)
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.
"""
# The factors we create here are based on fundamentals data and a moving
# average of sentiment data
enterprise = SimpleMovingAverage(
inputs=[Fundamentals.enterprise_value],
window_length=10,
)
marketcap = SimpleMovingAverage(
inputs=[Fundamentals.market_cap],
window_length=10,
)
earnings = SimpleMovingAverage(inputs=[Fundamentals.ebit],
window_length=10)
equitymedia = (EquityPricing.high.latest + EquityPricing.low.latest) / 2
high = SimpleMovingAverage(inputs=[EquityPricing.high], window_length=10)
low = SimpleMovingAverage(inputs=[EquityPricing.low], window_length=10)
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
enterprise_winsorized = enterprise.winsorize(min_percentile=0.05,
max_percentile=0.95)
marketcap_winsorized = marketcap.winsorize(min_percentile=0.05,
max_percentile=0.95)
earnings_winsorized = earnings.winsorize(min_percentile=0.05,
max_percentile=0.95)
equitymedia_winsorized = equitymedia.winsorize(min_percentile=0.05,
max_percentile=0.95)
high_winsorized = high.winsorize(min_percentile=0.05, max_percentile=0.95)
low_winsorized = low.winsorize(min_percentile=0.05, max_percentile=0.95)
# Here we combine our winsorized factors, z-scoring them to equalize their influence
combined_factor = (enterprise_winsorized.zscore() +
marketcap_winsorized.zscore() +
earnings_winsorized.zscore() +
high_winsorized.zscore() + low_winsorized.zscore() +
equitymedia_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)
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
def make_pipeline():
# Ganancias por acción con frecuencia semestral reportada mas recientemente
profits = fe.Actuals.slice('EPS', 'qf', 0).actual_value.latest
# Estimación de ganancias por acción con frecuencia trimentral
profits_estimation = fe.PeriodicConsensus.slice('EPS', 'qf', 0).mean.latest
# Diferencia entre el valor real y el estimado
difference_profits = (profits - profits_estimation) / profits_estimation
# Flujo de caja por acción con frecuencia semestral reportada mas recientemente
flow = fe.Actuals.slice('CFPS', 'qf', 0).actual_value.latest
# Flujo de caja por acción con frecuencia trimentral
flow_estimation = fe.PeriodicConsensus.slice('CFPS', 'qf', 0).mean.latest
# Diferencia entre el valor real y el estimado
difference_flow = (flow - flow_estimation) / flow_estimation
# Precio de cierre
close = EquityPricing.close.latest
# Precio de apertura
open = EquityPricing.open.latest
# Diferencia entre el precio de apretura y de cierre
difference_open_close_price = (close - open) / open
# Precio mas alto
high = EquityPricing.high.latest
# Precio mas bajo
low = EquityPricing.low.latest
# diferencia entre el precio mas alto y el mas bajo
difference_high_low_price = (high - low) / low
# N° acciones negociadas
volume = EquityPricing.volume.latest
# factores basado en datos de fundamentals y SimpleMovingAverage
value = Fundamentals.ebit.latest / Fundamentals.market_cap.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
# Obtener universo de acciones negociables
universe = QTradableStocksUS()
# Cambiar los valores por fuera de los percentiles minimo y maximo por el valor del percentil del extremo correspondiente
difference_profits_winsorized = -difference_profits.winsorize(
min_percentile=0.05, max_percentile=0.95)
difference_flow_winsorized = -difference_flow.winsorize(
min_percentile=0.05, max_percentile=0.95)
difference_open_close_price_winsorized = -difference_open_close_price.winsorize(
min_percentile=0.05, max_percentile=0.95)
difference_high_low_price_winsorized = -difference_high_low_price.winsorize(
min_percentile=0.05, max_percentile=0.95)
volume_winsorized = -volume.winsorize(min_percentile=0.05,
max_percentile=0.95)
value_winsorized = -value.winsorize(min_percentile=0.05,
max_percentile=0.95)
quality_winsorized = -quality.winsorize(min_percentile=0.05,
max_percentile=0.95)
sentiment_score_winsorized = -sentiment_score.winsorize(
min_percentile=0.05, max_percentile=0.95)
# Normalizar los datos y sumarlos para amplificar
combined_factor = (difference_profits_winsorized.zscore() +
difference_flow_winsorized.zscore() +
difference_open_close_price_winsorized.zscore() +
difference_high_low_price_winsorized.zscore() +
volume_winsorized.zscore() + value_winsorized.zscore() +
quality_winsorized.zscore() +
sentiment_score_winsorized.zscore())
# Filtrar los primeros y los ultimos valores
longs = combined_factor.top(TOTAL_POSITIONS // 2, mask=universe)
shorts = combined_factor.bottom(TOTAL_POSITIONS // 2, mask=universe)
# Unir los primeros y los ultimos valores
long_short_screen = (longs | shorts)
# Crear tuberia
pipe = Pipeline(columns={
'longs': longs,
'shorts': shorts,
'combined_factor': combined_factor
},
screen=long_short_screen)
return pipe
def make_pipeline():
"""
パイプラインを作成して返す関数
パイプラインの構築は、NotebookとAlgorithmの両方で動作させることができるので、Notebook
で検証したアルファをコピペしてアルゴリズムで使うことが可能。
【Execise】make_pipeline をNotebookにコピーして動作することを確認しよう。
Returns
-------
pipe : Pipeline
Represents computation we would like to perform on the assets that make
it through the pipeline screen.
"""
# ファクターその１：バリューファクター（この時点では生データ）
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
# ファクターその２：クオリティファクター（ROE）（この時点では生データ）
quality = Fundamentals.roe.latest
# ファクターその３：センチメントファクター（この時点では生データ）
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
# ユニバースはQTradableStockUS()を使う
universe = QTradableStocksUS()
# 生データからwinsorizeによってスコア化
# バリューファクタースコア
value_winsorized = value.winsorize(min_percentile=0.05,
max_percentile=0.95)
# クオリティファクタースコア
quality_winsorized = quality.winsorize(min_percentile=0.05,
max_percentile=0.95)
# センチメントファクタースコア
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
# ３つのファクターを合成して、１つのアルファを構成
# 【Execise】ファクターの合成方法を変えてバックテストしてみよう！
combined_factor = (value_winsorized.zscore() +
quality_winsorized.zscore() +
sentiment_score_winsorized.zscore())
# QTradableStocks構成銘柄の中から、さらに、combined_fatctorが上位のものだけを取得
longs = combined_factor.top(TOTAL_POSITIONS // 2, mask=universe)
# QTradableStocks構成銘柄の中から、さらに、combined_fatctorが下位のものだけを取得
shorts = combined_factor.bottom(TOTAL_POSITIONS // 2, mask=universe)
# longとshortsの構成銘柄を組み合わせることで、最終的なフィルタを構築
long_short_screen = (longs | shorts)
# パイプラインを作成
pipe = Pipeline(
columns={
'longs': longs,
'shorts': shorts,
'combined_factor': combined_factor
},
# フィルタリング（longs または shorts に含まれる銘柄でフィルタリング）
screen=long_short_screen)
# 作成したパイプラインを返して終了
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.
"""
#1
positive_sentiment_pct = (twitter_sentiment.bull_scored_messages.latest /
twitter_sentiment.total_scanned_messages.latest)
#2
quarterly_sales = Fundamentals2.sales_qf.latest
#3 bueno
research_development = Fundamentals2.rd_exp_qf.latest
#4
pPEI = Fundamentals2.ppe_impair_qf.latest #un poco mala
#5 re malo :´v
other_income = Fundamentals2.oper_inc_oth_qf.latest
#6 un tris malo
investment_income = Fundamentals2.invest_inc_qf.latest
# The factors we create here are based on fundamentals data and a moving
# average of sentiment data
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
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
investment_income_winsorized = investment_income.winsorize(
min_percentile=0.05, max_percentile=0.95)
other_income_winsorized = other_income.winsorize(min_percentile=0.05,
max_percentile=0.95)
pPEI_winsorized = pPEI.winsorize(min_percentile=0.05, max_percentile=0.95)
research_development_winsorized = research_development.winsorize(
min_percentile=0.05, max_percentile=0.95)
quarterly_sales_winsorized = quarterly_sales.winsorize(min_percentile=0.05,
max_percentile=0.95)
positive_sentiment_pct_winsorized = positive_sentiment_pct.winsorize(
min_percentile=0.05, max_percentile=0.95)
value_winsorized = value.winsorize(min_percentile=0.05,
max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05,
max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
# Here we combine our winsorized factors, z-scoring them to equalize their influence
combined_factor = (investment_income_winsorized.zscore() +
other_income_winsorized.zscore() +
pPEI_winsorized.zscore() +
research_development_winsorized.zscore() +
quarterly_sales_winsorized.zscore() +
positive_sentiment_pct_winsorized.zscore() +
value_winsorized.zscore() +
quality_winsorized.zscore() +
sentiment_score_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={
'positive_sentiment_pct': positive_sentiment_pct,
'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.
"""
# Ganancias menos gastos sobre el valor total de la empresa incluida la deuda
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
# Utilidad
quality = Fundamentals.roe.latest
# Ingresos netos sobre activos totales promedio de los ultimos 5 años
interest = Fundamentals.roa5_yr_avg.latest
# Los gastos operativos son costos recurrentes primarios asociados con las operaciones centrales (que no sean el costo de los bienes vendidos) en los que se incurre para generar ventas.
oExpense = Fundamentals.operating_expense.latest
# Costo total del personal que se paga a los empleados que no forma parte de los gastos de venta, generales y administrativos.
cost = Fundamentals.staff_costs.latest
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
universe = QTradableStocksUS()
# Transformacion de las estadisticas al limitar los valores extremos
value_winsorized = value.winsorize(min_percentile=0.05,
max_percentile=0.95)
quality_winsorized = quality.winsorize(min_percentile=0.05,
max_percentile=0.95)
interest_winsorized = interest.winsorize(min_percentile=0.05,
max_percentile=0.95)
oExpense_winsorized = oExpense.winsorize(min_percentile=0.1,
max_percentile=0.9)
cost_winsorized = cost.winsorize(min_percentile=0.05, max_percentile=0.95)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05,
max_percentile=0.95)
# Here we combine our winsorized factors, z-scoring them to equalize their influence
combined_factor = (
value_winsorized.zscore() + quality_winsorized.zscore() +
oExpense_winsorized.zscore() +
#interest_winsorized.zscore() +
#cost_winsorized.zscore() +
sentiment_score_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)
return pipe
