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(context):
universe = QTradableStocksUS()
# Mean Average Multiplier calculation
mean_close_50 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=50,
mask=universe)
mean_close_200 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=200,
mask=universe)
mean_average_multiplier = mean_close_200 / mean_close_50
#Sentiment Score calculation
bull_m_bear = (SimpleMovingAverage(inputs=[stocktwits.bull_minus_bear],
window_length=RETURNS_LOOKBACK_DAYS,
mask=universe))
total_messages = (SimpleMovingAverage(
inputs=[stocktwits.total_scanned_messages],
window_length=RETURNS_LOOKBACK_DAYS,
mask=universe))
sentiment_score = total_messages / (bull_m_bear)
sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.01,
max_percentile=0.95,
mask=universe)
#Returns with lookback calculations
recent_returns = Returns(window_length=RETURNS_LOOKBACK_DAYS,
mask=universe)
recent_returns_zscore = recent_returns.zscore()
#Universe and low-high calculations
low_returns = recent_returns_zscore.percentile_between(0,
25,
mask=universe)
high_returns = recent_returns_zscore.percentile_between(75,
100,
mask=universe)
securities_to_trade = (low_returns | high_returns) & universe
#Total score calculation
total_score = sentiment_score_winsorized * recent_returns_zscore * mean_average_multiplier
pipe = Pipeline(columns={
'total_score': total_score,
},
screen=securities_to_trade)
return pipe
def make_pipeline(context):
"""
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.
Parameters
-------
context : AlgorithmContext
See description above.
Returns
-------
pipe : Pipeline
Represents computation we would like to perform on the assets that make
it through the pipeline screen.
"""
# Filter for stocks in the QTradableStocksUS universe. For more detail, see
# the documentation:
# https://www.quantopian.com/help#quantopian_pipeline_filters_QTradableStocksUS
universe = QTradableStocksUS()
# Create a Returns factor with a 5-day lookback window for all securities
# in our QTradableStocksUS Filter.
recent_returns = Returns(window_length=RETURNS_LOOKBACK_DAYS,
mask=universe)
# Turn our recent_returns factor into a z-score factor to normalize the results.
recent_returns_zscore = recent_returns.zscore()
# Define high and low returns filters to be the bottom 10% and top 10% of
# securities in the QTradableStocksUS.
low_returns = recent_returns_zscore.percentile_between(0, 10)
high_returns = recent_returns_zscore.percentile_between(90, 100)
# Add a filter to the pipeline such that only high-return and low-return
# securities are kept.
securities_to_trade = (low_returns | high_returns)
# Create a pipeline object to computes the recent_returns_zscore for securities
# in the top 10% and bottom 10% (ranked by recent_returns_zscore) every day.
pipe = Pipeline(columns={'recent_returns_zscore': recent_returns_zscore},
screen=securities_to_trade)
return pipe
def make_pipeline(context):
# Universe of top 2000 liquid stocks
universe = QTradableStocksUS()
recent_returns = Returns(window_length=lookback_days, mask=universe)
# Convert returns into a factor
recent_returns_z_score = recent_returns.zscore()
low_returns = recent_returns_z_score.percentile_between(0, 10)
high_returns = recent_returns_z_score.percentile_between(90, 100)
securities_to_trade = (low_returns | high_returns)
pipe = Pipeline(columns={'recent_returns_zscore': recent_returns_z_score},
screen=securities_to_trade)
return pipe
def make_pipeline():
# Base universe set to the Q500US
universe = Q500US()
roe = Fundamentals.roe.latest
new_returns = Returns(window_length=5, mask=universe)
new_returns = new_returns.zscore()
returns_range = new_returns.percentile_between(1, 30)
new_volatility = AnnualizedVolatility(mask=universe)
new_volatility = new_volatility.zscore()
volatility_range = new_volatility.percentile_between(1, 30)
pipe = Pipeline(columns={
'roe': roe,
'returns': returns_range,
'volatility': volatility_range
},
screen=universe)
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():
"""
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
# Importar objetos para tuberias de Quantopian from quantopian.pipeline.factors import Returns # Obtiene los retornos porcentuales totales en los ultimos n dias en el universo especificado columna = Returns(window_length=n, mask=universo) # Obtiene los z-score de una columna ((valor-media)/desviación estandar) columna2 = columna.zscore()
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.
qtu = QTradableStocksUS()
up_and_down_factor, screen_up_and_down = up_and_down()
factor_growth, screen_growth = growth()
# Create a Returns factor with a 5-day lookback window for all securities
# in our QTradableStocksUS Filter.
recent_returns = Returns(window_length=5, mask=qtu)
# Turn our recent_returns factor into a z-score factor to normalize the results.
recent_returns_zscore = recent_returns.zscore()
# Define high and low returns filters to be the bottom 10% and top 10% of
# securities in the QTradableStocksUS.
low_returns = recent_returns_zscore.percentile_between(0, 10)
high_returns = recent_returns_zscore.percentile_between(90, 100)
factor_last = 2 * factor_growth + recent_returns_zscore + up_and_down_factor
# Add a filter to the pipeline such that only high-return and low-return
# securities are kept.
securities_to_trade = (low_returns | high_returns)
# Create a pipeline object to computes the recent_returns_zscore for securities
# in the top 10% and bottom 10% (ranked by recent_returns_zscore) every day.
pipe = Pipeline(columns={'Factor': factor_last},
screen=screen_growth & screen_up_and_down)
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