def make_pipeline():
# Universe Q1500US
base_universe = Q1500US()
# Energy Sector
sector = morningstar.asset_classification.morningstar_sector_code.latest
energy_sector = sector.eq(309)
# Make Mask of 1500US and Energy
base_energy = base_universe & energy_sector
# Dollar Volume (30 Days) Grab the Info
dollar_volume = AverageDollarVolume(window_length=30)
# Grab the top 5% in avg dollar volume
high_dollar_volume = dollar_volume.percentile_between(95,100)
# Combine the filters
top_five_base_energy = base_energy & high_dollar_volume
# 10 day mean close
mean_10 = SimpleMovingAverage(inputs=[USEquityPricing.close],window_length=10,mask=top_five_base_energy)
# 30 day mean close
mean_30 = SimpleMovingAverage(inputs=[USEquityPricing.close],window_length=30,mask=top_five_base_energy)
# Percent Difference
percent_difference = (mean_10-mean_30)/mean_30
# List of Shorts
shorts = percent_difference < 0
# List of Longs
longs = percent_difference > 0
# Final Mask/Filter for anything in shorts or longs
securities_to_trade = (shorts | longs)
# Return Pipeline
return Pipeline(columns={
'longs':longs,
'shorts':shorts,
'perc_diff':percent_difference
},screen=securities_to_trade)
def make_pipeline10():
# Create Filters for Masks First
latest_close = USEquityPricing.close.latest
small_price = latest_close < 5
# Classifier
nyse_filter = exchange.eq('NYS')
# Pass in the mask
mean_close_10 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=10,
mask=small_price)
mean_close_30 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=30,
mask=small_price)
percent_difference = (mean_close_10 - mean_close_30) / mean_close_30
perc_diff_check = percent_difference > 0
final_filter = perc_diff_check & nyse_filter
return Pipeline(columns={
'Percent Difference': percent_difference,
'30 Day Mean Close': mean_close_30,
'Latest Close': latest_close,
'Positive Percent Diff': perc_diff_check
},
screen=final_filter)
def make_pipeline():
mean_close_10 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=10)
mean_close_30 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=30)
rsi_7 = RSI(inputs=[USEquityPricing.close], window_length=10)
percent_difference = (mean_close_10 - mean_close_30) / mean_close_30
rsi_under_35 = (rsi_7 < 35)
latest_close = USEquityPricing.close.latest
dollar_volume = AverageDollarVolume(window_length=30)
high_dollar_volume = dollar_volume.percentile_between(85, 100)
filters = high_dollar_volume & rsi_under_35
return Pipeline(columns={
'10_day_mean_close': mean_close_10,
'latest_close_price': latest_close,
'percent_difference': percent_difference,
'dollar_value': dollar_volume
},
screen=filters)
def make_pipeline():
# Base universe filter.
base_universe = QTradableStocksUS()
# 10-day close price average.
mean_30 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=30,
mask=base_universe)
# 30-day close price average.
mean_60 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=60,
mask=base_universe)
# Percent difference factor.
percent_difference = (mean_30 - mean_60) / mean_60
# Create a filter to select securities to short.
shorts = percent_difference.top(50)
# Create a filter to select securities to long.
longs = percent_difference.bottom(50)
# Filter for the securities that we want to trade.
securities_to_trade = (shorts | longs)
return Pipeline(
columns={
'longs': longs,
'shorts': shorts
},
screen=(securities_to_trade),
)
def make_pipeline():
# Base universe filter.
base_universe = Q1500US()
# Sector Classifier as Filter
energy_sector = sector.eq(309)
# Masking Base Energy Stocks
base_energy = base_universe & energy_sector
# Dollar volume factor
dollar_volume = AverageDollarVolume(window_length=30)
# Top half of dollar volume filter
high_dollar_volume = dollar_volume.percentile_between(95,100)
# Final Filter Mask
top_half_base_energy = base_energy & high_dollar_volume
# 10-day close price average.
mean_10 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=10, mask=top_half_base_energy)
# 30-day close price average.
mean_30 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=30, mask=top_half_base_energy)
# Percent difference factor.
percent_difference = (mean_10 - mean_30) / mean_30
# Create a filter to select securities to short.
shorts = percent_difference < 0
# Create a filter to select securities to long.
longs = percent_difference > 0
# Filter for the securities that we want to trade.
securities_to_trade = (shorts | longs)
return Pipeline(
columns={
'longs': longs,
'shorts': shorts,
'percent_diff':percent_difference
},
screen=securities_to_trade
)
def initialize(context):
# 空のパイプラインを作ります
pipe = Pipeline()
# research では run_pipeline
# algorithm では，initilize 内で attach_pipeline を実行して，パイプラインを毎日実行するように設定します．
# ほしいデータを定義
# pipeline 用に定義された 移動平均 SimpleMovingAverage を使う
SMA10 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=10)
SMA30 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=30)
pipe.add(SMA10, 'SMA10')
pipe.add(SMA30, 'SMA30')
## raw_weights
raw_weights = (SMA10 - SMA30) / SMA30
abs_raw_weights = raw_weights.abs()
pipe.add(raw_weights, 'raw_weights')
pipe.add(abs_raw_weights, 'abs_raw_weights')
pipe.set_screen(Q500US())
attach_pipeline(pipe, "my_pipeline")
# 毎週月曜日にどの銘柄を保有するか決定する
schedule_function(rebalance,
date_rules.week_start(),
time_rules.market_open())
# 毎週金曜日の取引時間終了時に持っている銘柄を全部クローズする．
schedule_function(all_position_close,
date_rule = date_rules.week_end(days_offset=0),
time_rule = time_rules.market_close())
def make_pipeline():
"""
A function to create our dynamic stock selector (pipeline). Documentation on
pipeline can be found here: https://www.quantopian.com/help#pipeline-title
"""
sma_short = 35
sma_long = 50
short_sma = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=sma_short)
long_sma = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=sma_long)
rsi = RSI(inputs=[USEquityPricing.close], window_length=10)
symbol = SidInList(sid_list=(8554)) # SPY 500
return Pipeline(
columns={
'RSI': rsi,
'SMA_short': short_sma,
'SMA_long': long_sma,
'symbol': symbol,
},
screen=symbol,
)
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():
"""
A function to create our dynamic stock selector (pipeline). Documentation
on pipeline can be found here:
https://www.quantopian.com/help#pipeline-title
"""
# Base universe set to the QTradableStocksUS
base_universe = QTradableStocksUS()
stocktwits_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
).zscore()
sentdex_score = SimpleMovingAverage(
inputs=[sentiment.sentiment_signal],
window_length=3,
).zscore()
combined_score = (stocktwits_score + sentdex_score) / 2
pipe = Pipeline(columns={
'sentiment': combined_score,
},
screen=base_universe)
return pipe
def initialize(context):
# 空のパイプラインを作ります
pipe = Pipeline()
# research では run_pipeline
# algorithm では，initilize 内で attach_pipeline を実行して，パイプラインを毎日実行するように設定します．
# ほしいデータを定義
# pipeline 用に定義された 移動平均 SimpleMovingAverage を使う
SMA10 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=10)
SMA30 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=30)
pipe.add(SMA10, 'SMA10')
pipe.add(SMA30, 'SMA30')
## raw_weights
raw_weights = (SMA10 - SMA30) / SMA30
abs_raw_weights = raw_weights.abs()
pipe.add(raw_weights, 'raw_weights')
pipe.add(abs_raw_weights, 'abs_raw_weights')
pipe.set_screen(Q500US())
attach_pipeline(pipe, "my_pipeline")
# Rebalance every Monday (or the first trading day if it's a holiday)
# at market open.
schedule_function(rebalance, date_rules.week_start(),
time_rules.market_open())
schedule_function(all_position_close,
date_rule=date_rules.week_end(),
time_rule=time_rules.market_close())
def make_pipeline(context):
# ベースユニバース
base_universe = QTradableStocksUS()
# make_pipelineを実行するのは before_trading_start（08：45ET）
# 最新のプライスは前日の終値。
yesterday_close = USEquityPricing.close.latest
min_price = yesterday_close >= context.MyLeastPrice
max_price = yesterday_close <= context.MyMostPrice
tradeable_stocks = base_universe & min_price & max_price
base_universe = AverageDollarVolume(
window_length=20, mask=tradeable_stocks).percentile_between(6, 40)
short_sma = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=3,
mask=base_universe)
long_sma = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=45,
mask=base_universe)
percent_difference = (short_sma - long_sma) / long_sma
#stocks_worst = percent_difference.bottom(context.MaxCandidates)
stocks_worst = percent_difference.bottom(context.MaxBuyOrdersAtOnce)
pipe = Pipeline(columns={
'percent_difference': percent_difference,
'close': yesterday_close,
},
screen=stocks_worst)
return pipe
def new_pipeline():
latest_close = USEquityPricing.close.latest
small_price_stocks = latest_close < 5
mean_close_30 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=30,
mask=small_price_stocks)
mean_close_10 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=10,
mask=small_price_stocks)
# using a classifier to check filter only stocks that belong to the NYSE
nyse_filter = exchange.eq('NYS')
percent_difference = (mean_close_10 - mean_close_30) / mean_close_30
percent_difference_filter = percent_difference > 0
combined_filter = percent_difference_filter & nyse_filter
return Pipeline(columns={
'30-Day Mean Close Price':
mean_close_30,
'Latest Close Price':
latest_close,
'Percent Difference':
percent_difference,
'Positive Percentage Difference Filter':
percent_difference_filter,
},
screen=combined_filter)
def make_pipeline():
latest_close = USEquityPricing.close.latest
small_price = latest_close < 5
mean_close_30 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=30,
mask=small_price)
mean_close_10 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=10,
mask=small_price)
percent_diff = (mean_close_10 - mean_close_30) / mean_close_30
perc_filter = percent_diff > 0
# Combine Filters
final_filter = perc_filter & small_price
return Pipeline(columns={
'30 Day Mean Close': mean_close_30,
'Percent Diff': percent_diff,
'Latest Close': latest_close,
'Percent Filter': perc_filter
},
screen=final_filter)
def new_pipeline():
latest_close = USEquityPricing.close.latest
small_price_stocks = latest_close < 5
mean_close_30 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=30,
mask=small_price_stocks)
mean_close_10 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=10,
mask=small_price_stocks)
percent_difference = (mean_close_10 - mean_close_30) / mean_close_30
percent_difference_filter = percent_difference > 0
combined_filter = percent_difference_filter
return Pipeline(columns={
'30-Day Mean Close Price':
mean_close_30,
'Latest Close Price':
latest_close,
'Percent Difference':
percent_difference,
'Positive Percentage Difference Filter':
percent_difference_filter,
},
screen=combined_filter)
def make_pipeline():
"""
Create our pipeline.
"""
# Base universe set to the Q1500US.
base_universe = Q1500US()
# 10-day close price average.
mean_10 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=10, mask=base_universe)
# 30-day close price average.
mean_30 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=30, mask=base_universe)
percent_difference = (mean_10 - mean_30) / mean_30
# Filter to select securities to short.
shorts = percent_difference.top(25)
# Filter to select securities to long.
longs = percent_difference.bottom(25)
# Filter for all securities that we want to trade.
securities_to_trade = (shorts | longs)
return Pipeline(
columns={
'longs': longs,
'shorts': shorts
},
screen=(securities_to_trade),
)
def make_pipeline():
# Base universe set to the QTradableStocksUS
base_universe = QTradableStocksUS()
#mean average difference 用乖離當作判斷依據
mean_10 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=10,
mask=base_universe)
mean_30 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=30,
mask=base_universe)
percent_diff = (mean_10 - mean_30) / mean_30
#dollar vol filter 月交易來看流動性
dollar_vol = AverageDollarVolume(window_length=30)
high_dollar_vol = dollar_vol > 10000000
#Filter to select securities to short
shorts = percent_diff.top(100, mask=high_dollar_vol)
#Filter to select securities to long
longs = percent_diff.bottom(100, mask=high_dollar_vol)
#Filter for all securities that we want to trade.
securities_to_trade = (shorts | longs) & high_dollar_vol
return Pipeline(columns={
'shorts': shorts,
'longs': longs
},
screen=securities_to_trade)
def make_pipeline(context):
base_universe = QTradableStocksUS()
#Create Price Filter
latest_close = USEquityPricing.close.latest
penny_stock = latest_close < 5
# Create Momentum Filter
SMA200 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=200)
SMA20 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=20)
momentum_filter = SMA20 > SMA200
momentum_and_price = momentum_filter & penny_stock
#Create Volatility Filter
volatility = AnnualizedVolatility(inputs=[USEquityPricing.close],
window_length=30,
mask=base_universe)
high_volatility = volatility.top(1, mask=momentum_and_price)
# Combine into final filter
final_filter = penny_stock & momentum_filter & high_volatility
return Pipeline(columns={
'SMA200': SMA200,
'SMA20': SMA20,
'Last Close': latest_close,
'Annualized Volatility': volatility,
'final filter': final_filter
},
screen=final_filter)
def make_pipeline():
base_universe = Q1500US()
sector = morningstar.asset_classification.morningstar_sector_code.latest
energy_sector = sector.eq(309)
base_energy = base_universe & energy_sector
dollar_volume = AverageDollarVolume(window_length=30)
high_dollar_volume = dollar_volume.percentile_between(95, 100)
top_half_base_energy = base_energy & high_dollar_volume
mean_10 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=10,
mask=top_half_base_energy)
mean_30 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=30,
mask=top_half_base_energy)
percent_difference = (mean_10 - mean_30) / mean_30
shorts = percent_difference < 0
longs = percent_difference > 0
securities_to_trade = (shorts | longs)
return Pipeline(columns={
'longs': longs,
'shorts': shorts,
'percent_diff': percent_difference
},
screen=securities_to_trade)
def make_pipeline():
# UNIVERSE Q1500US
base_universe = Q1500US()
# ENERGY SECTOR (OR: sector = Sector())
sector = morningstar.asset_classification.morningstar_sector_code.latest
energy_sector = sector.eq(309)
# MAKE MASK OF 1500US & ENERGY
base_energy = base_universe & energy_sector
# DOLLAR VOLUME (30 Days) GRAB THE INFO
dollar_volume = AverageDollarVolume(window_length=30)
# GRAB THE TOP 5% IN AVERAGE DOLLAR VOLUME
high_dollar_volume = dollar_volume.percentile_between(95, 100)
# COMBINE THE FILTERS
top_five_base_energy = base_energy & high_dollar_volume
# GRAB THE 10-day & 30-day MEAN CLOSE
mean_10 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=10, mast=top_five_base_energy)
mean_30 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=30, mast=top_five_base_energy)
# PERCENT DIFFERNENCE
percent_difference = (mean_10 - mean_30) / mean_30
# LIST OF SHORTS & LONGS
shorts, longs = (percent_difference < 0), (percent_difference > 0)
# FINAL MASK/FILTER FOR ANYTHING IN SHORTS / LONGS
securities_to_trade = shorts & longs
# RETURN THE PIPELINE
return Pipeline(columns={
'longs': longs, 'shorts': shorts, 'percent_diff': percent_difference
}, screen=securities_to_trade)
def make_pipeline():
latest_close = USEquityPricing.close.latest
small_price = latest_close < 5
#classifier:
#creates a filter to show only stocks listed on new york stock exchange.
nyse_filter = exchange.eq("NYS")
mean_close_30 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=30,
mask=small_price)
mean_close_10 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=10,
mask=small_price)
#*please note the mask. it is applied in simple moving average for the reason of saving computational
#efforts
percent_diff = (mean_close_10 - mean_close_30) / mean_close_30
perc_filter = percent_diff > 0
#final filter:
final_filter = perc_filter & small_price & nyse_filter
return Pipeline(columns={
'30 Day Mean Close': mean_close_30,
'Percent Diff': percent_diff,
'Latest Close': latest_close,
'Percent Filter': perc_filter
},
screen=final_filter)
def make_pipeline():
"""
Make a VWAP pipeline.
"""
vwap = VWAP(inputs=[USEquityPricing.close, USEquityPricing.volume],
window_length=14)
short_sma = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=35)
long_sma = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=50)
symbol = SidInList(sid_list=(8554)) # Only show values for the S&P500
return Pipeline(
columns={
'vwap': vwap,
'close': USEquityPricing.close.latest,
'short_sma': short_sma,
'long_sma': long_sma,
'symbol': symbol,
},
screen=symbol,
)
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():
"""
"""
sma = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=20)
adj_close = USEquityPricing.close.latest
myfactor = sma / adj_close
quantiled_myfactor = myfactor.quantiles(10)
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)
myfactor_windorized = myfactor.winsorize(min_percentile=0.05,
max_percentile=0.95)
# Here we combine our winsorized factors, z-scoring them to equalize their influence
combined_factor = (
#myfactor.zscore()
myfactor_windorized.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)
# longs = combined_factor.eq(9)
# shorts = combined_factor.eq(0)
# 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,
'myfactor': myfactor,
'quantiled_myfactor': quantiled_myfactor,
},
screen=long_short_screen)
return pipe
def initialize(context):
set_slippage(
slippage.VolumeShareSlippage(volume_limit=0.25, price_impact=0.1))
set_commission(commission.PerShare(
cost=0.000, min_trade_cost=0.00)) # Assume Robinhood zero-commissions
# Filter order:
# 1. MktCap > X # filters stocks with minimum size
# 2. Volume > X # filters stocks with minimum volume
# 3. X < SMA_200 & X < SMA_10 # filter stocks with gaps more likely to fill
# 4. Y < RSI < X # filters stocks with (+) momentum and strong trend
mkt_cap = MarketCap()
last_close = LastClose()
dollar_volume = AverageDollarVolume(window_length=10)
sma_200 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=200)
sma_10 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=10)
rsi = RSI(inputs=[USEquityPricing.close], window_length=15)
mkt_cap_rank = mkt_cap.rank(ascending=False)
mkt_cap_filter = (mkt_cap_rank < 4000)
#sma_filter = (last_close > sma_10) # & (last_close < sma_200) # shouldnt be last close, should be open price
dollar_volume_filter = (dollar_volume > 100000)
rsi_filter = (rsi > 15) & (rsi < 50)
pipe = Pipeline(
columns={
'mkt_cap_rank': mkt_cap_rank,
'last_close': last_close,
'$volume': dollar_volume,
'sma_200%': last_close / sma_200,
'sma_10%': last_close / sma_10,
'rsi/50': rsi / 50
},
screen=(mkt_cap_filter & dollar_volume_filter & rsi_filter
) # & sma_filter)
)
attach_pipeline(pipe, name='my_pipeline')
for i in range(2, 5):
schedule_function( # This will execute buys at 9:31AM
openFunction,
date_rules.week_start(
days_offset=i), #do not run on Mondays (poor success)
time_rules.market_open(minutes=1),
False #exclude half days
)
schedule_function( # This will sell open positions before close
closeFunction,
date_rules.week_start(
days_offset=i), #do not run on Mondays (poor success)
time_rules.market_close(minutes=30),
False #exclude half days
)
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():
"""
A function to create our dynamic stock selector (pipeline). Documentation on
pipeline can be found here: https://www.quantopian.com/help#pipeline-title
"""
to_exclude_symbol_filter = StaticSids([sid(40515), sid(41969), sid(38054)])
sp_100_filter = StaticSids(getSP100())
# exchange = Fundamentals.exchange_id.latest
# nyse_filter = exchange.eq('NYS')
# dollar_volume = AverageDollarVolume(window_length=10)
# high_dollar_volume = dollar_volume.top(TOP_NUM_STOCKS_BY_DOLLAR_VOLUME)
vol_sma = SimpleMovingAverage(inputs=[USEquityPricing.volume],
window_length=90)
price_sma = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=30)
vol_filter = ~(to_exclude_symbol_filter) & (vol_sma > VOLUME_MIN_AVG) & (
price_sma > CLOSE_PRICE_MIN_AVG)
weekly_high = WeeklyHigh(inputs=[USEquityPricing.high], mask=vol_filter)
weekly_low = WeeklyLow(inputs=[USEquityPricing.low], mask=vol_filter)
weekly_gamma_filter = WeeklyGammaFilter(inputs=[
USEquityPricing.open, USEquityPricing.high, USEquityPricing.low,
USEquityPricing.close
],
mask=vol_filter)
monthly_outside_filter = MonthlyOutsideFilter(inputs=[
USEquityPricing.open, USEquityPricing.high, USEquityPricing.low,
USEquityPricing.close
],
mask=vol_filter)
monthly_current_open = MonthlyCurrentOpen(inputs=[USEquityPricing.open],
mask=vol_filter)
weekly_current_open = WeeklyCurrentOpen(inputs=[USEquityPricing.open],
mask=vol_filter)
pipe = Pipeline(
screen=(weekly_gamma_filter) & (monthly_outside_filter),
# screen = symbol_filter,
columns={
# 'daily_classifier': daily_classifier,
# 'daily_high': USEquityPricing.high.latest,
# 'daily_low': USEquityPricing.low.latest,
'weekly_gamma_filter': weekly_gamma_filter,
'monthly_outside_filter': monthly_outside_filter,
'weekly_high': weekly_high,
'weekly_low': weekly_low,
'monthly_current_open': monthly_current_open,
'weekly_current_open': weekly_current_open,
'is_sp_100': sp_100_filter
})
return pipe
def make_pipeline():
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
return Pipeline(columns={'percent_difference': percent_difference})
def initialize(context):
#sell criteria the morning of -_-
schedule_function(sellaII, date_rules.every_day(),
time_rules.market_open(minutes=1))
#cancel all open orders before buy criteria
schedule_function(sellaIII, date_rules.every_day(),
time_rules.market_close(minutes=8))
#sell all (4 mins before sell all positions)
schedule_function(selaV, date_rules.every_day(),
time_rules.market_close(minutes=7))
for i in range(30, 380):
schedule_function(sell_criteria, date_rules.every_day(),
time_rules.market_open(minutes=i))
#pipeine initialization (2 mins before buy criteria)
schedule_function(be4tradestart, date_rules.every_day(),
time_rules.market_close(minutes=6))
#buy criteria (3 mins before sell criteria day of)
schedule_function(examplee, date_rules.every_day(),
time_rules.market_close(minutes=5))
schedule_function(mmarket, date_rules.every_day(),
time_rules.market_close(minutes=1))
set_commission(commission.PerTrade(cost=0))
set_commission(commission.PerShare(cost=0, min_trade_cost=0))
set_slippage(slippage.VolumeShareSlippage(volume_limit=1, price_impact=0))
avg_volume = AverageDollarVolume(window_length=10)
sma_2 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=2)
sma_200 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=200)
avg_volumee = AverageDollarVolume(window_length=2)
volatilityy = AnnualizedVolatility(annualization_factor=252)
pipe_screen = ((avg_volume >= 15000000) & (sma_2 >= sma_200) &
(avg_volumee >= 3000000) & (sma_2 >= 5.0) &
(volatilityy <= 0.93))
pipe_columns = {
'avg voI': avg_volume,
'sma2': sma_2,
'sma200': sma_200,
'avg voII': avg_volumee,
'volatility': volatilityy
}
pipe = Pipeline(columns=pipe_columns, screen=pipe_screen)
attach_pipeline(pipe, 'example')
set_long_only()
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
