def make_pipeline():
return Pipeline(
columns={
# Your pipeline columns go here.
},
screen=QTradableStocksUS())
def make_pipeline():
base_universe = QTradableStocksUS()
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
# Create filter for top 350 and bottom 350
# assets based on their sentiment scores
top_bottom_scores = (
sentiment_score.top(350) | sentiment_score.bottom(350)
)
return Pipeline(
columns={
'sentiment_score': sentiment_score,
},
# Set screen as the intersection between our filter
# and trading universe
screen=(
base_universe
& top_bottom_scores
)
)
def make_pipeline(context):
## symbol universe
base_universe = QTradableStocksUS() #Q500US() if False else Q1500US()
## filters
# Filter for primary share equities. IsPrimaryShare is a built-in filter.
primary_share = IsPrimaryShare()
# Equities listed as common stock (as opposed to, say, preferred stock).
# 'ST00000001' indicates common stock.
common_stock = morningstar.share_class_reference.security_type.latest.eq(
'ST00000001')
# Non-depositary receipts. Recall that the ~ operator inverts filters,
# turning Trues into Falses and vice versa
not_depositary = ~morningstar.share_class_reference.is_depositary_receipt.latest
# Equities not trading over-the-counter.
not_otc = ~morningstar.share_class_reference.exchange_id.latest.startswith(
'OTC')
# Not when-issued equities.
not_wi = ~morningstar.share_class_reference.symbol.latest.endswith('.WI')
# Equities without LP in their name, .matches does a match using a regular expression
not_lp_name = ~morningstar.company_reference.standard_name.latest.matches(
'.* L[. ]?P.?$')
# Equities with a null value in the limited_partnership Morningstar fundamental field.
not_lp_balance_sheet = morningstar.balance_sheet.limited_partnership.latest.isnull(
)
# Equities whose most recent Morningstar market cap is not null have fundamental data and therefore are not ETFs.
have_market_cap = morningstar.valuation.market_cap.latest.notnull()
is_cyclical = SuperSector().eq(SuperSector.CYCLICAL)
is_defensive = SuperSector().eq(SuperSector.DEFENSIVE)
is_sensitive = SuperSector().eq(SuperSector.SENSITIVE)
sector = Sector()
close = USEquityPricing.close
# For filter
tradeable_stocks = (primary_share
& common_stock
& not_depositary
& not_otc
& not_wi
& not_lp_name
& not_lp_balance_sheet
& have_market_cap
& (is_cyclical | is_defensive | is_sensitive))
dollar_volume = AverageDollarVolume(window_length=30)
high_dollar_volume = dollar_volume.percentile_between(98, 100)
myscreen = (base_universe & high_dollar_volume) # & tradeable_stocks
# Pipeline
pipe = Pipeline(columns={
'yesterday_close': close.latest,
'day_before_yesterday_close': PrevClose(),
'day_before_yesterday_volume': PrevVolume(),
'morningstar_sector_code': sector,
},
screen=myscreen)
return pipe
def make_pipeline():
"""
Create and return 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.
"""
# By appending .latest to the imported morningstar data, we get builtin Factors
# so there's no need to define a CustomFactor
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest
quality = Fundamentals.roe.latest
# Classify all securities by sector so that we can enforce sector neutrality later
sector = Sector()
# Screen out non-desirable securities by defining our universe.
# Removes ADRs, OTCs, non-primary shares, LP, etc.
# Also sets a minimum $500MM market cap filter and $5 price filter
mkt_cap_filter = Fundamentals.market_cap.latest >= 500000000
price_filter = USEquityPricing.close.latest >= 5
universe = QTradableStocksUS() & price_filter & mkt_cap_filter
# Construct a Factor representing the rank of each asset by our value
# quality metrics. We aggregate them together here using simple addition
# after zscore-ing them
combined_rank = (value.zscore() + quality.zscore())
# Build Filters representing the top and bottom 150 stocks by our combined ranking system.
# We'll use these as our tradeable universe each day.
longs = combined_rank.top(NUM_LONG_POSITIONS, mask=universe)
shorts = combined_rank.bottom(NUM_SHORT_POSITIONS, 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_rank': combined_rank,
'quality': quality,
'value': value,
'sector': sector
},
screen=long_short_screen)
return pipe
def make_pipeline(context):
pipe = Pipeline()
base_universe = QTradableStocksUS()
dollar_volume = AverageDollarVolume(window_length=30)
high_dollar_volume = dollar_volume.percentile_between(98, 100)
close_day_before_yeseterday = ValueDaybeforeYesterday(
inputs=[USEquityPricing.close])
volume_day_before_yeseterday = ValueDaybeforeYesterday(
inputs=[USEquityPricing.volume])
pipe.add(close_day_before_yeseterday, "close_day_before_yeseterday")
my_screen = base_universe & high_dollar_volume
pipe.set_screen(my_screen)
return pipe
def make_pipeline():
base_universe = QTradableStocksUS()
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
return Pipeline(
columns={
'sentiment_score': sentiment_score,
},
screen=base_universe
)
def make_pipeline():
not_near_earnings = ~((BusinessDaysUntilNextEarnings() <= 2) |
(BusinessDaysSincePreviousEarnings() <= 2))
not_acq_tar = ~IsAnnouncedAcqTarget()
universe = (QTradableStocksUS() & not_near_earnings & not_acq_tar)
sentiment_score = SimpleMovingAverage(inputs=[stocktwits.bull_minus_bear],
window_length=5,
mask=universe)
return Pipeline(columns={
'sentiment_score': sentiment_score,
},
screen=sentiment_score.notnull())
def make_pipeline():
cash_return = Fundamentals.cash_return.latest
universe = QTradableStocksUS() & cash_return.notnull()
ranking = cash_return.rank(mask=universe)
longs = ranking.percentile_between(95, 100)
shorts = ranking.percentile_between(1, 5)
long_short_screen = (longs | shorts)
pipe = Pipeline(columns={
'longs': longs,
'shorts': shorts,
},
screen=long_short_screen)
return pipe
def make_pipeline():
base_universe = QTradableStocksUS()
# STEP TWO, CREATE A FILTER
# sentiment_score is a simple moving average ...
# where the inputs come from stocktwits.bull_minus_bear ...
# ... there's a bit of magic here with how this data works
# but not that it comes from pipeline.data.psychsignal import stocktwits
# so it's obviously a data set that comes from a "psychsignal" library
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
# Create filter for top 350 and bottom 350
# assets based on their sentiment scores
# NOTE: The "|" operator here is "OR" and creates a UNION.
# The .top(350) and .bottom(350) is so powerful, wow.
top_bottom_scores = (
sentiment_score.top(350) | sentiment_score.bottom(350)
)
# STEP THREE, return PIPELINE, AKA CREATE DATAFRAME AND SPECIFY SCREENING UNIVERSE.
# In place of "return Pipeline" maybe it's clearer to think...
# return pandas dataframe ...
# What is Pipeline( ... ) doing? It's just defining columns of a pandas dataframe
# and it's also inputing our specific SCREEN, that we've DEFINED
# DEFINED as in... both base_universe and top_bottom_scores (that union we defined earlier)
# so Pipeline(...) is really taking these partial abstractions and running this targeted analysis.
return Pipeline(
columns={
'sentiment_score': sentiment_score,
},
# Set screen as the intersection between our filter
# and trading universe
screen=(
base_universe
& top_bottom_scores
)
)
def make_pipeline():
# Create a reference to our trading universe
base_universe = QTradableStocksUS()
# Get latest closing price
close_price = USEquityPricing.close.latest
# Calculate 3 day average of bull_minus_bear scores
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
# Return Pipeline containing close_price and
# sentiment_score that has our trading universe as screen
return Pipeline(columns={
'close_price': close_price,
'sentiment_score': sentiment_score,
},
screen=base_universe)
def make_pipeline():
base_universe = QTradableStocksUS()
sector = Sector()
sector2 = morningstar.asset_classification.morningstar_sector_code.latest
healthcare_sector = sector.eq(206)
airline_sector = sector2.eq(31053108)
sent = sentiment.sentiment_signal.latest
longs = healthcare_sector and (sent > 3)
shorts = airline_sector and (sent < 1)
tradable_securities = (longs | shorts) and base_universe
return Pipeline(columns={
'longs': longs,
'shorts': shorts
},
screen=tradable_securities)
def make_pipeline():
# Latest p/e ratio.
pe_ratio = Fundamentals.pe_ratio.latest
# Number of days since the fundamental data was updated. In this example, we consider the p/e ratio
# to be fresh if it's less than 4 days old.
is_fresh = (BusinessDaysSincePreviousEvent(
inputs=[Fundamentals.pe_ratio_asof_date]) <= 4)
# Other indicators
market_c = Fundamentals.market_cap.latest
shares_o = Fundamentals.shares_outstanding.latest
basic_e = Fundamentals.basic_eps.latest
fcf = Fundamentals.free_cash_flow.latest
# QTradableStocksUS is a pre-defined universe of liquid securities.
universe = QTradableStocksUS() & is_fresh
# Filter
temp = market_c.filter()
# Top 50 and bottom 50 stocks ranked by p/e ratio
top_pe_stocks = pe_ratio.top(100, mask=universe)
bottom_pe_stocks = pe_ratio.bottom(100, mask=universe)
# Screen to include only securities tradable for the day
securities_to_trade = (top_pe_stocks | bottom_pe_stocks)
pipe = Pipeline(columns={
'pe_ratio': pe_ratio,
'longs': top_pe_stocks,
'shorts': bottom_pe_stocks,
},
screen=securities_to_trade)
return pipe
def initialize(context):
# Universe Selection
# ------------------
base_universe = QTradableStocksUS()
# From what remains, each month, take the top UNIVERSE_SIZE stocks by average dollar
# volume traded.
monthly_top_volume = (AverageDollarVolume(
window_length=LIQUIDITY_LOOKBACK_LENGTH).top(
UNIVERSE_SIZE, mask=base_universe).downsample('week_start'))
# The final universe is the monthly top volume &-ed with the original base universe.
# &-ing these is necessary because the top volume universe is calculated at the start
# of each month, and an asset might fall out of the base universe during that month.
universe = monthly_top_volume & base_universe
# Alpha Generation
# ----------------
# Compute Z-scores of free cash flow yield and earnings yield.
# Both of these are fundamental value measures.
fcf_zscore = Fundamentals.fcf_yield.latest.zscore(mask=universe)
yield_zscore = Fundamentals.earning_yield.latest.zscore(mask=universe)
sentiment_zscore = psychsignal.stocktwits.bull_minus_bear.latest.zscore(
mask=universe)
# Alpha Combination
# -----------------
# Assign every asset a combined rank and center the values at 0.
# For UNIVERSE_SIZE=500, the range of values should be roughly -250 to 250.
combined_alpha = (fcf_zscore + yield_zscore +
sentiment_zscore).rank().demean()
beta = 0.66 * RollingLinearRegressionOfReturns(
target=sid(8554),
returns_length=5,
regression_length=260,
mask=combined_alpha.notnull() & Sector().notnull()).beta + 0.33 * 1.0
# Schedule Tasks
# --------------
# Create and register a pipeline computing our combined alpha and a sector
# code for every stock in our universe. We'll use these values in our
# optimization below.
pipe = Pipeline(
columns={
'alpha': combined_alpha,
'sector': Sector(),
'sentiment': sentiment_zscore,
'beta': beta,
},
# combined_alpha will be NaN for all stocks not in our universe,
# but we also want to make sure that we have a sector code for everything
# we trade.
screen=combined_alpha.notnull() & Sector().notnull() & beta.notnull(),
)
# Multiple pipelines can be used in a single algorithm.
algo.attach_pipeline(pipe, 'pipe')
algo.attach_pipeline(risk_loading_pipeline(), 'risk_loading_pipeline')
# Schedule a function, 'do_portfolio_construction', to run twice a week
# ten minutes after market open.
algo.schedule_function(
do_portfolio_construction,
date_rule=algo.date_rules.week_start(),
time_rule=algo.time_rules.market_open(
minutes=MINUTES_AFTER_OPEN_TO_TRADE),
half_days=False,
)
def initialize(context):
set_slippage(
slippage.VolumeShareSlippage(volume_limit=0.025,
price_impact=0.1)) # Default
set_commission(commission.PerShare(cost=0.005,
min_trade_cost=1.0)) # FSC for IB
#Fetch SP500 DNN predictions
fetch_csv(
'https://docs.google.com/spreadsheets/d/e/2PACX-1vQAtxWEdzLnzdmU6zfTxk7RYgOIieg27z9em6sSd9Mm2mBpK46CXOY7EFrYjnZ3Vy9L8vYxBSICyEFz/pub?output=csv',
date_column='Date',
date_format='%m/%d/%y')
context.stock = symbol('SPY')
# Universe Selection
# ------------------
base_universe = QTradableStocksUS()
# From what remains, each month, take the top UNIVERSE_SIZE stocks by average dollar
# volume traded.
monthly_top_volume = (AverageDollarVolume(
window_length=LIQUIDITY_LOOKBACK_LENGTH).top(
UNIVERSE_SIZE, mask=base_universe).downsample('week_start'))
# The final universe is the monthly top volume &-ed with the original base universe.
# &-ing these is necessary because the top volume universe is calculated at the start
# of each month, and an asset might fall out of the base universe during that month.
universe = monthly_top_volume & base_universe
# Market Beta Factor
# ------------------
stock_beta = MyBeta() #SimpleBeta(
#target=sid(8554),
#regression_length=21,
#)
# Alpha Generation
# ----------------
# Compute Z-scores of free cash flow yield and earnings yield.
# Both of these are fundamental value measures.
#fcf_zscore = Fundamentals.fcf_yield.latest.zscore(mask=universe)
#yield_zscore = Fundamentals.earning_yield.latest.zscore(mask=universe)
#sentiment_zscore = psychsignal.stocktwits.bull_minus_bear.latest.zscore(mask=universe)
b = stock_beta.rank(mask=universe)
# Alpha Combination
# -----------------
# Assign every asset a combined rank and center the values at 0.
# For UNIVERSE_SIZE=500, the range of values should be roughly -250 to 250.
#combined_alpha = (fcf_zscore + yield_zscore + sentiment_zscore).rank().demean()
alpha = b.top(100)
# Schedule Tasks
# --------------
# Create and register a pipeline computing our combined alpha and a sector
# code for every stock in our universe. We'll use these values in our
# optimization below.
pipe = Pipeline(
columns={
'alpha': alpha,
},
# combined_alpha will be NaN for all stocks not in our universe,
# but we also want to make sure that we have a sector code for everything
# we trade.
screen=alpha,
)
algo.attach_pipeline(pipe, 'pipe')
# Schedule a function, 'do_portfolio_construction', to run twice a week
# ten minutes after market open.
algo.schedule_function(
do_portfolio_construction,
date_rule=algo.date_rules.every_day(),
time_rule=algo.time_rules.market_open(
minutes=MINUTES_AFTER_OPEN_TO_TRADE),
half_days=False,
)
schedule_function(func=record_vars,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_close(),
half_days=True)
set_slippage(
slippage.VolumeShareSlippage(volume_limit=0.025,
price_impact=0.1)) # Default
set_commission(commission.PerShare(cost=0.005,
min_trade_cost=1.0)) # FSC for IB
def initialize(context):
context.s1 = sid(5061)
context.s2 = sid(8655)
context.s3 = sid(17787)
context.s4 = sid(4707)
context.s5 = sid(35920)
context.s6 = sid(32146)
context.s7 = sid(4283)
context.s8 = sid(5885)
context.s9 = sid(12002)
context.s10 = sid(15596)
context.s11 = sid(679)
context.s12 = sid(1937)
context.s13 = sid(8347)
context.s14 = sid(3895)
context.s15 = sid(14516)
context.s16 = sid(14517)
context.s17 = sid(21787)
context.s18 = sid(21520)
context.s19 = sid(21524)
context.s20 = sid(23216)
context.s21 = sid(19725)
context.s22 = sid(3766)
context.s23 = sid(49506)
context.s24 = sid(5692)
context.s25 = sid(24757)
context.s26 = sid(7904)
context.s27 = sid(20774)
context.s28 = sid(7493)
context.s29 = sid(438)
context.s30 = sid(4914)
context.asset_pairs = [[
context.s1, context.s2, {
'in_short': False,
'in_long': False,
'spread': np.array([]),
'hedge_history': np.array([]),
'residual': np.array([]),
'z_scores': np.array([])
}
],
[
context.s3, context.s4, {
'in_short': False,
'in_long': False,
'spread': np.array([]),
'hedge_history': np.array([]),
'residual': np.array([]),
'z_scores': np.array([])
}
],
[
context.s5, context.s6, {
'in_short': False,
'in_long': False,
'spread': np.array([]),
'hedge_history': np.array([]),
'residual': np.array([]),
'z_scores': np.array([])
}
],
[
context.s7, context.s8, {
'in_short': False,
'in_long': False,
'spread': np.array([]),
'hedge_history': np.array([]),
'residual': np.array([]),
'z_scores': np.array([])
}
],
[
context.s9, context.s10, {
'in_short': False,
'in_long': False,
'spread': np.array([]),
'hedge_history': np.array([]),
'residual': np.array([]),
'z_scores': np.array([])
}
],
[
context.s11, context.s12, {
'in_short': False,
'in_long': False,
'spread': np.array([]),
'hedge_history': np.array([]),
'residual': np.array([]),
'z_scores': np.array([])
}
],
[
context.s13, context.s14, {
'in_short': False,
'in_long': False,
'spread': np.array([]),
'hedge_history': np.array([]),
'residual': np.array([]),
'z_scores': np.array([])
}
],
[
context.s15, context.s16, {
'in_short': False,
'in_long': False,
'spread': np.array([]),
'hedge_history': np.array([]),
'residual': np.array([]),
'z_scores': np.array([])
}
],
[
context.s17, context.s18, {
'in_short': False,
'in_long': False,
'spread': np.array([]),
'hedge_history': np.array([]),
'residual': np.array([]),
'z_scores': np.array([])
}
],
[
context.s19, context.s20, {
'in_short': False,
'in_long': False,
'spread': np.array([]),
'hedge_history': np.array([]),
'residual': np.array([]),
'z_scores': np.array([])
}
],
[
context.s21, context.s22, {
'in_short': False,
'in_long': False,
'spread': np.array([]),
'hedge_history': np.array([]),
'residual': np.array([]),
'z_scores': np.array([])
}
],
[
context.s23, context.s24, {
'in_short': False,
'in_long': False,
'spread': np.array([]),
'hedge_history': np.array([]),
'residual': np.array([]),
'z_scores': np.array([])
}
],
[
context.s25, context.s26, {
'in_short': False,
'in_long': False,
'spread': np.array([]),
'hedge_history': np.array([]),
'residual': np.array([]),
'z_scores': np.array([])
}
],
[
context.s27, context.s28, {
'in_short': False,
'in_long': False,
'spread': np.array([]),
'hedge_history': np.array([]),
'residual': np.array([]),
'z_scores': np.array([])
}
],
[
context.s29, context.s30, {
'in_short': False,
'in_long': False,
'spread': np.array([]),
'hedge_history': np.array([]),
'residual': np.array([]),
'z_scores': np.array([])
}
]]
context.z_back = 20
context.hedge_lag = 2
context.z_entry = 2.0
context.z_exit = 0.5
context.pair_count = len(context.asset_pairs)
context.margin = 0.5
context.universe = QTradableStocksUS()
#attach_pipeline(pipeline_data(context), 'fund_data')
schedule_function(my_handle_data, date_rules.every_day(),
time_rules.market_close(hours=4))
def initialize(context):
schedule_function(
func=periodic_rebalance,
date_rule=date_rules.every_day(), #week_start(days_offset=1),
time_rule=time_rules.market_open(hours=.5))
schedule_function(my_rebalance, date_rules.every_day(),
time_rules.market_open(hours=0, minutes=1))
#schedule_function(my_rebalance, date_rules.every_day(), time_rules.market_open(hours=1, minutes=1))
schedule_function(my_rebalance, date_rules.every_day(),
time_rules.market_open(hours=2, minutes=1))
#schedule_function(my_rebalance, date_rules.every_day(), time_rules.market_open(hours=3, minutes=1))
schedule_function(my_rebalance, date_rules.every_day(),
time_rules.market_open(hours=4, minutes=1))
#schedule_function(my_rebalance, date_rules.every_day(), time_rules.market_open(hours=5, minutes=1))
schedule_function(my_rebalance, date_rules.every_day(),
time_rules.market_open(hours=6, minutes=1))
#schedule_function(
# do_portfolio_construction,
# date_rule=algo.date_rules.week_start(),
# time_rule=algo.time_rules.market_open(minutes=30),
# half_days=False,
# )
for hours_offset in range(7):
schedule_function(my_rebalance,
date_rules.every_day(),
time_rules.market_open(hours=hours_offset,
minutes=10),
half_days=True)
#
# set portfolis parameters
#
set_asset_restrictions(security_lists.restrict_leveraged_etfs)
context.acc_leverage = 1.00
context.min_holdings = 20
context.s_min_holdings = 10
#
# set profit taking and stop loss parameters
#
context.profit_taking_factor = 0.01
context.profit_taking_target = 100.0 #set much larger than 1.0 to disable
context.profit_target = {}
context.profit_taken = {}
context.stop_pct = 0.97 # set to 0.0 to disable
context.stop_price = defaultdict(lambda: 0)
#
# Set commission model to be used
#
set_slippage(
slippage.VolumeShareSlippage(volume_limit=0.025,
price_impact=0.1)) # Default
set_commission(commission.PerShare(cost=0.005,
min_trade_cost=1.0)) # FSC for IB
# Define safe set (of bonds)
#sid(32268) #SH
context.safe = [
sid(23870), #IEF
sid(23921), #TLT
#sid(8554), #SPY
]
#
# Define proxy to be used as proxy for overall stock behavior
# set default position to be in safe set (context.buy_stocks = False)
#
context.canary = sid(22739) #why not spy
context.buy_stocks = False
#
# Establish pipeline
#
pipe = Pipeline()
attach_pipeline(pipe, 'ranked_stocks')
#
# Define the four momentum factors used in ranking stocks
#
factor1 = simple_momentum(window_length=1)
pipe.add(factor1, 'factor_1')
factor2 = simple_momentum(window_length=60) / Volatility(window_length=60)
pipe.add(factor2, 'factor_2')
factor3 = simple_momentum(window_length=252)
pipe.add(factor3, 'factor_3')
factor4 = ((Momentum() / Volatility()) + Momentum()) #or Downside_Risk()
pipe.add(factor4, 'factor_4')
factor8 = earning_yield()
pipe.add(factor8, 'factor8')
factor9 = roe() + roic() + roa()
pipe.add(factor9, 'factor9')
factor10 = cash_return()
pipe.add(factor10, 'factor10')
factor11 = fcf_yield()
pipe.add(factor11, 'factor11')
factor12 = current_ratio()
pipe.add(factor12, 'factor12')
factor13 = Quality()
pipe.add(factor13, 'factor13')
factor14 = market_cap()
pipe.add(factor14, 'factor14')
factor15 = RnD_to_market() + capex()
pipe.add(factor15, 'factor15')
factor18 = EPS_Growth_3M()
pipe.add(factor18, 'factor18')
factor19 = Piotroski4()
pipe.add(factor19, 'factor19')
factor20 = capex()
pipe.add(factor20, 'factor20')
#
# Define other factors that may be used in stock screening
#
#factor5 = get_fcf_per_share()
#pipe.add(factor5, 'factor_5')
factor6 = AverageDollarVolume(window_length=60)
pipe.add(factor6, 'factor_6')
factor7 = get_last_close()
pipe.add(factor7, 'factor_7')
#factor_4_filter = factor4 > 1.03 # only consider stocks with positive 1y growth
#factor_5_filter = factor5 > 0.0 # only consider stocks with positive FCF
factor_6_filter = factor6 > .5e6 # only consider stocks trading >$500k per day
#factor_7_filter = factor7 > 3.00 # only consider stocks that close above this value
factor_12_filter = factor12 > .99
#factor_8_filter = factor8 > 0
#factor_15_filter = factor15 > factor6
#factor_1_filter = factor1 > 1.1
#factor_2_filter = factor2 > 1
#factor_20_filter = factor20 > 0
utilities_filter = Sector() != 207
materials_filter = Sector() != 101
energy_filter = Sector() != 309
industrial_filter = Sector() != 310
health_filter = Sector() != 206
staples_filter = Sector() != 205
real_estate_filter = Sector() != 104
#sentiment_filter = ((0.5*st.bull_scored_messages.latest)>(st.bear_scored_messages.latest)) & (st.bear_scored_messages.latest > 10)
consumer_cyclical_filter = Sector() != 102
financial_filter = Sector() != 103
communication_filter = Sector() != 308
technology_filter = Sector != 311
#Basic_Materials = context.output[context.output.sector == 101]
#Consumer_Cyclical = context.output[context.output.sector == 102]
#Financial_Services = context.output[context.output.sector == 103]
#Real_Estate = context.output[context.output.sector == 104]
#Consumer_Defensive = context.output[context.output.sector == 205]
#Healthcare = context.output[context.output.sector == 206]
#Utilities = context.output[context.output.sector == 207]
#Communication_Services = context.output[context.output.sector == 308]
#Energy = context.output[context.output.sector == 309]
#Industrials = context.output[context.output.sector == 310]
#Technology = context.output[context.output.sector == 311]
#
# Establish screen used to establish candidate stock list
#
mkt_screen = market_cap()
cash_flow = factor10 + factor11
price = factor14
profitability = factor9
#earning_quality = factor15
stocks = QTradableStocksUS(
) #mkt_screen.top(3500)&profitability.top(3500)&factor19.top(2000)#&factor8.top(2000)#&price.top(2000)#&factor15.top(3000)#
total_filter = (
stocks
& factor_6_filter
#& factor_15_filter
#& factor_8_filter
#& factor_9_filter
#& factor_1_filter
#& factor_20_filter
#& communication_filter
#& consumer_cyclical_filter
#& financial_filter
#& staples_filter
#& materials_filter
#& industrial_filter
#& factor_12_filter
#& technology_filter
)
pipe.set_screen(total_filter)
#
# Establish ranked stock list
#
factor1_rank = factor1.rank(mask=total_filter, ascending=False)
pipe.add(factor1_rank, 'f1_rank')
factor2_rank = factor2.rank(mask=total_filter, ascending=False)
pipe.add(factor2_rank, 'f2_rank')
factor3_rank = factor3.rank(mask=total_filter,
ascending=False) #significant effect
pipe.add(factor3_rank, 'f3_rank')
factor4_rank = factor4.rank(mask=total_filter,
ascending=False) #significant effect
pipe.add(factor4_rank, 'f4_rank')
factor8_rank = factor8.rank(mask=total_filter,
ascending=False) #significant effect
pipe.add(factor8_rank, 'f8_rank')
factor9_rank = factor9.rank(mask=total_filter,
ascending=False) #very big effect
pipe.add(factor9_rank, 'f9_rank')
factor10_rank = factor10.rank(mask=total_filter, ascending=False)
pipe.add(factor10_rank, 'f10_rank')
factor11_rank = factor11.rank(mask=total_filter, ascending=False)
pipe.add(factor11_rank, 'f11_rank')
factor13_rank = factor13.rank(mask=total_filter,
ascending=False) #may want to remove
pipe.add(factor13_rank, 'f13_rank')
factor14_rank = factor14.rank(mask=total_filter, ascending=True)
pipe.add(factor14_rank, 'f14_rank')
factor15_rank = factor15.rank(mask=total_filter, ascending=False)
pipe.add(factor15_rank, 'f15_rank')
factor18_rank = factor18.rank(mask=total_filter, ascending=False)
pipe.add(factor18_rank, 'f18_rank')
factor19_rank = factor19.rank(mask=total_filter, ascending=False)
pipe.add(factor19_rank, 'f19_rank')
factor20_rank = factor20.rank(mask=total_filter, ascending=False)
pipe.add(factor20_rank, 'f20_rank')
combo_raw = (factor8_rank + factor18_rank + factor1_rank + factor4_rank +
factor10_rank + factor11_rank + factor15_rank + factor9_rank +
factor19_rank) #+factor14_rank*.5)
pipe.add(combo_raw, 'combo_raw')
pipe.add(combo_raw.rank(mask=total_filter), 'combo_rank')