def make_pipeline(context):
"""
Builds a Pipeline with Bollinger bands for top NUM_SYMBOLS stocks by market cap in the tech sector.
Starts out from the Q500US base universe.
"""
# Base universe of top 500 US stocks.
base_universe_filter = Q500US()
# Stocks of only tech sector.
tech_sector = Sector(mask=base_universe_filter)
tech_universe_filter = base_universe_filter & tech_sector.eq(311)
# Top 10 tech stocks with largest market cap.
mkt_cap_filter = morningstar.valuation.market_cap.latest
top_mkt_cap_tech_filter = mkt_cap_filter.top(context.NUM_SYMBOLS,
mask=tech_universe_filter)
# Bollinger band factor with Stdev factor 2.
lower_band_factor, middle_factor, upper_band_factor = BollingerBands(
window_length=22, k=2, mask=top_mkt_cap_tech_filter)
# Percent difference between (price, lower_band) and (price, upper_band).
price = USEquityPricing.close.latest
buy_percent_factor = ((lower_band_factor - price) * 100) / price
sell_percent_factor = ((price - upper_band_factor) * 100) / price
# Mean reversion buy and sell filters.
# Sell when price exceeds upper-band and buy when price is below lower-band.
buy_filter = buy_percent_factor > 0
sell_filter = sell_percent_factor > 0
# Build and return the Pipeline.
pipe_bbands = Pipeline(columns={
'buy_percent': buy_percent_factor,
'lower_band': lower_band_factor,
'buy': buy_filter,
'price': price,
'sell': sell_filter,
'upper_band': upper_band_factor,
'sell_percent': sell_percent_factor
},
screen=top_mkt_cap_tech_filter)
return pipe_bbands
def make_pipeline(context):
## symbol universe
base_universe = 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)
# Filter for stocks that pass all of our previous filters.
tradeable_stocks = (
primary_share
&common_stock
¬_depositary
¬_otc
¬_wi
¬_lp_name
¬_lp_balance_sheet
&have_market_cap
#&(is_cyclical)
#&(is_defensive)
#&(is_sensitive)
&(is_cyclical | is_defensive | is_sensitive)
)
# ToDo この範囲を色々変えてみる．
dollar_volume = AverageDollarVolume(window_length=30)
high_dollar_volume = dollar_volume.percentile_between(98, 100)
daily_volatility = AnnualizedVolatility(window_length=20)/math.sqrt(252)
sme20 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=20)
rsi = RSI(inputs=[USEquityPricing.close])
#sector = ms.asset_classification.morningstar_sector_code
sector = Sector()
static_sectors = (sector.eq(311) |
sector.eq(309) |
sector.eq(103) |
sector.eq(101) |
sector.eq(308) |
sector.eq(206) )
if context.static_asset:
myscreen = StaticSids(context.special_sids) #context.special_assets
else:
myscreen = (base_universe & tradeable_stocks & high_dollar_volume ) # & static_sectors
pipe = Pipeline(
columns={
'prev_close': PrevClose(),
'prev_volume': PrevVolume(),
'prev_turnover': PrevClose()*PrevVolume(),
'dollar_volume': dollar_volume,
'high_dollar_volume': high_dollar_volume,
'daily_volatility': daily_volatility,
'sma20': sme20,
'rsi': rsi,
'morningstar_sector_code': sector,
},
screen=myscreen,
)
return pipe