def make_pipeline(context):
base_universe = Q500US()
# 昨日の終値
yesterday_close = USEquityPricing.close.latest
yesterday_volume = USEquityPricing.volume.latest
dollar_volume = AverageDollarVolume(window_length=30)
high_dollar_volume = dollar_volume.percentile_between(
context.high_dollar_volume_thresh_min,
context.high_dollar_volume_thresh_max)
sector = Sector()
rsi = RSI(inputs=[USEquityPricing.close])
pipe = Pipeline(screen=base_universe & high_dollar_volume,
columns={
'high_dollar_volume': high_dollar_volume,
'sector': sector,
'rsi': rsi,
'roa': ROA(),
'roe': ROE(),
'normalized_basic_eps': NormalizedBasicEps(),
'net_income_growth': NetIncomeGrowth(),
'pe': PE(),
'book_value_yield': BookValueYield(),
'dividend_yield': DividendYield(),
'period_ending_date': PeriodEndingDate(),
'prev_close': yesterday_close,
})
return pipe
def make_pipeline():
base_universe = Q500US()
yesterday_close = USEquityPricing.close.latest
returns = Returns(window_length=2)
returns_over_5p = returns > 0.05
pipe = Pipeline(screen=base_universe & returns_over_5p,
columns={
'close': yesterday_close,
'return': returns
})
return pipe
def make_pipeline():
#context.features = ['RSI', 'MACD', 'EMA','SMA_5','SMA_10','ADX']
base_universe = Q500US()
sector = mstar.asset_classification.morningstar_sector_code.latest
sectors_311 = sector.eq(311)
returns_1 = Returns(window_length=2)
rsi = RSI(inputs=[USEquityPricing.close])
macd = MovingAverageConvergenceDivergenceSignal(
mask=base_universe
)
ema = ExponentialWeightedMovingAverage(
mask=base_universe,
inputs=[USEquityPricing.close],
window_length=30,
decay_rate=(1 - (2.0 / (1 + 15.0)))
)
mean_5 = SimpleMovingAverage(
inputs=[USEquityPricing.close],
window_length=5,
mask=base_universe
)
mean_10 = SimpleMovingAverage(
inputs=[USEquityPricing.close],
window_length=10,
mask=base_universe
)
bb = BollingerBands(
inputs=[USEquityPricing.close],
window_length=20,
k=2
)
diluted_eps = Fundamentals.diluted_eps_earnings_reports.latest
growth_score = Fundamentals.growth_score.latest
tangible_bv = Fundamentals.tangible_book_value.latest
return Pipeline(
columns={
'returns_1': returns_1,
'RSI': rsi,
'MACD': macd,
'EMA': ema,
'SMA_5': mean_5,
'SMA_10': mean_10,
'bb_upper': bb.upper,
'bb_middle': bb.middle,
'bb_lower': bb.lower,
'diluted_eps': diluted_eps,
'growth_score': growth_score,
'tangible_bv': tangible_bv
},
screen=(base_universe & sectors_311),
)
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 Q1500US
# base_universe = Q1500US()
# initial asset funnel
# base_universe = Q1500US()
base_universe = Q500US()
yesterday_close = USEquityPricing.close.latest
# roll_mean = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=10, mask=base_universe)
bb = BollingerBands(inputs=[USEquityPricing.close],
window_length=10,
k=1,
mask=base_universe)
# bb = BollingerBands(inputs = context.aapl, window_length=10, k=1)
upper_band = bb.upper
mean_band = bb.middle
lower_band = bb.lower
# print bb
# print upper_band
percent_diff_upper = (upper_band - mean_band) / mean_band
# shorts = percent_diff_upper.top(30)
percent_diff_lower = (lower_band - mean_band) / mean_band
longs = lower_band.top(20)
shorts = upper_band.bottom(30)
# print shorts
# print longs
securities_to_trade = (shorts | longs)
#-------------------------------------------------
# mean_10 = SimpleMovingAverage(inputs=[USEquityPricing.close],window=10,mask=base_universe)
# mean_30 = SimpleMovingAverage(inputs=[USEquityPricing.close],window=10,mase=base_universe)
# percent_diff = (mean_10 - mean_30)/mean_30
# shorts = percent_diff.top(25)
# longs = percent_diff.bottom(25)
# securities_to_trade = (shorts | longs)
#-------------------------------------------------
# Factor of yesterday's close price.
pipe = Pipeline(
columns={
'longs': longs,
'shorts': shorts,
},
screen=(securities_to_trade),
)
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 Q500US
base_universe = Q500US()
# Factor of yesterday's close price.
yesterday_close = USEquityPricing.close.latest
pipe = Pipeline(screen=base_universe, columns={
'close': yesterday_close,
})
return pipe
def make_pipeline(context):
mask = Q500US()
#lb_13 = -Returns(window_length=13, mask=mask)
weekly_return = Returns(window_length=6, mask=mask)
pipe = Pipeline(
columns={
'weekly_return': weekly_return,
'sector': Sector(),
},
# 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=weekly_return.notnull() & Sector().notnull(),
)
return pipe
def make_pipeline():
base_universe = Q500US()
yesterday_close = PrevClose()
yesterday_volume = PrevVolume()
dollar_volume = AverageDollarVolume(window_length=30)
#ToDo この範囲を色々変えてみる．
high_dollar_volume = dollar_volume.percentile_between(98, 100)
pipe = Pipeline(columns={
'yesterday_close': yesterday_close,
'yesterday_volume': yesterday_volume,
'yesterday_turnover': yesterday_close * yesterday_volume,
'dollar_volume': dollar_volume,
'high_dollar_volume': high_dollar_volume,
},
screen=base_universe & high_dollar_volume)
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()#Q500US()
base_universe = (base_universe & Q500US())
#base_universe = (base_universe & Fundamentals.market_cap.latest.top(150))
# Factor of yesterday's close price.
#yesterday_close = USEquityPricing.close.latest
pipe = Pipeline(
columns={
#'close': yesterday_close,
'sector': Sector(),
},
screen=base_universe
)
return pipe
def initialize(context):
"""
Setting our variables. Modify here to test different
iterations of the model.
"""
# Opt Method 1 = original, 2 = opt api
context.opt_method = 1
# Investment Universe 1 = Q500US, 2 = Q1500US
context.investment_set = 1
# This version uses the average of two momentum slopes.
# Want just one? Set them both to the same number.
context.momentum_window = 128 #88 # first momentum window.
context.momentum_window2 = 172 #128 # second momentum window
# Limit minimum slope. Keep in mind that shorter momentum windows
# yield more extreme slope numbers. Adjust one, and you may want
# to adjust the other.
context.minimum_momentum = 88 #78 #70 #88 # momentum score cap
# Fixed number of stocks in the portfolio. How diversified
# do you want to be?
context.number_of_stocks = 50 #150 #100 #64 # portfolio size
context.index_id = sid(
8554) # identifier for the SPY. used for trend filter.
context.index_average_window = 150 # moving average periods for index filter
# enable/disable trend filter.
context.index_trend_filter = True
# Most momentum research excludes most recent data.
context.exclude_days = 5 # excludes most recent days from momentum calculation
# Set trading frequency here.
#context.trading_frequency = date_rules.week_start(days_offset=3)
context.trading_frequency = date_rules.month_start(days_offset=3)
# identifier for the cash management etf, if used.
context.use_bond_etf = True
#context.bond_etf = sid(23870) #ief
context.bond_etf = sid(23921) #tlt
# 1 = inv.vola. 2 = equal size. Suggest to implement
# market cap and inverse market cap as well. There's
# lots of room for development here.
context.size_method = 1
if (context.opt_method == 1):
rebalance = my_rebalance
elif (context.investment_set == 2):
rebalance = my_rebalance_opt
# Schedule rebalance
schedule_function(rebalance, context.trading_frequency,
time_rules.market_open(hours=1))
# Schedule daily recording of number of positions - For display in back
# test results only.
schedule_function(my_record_vars, date_rules.every_day(),
time_rules.market_close())
# Create our dynamic stock selector - getting the top 500 most liquid US
# stocks.
if (context.investment_set == 1):
inv_set = Q500US()
elif (context.investment_set == 2):
inv_set = Q1500US()
attach_pipeline(make_pipeline(inv_set), 'investment_universe')
def initialize(context):
"""
Setting our variables. Modify here to test different
iterations of the model.
"""
# Investment Universe 1 = Q500US, 2 = Q1500US
context.investment_set = 1
# This version uses the average of two momentum slopes.
# Want just one? Set them both to the same number.
context.momentum_window = 60 # first momentum window.
context.momentum_window2 = 90 # second momentum window
# Limit minimum slope. Keep in mind that shorter momentum windows
# yield more extreme slope numbers. Adjust one, and you may want
# to adjust the other.
context.minimum_momentum = 60 # momentum score cap
# Fixed number of stocks in the portfolio. How diversified
# do you want to be?
context.number_of_stocks = 25 # portfolio size
context.index_id = sid(
8554) # identifier for the SPY. used for trend filter.
context.index_average_window = 100 # moving average periods for index filter
# enable/disable trend filter.
context.index_trend_filter = True
# Most momentum research excludes most recent data.
context.exclude_days = 5 # excludes most recent days from momentum calculation
# Set trading frequency here.
context.trading_frequency = date_rules.month_start()
# identifier for the cash management etf, if used.
context.use_bond_etf = True
context.bond_etf = sid(23870)
# 1 = inv.vola. 2 = equal size. Suggest to implement
# market cap and inverse market cap as well. There's
# lots of room for development here.
context.size_method = 2
# Schedule rebalance
schedule_function(my_rebalance, context.trading_frequency,
time_rules.market_open(hours=1))
# Schedule daily recording of number of positions - For display in back
# test results only.
schedule_function(my_record_vars, date_rules.every_day(),
time_rules.market_close())
# Create our dynamic stock selector - getting the top 500 most liquid US
# stocks.
if (context.investment_set == 1):
inv_set = Q500US()
elif (context.investment_set == 2):
inv_set = Q1500US()
attach_pipeline(make_pipeline(inv_set), 'investment_universe')
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 make_pipeline():
Universe = Q500US()
pipe = Pipeline(screen=Universe)
return pipe