def make_pipeline():
have_market_cap = IEXKeyStats.marketcap.latest >= 1
avg_volume = SimpleMovingAverage(inputs=[USEquityPricing.volume], window_length=50)
filter_volume = avg_volume > 500000
last_price = Latest(inputs=[USEquityPricing.close], window_length=1)
filter_price = last_price > 1
dollar_volume = AverageDollarVolume(window_length=50)
filter_dollar_volume = dollar_volume > 2500000
sma_150 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=150)
filter_sma_150 = USEquityPricing.close.latest > sma_150
atr_10_percent = atr_10days_percent()
filter_atr_10 = atr_10_percent > 4
rsi = RSI(inputs=[USEquityPricing.close], window_length=3)
filter_overbought = rsi < 30
atr_10 = atr_10days()
stocks_to_trade = have_market_cap & filter_volume & filter_price & filter_dollar_volume & filter_sma_150 & filter_atr_10 & filter_overbought
return Pipeline(
columns={
'stocks': stocks_to_trade,
'rsi': rsi,
'atr': atr_10
},
screen=(stocks_to_trade)
)
def make_pipeline():
# Filter for primary share equities. IsPrimaryShare is a built-in filter.
primary_share = IsPrimaryShare()
# Not when-issued equities.
not_wi = ~IEXCompany.symbol.latest.endswith('.WI')
# Equities without LP in their name, .matches does a match using a regular
# expression
not_lp_name = ~IEXCompany.companyName.latest.matches('.* L[. ]?P.?$')
# Equities whose most recent Morningstar market cap is not null have
# fundamental data and therefore are not ETFs.
have_market_cap = IEXKeyStats.marketcap.latest >= 1
tradeable_stocks = (
primary_share
& not_wi
& not_lp_name
& have_market_cap)
longs = USEquityPricing.close.latest.top(50,mask=tradeable_stocks)
shorts = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=10, mask=tradeable_stocks).percentile_between(20,22)
return Pipeline(
columns={
'longs': longs,
'shorts': shorts,
},
screen=tradeable_stocks& (shorts | longs)
)
def make_pipeline(context):
advFilter = curVsAvgVolFilter(context.params.get('lookback'))
# midToLargeFilter = isMidToLargeCap(context.params.get('lookback'))
smaSlow = SimpleMovingAverage(
inputs=[USEquityPricing.close],
window_length=context.params.get('smaSlowLookback'))
smaFast = SimpleMovingAverage(
inputs=[USEquityPricing.close],
window_length=context.params.get('smaFastLookback'))
top50 = AverageDollarVolume(window_length=20).top(50)
pipe = Pipeline(screen=top50)
pipe.add(advFilter, 'advFilter')
pipe.add(smaSlow, 'smaSlow')
pipe.add(smaFast, 'smaFast')
# pipe.add(midToLargeFilter, 'midToLargeFilter')
return pipe
def make_pipeline():
# My list of ETFS
base_universe = StaticSymbols(symbols = ("DGAZ", "UGAZ", "JDST", "JNUG", "UWT", "DWT", "GUSH", "DRIP", "TQQQ", "SQQQ", "SPXS", "SPXL"))
dollar_volume = AverageDollarVolume(
window_length=14
)
high_dollar_volume = (dollar_volume > 10000000)
mean_close_10 = SimpleMovingAverage(
inputs=[USEquityPricing.close],
window_length=10
)
mean_close_30 = SimpleMovingAverage(
inputs=[USEquityPricing.close],
window_length=30
)
rsi = RSI()
percent_difference = (mean_close_10 - mean_close_30) / mean_close_30
rsi_low = (rsi <= 40)
rsi_high = (rsi >= 65)
shorts = percent_difference.percentile_between(90, 100) & rsi_high
longs = percent_difference.percentile_between(0, 15) & rsi_low
securities_to_trade = high_dollar_volume
return Pipeline(
columns={
'longs': longs,
'shorts': shorts,
'rsi': rsi,
'percent_difference': percent_difference
},
screen=base_universe
)
def make_pipeline(context):
"""
Create our pipeline.
"""
# Filter for primary share equities. IsPrimaryShare is a built-in filter.
primary_share = IsPrimaryShare()
# Not when-issued equities.
not_wi = ~IEXCompany.symbol.latest.endswith('.WI')
# Equities without LP in their name, .matches does a match using a regular
# expression
not_lp_name = ~IEXCompany.companyName.latest.matches('.* L[. ]?P.?$')
# Equities whose most recent Morningstar market cap is not null have
# fundamental data and therefore are not ETFs.
have_market_cap = IEXKeyStats.marketcap.latest >= 1
# At least a certain price
price = USEquityPricing.close.latest
AtLeastPrice = (price >= context.MyLeastPrice)
AtMostPrice = (price <= context.MyMostPrice)
# Filter for stocks that pass all of our previous filters.
tradeable_stocks = (primary_share
& not_wi
& not_lp_name
& have_market_cap
& AtLeastPrice
& AtMostPrice)
LowVar = 6
HighVar = 40
log.info('''
Algorithm initialized variables:
context.MaxCandidates %s
LowVar %s
HighVar %s''' % (context.MaxCandidates, LowVar, HighVar))
# High dollar volume filter.
base_universe = AverageDollarVolume(
window_length=20,
mask=tradeable_stocks).percentile_between(LowVar, HighVar)
# Short close price average.
ShortAvg = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=3,
mask=base_universe)
# Long close price average.
LongAvg = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=45,
mask=base_universe)
percent_difference = (ShortAvg - LongAvg) / LongAvg
# Filter to select securities to long.
stocks_worst = percent_difference.bottom(context.MaxCandidates)
securities_to_trade = (stocks_worst)
return Pipeline(
columns={'stocks_worst': stocks_worst},
screen=(securities_to_trade),
)