def test_engine(refdata, stocks, alpaca_tradeapi):
def list_symbols():
return ['A', 'AA']
mock_iex.get_available_symbols(refdata)
mock_iex.get_key_stats(stocks)
mock_iex.get_chart(stocks)
mock_tradeapi.list_assets(alpaca_tradeapi)
eng = LivePipelineEngine(list_symbols)
ADV = AverageDollarVolume(window_length=20, )
top5 = ADV.top(5, groupby=Sector())
pipe = Pipeline({
'top5': top5,
'close': USEquityPricing.close.latest,
},
screen=top5)
df = eng.run_pipeline(pipe)
assert sorted(df.columns) == ['close', 'top5']
pipe = Pipeline({
'close': USEquityPricing.close.latest,
})
df = eng.run_pipeline(pipe)
assert df.shape == (2, 1)
assert df.close['AA'] > 40
def initialize(context):
# The initialize method is called at the very start of your script's
# execution. You can set up anything you'll be needing later here. The
# context argument will be received by all pylivetrader methods in
# your script, and you can store information on it that you'd like to
# share between methods, or in later trades
# let's create our pipeline and attach it to pylivetrader execution
top5 = AverageDollarVolume(window_length=20).top(5)
pipe = Pipeline({
'close': USEquityPricing.close.latest,
}, screen=top5)
# this line connects the pipeline to pylivetrader. this is done once,
# and we get a new and it's stored in the context. we will get a fresh list
# of assets every morning in before_trading_start()
context.attach_pipeline(pipe, "pipe")
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
from pipeline_live.engine import LivePipelineEngine
from pipeline_live.data.sources.iex import list_symbols
from pipeline_live.data.alpaca.pricing import USEquityPricing
from pipeline_live.data.alpaca.factors import AverageDollarVolume
from pipeline_live.data.polygon.fundamentals import PolygonCompany
from zipline.pipeline import Pipeline
from dotenv import load_dotenv
load_dotenv()
engine = LivePipelineEngine(list_symbols)
top5 = AverageDollarVolume(window_length=20).top(5)
pipe = Pipeline(
{
"close": USEquityPricing.close.latest,
"marketcap": PolygonCompany.marketcap.latest,
},
screen=top5,
)
result = engine.run_pipeline(pipe)
print(result)
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')
not_wi = ~PolygonCompany.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.?$')
not_lp_name = ~PolygonCompany.name.latest.matches(".* L[. ]?P.?$")
# 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
have_market_cap = PolygonCompany.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),
)