def test_engine(refdata, stocks, data_path):
def list_symbols():
return ['A', 'AA']
mock_iex.get_available_symbols(refdata)
mock_iex.get_key_stats(stocks)
mock_iex.get_chart(stocks)
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 pipeline_output(self, name):
try:
from pipeline_live.engine import LivePipelineEngine
except ImportError:
raise RuntimeError('pipeline-live is not installed')
finder = self.asset_finder
def list_symbols():
return sorted([a.symbol for a in finder._asset_cache.values()])
eng = LivePipelineEngine(list_symbols)
output = eng.run_pipeline(self._pipelines[name])
output.index = pd.Index(finder.lookup_symbols(output.index))
return output
def initialize (context): # runs once when script starts
log.info("Welcome Vincent Perkins")
#context is a python dictionary that contains information on portfolio/performance.
context.idr_losers = pd.Series(([])) #intraday losing stocks
context.day_count = 0
context.daily_message = "Day {}."
context.open_orders = get_open_orders()
context.backup_stocks = symbols('BAM')
context.age = {} #empty dictionary maps one value to another
#context.usep = USEquityPricing() #USEquityPricing object
#Factor criteria
close_price = USEquityPricing.close.latest
vol = USEquityPricing.volume.latest
ann_var = AnnualizedVolatility()
rsi = RSI()
#screening
mask_custom = (IsPrimaryShare() & (vol < 150000) & (close_price > 1) & (close_price < 3) & (ann_var > 0.815) & (rsi < 50))
stockBasket = USEquityPricing.close.latest.top(3000, mask = mask_custom)
#Column construction
pipe_columns = {'close_price': close_price, 'volume': vol, 'ann_var': ann_var}
#Creation of actual pipeline
pipe = Pipeline(columns = pipe_columns, screen = stockBasket)
attach_pipeline(pipe, 'Stocks')
#Testing
log.info(USEquityPricing.get_loader())
eng = LivePipelineEngine(list_symbols)
df = eng.run_pipeline(pipe)
log.info(df)
#Schedule functions
schedule_function(day_start, date_rules.every_day(), time_rules.market_open(hours = 0, minutes = 1))
schedule_function(late_day_trade, date_rules.every_day(), time_rules.market_open(hours = 5, minutes = 56)) #offset open tells when to run a user defined function
schedule_function(check_portfolio, date_rules.every_day(), time_rules.market_open(hours = 0, minutes = 1))
schedule_function(morning_day_trade1, date_rules.every_day(), time_rules.market_open(hours = 0, minutes = 15))
schedule_function(morning_day_trade2, date_rules.every_day(), time_rules.market_open(hours = 0, minutes = 45))
schedule_function(check_portfolio, date_rules.every_day(), time_rules.market_open(hours = 0, minutes = 48))
schedule_function(cancel_open_orders, date_rules.every_day(),time_rules.market_close(hours=0, minutes=1))
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)
from pipeline_live.engine import LivePipelineEngine
from pipeline_live.data.alpaca.pricing import USEquityPricing
from pipeline_live.data.alpaca.factors import AverageDollarVolume
from pipeline_live.data.sources.polygon import list_symbols
from zipline.pipeline import Pipeline
from src.ziplineStrategies.Filters.CurVsAvgVolFilter import curVsAvgVolFilter
# from src.ziplineStrategies.Filters.CompanySizeFilter import isMidToLargeCap
from pipeline_live.data.iex.factors import SimpleMovingAverage
from pylivetrader.api import (symbol, pipeline_output, order_target_percent)
import logging
ENGINE = LivePipelineEngine(list_symbols)
def initialize(context):
context.params = {
'lookback': 20,
'smaSlowLookback': 30,
'smaFastLookback': 10
}
context.position_size = .1
context.stopLevel = .9
context.leverageLimit = 1.5
context.stopPriceMap = {}
def make_pipeline(context):
advFilter = curVsAvgVolFilter(context.params.get('lookback'))
# midToLargeFilter = isMidToLargeCap(context.params.get('lookback'))
smaSlow = SimpleMovingAverage(