to read more about marketstore. MarketStore is a database server optimized for financial timeseries data. I am integrating MS because it as well allows for real-time streaming via a websocket of the bars from the db. IMO this will allow for consistency between realtime and historical testing. Data is stored in tick compression and then using On-disk Aggregate updates the downsample data compressed into any timeframe you want. Easiest way to get this to work is to build MS from source in a docker, its already integrated with GDAX and works out the box.
The next step for me is to integrate polygon.io which is REALTIME trade by trade SIP data for 200$ a month, latency is under 10ms too if you are colocated in or around ny4.
will update with the polygon connector to MS once it is complete and will post the live datafeed code as well once done. Will update this code too once all the rest is complete.
thanks,
datafeed:
import backtrader as bt
from ..utils import date2num
import backtrader.feed as feed
import datetime
import pytz
import pymarketstore as pymkts
class MarketStore(feed.DataBase):
params = (
('dataname', None),
('fromdate', datetime.date(1990, 1, 1)),
('todate', datetime.date(2050,1,1)),
('name', ''),
('compression', 1),
('timeframe', bt.TimeFrame.Days),
('host', '127.0.0.1'),
('port', '5993'),
('symbol', None),
('query_timeframe', None),
)
def start(self):
super(MarketStore, self).start()
self.ndb = pymkts.Client('http://{host}:{port}/rpc'.format(
host=self.p.host,
port=self.p.port
))
qstr = pymkts.Params(self.p.symbol,
self.p.query_timeframe,
'OHLCV',
start=self.p.fromdate.isoformat(),
end=self.p.todate.isoformat())
dbars = list(self.ndb.query(qstr).first().array)
self.biter = iter(dbars)
def _load(self):
try:
bar = next(self.biter)
except StopIteration:
return False
self.l.datetime[0] = date2num(datetime.datetime.fromtimestamp(bar[0],pytz.utc))
self.l.open[0] = bar[1]
self.l.high[0] = bar[2]
self.l.low[0] = bar[3]
self.l.close[0] = bar[4]
self.l.volume[0] = bar[5]
return True
simple test strategy:
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import pytz
import datetime
import backtrader as bt
class Master(bt.Strategy):
def __init__(self):
self.dataopen = self.datas[0].open
self.datahigh = self.datas[0].high
self.datalow = self.datas[0].low
self.dataclose = self.datas[0].close
def next(self):
# self.log('Line')
print(self.datas[0].datetime.date(0), self.datas[0].datetime.time(0),
self.dataopen[0],
self.datahigh[0],
self.datalow[0],
self.dataclose[0],
self.datas[0]._name
)
def runstrat():
cerebro = bt.Cerebro()
cerebro.broker.setcash(550000.0)
data123 = bt.feeds.MarketStore(
symbol='ETH',
name='ETH',
query_timeframe='1Hour',
timeframe=bt.TimeFrame.Minutes,
fromdate=datetime.date(2018,6,3),
todate=datetime.date(2018, 6,3),
# sessionstart=datetime.time(7),
# sessionend=datetime.time(10),
tz=pytz.timezone('US/Eastern'),
)
cerebro.adddata(data123)
cerebro.addstrategy(Master)
cerebro.run()
print('finished')
if __name__ == '__main__':
runstrat()