Optimizing with IBStore causes redundant connections/downloads
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I'm trying to optimize a Simple Moving Average cross-over strategy using Interactive Brokers data via IBStore.
While optimizing, the processes initiate numerous connects/disconnects to the IB Trader Workstation as the parameters are iterated - that is, one connection for each permutation of parameters. Both the TWS data window shows this, and a similar number of "TWS Time at connection:20170514 16:06:03 EST" lines are printed on the console. (If the optimization is iterating through 100 permutations, there are 100 connections)
I'm guessing that with each iteration, no matter what the data source, the data is loaded into the strategy? I figured the default values for preload/runonce/optdatas would handle this.
If this is not the case, is the proper way to do this just pre-load the required data set into Pandas or influxdb and use it from there?
from __future__ import (absolute_import, division, print_function, unicode_literals) import datetime # For datetime objects import os.path # To manage paths import sys # To find out the script name (in argv[0]) # Import the backtrader platform import backtrader as bt # Create a Data Feed ibstore = bt.stores.IBStore(host='127.0.0.1',port=7496,clientId=5) mysymbol = 'EUR.USD-CASH-IDEALPRO' fromdate = datetime.datetime(2015,1,1,00,00) todate = fromdate = datetime.datetime(2017,3,30,00,00) data = ibstore.getdata(dataname=mysymbol, timeframe=bt.TimeFrame.Days, compression=1, fromdate=fromdate, todate=todate, historical=True) # Create a Strategy class SmaCross(bt.SignalStrategy): params = ( ('sma1', 10), ('sma2', 30), ) def __init__(self): SMA1 = bt.ind.SMA(period=int(self.params.sma1)) SMA2 = bt.ind.SMA(period=int(self.params.sma2)) crossover = bt.ind.CrossOver(SMA1, SMA2) self.signal_add(bt.SIGNAL_LONG, crossover) if __name__ == '__main__': # Create a cerebro entity cerebro = bt.Cerebro(maxcpus=1) # Add a strategy strats = cerebro.optstrategy( SmaCross, sma1=range(50, 60), sma2=range(50,60)) # Add the Data Feed to Cerebro cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(10000.0) # Add a FixedSize sizer according to the stake cerebro.addsizer(bt.sizers.FixedSize, stake=1000) # Set the commission cerebro.broker.setcommission(commission=0.0) # Run over everything cerebro.run()
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Optimization was in the platform long before any kind of live streams was added and will simply ask the streams to load its data if the data is not preloaded.
In this case the data is not seen as preloaded because of the live nature of the feed and that's why it is being recollected.
It was never envisioned (also taking into account the historical download limitations inherent to IB) that such feeds would be used in an optimization attempt.
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@backtrader Than you for the confirm (and this gift of software!)
It seems the answer then is to
- Get a snapshot of IB historical data and load into a more optimal store, such as a Pandas data frame feed
- Use the Pandas data frame as a data feed in the optimization
On point 1, is there an easy way you can think of to copy the ibstore data into a Pandas data frame? Or perhaps this would best be handled using the external IBpy methods to prep the data.
-D
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Resolved: see this post for a solution...
https://community.backtrader.com/topic/447/code-snippet-copying-ib-data-to-pandas-for-efficiency
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The message above got under the radar. A potential solution (untested) would be:
class MyData(bt.feeds.IBData): def islive(self): '''Usually Returns ``True`` to notify ``Cerebro`` that preloading and runonce should be deactivated --- Returns False in subclass to allow optimization ''' return Falseand then
bt.stores.IBStore.DataCls = MyData
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@backtrader
I have a similar issue.
I am trying to run optimization on IB data(live or historical) source.
When using optStrategy I am getting this error(attached below) ,but with other data sources(csv file using GenericCSVData) it is running fine.
Even In addstrategy method this IB data source works , thus it seems to me the problem is using IB data source with optStrategy only.Traceback (most recent call last): File "D:/Users/R/BackTestLiveIBv2.py", line 219, in <module> runstrategy() File "D:/Users/R/BackTestLiveIBv2.py", line 216, in runstrategy cerebro.run() File "C:\Users\R\Anaconda3\lib\site-packages\backtrader\cerebro.py", line 1143, in run for r in pool.imap(self, iterstrats): File "C:\Users\R\Anaconda3\lib\multiprocessing\pool.py", line 735, in next raise value File "C:\Users\R\Anaconda3\lib\multiprocessing\pool.py", line 424, in _handle_tasks put(task) File "C:\Users\R\Anaconda3\lib\multiprocessing\connection.py", line 206, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "C:\Users\R\Anaconda3\lib\multiprocessing\reduction.py", line 51, in dumps cls(buf, protocol).dump(obj) TypeError: can't pickle _thread.lock objectsHere is the code :
import argparse import datetime import backtrader as bt from backtrader.utils import flushfile import os class TestStrategy(bt.Strategy): params = dict( smaperiod = 5, trade=False, stake=10, exectype=bt.Order.Market, stopafter=0, valid=None, cancel=0, donotsell=False, optim=False, optimParams= (0, 0), ) def __init__(self): # To control operation entries self.orderid = list() self.order = None self.counttostop = 0 self.datastatus = 0 # Create SMA on 2nd data if self.p.optim: # Use a tuple during optimization self.p.smaperiod,self.p.stake = self.p.optimParams self.sma = bt.indicators.MovAv.SMA(self.data, period=self.p.smaperiod) def notify_data(self, data, status, *args, **kwargs): print('*' * 5, 'DATA NOTIF:', data._getstatusname(status), *args) if status == data.LIVE: self.counttostop = self.p.stopafter self.datastatus = 1 def notify_order(self, order): if order.status in [order.Completed, order.Cancelled, order.Rejected]: self.order = None print('-' * 50, 'ORDER BEGIN', datetime.datetime.now()) # print(order) print('-' * 50, 'ORDER END') def notify_trade(self, trade): print('-' * 50, 'TRADE BEGIN', datetime.datetime.now()) print(trade) print('-' * 50, 'TRADE END') def prenext(self): self.next(frompre=True) def next(self, frompre=False): txt = list() txt.append('%04d' % len(self)) dtfmt = '%Y-%m-%d %H:%M:%S.%f' txt.append('%s' % self.data.datetime.datetime(0).strftime(dtfmt)) txt.append('{}'.format(self.data.open[0])) txt.append('{}'.format(self.data.high[0])) txt.append('{}'.format(self.data.low[0])) txt.append('{}'.format(self.data.close[0])) txt.append('{}'.format(self.data.volume[0])) txt.append('{}'.format(self.data.openinterest[0])) txt.append('{}'.format(self.sma[0])) print(', '.join(txt)) if self.counttostop: # stop after x live lines self.counttostop -= 1 if not self.counttostop: self.env.runstop() return if not self.p.trade: return if self.datastatus and not self.position and len(self.orderid) < 1: self.order = self.buy(size=self.p.stake, exectype=self.p.exectype, price=round(self.data0.close[0] * 0.90, 2), valid=self.p.valid) self.orderid.append(self.order) elif self.position.size > 0 and not self.p.donotsell: if self.order is None: self.order = self.sell(size=self.p.stake // 2, exectype=bt.Order.Market, price=self.data0.close[0]) elif self.order is not None and self.p.cancel: if self.datastatus > self.p.cancel: self.cancel(self.order) if self.datastatus: self.datastatus += 1 def start(self): header = ['Datetime', 'Open', 'High', 'Low', 'Close', 'Volume', 'OpenInterest', 'SMA'] print(', '.join(header)) self.done = False def runstrategy(): cerebro = bt.Cerebro(stdstats=False) storekwargs = dict(host='127.0.0.1', port=7497, clientId=0, notifyall=False, _debug=False, reconnect = 3, timeout = 3, timeoffset = False, timerefresh = 60.0, ) store = bt.stores.IBStore(**storekwargs) datakwargs = dict( timeframe=bt.TimeFrame.Seconds, compression=1, historical= True, rtbar=False, # real time bars qcheck=0.5, backfill_start=True, backfill=True, latethrough=True, tz='GMT', ) data = store.getdata(dataname='USDINR-IND-NSE-INR', **datakwargs) cerebro.resampledata(dataname= data) # Add the strategy # cerebro.addstrategy(TestStrategy, # smaperiod = 5, # trade = True) cerebro.optstrategy(TestStrategy, optim=True, optimParams=[[5,10],[10,10]], trade=True) cerebro.run() if __name__ == '__main__': runstrategy()