There's a small inefficiency issue when optimizing parameters using Interactive Brokers historical data, whether it's with backtrader's optimization module or an external optimization library such as Optunity. The issue is that a new connection is made to download the data from IB for each iteration of each permutation in parameter ranges.
To speed things up, I use the IbPy library to copy the data into a pandas dataframe then pass this over to backtrader as a data feed. In this way, the IB data is read once from TWS, but is made available to the optimizer via a data frame in memory as often as the optimization engine requires it.
There may be a simpler way of doing this with ibStore, but if there is I wan't able to figure it out.
Code snippet for the IbPy/pandas solution is below using backtrader's optimization example code as a base...
import os.path # To manage paths
import sys # To find out the script name (in argv[0])
import backtrader as bt
from datetime import datetime
import pytz, tzlocal
from time import sleep, strftime, localtime
from ib.ext.Contract import Contract
from ib.opt import ibConnection, message
import pandas as pd
# Set up IB message handler to dump to pandas dataframe
df = pd.DataFrame( columns=['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'OpenInterest'])
s = pd.Series()
# define historical data handler for IB - this will populate our pandas data frame
def historical_data_handler(msg):
global df
# print (msg.reqId, msg.date, msg.open, msg.close, msg.high, msg.low)
if ('finished' in str(msg.date)) == False:
s = ([datetime.fromtimestamp(int(msg.date)), msg.open, msg.high, msg.low, msg.close, msg.volume, 0])
df.loc[len(df)] = s
else:
df.set_index('Date',inplace=True)
con = ibConnection(host='127.0.0.1',port=7496,clientId=77)
con.register(historical_data_handler, message.historicalData)
con.connect()
# IBpy - set up contract details and historical data request
qqq = Contract()
qqq.m_symbol = 'ES'
qqq.m_secType = 'FUT'
qqq.m_exchange = 'GLOBEX'
qqq.m_currency = 'USD'
qqq.m_expiry = '201709'
print(qqq.m_symbol)
con.reqHistoricalData(0, qqq, '', '3 W', '1 hour', 'TRADES', 1, 2)
sleep(10)
print('---------------')
print(df)
data = bt.feeds.PandasData(dataname = df, tz=pytz.timezone('US/Eastern'))
# assign our newly created dataframe to a bt.feed
data = bt.feeds.PandasData(dataname = df, tz=pytz.timezone('US/Eastern'))
# Create a Strategy
class TestStrategy(bt.Strategy):
params = (
('maperiod', 15),
('printlog', False),
)
def log(self, txt, dt=None, doprint=False):
''' Logging function fot this strategy'''
if self.params.printlog or doprint:
dt = dt or self.datas[0].datetime.date(0)
print('%s, %s' % (dt.isoformat(), txt))
def __init__(self):
# Keep a reference to the "close" line in the data[0] dataseries
self.dataclose = self.datas[0].close
# To keep track of pending orders and buy price/commission
self.order = None
self.buyprice = None
self.buycomm = None
# Add a MovingAverageSimple indicator
self.sma = bt.indicators.SimpleMovingAverage(
self.datas[0], period=self.params.maperiod)
def notify_order(self, order):
if order.status in [order.Submitted, order.Accepted]:
# Buy/Sell order submitted/accepted to/by broker - Nothing to do
return
# Check if an order has been completed
# Attention: broker could reject order if not enougth cash
if order.status in [order.Completed]:
if order.isbuy():
self.log(
'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
(order.executed.price,
order.executed.value,
order.executed.comm))
self.buyprice = order.executed.price
self.buycomm = order.executed.comm
else: # Sell
self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
(order.executed.price,
order.executed.value,
order.executed.comm))
self.bar_executed = len(self)
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
self.log('Order Canceled/Margin/Rejected')
# Write down: no pending order
self.order = None
def notify_trade(self, trade):
if not trade.isclosed:
return
self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
(trade.pnl, trade.pnlcomm))
def next(self):
# Simply log the closing price of the series from the reference
self.log('Close, %.2f' % self.dataclose[0])
# Check if an order is pending ... if yes, we cannot send a 2nd one
if self.order:
return
# Check if we are in the market
if not self.position:
# Not yet ... we MIGHT BUY if ...
if self.dataclose[0] > self.sma[0]:
# BUY, BUY, BUY!!! (with all possible default parameters)
self.log('BUY CREATE, %.2f' % self.dataclose[0])
# Keep track of the created order to avoid a 2nd order
self.order = self.buy()
else:
if self.dataclose[0] < self.sma[0]:
# SELL, SELL, SELL!!! (with all possible default parameters)
self.log('SELL CREATE, %.2f' % self.dataclose[0])
# Keep track of the created order to avoid a 2nd order
self.order = self.sell()
def stop(self):
self.log('(MA Period %2d) Ending Value %.2f' %
(self.params.maperiod, self.broker.getvalue()), doprint=True)
# Create a cerebro entity
cerebro = bt.Cerebro(maxcpus=1)
# Add the Data Feed to Cerebro
cerebro.adddata(data)
# Add a strategy
strats = cerebro.optstrategy(
TestStrategy,
maperiod=range(5, 10))
# Run over everything
cerebro.run()
