MultiData StrategyΒΆ

Because nothing in the world lives in isolation it can well be that the trigger to buy an asset is actually another asset.

Using different analysis techniques a correlation may have been found between two different datas.

backtrader supports using different data sources simultaneously so it can possibly be used for the purpose in most cases.

Let’s assume that a correlation has been found between the following companies:

  • Oracle
  • Yahoo

One could imagine that when things go well for Yahoo, the company buys more servers, more databases and more professional services from Oracle, which in turn pushes the stock prices up.

As such and having run a profound analysis a strategy is devised:

  • If the close price of Yahoo goes over the Simple Moving Average (period 15)
  • Buy Oracle

To exit the position:

  • Use the crossing downwards of the close price

Order Execution Type:

  • Market

In summary what’s needed to set this up with backtrader:

  • Create a cerebro
  • Load the Data Source 1 (Oracle) and add it to cerebro
  • Load the Data Source 2 (Yahoo) and add it to cerebro
  • Load the Strategy we have devised

The details of the strategy:

  • Create a Simple Moving Average on Data Source 2 (Yahoo)
  • Create a CrossOver indicator using Yahoo’s close price and the Moving Average

And then execute the buy/sell orders on Data Source 1 (Oracle) as described above.

The script below uses the following defaults:

  • Oracle (Data Source 1)
  • Yahoo (Data Source 2)
  • Cash: 10000 (system default)
  • Stake: 10 shares
  • Commission: 0.5% for each round (expressed as 0.005)
  • Period: 15 trading days
  • Period: 2003, 2004 and 2005

The script can take arguments to modify the above settings as seen in the help text:

$ ./multidata-strategy.py --help
usage: multidata-strategy.py [-h] [--data0 DATA0] [--data1 DATA1]
                             [--fromdate FROMDATE] [--todate TODATE]
                             [--period PERIOD] [--cash CASH]
                             [--commperc COMMPERC] [--stake STAKE] [--plot]
                             [--numfigs NUMFIGS]

MultiData Strategy

optional arguments:
  -h, --help            show this help message and exit
  --data0 DATA0, -d0 DATA0
                        1st data into the system
  --data1 DATA1, -d1 DATA1
                        2nd data into the system
  --fromdate FROMDATE, -f FROMDATE
                        Starting date in YYYY-MM-DD format
  --todate TODATE, -t TODATE
                        Starting date in YYYY-MM-DD format
  --period PERIOD       Period to apply to the Simple Moving Average
  --cash CASH           Starting Cash
  --commperc COMMPERC   Percentage commission for operation (0.005 is 0.5%
  --stake STAKE         Stake to apply in each operation
  --plot, -p            Plot the read data
  --numfigs NUMFIGS, -n NUMFIGS
                        Plot using numfigs figures

The result of a standard execution:

$ ./multidata-strategy.py
2003-02-11T23:59:59+00:00, BUY CREATE , 9.14
2003-02-12T23:59:59+00:00, BUY COMPLETE, 11.14
2003-02-12T23:59:59+00:00, SELL CREATE , 9.09
2003-02-13T23:59:59+00:00, SELL COMPLETE, 10.90
2003-02-14T23:59:59+00:00, BUY CREATE , 9.45
2003-02-18T23:59:59+00:00, BUY COMPLETE, 11.22
2003-03-06T23:59:59+00:00, SELL CREATE , 9.72
2003-03-07T23:59:59+00:00, SELL COMPLETE, 10.32
...
...
2005-12-22T23:59:59+00:00, BUY CREATE , 40.83
2005-12-23T23:59:59+00:00, BUY COMPLETE, 11.68
2005-12-23T23:59:59+00:00, SELL CREATE , 40.63
2005-12-27T23:59:59+00:00, SELL COMPLETE, 11.63
==================================================
Starting Value - 100000.00
Ending   Value - 99959.26
==================================================

After two complete years of execution the Strategy:

  • Has lost 40.74 monetary units

So much for the correlation between Yahoo and Oracle

The Visual Ouput (add --plot to produce a chart)

And the script (which has been added to the source distribution of backtrader under the samples/multidata-strategy directory.

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import argparse
import datetime

# The above could be sent to an independent module
import backtrader as bt
import backtrader.feeds as btfeeds
import backtrader.indicators as btind


class MultiDataStrategy(bt.Strategy):
    '''
    This strategy operates on 2 datas. The expectation is that the 2 datas are
    correlated and the 2nd data is used to generate signals on the 1st

      - Buy/Sell Operationss will be executed on the 1st data
      - The signals are generated using a Simple Moving Average on the 2nd data
        when the close price crosses upwwards/downwards

    The strategy is a long-only strategy
    '''
    params = dict(
        period=15,
        stake=10,
        printout=True,
    )

    def log(self, txt, dt=None):
        if self.p.printout:
            dt = dt or self.data.datetime[0]
            dt = bt.num2date(dt)
            print('%s, %s' % (dt.isoformat(), txt))

    def notify_order(self, order):
        if order.status in [bt.Order.Submitted, bt.Order.Accepted]:
            return  # Await further notifications

        if order.status == order.Completed:
            if order.isbuy():
                buytxt = 'BUY COMPLETE, %.2f' % order.executed.price
                self.log(buytxt, order.executed.dt)
            else:
                selltxt = 'SELL COMPLETE, %.2f' % order.executed.price
                self.log(selltxt, order.executed.dt)

        elif order.status in [order.Expired, order.Canceled, order.Margin]:
            self.log('%s ,' % order.Status[order.status])
            pass  # Simply log

        # Allow new orders
        self.orderid = None

    def __init__(self):
        # To control operation entries
        self.orderid = None

        # Create SMA on 2nd data
        sma = btind.MovAv.SMA(self.data1, period=self.p.period)
        # Create a CrossOver Signal from close an moving average
        self.signal = btind.CrossOver(self.data1.close, sma)

    def next(self):
        if self.orderid:
            return  # if an order is active, no new orders are allowed

        if not self.position:  # not yet in market
            if self.signal > 0.0:  # cross upwards
                self.log('BUY CREATE , %.2f' % self.data1.close[0])
                self.buy(size=self.p.stake)

        else:  # in the market
            if self.signal < 0.0:  # crosss downwards
                self.log('SELL CREATE , %.2f' % self.data1.close[0])
                self.sell(size=self.p.stake)

    def stop(self):
        print('==================================================')
        print('Starting Value - %.2f' % self.broker.startingcash)
        print('Ending   Value - %.2f' % self.broker.getvalue())
        print('==================================================')


def runstrategy():
    args = parse_args()

    # Create a cerebro
    cerebro = bt.Cerebro()

    # Get the dates from the args
    fromdate = datetime.datetime.strptime(args.fromdate, '%Y-%m-%d')
    todate = datetime.datetime.strptime(args.todate, '%Y-%m-%d')

    # Create the 1st data
    data0 = btfeeds.YahooFinanceCSVData(
        dataname=args.data0,
        fromdate=fromdate,
        todate=todate)

    # Add the 1st data to cerebro
    cerebro.adddata(data0)

    # Create the 2nd data
    data1 = btfeeds.YahooFinanceCSVData(
        dataname=args.data1,
        fromdate=fromdate,
        todate=todate)

    # Add the 2nd data to cerebro
    cerebro.adddata(data1)

    # Add the strategy
    cerebro.addstrategy(MultiDataStrategy,
                        period=args.period,
                        stake=args.stake)

    # Add the commission - only stocks like a for each operation
    cerebro.broker.setcash(args.cash)

    # Add the commission - only stocks like a for each operation
    cerebro.broker.setcommission(commission=args.commperc)

    # And run it
    cerebro.run()

    # Plot if requested
    if args.plot:
        cerebro.plot(numfigs=args.numfigs, volume=False, zdown=False)


def parse_args():
    parser = argparse.ArgumentParser(description='MultiData Strategy')

    parser.add_argument('--data0', '-d0',
                        default='../../datas/orcl-1995-2014.txt',
                        help='1st data into the system')

    parser.add_argument('--data1', '-d1',
                        default='../../datas/yhoo-1996-2014.txt',
                        help='2nd data into the system')

    parser.add_argument('--fromdate', '-f',
                        default='2003-01-01',
                        help='Starting date in YYYY-MM-DD format')

    parser.add_argument('--todate', '-t',
                        default='2005-12-31',
                        help='Starting date in YYYY-MM-DD format')

    parser.add_argument('--period', default=15, type=int,
                        help='Period to apply to the Simple Moving Average')

    parser.add_argument('--cash', default=100000, type=int,
                        help='Starting Cash')

    parser.add_argument('--commperc', default=0.005, type=float,
                        help='Percentage commission for operation (0.005 is 0.5%%')

    parser.add_argument('--stake', default=10, type=int,
                        help='Stake to apply in each operation')

    parser.add_argument('--plot', '-p', action='store_true',
                        help='Plot the read data')

    parser.add_argument('--numfigs', '-n', default=1,
                        help='Plot using numfigs figures')

    return parser.parse_args()


if __name__ == '__main__':
    runstrategy()

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