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Commissions: Stocks vs Futures

backtrader has been born out of necessity. My own … to have the feeling I control my own backtesting platform and can experiment new ideas. But in doing so and fully open sourcing it from the very beginning it was clear it has to have a way to fulfill the needs and wishes of others.

Being a traders future I could have chosen to code point based calculations and fixed price per round commissions, but it would have been a mistake.

Note

Jul 31, 2015

Follow up post with newly added operations/trades notifications, fixing the plotting of trades P&L figures and avoiding manual calculation like in the example below

Improving Commissions: Stocks vs Futures

Instead, backtrader offers the possibility to play with regular % size/price based schemes and fixed price/point schemes. The choice is yours.

Agnosticity

Before going forward let’s remember that backtrader tries to remain agnostic as to what the data represents. Different commission schemes can be applied to the same data set.

Let’s see how it can be done.

Using the broker shortcuts

This keeps the end user away from CommissionInfo objects because a commission scheme can be created/set with a single function call. Within the regular cerebro creation/set-up process, just add a call to setcomission over the broker member variable. The following call sets a usual commission scheme for Eurostoxx50 futures when working with InteractiveBrokers:

cerebro.broker.setcommission(commission=2.0, margin=2000.0, mult=10.0)

Since most users will usually just test a single instrument, that’s all that’s down to it. If you have given a name to your data feed, because several instruments are being considered simultaneously on a chart, this call can be slightly extended to look as follows:

cerebro.broker.setcommission(commission=2.0, margin=2000.0, mult=10.0,
name='Eurostoxxx50')

In this case this on-the-fly commission scheme will only applied to instruments whose name matches Eurostoxx50.

The meaning of the setcommission parameters

  • commission (default: 0.0)

    Monetary units in absolute or percentage terms each action costs.

    In the above example it is 2.0 euros per contract for a buy and again 2.0 euros per contract for a sell.

    The important issue here is when to use absolute or percentage values.

    • If margin evaluates to False (it is False, 0 or None for example) then it will be considered that commission expresses a percentage of the price times size operatin value

    • If margin is something else, it is considered the operations are happenning on a futures like intstrument and commission is a fixed price per size contracts

  • margin (default: None)

    Margin money needed when operating with futures like instruments. As expressed above

    • If a no margin is set, the commission will be understood to be indicated in percentage and applied to price \* size components of a buy or sell operation

    • If a margin is set, the commission will be understood to be a fixed value which is multiplied by the size component of buy or sell operation

  • mult (default: 1.0)

    For future like instruments this determines the multiplicator to apply to profit and loss calculations.

    This is what makes futures attractive and risky at the same time.

  • name (default: None)

    Limit the application of the commission scheme to instruments matching name

    This can be set during the creation of a data feed.

    If left unset, the scheme will apply to any data present in the system.

Two examples now: stocks vs futures

The futures example from above:

cerebro.broker.setcommission(commission=2.0, margin=2000.0, mult=10.0)

A example for stocks:

cerebro.broker.setcommission(commission=0.005)  # 0.5% of the operation value

Creating permanent Commission schemes

A more permanent commission scheme can be created by working directly with CommissionInfo classes. The user could choose to have this definition somewhere:

from bt import CommissionInfo

commEurostoxx50 = CommissionInfo(commission=2.0, margin=2000.0, mult=10.0)

To later apply it in another Python module with addcommissioninfo:

from mycomm import commEurostoxx50

...

cerebro.broker.addcomissioninfo(commEuroStoxx50, name='Eurostoxxx50')

CommissionInfo is an object which uses a params declaration just like other objects in the backtrader environment. As such the above can be also expressed as:

from bt import CommissionInfo

class CommEurostoxx50(CommissionInfo):
    params = dict(commission=2.0, margin=2000.0, mult=10.0)

And later:

from mycomm import CommEurostoxx50

...

cerebro.broker.addcomissioninfoCommEuroStoxx50(), name='Eurostoxxx50')

Now a “real” comparison with a SMA Crossover

Using a SimpleMovingAverage crossover as the entry/exit signal the same data set is going to be tested with a futures like commission scheme and then with a stocks like one.

Note

Futures positions could also not only be given the enter/exit behavior but a reversal behavior on each occassion. But this example is about comparing the commission schemes.

The code (see at the bottom for the full strategy) is the same and the scheme can be chosen before the strategy is defined.

futures_like = True

if futures_like:
    commission, margin, mult = 2.0, 2000.0, 10.0
else:
    commission, margin, mult = 0.005, None, 1

Just set futures_like to false to run with the stocks like scheme.

Some logging code has been added to evaluate the impact of the differrent commission schemes. Let’s concentrate on just the 2 first operations.

For futures:

2006-03-09, BUY CREATE, 3757.59
2006-03-10, BUY EXECUTED, Price: 3754.13, Cost: 2000.00, Comm 2.00
2006-04-11, SELL CREATE, 3788.81
2006-04-12, SELL EXECUTED, Price: 3786.93, Cost: 2000.00, Comm 2.00
2006-04-12, OPERATION PROFIT, GROSS 328.00, NET 324.00
2006-04-20, BUY CREATE, 3860.00
2006-04-21, BUY EXECUTED, Price: 3863.57, Cost: 2000.00, Comm 2.00
2006-04-28, SELL CREATE, 3839.90
2006-05-02, SELL EXECUTED, Price: 3839.24, Cost: 2000.00, Comm 2.00
2006-05-02, OPERATION PROFIT, GROSS -243.30, NET -247.30

For stocks:

2006-03-09, BUY CREATE, 3757.59
2006-03-10, BUY EXECUTED, Price: 3754.13, Cost: 3754.13, Comm 18.77
2006-04-11, SELL CREATE, 3788.81
2006-04-12, SELL EXECUTED, Price: 3786.93, Cost: 3786.93, Comm 18.93
2006-04-12, OPERATION PROFIT, GROSS 32.80, NET -4.91
2006-04-20, BUY CREATE, 3860.00
2006-04-21, BUY EXECUTED, Price: 3863.57, Cost: 3863.57, Comm 19.32
2006-04-28, SELL CREATE, 3839.90
2006-05-02, SELL EXECUTED, Price: 3839.24, Cost: 3839.24, Comm 19.20
2006-05-02, OPERATION PROFIT, GROSS -24.33, NET -62.84

The 1st operation has the following prices:

  • BUY (Execution) -> 3754.13 / SELL (Execution) -> 3786.93

    • Futures Profit & Loss (with comission): 324.0

    • Stocks Profit & Loss (with commission): -4.91

    Hey!! Commission has fully eaten up any profit on the stocks operation but has only meant a small dent to the futures one.

The 2nd operation:

  • BUY (Execution) -> 3863.57 / SELL (Execution) -> 3389.24

    • Futures Profit & Loss (with commission): -247.30

    • Stocks Profit & Loss (with commission): -62.84

    The bite has been sensibly larger for this negative operation with futures

But:

  • Futures accumulated net profit & loss: 324.00 + (-247.30) = 76.70

  • Stocks accumulated net profit & loss: (-4.91) + (-62.84) = -67.75

The accumulated effect can be seen on the charts below, where it can also be seen that at the end of the full year, futures have produced a larger profit, but have also suffered a larger drawdown (were deeper underwater)

But the important thing: whether futures or stocks … it can be backtested.

Commissions for futures

image

Commissions for stocks

image

The code

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

import backtrader as bt
import backtrader.feeds as btfeeds
import backtrader.indicators as btind


futures_like = True

if futures_like:
    commission, margin, mult = 2.0, 2000.0, 10.0
else:
    commission, margin, mult = 0.005, None, 1


class SMACrossOver(bt.Strategy):
    def log(self, txt, dt=None):
        ''' Logging function fot this strategy'''
        dt = dt or self.datas[0].datetime.date(0)
        print('%s, %s' % (dt.isoformat(), txt))

    def notify(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, order.Canceled, order.Margin]:
            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
                self.opsize = order.executed.size
            else:  # Sell
                self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
                         (order.executed.price,
                          order.executed.value,
                          order.executed.comm))

                gross_pnl = (order.executed.price - self.buyprice) * \
                    self.opsize

                if margin:
                    gross_pnl *= mult

                net_pnl = gross_pnl - self.buycomm - order.executed.comm
                self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
                         (gross_pnl, net_pnl))

    def __init__(self):
        sma = btind.SMA(self.data)
        # > 0 crossing up / < 0 crossing down
        self.buysell_sig = btind.CrossOver(self.data, sma)

    def next(self):
        if self.buysell_sig > 0:
            self.log('BUY CREATE, %.2f' % self.data.close[0])
            self.buy()  # keep order ref to avoid 2nd orders

        elif self.position and self.buysell_sig < 0:
            self.log('SELL CREATE, %.2f' % self.data.close[0])
            self.sell()


if __name__ == '__main__':
    # Create a cerebro entity
    cerebro = bt.Cerebro()

    # Add a strategy
    cerebro.addstrategy(SMACrossOver)

    # Create a Data Feed
    datapath = ('../datas/2006-day-001.txt')
    data = bt.feeds.BacktraderCSVData(dataname=datapath)

    # Add the Data Feed to Cerebro
    cerebro.adddata(data)

    # set commission scheme -- CHANGE HERE TO PLAY
    cerebro.broker.setcommission(
        commission=commission, margin=margin, mult=mult)

    # Run over everything
    cerebro.run()

    # Plot the result
    cerebro.plot()