Backtrader: First Script

Scope

if __name__ == '__main__’:

The Strategy

• When RSI < 30

• When RSI > 70

• No trade management shall be implemented. No scaling in / out. Just simple buying and selling with a single open position at a time.
• Position size wise, we will keep things simple and just buy / sell 100 shares at a time without doing any calculations to see if we have enough cash for position size. (if we don’t have enough cash, backtrader is smart enough to reject the order)

• Period = 21
• Lets use a longer look back period than the default 14. In theory this should result in less false signals and price should have to come down / rise much further before it is considered overbought / over sold.

Before we start

Requirements

pip3 list

pip3 list | grep backtrader

import backtrader as bt
print(bt.__version__)

pip3 install --upgrade backtrader

pip3 install matplotlib

And so we begin….

'''
Author: www.backtest-rookies.com

MIT License

Copyright (c) 2017 backtest-rookies.com

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''

import backtrader as bt
from datetime import datetime

class firstStrategy(bt.Strategy):

    def __init__(self):
        self.rsi = bt.indicators.RSI_SMA(self.data.close, period=21)

    def next(self):
        if not self.position:
            if self.rsi < 30:
                self.buy(size=100)
        else:
            if self.rsi > 70:
                self.sell(size=100)


#Variable for our starting cash
startcash = 10000

#Create an instance of cerebro
cerebro = bt.Cerebro()

#Add our strategy
cerebro.addstrategy(firstStrategy)

#Get Apple data from Yahoo Finance.
data = bt.feeds.Quandl(
    dataname='AAPL',
    fromdate = datetime(2016,1,1),
    todate = datetime(2017,1,1),
    buffered= True
    )

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

# Set our desired cash start
cerebro.broker.setcash(startcash)

# Run over everything
cerebro.run()

#Get final portfolio Value
portvalue = cerebro.broker.getvalue()
pnl = portvalue - startcash

#Print out the final result
print('Final Portfolio Value: ${}'.format(portvalue))
print('P/L: ${}'.format(pnl))

#Finally plot the end results
cerebro.plot(style='candlestick')

Important Note: The data in this tutorial uses the Quandl API. With this API you are limited to the number calls you can make per day. To have unlimited access to their free data, sign up for an API key here and add the apikeykeyword to the quandl data call like so:

data = bt.feeds.Quandl(
    dataname='F',
    fromdate = datetime(2016,1,1),
    todate = datetime(2017,1,1),
    buffered= True,
    apikey="INSERT YOUR API KEY"
    )

Reference: https://www.quandl.com/?modal=register

Imports

import backtrader as bt
from datetime import datetime

The first import should be pretty obvious. We are importing the backtrader framework. In addition to this, we import the datetime module from the standard python library. This is used for setting the start and end dates for our backtest. Backtrader expects to receive datetime objects when creating data feeds. For more information regarding the datetime module, please check out the documentation here:

The Strategy.

class firstStrategy(bt.Strategy):

    def __init__(self):
        self.rsi = bt.indicators.RSI_SMA(self.data.close, period=21)

    def next(self):
        if not self.position:
            if self.rsi < 30:
                self.buy(size=100)
        else:
            if self.rsi > 70:
                self.sell(size=100)

When you create a strategy in backtrader, you inherit many methods and attributes from the base class `bt.Strategy`. If that sounds confusing, basically we are taking an existing skeleton strategy that has been written in the backtest framework and adding our logic to it. In programming when we use inheritance, we get to use all of the code that was written for the base strategy and just overwrite the bits we want to change. This means you don’t have to worry about all the code behind the scenes which allows you to interact with the data, orders, broker notifications and so on. The framework takes care of that! The new strategy class can be short, clean and easy to read. However, if you wish, you can dig deeper and tweak to your heart’s content.

The Setup

• Calling the backtrader engine (cerebro)
• Getting some data and loading it into the engine.
• Setting how much cash we have to trade (startcash)
• Loading our strategy
• Running the test
• Plotting the results

A few things to point out:

cerebro.plot(style='candlestick')

data = bt.feeds.YahooFinanceData(
    dataname='AAPL',
    fromdate = datetime(2016,1,1),
    todate = datetime(2017,1,1),
    buffered= True
    )

Running the script

The results