Building a Trading Backtesting System — Part 2: Crafting the backbone

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Since we’re leveraging backtrader, our primary aim is to adopt an evolutionary approach in designing and implementing the system.

Initially, we establish a Backtesting class containing the initialization of needed collaborators and arun method responsible of executing the entire backtesting process flow:

import backtrader as bt

class Backtesting:
    def __init__(self, initial_cash):  
        self._cerebro = bt.Cerebro()  
        self._cerebro.broker = bt.brokers.BackBroker()  
        self._cerebro.broker.setcash(initial_cash)  
    def run(self):          
        print('Starting Portfolio Value: %.2f' % self._cerebro.broker.getvalue())  
        self._cerebro.run()  
        print('Final Portfolio Value: %.2f' % self._cerebro.broker.getvalue())

if __name__ == "__main__":
    backtesting = Backtesting(100000)
    backtesting.run()

Running this main code will produce the following output in the console:

Starting Portfolio Value: 100000.00
Final Portfolio Value: 100000.00

As of now, no strategy or data has been incorporated, resulting in the expected initial value as the final outcome.

Adding which data to analyze