Building a Portfolio of Strategies with backtrader

When it comes to executing algorithmic strategies and backtesting, backtrader is a powerful Python library that remains popular among traders for its capability to perform extensive strategy analyses. Let's explore how you can build a portfolio of strategies using backtrader and execute them as part of your investment decisions.

Getting Started with backtrader

To embark on this journey, you first need to ensure that you have installed the backtrader library. You can install the library via pip:

pip install backtrader

Once installed, let's begin by understanding the basic components of a backtrader structure, which is comprised of the Cerebro engine, Strategy class, Data Feeds, and Brokers.

Step 1: Setting Up the Cerebro Engine

The Cerebro engine is the heart of backtrader, which orchestrates everything. You initiate it as follows:

import backtrader as bt

cerebro = bt.Cerebro()

This forms the base upon which your brokers, strategies, and data feeds will be applied.

Step 2: Creating a Strategy

A Strategy dictates how trades are executed. Here's a simple example of a basic strategy:

class MyStrategy(bt.Strategy):
    def __init__(self):
        self.sma = bt.indicators.SimpleMovingAverage(self.data, period=15)

    def next(self):
        if self.data.close[0] > self.sma[0]:
            self.buy()
        elif self.data.close[0] < self.sma[0]:
            self.sell()

This strategy buys if the current price exceeds a 15-day Simple Moving Average (SMA) and sells if the price falls below it.

Step 3: Adding Data Feed

Data Feeds provide the historical data needed to backtest strategies. It's critical to select data that is comprehensive and accurate:

data = bt.feeds.YahooFinanceData(dataname='AAPL', fromdate=datetime(2020, 1, 1), todate=datetime(2023, 1, 1))
cerebro.adddata(data)

This code uses Yahoo Finance data for Apple (AAPL) to test your implemented strategies over specific years.

Step 4: Adding Your Strategies to Cerebro

Once your strategies are defined, add them to Cerebro:

cerebro.addstrategy(MyStrategy)

You can include multiple strategies to operate simultaneously by adding them to Cerebro, forming a portfolio:

cerebro.addstrategy(MyOtherStrategy)
cerebro.addstrategy(YetAnotherStrategy)

Step 5: Configuring the Broker

The broker manages your cash and order execution. Configure your starting cash and transaction commission:

cerebro.broker.setcash(10000.0)
cerebro.broker.setcommission(commission=0.001)

With this setup, you start with a virtual $10,000 and pay a 0.1% commission on trade executions.

Step 6: Executing and Reviewing Performance

Now, execute your entire setup and evaluate performance through backtesting:

print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.run()
print('Ending Portfolio Value: %.2f' % cerebro.broker.getvalue())

With these steps, you can see how the specified strategies have performed with your selected parameters.

Visualization and Analysis

Finally, visualize to better understand how trades were executed regarding data trends:

cerebro.plot()

Building a portfolio of strategies can majorly impact your trading endeavors by pinpointing the best approach for different market conditions. Continuously enhance your strategies with backtrader, and explore more complex indicators or tailor strategies to fit your unique perspective.

By leveraging this platform, you maintain an edge through rigorous testing and analysis, which can lead to more informed investment decisions. Happy backtesting!