Creating Multi-Strategy Backtests and Analysis in backtrader

Backtesting is an essential part of algorithmic trading. It allows traders to evaluate the success of their strategies using historical data before risking actual capital. One popular open-source framework for this purpose is backtrader. In this article, we will walk through creating multi-strategy backtests and performing analysis using backtrader.

Setting Up backtrader

To get started, you need to have a Python environment set up, as backtrader is a Python library. You can install it using pip:

pip install backtrader

Creating Your First Strategy

Before diving into multi-strategy backtesting, let's create a single strategy. We will create a simple moving average crossover strategy. Here's a basic implementation:

import backtrader as bt

# Create a simple strategy
class SmaCross(bt.Strategy):
    # List of parameters defined by the end user
    params = dict(
        pfast=10,  # period for the fast moving average
        pslow=30   # period for the slow moving average
    )

    def __init__(self):
        sma1 = bt.ind.SMA(period=self.p.pfast)  # fast moving average
        sma2 = bt.ind.SMA(period=self.p.pslow)  # slow moving average
        self.crossover = bt.ind.CrossOver(sma1, sma2)  # crossover signal

    def next(self):
        if self.crossover > 0:  # if fast crosses slow to the upside
            self.buy()
        elif self.crossover < 0:  # if fast crosses slow to the downside
            self.sell()

Running the Backtest

To run the backtest for our strategy, we need some historical data. Backtrader supports multiple formats and sources of data. Here's a simple example using Yahoo Finance data:

# Create a cerebro instance
cerebro = bt.Cerebro()

# Add a data feed
data = bt.feeds.YahooFinanceData(
    dataname='AAPL',
    fromdate=datetime(2021, 1, 1),
    todate=datetime(2022, 1, 1)
)
cerebro.adddata(data)

# Add the strategy
cerebro.addstrategy(SmaCross)

# Run the backtest
cerebro.run()

# Plot the result
cerebro.plot()

Implementing Multi-Strategy Backtesting

For multi-strategy backtesting, backtrader provides a straightforward way to combine multiple strategies using the optimize feature. You can run multiple strategies with different parameter sets:

class RsiSmaStrategy(bt.Strategy):
    params = dict(rsi_period=14, sma_period=20)

    def __init__(self):
        self.rsi = bt.ind.RSI(period=self.params.rsi_period)
        self.sma = bt.ind.SMA(period=self.params.sma_period)

    def next(self):
        if self.rsi < 30 and self.data.close > self.sma:
            self.buy(size=1)
        elif self.rsi > 70:
            self.sell(size=1)

We can test multiple configurations of these strategies:

cerebro = bt.Cerebro()

# Optimizing strategies with different parameters
cerebro.optstrategy(SmaCross, pfast=range(5, 20), pslow=range(20, 50))
cerebro.optstrategy(RsiSmaStrategy, rsi_period=range(10, 20), sma_period=range(15, 25))

Analyzing the Results

One of backtrader's strengths is its robust analysis capabilities. After running multiple strategies, you can retrieve detailed statistics for performance analysis:

results = cerebro.run()

for res in results:
    for strategy in res:
        print('Strat:', strategy.__class__.__name__)
        print('Params:', strategy.params.__dict__)
        print('PnL:', strategy.broker.getvalue())

Analyzing these results will allow you to determine the most profitable strategy parameters and make data-informed decisions in future trading endeavors.

Conclusion

Combining multiple strategies in backtrader allows for comprehensive testing and optimization of trading strategies under a single framework. By utilizing its powerful backtesting features, traders can enhance their strategies' effectiveness and ultimately improve their trading performance. Remember to always validate your results by performing robust analysis on your strategy's performance metrics. Happy trading!