In recent years, algorithmic trading has gained a lot of traction, leading to an increased demand for robust tools that can simulate trading strategies. In the Python ecosystem, there are several frameworks designed for this purpose, with backtrader being one of the most popular. This article compares backtrader to other notable Python backtesting frameworks, helping traders choose the right tool for their needs.
Backtrader Overview
Backtrader is a popular open-source Python backtesting framework created by Daniel Rodriguez. One of the key features of backtrader is its flexibility, supporting different data feeds, trading strategies, and types of orders. It also has extensive documentation, helping users get started quickly.
import backtrader as bt
class MyStrategy(bt.Strategy):
def __init__(self):
self.sma = bt.indicators.SimpleMovingAverage(self.data)
def next(self):
if self.data.close > self.sma:
self.buy()
elif self.data.close < self.sma:
self.sell()
cerebro = bt.Cerebro()
data = bt.feeds.YahooFinanceData(dataname='AAPL', fromdate=datetime(2019, 1, 1), todate=datetime(2020, 1, 1))
cerebro.adddata(data)
cerebro.addstrategy(MyStrategy)
cerebro.run()Other Frameworks Overview
1. Zipline
Zipline, maintained by Quantopian, once enjoyed immense popularity due to its support by the Zipline research platform. It runs on a simple syntax similar to backtrader and offers integration with numerous data sources, but it has lost some users since the inaccessibility of its parent platform.
from zipline.api import order, record, symbol
def initialize(context):
context.asset = symbol('AAPL')
def handle_data(context, data):
order(context.asset, 10)
record(AAPL=data.current(context.asset, 'price'))2. QuantConnect's Lean Engine
The Lean engine by QuantConnect is a powerful backtesting platform supporting multiple asset classes. Its strength lies in its extensive API, capable of much more complex strategies than some other frameworks, although it brings a steeper learning curve.
# A strategy written for Lean engine in C#. Python also supported.
public class BasicTemplateAlgorithm : QCAlgorithm
{
public override void Initialize()
{
SetStartDate(2019, 1, 1);
AddEquity("AAPL");
}
public override void OnData(Slice data)
{
if (!Portfolio.Invested)
{
SetHoldings("AAPL", 1.0);
}
}
}3. PyAlgoTrade
PyAlgoTrade is another significant framework that focuses on simplicity and fast computations. It’s less feature-rich than backtrader but might be sufficient for straightforward backtests.
from pyalgotrade.strategy import BacktestingStrategy
from pyalgotrade.technical import ma
class MyStrategy(BacktestingStrategy):
def __init__(self, feed, instrument):
super(MyStrategy, self).__init__(feed)
self.__sma = ma.SMA(feed[instrument].getCloseDataSeries(), 15)
def onBars(self, bars):
bar = bars.getBar('AAPL')
if bar.getClose() > self.__sma[-1]:
self.enterLong('AAPL', 100)
feed = yahoofeed.Feed()
feed.addBarsFromCSV('AAPL', 'AAPL-2019-yahoo.csv')
myStrategy = MyStrategy(feed, 'AAPL')
myStrategy.run()Choosing the Right Framework
Every trading strategy has different requirements, so choosing the right backtesting platform depends largely on individual needs and expertise. While backtrader boasts excellent documentation and flexibility, QuantConnect’s Lean is robust for advanced strategies involving multiple asset classes. On the simpler side, PyAlgoTrade might appeal to beginners looking for speed and ease of use, and Zipline still interests those preferring its syntax and feature set.
Conclusion
Whether you’re a beginner or an experienced algorithmic trader, the Python ecosystem provides numerous options for backtesting. Backtrader is a versatile choice, but it’s essential to evaluate other frameworks like Zipline, Lean, and PyAlgoTrade based on your specific needs and technical skills. Spend some time experimenting to find the framework that aligns best with your trading goals and preferred coding practices.
