Framework

Backtrader Review: Python Backtesting Framework

Item: Backtrader
Author: CodeForFinance

Backtrader is an open-source Python backtesting framework that lets you test trading strategies against historical data. It is event-driven, meaning it simulates the market tick by tick, which is more realistic than vectorised backtesting. Backtrader includes built-in indicators, plotting, position sizing, and can connect to live brokers for paper or real trading.

Pricing

Open SourceFree forever

✓ Full backtesting engine
✓ 100+ built-in indicators
✓ Plotting and analysis
✓ Live broker connections
✓ No restrictions

Pros

+ Completely free and open source
+ Event-driven (more realistic than vectorised)
+ 100+ built-in technical indicators
+ Built-in plotting and analysis tools
+ Can connect to Interactive Brokers and Oanda for live trading

Cons

- Documentation is sparse and sometimes confusing
- Learning curve is steep for the framework patterns
- Not actively maintained (last commit 2021)
- Plotting can be slow for large datasets
- VectorBT and other modern alternatives are faster

Who Is It For?

Backtrader is for Python developers who want a free, local, event-driven backtesting engine. It is great for learning the fundamentals of strategy development. However, for new projects, consider VectorBT (faster) or QuantConnect (cloud-based with better data).

Code Example

import backtrader as bt
import datetime

class SmaCross(bt.Strategy):
    params = (
        ("fast", 10),
        ("slow", 30),
    )

    def __init__(self):
        sma_fast = bt.ind.SMA(period=self.params.fast)
        sma_slow = bt.ind.SMA(period=self.params.slow)
        self.crossover = bt.ind.CrossOver(sma_fast, sma_slow)

    def next(self):
        if not self.position:
            if self.crossover > 0:
                self.buy()
        elif self.crossover < 0:
            self.close()

    def stop(self):
        print(f"Final Value: {self.broker.getvalue():.2f}")

# Run backtest
cerebro = bt.Cerebro()
cerebro.addstrategy(SmaCross)
cerebro.broker.setcash(100000)
cerebro.broker.setcommission(commission=0.001)

# Add data feed
data = bt.feeds.YahooFinanceData(
    dataname="AAPL",
    fromdate=datetime.datetime(2020, 1, 1),
    todate=datetime.datetime(2024, 1, 1)
)
cerebro.adddata(data)

# Add analyzers
cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name="sharpe")
cerebro.addanalyzer(bt.analyzers.DrawDown, _name="drawdown")

results = cerebro.run()
strategy = results[0]

print(f"Sharpe Ratio: {strategy.analyzers.sharpe.get_analysis().get('sharperatio', 'N/A')}")
print(f"Max Drawdown: {strategy.analyzers.drawdown.get_analysis().max.drawdown:.2f}%")

cerebro.plot()

Alternatives

VectorBTZiplineQuantConnect