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Extending backtrader with Custom Observers and Analyzers

Last updated: December 22, 2024

Backtrader is a popular Python library for algorithmic trading, valued for its simplicity and flexibility. However, to truly leverage its power, it can often be necessary to extend its functionality with custom components. In this article, we'll dive into creating custom Observers and Analyzers, two integral parts of the backtrader library that help traders gain insights from their strategies.

Understanding Observers and Analyzers

Before delving into customization, it’s crucial to understand what Observers and Analyzers do in backtrader:

  • Observers: These are used to track changes for each bar (or time period) in your stock data. Observers can keep an eye on metrics like cash, trades, and orders.
  • Analyzers: In contrast, Analyzers are primarily concerned with processing and summarizing the data after the strategy execution. They can provide insights into performance metrics like returns, Sharpe ratios, and more.

Creating Custom Observers

To create a custom observer, you need to inherit from the Observer class and implement the next method. Here is an example where we create a simple observer to count the number of trades:


import backtrader as bt

class TradeCounter(bt.Observer):
    lines = ('trade_count',)

    def __init__(self):
        self.trade_count = 0

    def next(self):
        if self._owner.buy_sell_hist_indicator():  # Check if a trade happened
            self.trade_count += 1
        self.lines.trade_count[0] = self.trade_count

In this observer, we're counting trades on each bar and storing the result in our defined line, trade_count.

Implementing Custom Analyzers

When building a custom analyzer, you must inherit from the Analyzer class. Define useful methods like start, next, and stop to manage pre-run, per-bar, and post-run computations.

Here's an example where we create an analyzer to calculate average trade size:


class AvgTradeSizeAnalyzer(bt.Analyzer):
    def __init__(self):
        self.total_size = 0
        self.num_trades = 0

    def get_analysis(self):
        if self.num_trades == 0:
            return 0
        return self.total_size / self.num_trades

    def notify_trade(self, trade):
        self.total_size += trade.size
        self.num_trades += 1

In this example, we utilize the notify_trade event to accumulate the trade sizes and count these to determine average size.

Using Custom Components

After creating these custom components, include them in your backtrader strategy setup as follows:


cerebro = bt.Cerebro()
cerebro.addobserver(TradeCounter)
cerebro.addanalyzer(AvgTradeSizeAnalyzer, _name='avgt')

# Running your strategy
results = cerebro.run()

# Accessing analysis results
avg_trade_size = results[0].analyzers.avgt.get_analysis()
print(f"Average Trade Size: {avg_trade_size}")

Notice how the custom observer and analyzer are added to the cerebro engine and how the results from the analyzer can be accessed post-run. Integrating these components can provide deeper insights and tailor backtrader to fit specific trading needs.

Conclusion

By extending backtrader with custom Observers and Analyzers, you unlock powerful tools to automate insights and metrics difficult to glean from basic data operations. Armed with this understanding, you can break beyond the bounds of standard trading with creativity and complexity uniquely suited to your strategies.

Next Article: Comparing backtrader to Other Python Backtesting Frameworks

Previous Article: Running backtrader in Docker for Scalable Trading Infrastructures

Series: Algorithmic trading with Python

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