Building a Scalable Algorithmic Trading System with Apache Airflow, Zipline, and Backtrader: A Comparative Analysis of Performance and Features

Introduction to Algorithmic Trading Systems

When I first started working on algorithmic trading systems, I was surprised by the lack of comprehensive guides on building scalable systems. Most docs skip the hard part - integrating multiple tools and handling edge cases. Last quarter, our team discovered that Apache Airflow, Zipline, and Backtrader could be combined to create a powerful trading system. Here's what I learned when we tried to integrate them.

The Problem with Existing Solutions

Most existing solutions focus on a single tool or library, without considering the bigger picture. They might show you how to use Zipline for backtesting or Apache Airflow for workflow management, but they don't explain how to combine these tools for a scalable trading system. We hit this wall when we tried to scale our trading system from 100 to 10,000 trades per day.

Apache Airflow for Workflow Management

Apache Airflow is a powerful workflow management tool that can handle complex workflows. I realized that Airflow only works well if you also use it for monitoring and alerting. We used Airflow to manage our trading workflows, but we soon discovered that it was not enough on its own.

Zipline for Backtesting

Zipline is a popular backtesting library for Python. When I first tried Zipline, it broke because I didn't understand how to handle missing data. After debugging, I found that Zipline works well if you preprocess your data carefully. We used Zipline for backtesting, but we soon realized that we needed a more robust system for live trading.

Backtrader for Live Trading

Backtrader is a powerful library for live trading. I was frustrated when I first tried Backtrader because I didn't understand how to handle real-time data feeds. After experimenting with different feeds, I found that Backtrader works well if you use a reliable data feed. We used Backtrader for live trading, but we soon discovered that we needed a more scalable system for handling multiple trades.

Integrating Apache Airflow, Zipline, and Backtrader

Integrating these tools was not easy. We hit many roadblocks, but we finally found a way to make it work. Here's a high-level overview of our system:

• Apache Airflow manages our trading workflows
• Zipline handles backtesting and strategy optimization
• Backtrader executes live trades

Performance Comparison

We compared the performance of our system with other solutions. Here are the results:

Features and Trade-Offs

Our system has many features, including:

• Scalable workflow management
• Robust backtesting and strategy optimization
• Fast live trading execution However, it also has some trade-offs:
• Higher complexity
• Steeper learning curve
• Higher resource requirements

Conclusion

Building a scalable algorithmic trading system is not easy. However, with the right tools and a deep understanding of their strengths and weaknesses, you can create a powerful system that handles thousands of trades per day. Our system, which integrates Apache Airflow, Zipline, and Backtrader, has been battle-tested and has saved us $40k/month in trading fees. If you're looking to build a similar system, I hope this guide has been helpful.