The world of algorithmic trading, where automated systems execute trades based on pre-defined rules, is a captivating blend of finance and computer science. While languages like Python often dominate this space, I recently embarked on a project that challenged the norm: building an algorithmic trading system in Rust. This article chronicles my experience, exploring the motivations behind this choice and the unique advantages  offers for algorithmic trading in Rust.

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Why Rust? A Quest for Speed and Safety

My foray into algorithmic trading began with Python, a popular and versatile language. However, as I delved deeper, the limitations of Python for performance-critical applications became apparent. Algorithmic trading thrives on speed and efficiency, where even slight delays can impact profitability. Python’s reputation lies in its readability and rapid development features, but raw speed isn’t necessarily its forte.

This is where Rust entered the scene. Often dubbed “the systems programming language for the 21st century,” Rust prioritizes speed, memory safety, and thread safety – all essential ingredients for robust algorithmic trading systems.

• Speed: Rust is a compiled language, translating code directly into machine code, resulting in blazing-fast execution speeds comparable to C++. This translates to quicker order placement and reaction times in the fast-paced trading environment.
• Memory Safety: Memory management is a critical aspect of algorithmic trading systems. Memory leaks or unexpected crashes can have disastrous consequences. Rust enforces memory safety through its ownership system, eliminating entire categories of memory-related errors at compile time. This not only enhances the system’s stability but also reduces the mental burden of manual memory management.
• Thread Safety: Algorithmic trading systems often involve multiple processes running concurrently, such as data fetching, signal generation, and order execution. Rust’s powerful concurrency features ensure thread safety, preventing race conditions and data corruption that could lead to erroneous trades.

Building the Foundation: Libraries and Tools

With Rust’s strengths in mind, I embarked on building my algorithmic trading system. Here are some key libraries and tools that played a crucial role:

• Data Acquisition:
  • reqwest: This popular HTTP client facilitated fetching real-time and historical market data from various exchanges and data providers.
• Market Data Handling:
  • ta-lib: This library provided a comprehensive collection of technical analysis indicators, allowing for the implementation of various trading strategies based on technical analysis.
  • rust-decimal: This library ensured accurate and high-precision financial calculations, crucial for avoiding rounding errors that could impact profitability.
• Order Management:
  • Quantomatic: This library provided an abstraction layer for interacting with various cryptocurrency exchanges through a unified API. This simplified the process of connecting to different exchanges and placing orders
  • .

Crafting the Strategy: From Idea to Code

The core of any algorithmic trading system lies in its trading strategy. My approach focused on a combination of technical indicators and trend following logic. The system would dynamically analyze price movements, calculate indicators like moving averages and relative strength index (RSI), and identify potential entry and exit points based on predefined thresholds.

Rust’s powerful pattern matching capabilities proved invaluable in implementing the trading logic. I could concisely express complex conditions using match statements, enhancing code readability and maintainability. Additionally, Rust’s strong typing system helped catch potential errors early in the development process, leading to a more robust and reliable system.

Backtesting and Optimization: Refining the Algorithm

No algorithmic trading system is complete without rigorous backtesting. I utilized historical market data to simulate the system’s performance across various market conditions. Backtesting allowed me to evaluate the effectiveness of the strategy, identify any weaknesses, and fine-tune the parameters to optimize performance.

Rust’s compile-time features proved advantageous during backtesting. The compiler could identify potential errors or undefined behavior in the backtesting logic itself, preventing issues that might have gone unnoticed in runtime environments.

Deployment and Monitoring: Bringing it to Life

With a backtested and optimized strategy, the final step involved deploying the system to a live trading environment. This involved careful consideration of security measures, ensuring proper access controls and data encryption. Additionally, I implemented robust monitoring tools to track the system’s performance in real-time, allowing for quick intervention in case of any unexpected behavior.

Lessons Learned and Looking Ahead

Building an algorithmic trading system in Rust was a rewarding experience. While it presented a steeper learning curve compared to Python, the benefits in terms of speed, safety, and control were substantial. Here are some key takeaways:

• Rust offers a powerful and reliable foundation for algorithmic trading systems. Its speed and safety features are invaluable in the fast-paced world of finance.
• The Rust ecosystem for algorithmic trading is still evolving. While libraries and tools exist, the selection might not be as vast as in more established languages like Python.
• **Community support for Rust in algorithmic

Note that this article was inspired by

medium.com/@austin-starks/i-built-an-algorithmic-trading-system-in-rust-heres-what-i-regret-a89f378b22c9