Why are classes bad in programming Rust for Algo Trading Platforms

There is a reason why are classes bad in programming rust for algo trading? It is clearly explained in this video and article below.

Rust, a systems programming language known for its performance, safety, and concurrency, is increasingly used in high-performance applications like algorithmic trading. While classes are a fundamental concept in object-oriented programming, their use in Rust for such applications is often discouraged. This article delves into the reasons why and explores alternative approaches.

The Case Against Classes in Rust

1. Performance Overhead:

   • Virtual Function Table (VFT): In object-oriented languages like C++, every object instance carries a VFT pointer, which adds overhead during method calls. This overhead can be significant in performance-critical applications like HFT.

   • Memory Allocation and Deallocation: Object-oriented programming often involves dynamic memory allocation and deallocation, which can introduce performance penalties due to garbage collection or manual memory management.

2. Complexity and Boilerplate Code:

   • Inheritance Hierarchies: Complex inheritance hierarchies can make code harder to understand, maintain, and reason about.

   • Boilerplate Code: Classes require boilerplate code for constructors, destructors, and other methods, which can clutter the codebase.

3. Lack of Flexibility:

   • Tight Coupling: Classes can tightly couple components, making it difficult to modify or reuse them independently.

   • Limited Flexibility: Once a class hierarchy is established, it can be challenging to make significant changes without breaking existing code.

Rust's Alternative: Composition Over Inheritance

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Rust promotes composition over inheritance, a design principle that favors the composition of smaller, independent units of functionality. This approach offers several advantages:

1. Performance:

   • Value Semantics: Rust's preference for value semantics avoids the overhead of dynamic memory allocation and deallocation.

   • Zero-Cost Abstractions: Rust's powerful type system and ownership model allow for the creation of efficient abstractions without sacrificing performance.

2. Clarity and Simplicity:

   • Smaller, More Focused Components: By breaking down complex systems into smaller, more focused components, Rust code becomes easier to understand, test, and maintain.

   • Reduced Boilerplate: Rust's concise syntax and powerful features minimize the need for boilerplate code.

3. Flexibility and Reusability:

   • Loose Coupling: Rust's composition-based approach promotes loose coupling between components, making it easier to modify and reuse them.

   • Adaptability: Rust's trait system allows for flexible and adaptable interfaces, enabling the creation of highly customizable and reusable components.

Practical Example: A Trading Strategy Implementation

Consider a simple trading strategy that involves calculating a moving average and generating buy/sell signals. In a class-based approach, we might define a TradingStrategy class with methods for calculating the moving average and generating signals. However, in Rust, we can achieve the same functionality using structs and traits:

Rust

// TradingStrategy.rs

struct TradingStrategy {

    // ... other fields

}

impl TradingStrategy {

    fn calculate_moving_average(&self, prices: &[f64], period: usize) -> f64 {

        // ... implementation

    }

    fn generate_signal(&self, current_price: f64, moving_average: f64) -> Signal {

        // ... implementation

    }

}

By using structs and traits, we can create flexible, reusable components that can be easily combined and customized to implement different trading strategies.

Conclusion

While classes can be a useful tool in certain programming contexts, their use in high-performance, low-latency applications like algorithmic trading can introduce significant overhead and complexity. Rust's focus on composition, value semantics, and zero-cost abstractions provides a more efficient and flexible approach to building robust and scalable trading systems. By understanding the trade-offs and embracing Rust's unique features, developers can create high-performance, low-latency trading platforms that can compete in today's fast-paced financial markets.

I spent 2 years rebuilding my algorithmic trading platform in Rust. I have no regrets.