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1. Why most people currently choose Python as the quantitative development language
For the vast majority of small to medium-sized quantitative traders, the Single-Individual Operator (SIO) model is typically adopted, with core strategy personnel primarily skilled in Python and Matlab. Python, with its simplicity, efficiency, rich financial libraries (such as Pandas, NumPy, Scikit-learn, etc.), and strong community support, has become an excellent choice for strategy implementation. Matlab, due to its powerful matrix computation capabilities and rich financial toolbox, also holds a place in quantitative trading.
In this environment, the speed and flexibility of strategy implementation are considered the most important demands. The ease of use and rapid development characteristics of Python and Matlab allow strategy personnel to quickly complete strategy design and implementation, thus providing valuable time for strategy backtesting and optimization.
2. Problems Encountered in Quantitative Trading
However, with the rapid development of quantitative trading, strategy returns have begun to decline significantly, and the number of market participants has increased, leading many traditional effective factors to gradually lose their effectiveness. The high competition in the market requires strategies to be continuously iterated and updated, seeking more dimensions and finer granularity of data to enhance factor returns. Increasing computational speed to improve returns (real-time calculations during trading) has led to exponential growth in data and computation, with tick-level data becoming standard. Novel and effective factors must be quickly applied in the market; otherwise, returns will soon be diluted.
Therefore, the complexity of business and the technical requirements have significantly increased. Traders often need to handle GB/TB-level data and are extremely strict about controlling execution costs. Additionally, strategies often lean towards high-frequency trading, requiring synchronization capabilities across multiple markets. As a result, traders have begun to use high-performance programming languages such as C++, Java, and even started to introduce Rust.
3. Advantages of Python
① High Development Efficiency: Rapid Prototyping and Implementation
Python is an interpreted language with a simple and readable syntax, allowing developers to quickly prototype and implement. This high development efficiency is particularly important for quantitative traders, as they need to rapidly test various strategies to respond to the fast-changing market environment.
② Comprehensive Third-Party Ecosystem: pandas, numpy, scipy, etc.
Python has a rich set of third-party libraries, such as pandas, numpy, and scipy, which provide powerful data processing and numerical computation capabilities, enabling developers to easily perform data analysis and modeling. Furthermore, the continuous development and support from the Python community ensure that these libraries are constantly updated and optimized.
③ Strong Machine Learning Support: tensorflow, pytorch, sklearn
Machine learning is increasingly applied in quantitative trading, and Python’s libraries like tensorflow, pytorch, and sklearn provide robust machine learning support, allowing quantitative traders to easily implement complex machine learning models.
④ Friendly Development Environment: No Compilation Process, Easy Debugging
The interpreted nature of Python means that the development process does not require compilation, greatly simplifying debugging and testing. For quantitative traders, this means they can iterate strategies faster, quickly identify and fix issues, thus improving development efficiency.
⑤ Smooth Learning Curve Suitable for Strategy Personnel with Backgrounds in Mathematics, Physics, and Finance
Python’s simple and easy-to-learn syntax is very suitable for those with backgrounds in mathematics, physics, and finance but lacking programming experience. These individuals can quickly get started with Python for strategy development and implementation.
4. Limitations of Python
① GIL Issue: Limits Concurrent Efficiency of Multithreading
The Global Interpreter Lock (GIL) in Python limits the concurrent efficiency of multithreading, which is a significant bottleneck for quantitative strategies that need to handle large amounts of data and perform high-frequency trading. Although the GIL issue can be circumvented through multiprocessing, this introduces additional complexity and resource consumption.
② High Memory Usage: Limits Scalability
Python’s dynamic typing and interpreted characteristics lead to high memory usage, with a single Python process + pandas/numpy occupying 120MB of memory, which limits its scalability when handling large datasets. For quantitative trading systems that need to process massive amounts of data, high memory usage not only affects performance but also increases operational costs.
③ Performance Bottleneck: Interpreted Language is Slower, Not Suitable for Large Datasets and Low Latency Scenarios
As an interpreted language, Python’s execution speed is slower, making it unsuitable for handling large datasets and low-latency scenarios. This poses a serious performance bottleneck for high-frequency trading and large-scale data analysis, limiting the execution efficiency and return rate of strategies.
④ Operational Costs:
Due to Python’s high memory usage and performance bottlenecks, it not only increases hardware costs but also raises operational costs.
5. Why Choose Rust
① Excellent Performance
Rust’s performance far exceeds that of Python, excelling in scenarios involving large datasets, low latency, and intensive computations. Its compiled code is close to low-level machine code, allowing it to fully leverage hardware performance. This makes Rust an ideal choice for handling high-frequency trading and large-scale data processing, improving the efficiency and return rate of strategy execution, achieving 0.7ms for processing 1-minute line data for a single factor.
② Memory Safety and Thread Safety: Guaranteed by the Compiler, No Data Race Issues
In Rust, the ownership system and borrowing checker enforce memory and thread safety rules at compile time, using smart pointers (such as Arc<T> and Mutex<T>) to ensure thread safety. This avoids common memory leaks and data race issues, enhancing system stability and reducing development and maintenance costs, allowing quantitative traders to focus more on strategy development and optimization.
③ No GIL or GC Issues: Low Memory Usage
Rust does not have a Global Interpreter Lock (GIL) or garbage collection (GC), resulting in superior multithreading performance and low memory usage. Rust provides the std::sync::mpsc module for message passing between threads. Through message passing, threads can communicate without sharing memory. This is a significant advantage for quantitative strategies that need to handle large amounts of data and perform high-frequency trading. Rust’s multithreading model makes concurrent programming more efficient, fully utilizing the performance of multi-core processors.
④ Development Efficiency: Supports Functional, Concurrent, and Object-Oriented Programming, Significantly Reducing Difficulty Compared to C++
Rust supports multiple programming paradigms, including functional, concurrent, and object-oriented programming, providing flexible programming options. Compared to C++, Rust’s syntax is simpler and easier to learn, reducing development difficulty. This allows quantitative traders to quickly get started with Rust for strategy development and implementation, thus improving development efficiency.
⑤ Perfect Use of Asynchronous Ecosystem
Although the Rust language itself only provides the basic capabilities for writing asynchronous code, the community-driven ecosystem makes Rust excel in asynchronous programming. Rust’s asynchronous programming model avoids performance fluctuations caused by garbage collection, further enhancing performance through fine-grained task scheduling and memory management. Compared to Go’s concurrency model, Rust’s lack of GC and flexibility gives it higher performance and stability in high-concurrency scenarios. Whether for web servers, microservices, or other high-concurrency applications, Rust’s asynchronous ecosystem provides strong support.
⑤ Cross-Platform Support: Official Package Manager and Comprehensive Support for Windows/macOS/Linux
Rust provides an official package manager, Cargo, and comprehensive support for Windows, macOS, and Linux. This allows developers to easily perform cross-platform development and deployment, ensuring that quantitative trading systems can run seamlessly on different operating systems. This is a significant advantage for quantitative trading systems that require global deployment and operation.
6. Disadvantages of Rust
① Slow Compilation Speed
Although Rust’s compiler provides powerful error checking and optimization features, the slow compilation speed affects development efficiency. However, for quantitative systems, runtime performance and safety are often more important, so this drawback can be accepted to some extent.
② Unsafe Mode Can Still Lead to Memory Safety Issues
Despite Rust’s excellent performance in safety, using Unsafe mode can still lead to memory issues. Unsafe mode allows developers to bypass the compiler’s safety checks and directly manipulate memory for higher performance.
7. Why Not Choose C++?
Although the performance gap with Rust is not significant, it is not the optimal solution for quantitative trading.
① No Compiler-Level Restrictions on User Behavior, Leading to Memory Leaks and Data Race Issues
While C offers excellent performance, its development efficiency is relatively low. C allows direct memory manipulation, which, while providing great flexibility, also leads to memory leaks and data race issues. These problems are often difficult to debug and reproduce, requiring developers to spend a lot of time and effort writing and maintaining C++ code, ultimately affecting overall development efficiency.
② No Package Manager, Limited Cross-Platform Support
Compared to modern programming languages, C++ lacks a unified package manager, and the standard libraries on different platforms may have slight differences, making dependency management and library usage cumbersome. Even with CMake, a cross-platform build system, CMake itself has a certain complexity in learning and usage.
③ High Development Time Costs: Complex Syntax and Difficult Error Handling Mechanisms
C++ has a complex syntax and a steep learning curve. Even experienced developers may encounter difficulties when writing C++. Additionally, C++’s error handling mechanisms (such as exception handling) are complex and hard to master. This requires developers to invest a lot of time and effort in writing, debugging, and maintaining code.
④ Trade-off Between Runtime Efficiency and Development Efficiency
Although C++ performs excellently in runtime performance, close to Rust, its development efficiency is relatively low. In specific quantitative trading scenarios such as high-frequency trading and large-scale data processing, development efficiency is also crucial. Long development cycles, high maintenance costs, and difficult debugging issues make C++ not the optimal solution in these scenarios. Developers may prefer to choose languages like Rust or Python that achieve a better balance between development efficiency and runtime performance.
Disclaimer: This is for knowledge organization and personal opinion output only. This public account does not guarantee the accuracy and completeness of this information. Investment involves risks, and one should be cautious when entering the market. This material does not constitute any investment advice; please carefully choose products and services that match your risk tolerance and investment goals.