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低延迟交易系统的C++技术与设计模式

文章 arXiv papers · 作者: Paul Bilokon et al.
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总结

这项研究探讨降低对性能敏感的C++应用程序延迟的方法，主要场景是高频交易。文中介绍了一个编程代码库，其中包含经过基准测试的示例、一种经过优化的市场中性统计套利配对策略，以及用于协调并发工作的Disruptor模式C++实现。

评估考察了速度、缓存使用和统计显著性。作者报告称，缓存预热和使用constexpr进行编译期计算带来了最明显的延迟改善，而Disruptor的表现优于传统队列方案。报告还称，该交易策略在速度和盈利能力方面均有改善，但摘录没有提供指标或实验细节来评估这些说法。实盘市场测试以及将Disruptor与交易算法集成仍是未来计划，因此这些结果并不能证明端到端实盘表现。

核心观点

  • 该研究将实用的C++优化示例与统计基准测试用于对延迟敏感的应用。
  • 研究将低延迟技术应用于市场中性统计套利配对策略。
  • Disruptor模式被描述为比传统队列方法更快的替代方案。
  • 据报告，缓存预热和constexpr对降低延迟尤其有效。
  • 实盘交易和集成系统测试仍属未来工作。

标签

高频交易配对交易套利交易执行统计学
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# C++ Design Patterns for Low-latency Applications Including High-frequency Trading


# C++ Design Patterns for Low-latency Applications Including High-frequency Trading









This work aims to bridge the existing knowledge gap in the optimisation of latency-critical code, specifically focusing on high-frequency trading (HFT) systems. The research culminates in three main contributions: the creation of a Low-Latency Programming Repository, the optimisation of a market-neutral statistical arbitrage pairs trading strategy, and the implementation of the Disruptor pattern in C++. The repository serves as a practical guide and is enriched with rigorous statistical benchmarking, while the trading strategy optimisation led to substantial improvements in speed and profitability. The Disruptor pattern showcased significant performance enhancement over traditional queuing methods. Evaluation metrics include speed, cache utilisation, and statistical significance, among others. Techniques like Cache Warming and Constexpr showed the most significant gains in latency reduction. Future directions involve expanding the repository, testing the optimised trading algorithm in a live trading environment, and integrating the Disruptor pattern with the trading algorithm for comprehensive system benchmarking. The work is oriented towards academics and industry practitioners seeking to improve performance in latency-sensitive applications.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。