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

ArXiv ID: 2309.04259 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: high-frequency trading, low-latency programming, pairs trading, Disruptor pattern, cache optimization

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders
  • Why: The paper focuses on practical C++ optimization techniques and includes backtesting with statistical significance metrics, but lacks advanced mathematical derivations beyond basic statistical arbitrage concepts.
  flowchart TD
    A["Research Goal<br>Bridge knowledge gap in<br>optimizing latency-critical C++ code<br>for HFT systems"] --> B["Key Methodology<br>Created LLPR & Implemented<br>Disruptor pattern; Optimized<br>Statistical Arbitrage Strategy"]
    B --> C["Data & Inputs<br>Market data streams<br>Algorithmic trading logic<br>Traditional queuing models"]
    C --> D["Computational Process<br>Rigorous statistical benchmarking<br>Cache optimization &<br>Latency analysis (speed, cache util)"]
    D --> E["Key Findings & Outcomes<br>• Disruptor > Traditional queues<br>• Significant latency reduction<br>• Validated profitable strategy<br>• Future: Live deployment"]