A Hybrid Genetic-Programming Swarm-Optimisation Approach for Examining the Nature and Stability of High Frequency Trading Strategies

2014 13th International Conference on Machine Learning and Applications Pub Date : 2014-12-03 DOI:10.1109/ICMLA.2014.11

Andreea-Ingrid Funie, Mark Salmon, W. Luk

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引用次数: 8

Abstract

Advances in high frequency trading in financial markets have exceeded the ability of regulators to monitor market stability, creating the need for tools that go beyond market microstructure theory and examine markets in real time, driven by algorithms, as employed in practice. This paper investigates the design, performance and stability of high frequency trading rules using a hybrid evolutionary algorithm based on genetic programming, with particle swarm optimisation layered on top to improve the genetic operators' performance. Our algorithm learns relevant trading signal information using Foreign Exchange market data. Execution time is significantly reduced by implementing computationally intensive tasks using Field Programmable Gate Array technology. This approach is shown to provide a reliable platform for examining the stability and nature of optimal trading strategies under different market conditions through robust statistical results on the optimal rules' performance and their economic value.

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研究高频交易策略性质与稳定性的混合遗传规划群优化方法

金融市场高频交易的进步已经超出了监管机构监控市场稳定性的能力，因此需要超越市场微观结构理论的工具，并在实践中使用的算法驱动下实时检查市场。本文采用基于遗传规划的混合进化算法研究高频交易规则的设计、性能和稳定性，并在遗传算子的基础上进行粒子群优化，以提高遗传算子的性能。我们的算法利用外汇市场数据学习相关的交易信号信息。通过使用现场可编程门阵列技术实现计算密集型任务，大大减少了执行时间。该方法通过对最优规则的性能及其经济价值的稳健统计结果，为检验不同市场条件下最优交易策略的稳定性和性质提供了可靠的平台。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2014 13th International Conference on Machine Learning and Applications

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