Combining technical trading rules using parallel particle swarm optimization based on Hadoop

Fei WANG, Leung Ho Philip YU, David W. CHEUNG

Research output: Chapter in Book/Report/Conference proceedingChapters

8 Citations (Scopus)

Abstract

Technical trading rules have been utilized in the stock markets to make profit for more than a century. However, no single trading rule can ever be expected to predict the stock price trend accurately. In fact, many investors and fund managers make trading decisions by combining a bunch of technical indicators. In this paper, we consider the complex stock trading strategy, called Performance-based Reward Strategy (PRS), proposed by [1]. Instead of combining two classes of technical trading rules, we expand the scope to combine the seven most popular classes of trading rules in financial markets, resulting in a total of 1059 component trading rules. Each component rule is assigned a starting weight and a reward/penalty mechanism based on rules' recent profit is proposed to update their weights over time. To determine the best parameter values of PRS, we employ an improved time variant particle swarm optimization (TVPSO) algorithm with the objective of maximizing the annual net profit generated by PRS. Due to a large number of component rules and swarm size, the optimization time is significant. A parallel PSO based on Hadoop, an open source parallel programming model of MapReduce, is employed to optimize PRS more efficiently. The experimental results show that PRS outperforms all of the component rules in the testing period. Copyright © 2014 IEEE.
Original languageEnglish
Title of host publicationProceedings of 2014 International Joint Conference on Neural Networks
Place of PublicationPiscataway, New Jersey
PublisherIEEE
Pages3987-3994
ISBN (Print)9781479914845
DOIs
Publication statusPublished - 2014

Citation

Wang, F., Yu, P. L. H., & Cheung, D. W. (2014). Combining technical trading rules using parallel particle swarm optimization based on Hadoop. In Proceedings of 2014 International Joint Conference on Neural Networks (pp. 3987-3994). Piscataway, New Jersey: IEEE.

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