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Complex Stock Trading Strategy Based on Parallel Particle Swarm Optimization

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Complex Stock Trading Strategy Based on Parallel Particle Swarm Optimization



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Complex Stock Trading Strategy Based on Parallel Particle Swarm Optimization by Fei Wang
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This dissertation, "Complex Stock Trading Strategy Based on Parallel Particle Swarm Optimization" by Fei, Wang, 王緋, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Trading rules have been utilized in the stock market to make profit for more than a century. However, only using a single trading rule may not be sufficient to predict the stock price trend accurately. Although some complex trading strategies combining various classes of trading rules have been proposed in the literature, they often pick only one rule for each class, which may lose valuable information from other rules in the same class. In this thesis, a complex stock trading strategy, namely Performance-based Reward Strategy (PRS), is proposed. PRS combines the seven most popular classes of trading rules in financial markets, and for each class of trading rule, PRS includes various combinations of the rule parameters to produce a universe of 1059 component trading rules in all. Each component rule is assigned a starting weight and a reward/penalty mechanism based on profit is proposed to update these rules' weights over time. To determine the best parameter values of PRS, we employ an improved time variant Particle Swarm Optimization (PSO) algorithm with the objective of maximizing the annual net profit generated by PRS. Due to the 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. By omitting the traditional reduce phase of MapReduce, the proposed parallel PSO avoids the I/O cost of intermediate data and gets higher speedup ratio than previous parallel PSO based on MapReduce. After being optimized in an eight years training period, PRS is tested on an out-of-sample data set. The experimental results show that PRS outperforms all of the component rules in the testing period. DOI: 10.5353/th_b4985888 Subjects: Investments - Data processingStocks - Data processingParallel processing (Electronic computers)Mathematical optimizationSwarm intelligence
Release date NZ
January 26th, 2017
Created by
colour illustrations
Country of Publication
United States
Open Dissertation Press
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