CUDA-Enabled Optimisation of Technical Analysis Parameters

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{ORourke:2012:DS-RT,

• author = "John O'Rourke and John Burns",
• booktitle = "16th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2012)",
• title = "CUDA-Enabled Optimisation of Technical Analysis Parameters",
• year = "2012",
• pages = "221--227",
• size = "7 pages",
• abstract = "The optimisation of Technical Trading parameters is a computationally intensive exercise. Models comprising a modest number of Technical Indicators require many thousands of simulations to be executed over a sample period of data, with the best performing sets of parameters employed to generate future trading signals. The purpose of this research is to investigate the suitability of GPU Computing for running the simulations in parallel and to develop a working Prototype optimiser based on the CUDA architecture. The cumulative nature of Profit and Loss over a sample period is a restricting factor in the design of a data-parallel trading simulator. Thus, different approaches to the distribution of the parallel workload are researched and an appropriate design for the Prototype is derived. Past studies are examined, including parallel Genetic Programming implementations. The remarkable speedups enjoyed by the Prototype are discussed in detail and a number of key design strategies are proposed. These include a per-thread solution identification methodology, a modification to Welford's Standard Deviation algorithm which results in the avoidance of divergent threads, and a suitable parameter distribution policy.",
• keywords = "genetic algorithms, genetic programming, graphics processing units, parallel architectures, software prototyping, CUDA, GPU computing, Welford standard deviation algorithm, data parallel trading simulator, key design strategy, optimisation, parallel architecture, parallel genetic programming, parallel workload distribution, parameter distribution, per-thread solution identification methodology, prototype optimiser, technical analysis parameter, technical indicator, Data models, Graphics processing units, Instruction sets, Instruments, Market research, Optimisation, Prototypes, GPU, Technical Trading",
• DOI = "doi:10.1109/DS-RT.2012.39",
• ISSN = "1550-6525",
• notes = "Also known as \cite{6365071}",

}

Genetic Programming entries for John O'Rourke John Burns

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