CUDA-Enabled Optimisation of Technical Analysis Parameters

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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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