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

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@InProceedings{Funie:2014:ICMLA,
  author =       "Andreea-Ingrid Funie and Mark Salmon and Wayne Luk",
  booktitle =    "13th International Conference on Machine Learning and
                 Applications (ICMLA 2014)",
  title =        "A Hybrid Genetic-Programming Swarm-Optimisation
                 Approach for Examining the Nature and Stability of High
                 Frequency Trading Strategies",
  year =         "2014",
  month =        dec,
  pages =        "29--34",
  keywords =     "genetic algorithms, genetic programming, PSO, FPGA",
  DOI =          "doi:10.1109/ICMLA.2014.11",
  size =         "6 pages",
  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.",
  notes =        "Dept. of Comput., Imperial Coll. London, London,
                 UK

                 Also known as \cite{7033087}",
}

Genetic Programming entries for Andreea-Ingrid Funie Mark Salmon Wayne Luk

Citations