Robustness Test of Genetic Algorithm on Generating Rules for Currency Trading

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{DENG:2012:PCS,
  author =       "Shangkun Deng and Yizhou Sun and Akito Sakurai",
  title =        "Robustness Test of Genetic Algorithm on Generating
                 Rules for Currency Trading",
  journal =      "Procedia Computer Science",
  volume =       "13",
  pages =        "86--98",
  year =         "2012",
  note =         "Proceedings of the International Neural Network
                 Society Winter Conference (INNS-WC2012)",
  keywords =     "genetic algorithms, genetic programming, Optimisation
                 algorithm, Foreign exchange, Robustness test, Technical
                 analysis, Financial prediction",
  ISSN =         "1877-0509",
  DOI =          "doi:10.1016/j.procs.2012.09.117",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1877050912007247",
  abstract =     "In trading in currency markets, reducing te mean of
                 absolute or squared errors of predicted values is not
                 valuable unless it results in profits. A trading rule
                 is a set of conditions that describe when to buy or
                 sell a currency or to close a position, which can be
                 used for automated trading. To optimise the rule to
                 obtain a profit in the future, a probabilistic method
                 such as a genetic algorithm (GA) or genetic programming
                 (GP) is used, since the profit is a discrete and
                 multimodal function with many parameters. Although the
                 rules optimised by GA/GP reportedly obtain a profit in
                 out-of-sample testing periods, it is hard to believe
                 that they yield a profit in distant out-of-sample
                 periods. In this paper, we first consider a framework
                 where we optimise the parameters of the trading rule in
                 an in-sample training period, and then execute trades
                 according to the rule in its succeeding out-of-sample
                 period. We experimentally show that the framework very
                 often results in a profit. We then consider a framework
                 in which we conduct optimization as above and then
                 execute trades in distant out-of-sample periods. We
                 empirically show that the results depend on the
                 similarity of the trends in the training and testing
                 periods.",
}

Genetic Programming entries for Shangkun Deng Yizhou Sun Akito Sakurai

Citations