Genetic Network Programming with Sarsa Learning and Its Application to Creating Stock Trading Rules

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

@InProceedings{Chen:2007:cec,
  author =       "Yan Chen and Shingo Mabu and Kotaro Hirasawa and 
                 Jinglu Hu",
  title =        "Genetic Network Programming with Sarsa Learning and
                 Its Application to Creating Stock Trading Rules",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "220--227",
  address =      "Singapore",
  month =        "25-28 " # sep,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "1-4244-1340-0",
  file =         "1636.pdf",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2007.4424475",
  abstract =     "In this paper, trading rules on stock market using the
                 Genetic Network Programming (GNP) with Sarsa learning
                 is described. GNP is an evolutionary computation, which
                 represents its solutions using graph structures and has
                 some useful features inherently. It has been clarified
                 that GNP works well especially in dynamic environments
                 since GNP can create quite compact programs and has an
                 implicit memory function. In this paper, GNP is applied
                 to creating a stock trading model. There are three
                 important points: The first important point is to
                 combine GNP with Sarsa Learning which is one of the
                 reinforcement learning algorithms. Evolution-based
                 methods evolve their programs after task execution
                 because they must calculate fitness values, while
                 reinforcement learning can change programs during task
                 execution, therefore the programs can be created
                 efficiently. The second important point is that GNP
                 uses candlestick chart and selects appropriate
                 technical indices to judge the timing of the buying and
                 selling stocks. The third important point is that
                 sub-nodes are used in each node to determine
                 appropriate actions (buying/selling) and to select
                 appropriate stock price information depending on the
                 situation. In the simulations, the trading model is
                 trained using the stock prices of 16 brands in 2001,
                 2002 and 2003. Then the generalisation ability is
                 tested using the stock prices in 2004. From the
                 simulation results, it is clarified that the trading
                 rules of the proposed method obtain much higher profits
                 than Buy&Hold method and its effectiveness has been
                 confirmed.",
  notes =        "CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",
}

Genetic Programming entries for Yan Chen Shingo Mabu Kotaro Hirasawa Jinglu Hu

Citations