Comprehensibility, Overfitting and Co-Evolution in Genetic Programming for Technical Trading Rules

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

@MastersThesis{seshadri:mastersthesis,
  author =       "Mukund Seshadri",
  title =        "Comprehensibility, Overfitting and Co-Evolution in
                 Genetic Programming for Technical Trading Rules",
  school =       "Worcester Polytechnic Institute",
  year =         "2003",
  type =         "Master's Thesis",
  address =      "6 Castle Road, Northboro, MA 10532, USA",
  month =        may,
  email =        "mukund@cs.wpi.edu",
  keywords =     "genetic algorithms, genetic programming, technical
                 analysis, coevolution, S&P500, market timing",
  URL =          "http://www.wpi.edu/Pubs/ETD/Available/etd-0430103-121518/unrestricted/Seshadri.pdf",
  URL =          "http://www.wpi.edu/Pubs/ETD/Available/etd-0430103-121518/",
  abstract =     "This thesis presents Genetic Programming methodologies
                 to find successful and understandable technical trading
                 rules for financial markets. The methods when applied
                 to the S&P500 consistently beat the buy-and-hold
                 strategy over a 12-year period, even when considering
                 transaction costs. Some of the methods described
                 discover rules that beat the S&P500 with 99%
                 significance. The work describes the use of a
                 complexity-penalising factor to avoid overfitting and
                 improve comprehensibility of the rules produced by GPs.
                 The effect of this factor on the returns for this
                 domain area is studied and the results indicated that
                 it increased the predictive ability of the rules. A
                 restricted set of operators and domain knowledge were
                 used to improve comprehensibility. In particular,
                 arithmetic operators were eliminated and a number of
                 technical indicators in addition to the widely used
                 moving averages, such as trend lines and local maxima
                 and minima were added. A new evaluation function that
                 tests for consistency of returns in addition to total
                 returns is introduced. Different cooperative
                 coevolutionary genetic programming strategies for
                 improving returns are studied and the results analysed.
                 We find that paired collaborator coevolution has the
                 best results.",
  size =         "87 pages",
}

Genetic Programming entries for Mukund Seshadri

Citations