A real-time adaptive trading system using genetic programming

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@Article{Dempster:2000:QF,
  author =       "M. A. H. Dempster and C. M. Jones",
  title =        "A real-time adaptive trading system using genetic
                 programming",
  journal =      "Quantitative Finance",
  year =         "2000",
  volume =       "1",
  pages =        "397--413",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2000/geneticprogramming.pdf",
  URL =          "http://citeseer.ist.psu.edu/dempster01realtime.html",
  size =         "17 pages",
  abstract =     "Technical analysis indicators are widely used by
                 traders in financial and commodity markets to predict
                 future price levels and enhance trading profitability.
                 We have previously shown a number of popular
                 indicator-based trading rules to be loss-making when
                 applied individually in a systematic manner. However,
                 technical traders typically use combinations of a broad
                 range of technical indicators. Moreover, successful
                 traders tend to adapt to market conditions by dropping
                 trading rules as soon as they become loss-making or
                 when more profitable rules are found. In this paper we
                 try to emulate such traders by developing a trading
                 system consisting of rules based on combinations of
                 different indicators at different frequencies and lags.
                 An initial portfolio of such rules is selected by a
                 genetic algorithm applied to a number of indicators
                 calculated on a set of US Dollar/British Pound spot
                 foreign exchange tick data from 1994 to 1997 aggregated
                 to various intraday frequencies. The genetic algorithm
                 is subsequently used at regular intervals on
                 out-of-sample data to provide new rules and a feedback
                 system is used to rebalance the rule portfolio, thus
                 creating two levels of adaptivity. Despite the
                 individual indicators being generally loss-making over
                 the data period, the best rule found by the developed
                 system is found to be modestly, but significantly,
                 profitable in the presence of realistic transaction
                 costs.",
  notes =        "INSTITUTE OF PHYSICS PUBLISHING quant.iop.org",
}

Genetic Programming entries for Michael Dempster Chris M Jones

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