Evolutionary learning of technical trading rules without data-mining bias

  • Agapitos A
  • O'Neill M
  • Brabazon A
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In this paper we investigate the profitability of
evolved technical trading rules when controlling for
data-mining bias. For the first time in the
evolutionary computation literature, a comprehensive
test for a rule's statistical significance using
Hansen's Superior Predictive Ability is explicitly
taken into account in the fitness function, and
multi-objective evolutionary optimisation is employed
to drive the search towards individual rules with
better generalisation abilities. Empirical results on a
spot foreign-exchange market index suggest that
increased out-of-sample performance can be obtained
after accounting for data-mining bias effects in a
multi-objective fitness function, as compared to a
single-criterion fitness measure that considers solely
the average return.

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  • Alexandros Agapitos

  • Michael O'Neill

  • Anthony Brabazon

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