Robustness test of genetic algorithm on generating rules for currency trading

Shangkun Deng, Yizhou Sun, Akito Sakurai

Research output: Contribution to journalConference articlepeer-review

7 Citations (Scopus)


In trading in currency markets, reducing the 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 optimize the rule to obtain a profit in the future, a probabilistic method such as a genetic algorithm (GA) or genetic programming (GP) is utilized, since the profit is a discrete and multimodal function with many parameters. Although the rules optimized 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 optimize 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.

Original languageEnglish
Pages (from-to)86-98
Number of pages13
JournalProcedia Computer Science
Publication statusPublished - 2012
Event3rd International Neural Network Society Winter Conference, INNS-WC 2012 - Bangkok, Thailand
Duration: 2012 Oct 32012 Oct 5


  • Financial prediction
  • Foreign exchange
  • Genetic algorithm
  • Optimization algorithm
  • Robustness test
  • Technical analysis

ASJC Scopus subject areas

  • Computer Science(all)


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