ABSTRACT
Foreign exchange (forex) market trading using evolutionary algorithms is an active and controversial area of research. We investigate the use of a linear genetic programming (LGP) system for automated forex trading of four major currency pairs. Fitness functions with varying degrees of conservatism through the incorporation of maximum drawdown are considered. The use of the fitness types in the LGP system for different currency value trends are examined in terms of performance over time, underlying trading strategies, and overall profitability. An analysis of trade profitability shows that the LGP system is very accurate at both buying to achieve profit and selling to prevent loss, with moderate levels of trading activity.
- A. Brabazon and M. O'Neill. Biologically Inspired Algorithms for Financial Modeling. Springer Verlag, Berlin, 2006. Google ScholarDigital Library
- M. Brameier and W. Banzhaf. Linear Genetic Programming. Springer, New York, 2007. Google ScholarDigital Library
- M. Dacorogna, R. Gençy, U. Muller, R. Olsen, and O. Picket. An Introduction to High-Frequency Finance. Academic Press, San Diego, 2001.Google Scholar
- M. A. H. Dempster and C. M. Jones. A real-time adaptive trading system using genetic programming. Quantitative Finance, 1:397--413, 2000.Google ScholarCross Ref
- M. A. H. Dempster, T. W. Payne, Y. Romahi, and G. W. P. Thompson. Computational learning techniques for intraday FX trading using popular technical indicators. IEEE Transactions on Neural Networks, 12(4):744--754, July 2001. Google ScholarDigital Library
- M. A. H. Dempster and Y. S. Romahi. Intraday fx trading: An evolutionary reinforcement learning approach. In IDEAL '02: Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning, pages 347--358, London, UK, 2002. Springer-Verlag. Google ScholarDigital Library
- C. Faith. Way of the Turtle: The Secret Methods that Turned Ordinary People into Legendary Traders. McGraw-Hill, New York, 2007.Google Scholar
- A. Hirabayashi, C. Aranha, and H. Iba. Optimization of the trading rule in foreign exchange using genetic algorithm. In GECCO 2009 Conference Proceedings, pages 1529--1536. ACM Press, July 2009. Google ScholarDigital Library
- A. Hryshko and T. Downs. System for foreign exchange trading using genetic algorithms and reinforcement learning. Intern. J. Syst. Sci., 35(13--14):763--774, 2004. Google ScholarDigital Library
- C. J. Neely and P. A. Weller. Intraday technical trading in the foreign exchange market. Journal of International Money and Finance, 22(2):223--237, 2003.Google ScholarCross Ref
- C. J. Neely, P. A. Weller, and R. Dittmar. Is technical analysis in the foreign exchange market profitable? A genetic programming approach. The Journal of Financial and Quantitative Analysis, 32(4):405--426, Dec. 1997.Google ScholarCross Ref
- C. J. Neely, P. A. Weller, and R. Dittmar. The adaptive market hypothesis: Evidence from the foreign exchange market. The Journal of Financial and Quantitative Analysis, 44(2):223--237, April 2009.Google ScholarCross Ref
- Bank of Canada. http://www.bank-banque-canada.ca.Google Scholar
- P. Saks and D. Maringer. Evolutionary money management. In EvoWorkshops '09: Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing, pages 162--171, Berlin, Heidelberg, 2009. Springer-Verlag. Google ScholarDigital Library
- R. Schwaerzel and T. Bylander. Predicting currency exchange rates by genetic programming with trigonometric functions and high-order statistics. In M. K. et al., editor, GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, volume 1, pages 955--956, Seattle, Washington, USA, 8-12 July 2006. ACM Press. Google ScholarDigital Library
- G. Wilson and W. Banzhaf. Prediction of interday stock prices using developmental and linear genetic programming. In EvoWorkshops '09: Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing, pages 172--181, Berlin, Heidelberg, 2009. Springer-Verlag. Google ScholarDigital Library
Index Terms
Interday foreign exchange trading using linear genetic programming
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