Abstract
Genetic algorithms are an application of evolutionary models derived and used in solving various problems of modelling, programming and optimisation. In this paper, we developed an optimisation methodology using the free Indicore SDK 2.0 software to increase the quality and performance of the results of autotrading robots programmed in the LUA 5.1 language with various parameters optimisable in number and range, by dividing the overall process optimisation into various sub phases. This methodology was applied to an autotrading robot built on the basis of the Moving Average Convergence Divergence technique for the currency pair of EUR/USD. This application was for a time scale of 1 h, during a period of annual in-sample optimisation between 2001 and 2007. We then tested this algorithm by applying the optimal configuration yielded by this process to an out-of-sample phase spanning 2008 to August 2011. The results show that the optimal configuration yielded by the optimisation methodology could be used as a tool to increase the quality of autotrading robots, because, in addition to producing positive results in the optimisation phase, the technique improves performance and behaviour when applied in the testing phase.
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References
Allen F, Karjalainen R (1999) Using genetic algorithms to find technical trading rules. J Financ Econ 51(2):245–271
Allen H, Hawkings J, Sato S (2001) Electronic trading and its implications for the financial systems. BIS Papers No 7: Electronic Finance. A New Perspect Challeng 2001(7):30–52
Boehmer E (2005) Dimensions of execution quality recent evidence for US equity markets. J Financ Econ 2005(78):553–582
Boehmer E, Jennings R, Wei L (2007) Public disclosure and private decisions: equity market execution quality and order routing. Rev Financ Stud 2007(20):315–358
Chan L, Wonk AWK (2012) Automated trading with genetic algorithm neural network. Risk cybernetics. An application on FX Markets. Finamatrix Journal, Leading & Dedicated Research in Risk Control for Sustainable Returns
Chen JS (2005) Trading strategy generation using genetic algorithms. Asian J Inform Technol 4(4):310–322
Chen JS, Deng SX, Lin PC (2000) Generation of trading strategies using genetic algorithms. In: Proceedings of the fifth joint conference on information sciences
Dempster MAH, Payne TW, Romahi Y, Thompson GWP (2001) Computational learning techniques for intraday FX. Trading using popular technical indicators. IEEE Trans Neural Network 12(4):744–754
Doherty CG (2003) Fundamental analysis using genetic programming for classification rule induction. Genetic algorithms and genetic programming at Stanford 2003. Standford Bookstore, 45–51
Drake AE, Marks RE (1998) Genetic algorithms in economics and finance forecasting stock market prices and foreign exchange. A review. Working paper series of the Australian Graduate School of Management. University of New South Wales, Sydney, Australia
Dunis C, Harris A, Leong S, Nacaskul P (1999) Optimising intraday trading models with genetic algorithms. Neural Network World 9(3):193–223
Ergin NH (2007) Architecting system of systems. Artificial life analysis of financial market behavior. Partial fulfillment of the requirements for the degree Doctor of philosophy in systems engineering, Faculty of the Graduate School of the University of Missouri-Rolla, USA
Farnsworth GV, Kelly JA, Othling AS, Pryor RJ (2004) Successful technical trading agents using genetic programming. Sandia Report. Sandia National Laboratories, Albuquerque, New Mexico
Fu F, Krishnamurti C, Sequeira J (2003) Stock exchange governance and market quality. J Bank Financ 27(9):1859–1878
Garvey R, Wu F (2009) Intraday time and order execution quality dimensions. J Financ Market 12(2):203–228
Hendershott T, Moulton PC (2010) Automation, speed, and stock market quality: the NYSE’s hybrid. J Financ Market 14(2011):568–604
Hirabayashi A, Aranha C, Iba H (2009) Optimisation of the trading rule in foreign exchange using Genetic algorithm. In: Proceedings of the 11th annual conference on genetic and evolutionary computation GECCO 09
Holland J (1975) Adaptation in natural and artificial systems. An introductory analysis with applications to biology, control and artificial intelligence. University of Michigan Press, Ann Arbor
Hryshko A, Downs T (2003) An implementation of genetic algorithms as a basis for a trading system on the foreign exchange market. The 2003 Congress on Evolutionary Computation 03(3):1695–1701
Kapishnikov A, Borisov A (2001) Technical rules optimisation using intelligent hybrid systems. Institute of Information Technology of Riga Technical University, Latvia
Koskinen J, Airas J, Nummelin T, Pekkala T, Starczewski J (2008) Exploring algorithms for automated FX trading constructing an hybrid model. Seminar on case studies in operations research. Helsinki University of Technology, Finnland
Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. Bradford Book, The MIT Press, Cambridge/London, MA/England
Lazarus C, Hu H (2001) Using genetic programming to evolve robot behaviours. In: Proceedings of the 3rd British conference on autonomous mobile robotics and autonomous systems, Manchester, UK
Li J, Tsang EPK (1999) Improving technical analysis predictions an application of genetic programming. American Association for Artificial Intelligence
Lin L, Cao L, Wang J, Zhang C (2004) The Applications of Genetic Algorithms in Stock Market Data Mining Optimisation. Wessex Institute of Technology Press, Faculty of Information Technology, University of Technology, Sydney, Australia
Lohpetch D, Corne D (2009) Discovering effective technical trading rules with genetic programming towards robustly outperforming buy-and-hold. World Cong Nat Biol Inspire Comput 1:439–444
Matsui K, Sato H (2011) A comparison of genotype representations to acquire stock trading strategy using genetic algorithms. Lect Note Comput Sci 6260(11):56–70
Mitchell M (1995) Genetic algorithms. An overview. Complexity 1(1):31–39
Neely CJ, Weller P, Dittmar R (1997) Is technical analysis in the foreign exchange market profitable. J Financ Quant Anal 32(4):405–26
Nieto MJ (2001) Reflections on the regulatory approach to E-finance. BIS Papers No 7: Electronic Finance. A New Perspect Challenge 2001(7):90–97
Noguer M (2010) Statistical arbitrage and algorithmic trading overview and applications. PhD. Thesis. Departamento de Economía Aplicada Cuantitativa II, Facultad de Ciencias Económicas y Empresariales, UNED
Payo-Molina L, Perez-Gonzalez P (2010) Sistemas Expertos: Trading. Inteligencia en Redes y Comunicaciones, 28–36
Pappu S (2011) Evolutionary algorithms. Project report. Department of Electronics & Telecommunication Engineering, Sardar Patel Institute of Technology, Munshi Nagar, Mumbai, India
Roberts MC (2002) The value of technical analysis. Departament of Agricultural, Environmental and Development Economics, The Ohio State University, Columbus, OH
Sato S, Hawkings J (2001) Electronic finance: an overview of the issues. BIS Papers No 7: Electronic Finance. A New Perspect Challenge 2001(7):1–12
Subramanian H (2004) Evolutionary algorithms in optimisation of technical rules for automated stock trading. Partial fulfillment of the requirements for the degree of master of science in engineering. Faculty of the Graduate School of The University of Texas, USA
Subramanian H, Ramamoorthy S, Stone P, Kuipers BJ (2006) Designing safe, profitable automated stock trading agents using evolutionary algorithms. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation
Weber S, Zysman J (2001) E-finance and the politics of transitions. BIS Papers No 7: Electronic Finance. A New Perspect Challenge 2001(7):26–29
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Alonso-Gonzalez, A., Almenar-Llongo, V., Peris-Ortiz, M. (2014). Optimisation Methodology Based on Genetic Algorithms to Increase the Quality and Performance in Autotrading Robots. In: Peris-Ortiz, M., Álvarez-García, J. (eds) Action-Based Quality Management. Springer, Cham. https://doi.org/10.1007/978-3-319-06453-6_12
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DOI: https://doi.org/10.1007/978-3-319-06453-6_12
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