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Multi-objective Optimization of Technical Stock Market Indicators using GAs

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International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 68 - Number 20
Year of Publication: 2013
Authors:
Magda B. Fayek
Hatem M. El-boghdadi
Sherin M. Omran
10.5120/11698-7428

Magda B Fayek, Hatem M El-boghdadi and Sherin M Omran. Article: Multi-objective Optimization of Technical Stock Market Indicators using GAs. International Journal of Computer Applications 68(20):41-48, April 2013. Full text available. BibTeX

@article{key:article,
	author = {Magda B. Fayek and Hatem M. El-boghdadi and Sherin M. Omran},
	title = {Article: Multi-objective Optimization of Technical Stock Market Indicators using GAs},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {68},
	number = {20},
	pages = {41-48},
	month = {April},
	note = {Full text available}
}

Abstract

Recent financial researches showed that technical indicators are useful tools for stock prediction. Technical indicators are used to generate trading signals (buy/sell) signals. The main problem of an indicator usage is to determine its appropriate parameters. In this paper a new GA based technique for optimizing the parameters of a collection of technical indicators over two objective functions Sharpe ratio and annual profit is proposed. The technique handles four indicators DEMAC (Double Exponential Moving Average Crossovers), RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), and MARSI (Moving Average RSI) indicators. The technique was tested on 30 years of historical data of DJIA (Dow Jones Industrial Average) stock index. Results showed that the optimized parameters obtained by the proposed technique improved the profits obtained by the indicators with their typical parameters, the Buy and Hold strategy and the random strategy.

References

  • S. Taylor. (1986). "Modeling Financial Time Series", John Wiley & Sons
  • Malkiel, Burton G. (1973). A Random Walk Down Wall Street (6th ed. ). W. W. Norton & Company, Inc. ISBN 0-393-06245-7
  • John J. Murphy. TECHNICAL ANALYSIS OF THE FINANCIAL MARKETS (A COMPREHENSIVE GUIDE TO TRADING METHODS AND APPLICATIONS). New York Institute of Finance 1999.
  • Teeples, Allan W. , "An Evolutionary Approach to Optimization of Compound Stock Trading Indicators Used to Confirm Buy Signals"(2010). All Graduate Thesis and Dissertations. Paper 820.
  • M. A. H. Dempster and Chris M. Jones. (2000). The profitability of intra-day FX trading using technical indicators. University of Cambridge Judge Institute of Management Studies. Research Papers, No. 35/2000. Cambridge: University of Cambridge.
  • Akinori Hirabayashi, Claus Aranha and Hitoshi Iba. Optimization of the Trading Rule in Foreign Exchange using Genetic Algorithm. In GECCO 2009.
  • Seung-kyu Lee and Byungo-Ro Moon. Finding attractive rules in stock markets using a modular genetic programming. In GECCO 2009.
  • P. Fernandez, D. Bodas, F. Soltero and J. I. Hidalgo . Technical market indicators optimization using EA. In GECCO 2008.
  • Adriano Simões, Rui Ferreira Neves, Nuno Horta . An Innovative GA Optimized Investment Strategy based on a New Technical Indicator using Multiple MAS. In proceeding of: ICEC 2010 - Proceedings of the International Conference on Evolutionary Computation, [part of the International Joint Conference on Computational Intelligence IJCCI 2010], Valencia, Spain, October 24 - 26, 2010.
  • V. Kapoor, S. Dey, and A. P Khurana. Genetic Algorithm: An Application to Technical Trading System Design. International Journal of Computer Applications (0975 – 8887), Volume 36– No. 5, December 2011.
  • Devayan Mallick, Vincent C. S. Lee and Yew Soon Ong. An Empirical Study of Genetic Programming Generated Trading Rules in Computerized Stock Trading Service System. In IEEE proceedings. (2008).
  • S. N. Sivanandam & S. N. Deepa . Introduction to Genetic Algorithms. Springer 2008.
  • Darrell Whitley. A Genetic Algorithm Tutorial. Statistics and Computing (1994) 4, 65-85.
  • Gerald Appel. Financial Times Prentice Hall. 2005 Pearson Education, Inc.
  • Diego J Bodas-Sagi , Pablo. Fernandez , J. lgnacio, Francisco J. Soltero and Jose L. Risco-Martin. Multi-objective optimization of technical market indicators. In GECCO 2009.
  • Abdullah Konaka,_, David W. Coitb, Alice E. Smithc. Multi-objective optimization using genetic algorithms: A tutorial. In Reliability Engineering and System Safety 91 (2006) 992–1007 (ELSEVIER).
  • Mark Choey and Andreas S. Weigend. Nonlinear Trading Models Through Sharpe Ratio Maximization. Leonard N. Stern School of Business, New York University. In: Decision Technologies for Financial Engineering (Proceedings of the Fourth International Conference on Neural Networks in the Capital Markets, NNCM-96), pp. 3-22.
  • Lohpetch, D, David Corne. Multiobjective algorithms for financial trading: Multiobjective out-trades single-objective. In proceeding of: Evolutionary Computation (CEC), 2011 IEEE Congress on