Journal of the Korean Data and Information Science Society

한국데이터정보과학회 (The Korean Data and Information Science Society)
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선물시장에서 러프집합 기반의 유전자 알고리즘을 이용한 최적화 거래전략 개발

Using genetic algorithm to optimize rough set strategy in KOSPI200 futures market

  • 정승환 (연세대학교 정보산업공학과) ;
  • 오경주 (연세대학교 정보산업공학과)
  • Chung, Seung Hwan (Department of Information and Industrial Engineering, Yonsei University) ;
  • Oh, Kyong Joo (Department of Information and Industrial Engineering, Yonsei University)
  • 투고 : 2013.12.02
  • 심사 : 2014.01.27
  • 발행 : 2014.03.31
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초록

최근 알고리즘 트레이딩에 대한 관심이 높아지면서, 인공지능 방법론을 이용한 매매 전략 구축에 관련된 연구들이 활발하게 진행되고 있다. 하지만 복수의 인공지능 방법론을 융합하여 매매 전략 개발에 이용한 사례는 아직 많지 않다. 본 연구는 주가지수선물시장을 바탕으로 인공지능 방법론 중 하나인 러프집합 이론을 적용하여 알고리즘 트레이딩 매매전략을 개발한다. 특히 유전자 알고리즘을 도입하여 생성된 매매전략을 현재시장상황에 최고의 수익률을 보일 수 있도록 최적화한다. 실증분석으로는 2009년부터 2012년까지 4년간의 매매수익률을 분석한 결과 매수 후 보유 전략과 비교하여 우수한 성과를 보였다.

As the importance of algorithm trading is getting stronger, researches for artificial intelligence (AI) based trading strategy is also being more important. However, there are not enough studies about using more than two AI methodologies in one trading system. The main aim of this study is development of algorithm trading strategy based on the rough set theory that is one of rule-based AI methodologies. Especially, this study used genetic algorithm for optimizing profit of rough set based strategy rule. The most important contribution of this study is proposing efficient convergence of two different AI methodology in algorithm trading system. Target of purposed trading system is KOPSI200 futures market. In empirical study, we prove that purposed trading system earns significant profit from 2009 to 2012. Moreover, our system is evaluated higher shape ratio than buy-and-hold strategy.

키워드

참고문헌

  1. Byun, H. W., Song, C, W., Han, S. K., Lee, T. K. and Oh, K. J. (2009). Using genetic algorithms to develop volatility index-assisted hierarchical portfolio optimization. Journal of Korean Data & Information Science Society, 20, 1049-1060.
  2. Chang, P. C. and Liu, C. H. (2008). A TSK type fuzzy rule based system for stock price prediction. Expert Systems with Applications, 34, 135-144 https://doi.org/10.1016/j.eswa.2006.08.020
  3. Chavarnakul, T. and Enke, D. (2008). Intelligent technical analysis based equivolume charting for stock trading using neural networks. Expert Systems with Applications, 34, 1004-1017. https://doi.org/10.1016/j.eswa.2006.10.028
  4. Dai, J. H. and Li, Y. X. (2002). Study on discretization based on rough set theory, Preceedings of the First International Conference On Machine Learning and Cybernetics, 4-5.
  5. Dong, M. and Zhou, X. S. (2002). Exploring the fuzzy nature of technical patterns of U.S stock market. ICONIP'02-SEAL'02-FSKD'02, Singapore, 18-22.
  6. Fazel Zarzndi, M. H., Rezaee, B., Turksen, I. B. and Neshat, E. (2009). A type-2 fuzzy rule-based expert system model for stock price analysis. Expert Systems with Applications, 36, 139-154. https://doi.org/10.1016/j.eswa.2007.09.034
  7. Fong, S., Tai, J. and Si, Y. W. (2011). Trend following algorithms for technical trading in stock market. Journal of Emerging Technologies in Web Intelligence, 3, 136-145.
  8. Kang, Y. J. and Oh, K. J. (2013). Using rough set to develop a volatility reverting strategy in options market. Journal of Korean Data & Information Science Society, 24, 135-150. https://doi.org/10.7465/jkdi.2013.24.1.135
  9. Kim, H, H. and Oh, K. J. (2012). Using rough set to develop the optimization strategy of evolving timedivision trading in the futures market. Journal of Korean Data & Information Science Society, 23, 881-893. https://doi.org/10.7465/jkdi.2012.23.5.881
  10. Kim, K. J. and Han, I. G. (2000). Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications, 19, 125-132. https://doi.org/10.1016/S0957-4174(00)00027-0
  11. Kim, S. and Ahn, H. (2010). Development of an intelligent trading system using support vector machines and genetic algorithms. Journal of Intelligence and Information Systems, 16, 71-92.
  12. Kuo, R. J., Chen, C. H. and Hwang, Y. C. (2001). An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets and Systems, 118, 21-45. https://doi.org/10.1016/S0165-0114(98)00399-6
  13. Lee, S. J., Ahn, J. J., Oh, K. J. and Kim, T. Y. (2010). Using rough set to support investment strategies of real-time trading in futures market. Applied Intelligence, 32, 364-337. https://doi.org/10.1007/s10489-008-0150-y
  14. Lo, A. W., Mamaysky, H. M. and Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. Journal of Finance, 55, 1705-1770. https://doi.org/10.1111/0022-1082.00265
  15. Marc, B. (2004). Khiops: A statistical discretization method of continuous attributes. Machine Learning, 55, 53-69. https://doi.org/10.1023/B:MACH.0000019804.29836.05
  16. Pawlak, Z. (1997). Rough set approach to knowledge-based decision support. European Journal of Operational Research, 99, 48-57. https://doi.org/10.1016/S0377-2217(96)00382-7

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