Evolving Profitable Trading Rules with Genetic Algorithms

被引：0
|
作者：
Shin, Kyung-shik [2 ]
Kim, Kyoung-jae [1 ]
机构：
[1] Dongguk Univ, Dept Management Informat Syst, Seoul 100715, South Korea
[2] Ewha Womans Univ, Coll Business Adm, Seoul 120750, South Korea
来源：
INFORMATION-AN INTERNATIONAL INTERDISCIPLINARY JOURNAL | 2012年 / 15卷 / 08期
关键词：
Genetic algorithms; Stock market prediction; Trading rule extraction; Technical indicators; Buy & hold strategy;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The goal of this study is to mine reasonable trading rules using genetic algorithms (GAs) for the Korea Stock Price Index 200 (KOSPI 200) futures. During the course of this study, we have found trading rules that would have yielded the highest returns over a certain time period using historical data. The simulated results of buying and selling according to the trading rules were outstanding. These experimental results suggest that genetic algorithms are promising methods for extracting profitable trading rules.
引用
收藏
页码：3313 / 3321
页数：9
相关论文
共 50 条
  • [11] Evolving TSK fuzzy rules for classification tasks by genetic algorithms
    Espíndola, RP
    Ebecken, NFF
    DATA MINING II, 2000, 2 : 467 - 476
  • [12] Are simple technical trading rules profitable in bitcoin markets?
    Deprez, Niek
    Frommel, Michael
    INTERNATIONAL REVIEW OF ECONOMICS & FINANCE, 2024, 93 : 858 - 874
  • [13] FX technical trading rules can be profitable sometimes!
    Zarrabi, Nima
    Snaith, Stuart
    Coakley, Jerry
    INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS, 2017, 49 : 113 - 127
  • [14] Computational Intelligence for Evolving Trading Rules
    Ghandar, Adam
    Michalewicz, Zbigniew
    Schmidt, Martin
    To, Thuy-Duong
    Zurbrugg, Ralf
    IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2009, 13 (01) : 71 - 86
  • [15] Generating Moving Average Trading Rules on the Oil Futures Market with Genetic Algorithms
    Wang, Lijun
    An, Haizhong
    Xia, Xiaohua
    Liu, Xiaojia
    Sun, Xiaoqi
    Huang, Xuan
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2014, 2014
  • [16] An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms
    Kim, Youngmin
    Ahn, Wonbin
    Oh, Kyong Joo
    Enke, David
    APPLIED SOFT COMPUTING, 2017, 55 : 127 - 140
  • [17] Discovering trading rules with genetic algorithms: An empirical study based on GARCH time series
    Chen, SH
    Lin, WY
    Tsao, CY
    INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL I AND II, 1999, : 430 - 436
  • [18] Evolving Technical Trading Strategies Using Genetic Algorithms: A Case About Pakistan Stock Exchange
    Khan, Basit Tanvir
    Javed, Noman
    Hanif, Ambreen
    Raja, Muhammad Adil
    INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2017, 2017, 10585 : 335 - 344
  • [19] Are moving average trading rules profitable? Evidence from the European stock markets
    Metghalchi, Massoud
    Marcucci, Juri
    Chang, Yung-Ho
    APPLIED ECONOMICS, 2012, 44 (12) : 1539 - 1559
  • [20] Evolving Quasigroups by Genetic Algorithms
    Snasel, Vaclav
    Dvorsky, Jiri
    Ochodkova, Eliska
    Kromer, Pavel
    Platos, Jan
    Abraham, Ajith
    PROCEEDINGS OF THE DATESO 2010 WORKSHOP - DATESO DATABASES, TEXTS, SPECIFICATIONS, AND OBJECTS, 2010, 567 : 108 - 117
12345