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A Genetic Algorithm for Rule-based Chart Pattern Search in Stock Market Prices

Authors:

Myoung Hoon Ha,

Byung-Ro MoonAuthors Info & Claims

GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016

Pages 909 - 916

https://doi.org/10.1145/2908812.2908828

Published: 20 July 2016 Publication History

Abstract

Chart pattern analysis uses knowledge extracted from graphical information of price movements. There are two representative types of problems in chart pattern analysis: the matching problem and the search problem. There have been extensive studies on chart pattern matching. However, chart pattern search has not yet drawn much interest. Instead of automatic search, most studies use chart patterns manually designed by financial experts. In this paper, we suggest an automatic algorithm that searches a rule-based chart pattern. We formulate rule-based chart pattern search as an optimization problem for a genetic algorithm. The suggested genetic algorithm includes a considerable amount of problem-specific manipulation. The algorithm successfully found attractive patterns working on the Korean stock market. We studied the rules used in the found patterns, noting that they are rising-support patterns. In addition, the automated pattern generation uses designs at a higher level of abstraction.

References

[1]

M. N. Bandara, R. M. Ranasinghe, R. Woranga Mudugamuwa Arachchi, C. G. Somathilaka, S. Perera, and D. C. Wimalasuriya. A complex event processing toolkit for detecting technical chart patterns. In Parallel and Distributed Processing Symposium Workshop (IPDPSW), 2015 IEEE International, pages 547--556. IEEE, 2015.

Digital Library

[2]

R. W. Banz. The relationship between return and market value of common stocks. Journal of nancial economics, 9(1):3--18, 1981.

[3]

L. Bo, S. Linyan, and R. Mweene. Empirical study of trading rule discovery in china stock market. Expert Systems with Applications, 28(3):531--535, 2005.

Digital Library

[4]

R. Cervello-Royo, F. Guijarro, and K. Michniuk. Stock market trading rule based on pattern recognition and technical analysis: Forecasting the djia index with intraday data. Expert Systems with Applications, 42(14):5963--5975, 2015.

Digital Library

[5]

E. F. Fama. Efficient capital markets: A review of theory and empirical work*. The journal of Finance, 25(2):383--417, 1970.

[6]

T.-c. Fu, F.-l. Chung, R. Luk, and C.-m. Ng. Stock time series pattern matching: Template-based vs. rule-based approaches. Engineering Applications of Artificial Intelligence, 20(3):347--364, 2007.

Digital Library

[7]

D. B. Keim. Size-related anomalies and stock return seasonality: Further empirical evidence. journal of Financial Economics, 12(1):13--32, 1983.

[8]

C.-H. L. Lee, A. Liu, and W.-S. Chen. Pattern discovery of fuzzy time series for nancial prediction. Knowledge and Data Engineering, IEEE Transactions on, 18(5):613--625, 2006.

Digital Library

[9]

W. Leigh, C. J. Frohlich, S. Hornik, R. L. Purvis, and T. L. Roberts. Trading with a stock chart heuristic. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 38(1):93--104, 2008.

Digital Library

[10]

W. Leigh, R. Purvis, and J. M. Ragusa. Forecasting the nyse composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support. Decision Support Systems, 32(4):361--377, 2002.

Digital Library

[11]

A. W. Lo, H. Mamaysky, and J. Wang. Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. Technical report, National bureau of economic research, 2000.

[12]

J.-L. Wang and S.-H. Chan. Stock market trading rule discovery using pattern recognition and technical analysis. Expert Systems with Applications, 33(2):304--315, 2007.

Digital Library

[13]

J.-L. Wang and S.-H. Chan. Trading rule discovery in the us stock market: An empirical study. Expert Systems with Applications, 36(3):5450--5455, 2009.

Digital Library

[14]

A. Zapranis and P. E. Tsinaslanidis. A novel, rule-based technical pattern identification mechanism: Identifying and evaluating saucers and resistant levels in the us stock market. Expert Systems with Applications, 39(7):6301--6308, 2012.

Digital Library

[15]

Z. Zhang, J. Jiang, X. Liu, R. Lau, H. Wang, and R. Zhang. A real time hybrid pattern matching scheme for stock time series. In Proceedings of the Twenty-First Australasian Conference on Database Technologies-Volume 104, pages 161--170. Australian Computer Society, Inc., 2010.

Digital Library

Cited By

Ma XZheng FTang D(2024)Identifying the Head-and-Shoulders Pattern Using Financial Key Points and Its Application in Consumer Electronic StocksIEEE Transactions on Consumer Electronics10.1109/TCE.2023.333914070:2(4945-4954)Online publication date: May-2024
https://doi.org/10.1109/TCE.2023.3339140
El Bakai MBoutyour YIdrissi A(2024)Creating a Customized Dataset for Financial Pattern Recognition in Deep LearningModern Artificial Intelligence and Data Science 202410.1007/978-3-031-65038-3_8(99-117)Online publication date: 4-Oct-2024
https://doi.org/10.1007/978-3-031-65038-3_8
Bhuyan BJaiswal VCherif A(2023)A Knowledge Representation System for the Indian Stock MarketComputers10.3390/computers1205009012:5(90)Online publication date: 24-Apr-2023
https://doi.org/10.3390/computers12050090
Show More Cited By

Index Terms

A Genetic Algorithm for Rule-based Chart Pattern Search in Stock Market Prices
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
  2. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches
        Genetic algorithms
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

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Published In

cover image ACM Conferences

GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016

July 2016

1196 pages

ISBN:9781450342063

DOI:10.1145/2908812

Editor:
Tobias Friedrich
Hasso Plattner Institute
,
General Chair:
Frank Neumann
University of Adelaide
,
Program Chair:
Andrew M. Sutton
Hasso Plattner Institute

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2016

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GECCO '16

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SIGEVO

GECCO '16: Genetic and Evolutionary Computation Conference

July 20 - 24, 2016

Colorado, Denver, USA

Acceptance Rates

GECCO '16 Paper Acceptance Rate 137 of 381 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Ma XZheng FTang D(2024)Identifying the Head-and-Shoulders Pattern Using Financial Key Points and Its Application in Consumer Electronic StocksIEEE Transactions on Consumer Electronics10.1109/TCE.2023.333914070:2(4945-4954)Online publication date: May-2024
https://doi.org/10.1109/TCE.2023.3339140
El Bakai MBoutyour YIdrissi A(2024)Creating a Customized Dataset for Financial Pattern Recognition in Deep LearningModern Artificial Intelligence and Data Science 202410.1007/978-3-031-65038-3_8(99-117)Online publication date: 4-Oct-2024
https://doi.org/10.1007/978-3-031-65038-3_8
Bhuyan BJaiswal VCherif A(2023)A Knowledge Representation System for the Indian Stock MarketComputers10.3390/computers1205009012:5(90)Online publication date: 24-Apr-2023
https://doi.org/10.3390/computers12050090
Ha MChi SLee SCha YByung-Ro MKrawiec K(2021)Evolutionary meta reinforcement learning for portfolio optimizationProceedings of the Genetic and Evolutionary Computation Conference10.1145/3449639.3459386(964-972)Online publication date: 26-Jun-2021
https://dl.acm.org/doi/10.1145/3449639.3459386
Ha SMoon B(2018)Finding attractive technical patterns in cryptocurrency marketsMemetic Computing10.1007/s12293-018-0252-y10:3(301-306)Online publication date: 9-Mar-2018
https://doi.org/10.1007/s12293-018-0252-y
Ha MMoon BBosman P(2017)The evolution of neural network-based chart patternsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071192(1113-1120)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1145/3071178.3071192

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