research-article

A new modular genetic programming for finding attractive technical patterns in stock markets

Authors:

Byung-Ro MoonAuthors Info & Claims

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

Pages 1219 - 1226

https://doi.org/10.1145/1830483.1830704

Published: 07 July 2010 Publication History

Abstract

We propose a new modular genetic programming for finding attractive and statistically sound technical patterns forstock trading. We restrict the problem space to combinations of modules for more effective space search. We carefully prepared the set of modules based on existing studies of technical indicators and our own experience. Our modular genetic programming successfully found unknown attractive technical patterns for the Korean stock market. A trading simulation with the generated patterns by a commercial tool showed significantly higher accumulative returns than the KOSPI index.

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Cited By

Ha SLee SMoon BTakadama K(2018)Investigation of the latent space of stock market patterns with genetic programmingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205493(1254-1261)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205493
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 SMoon BEsparcia-Alcázar A(2015)Fast Knowledge Discovery in Time Series with GPGPU on Genetic ProgrammingProceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739480.2754669(1159-1166)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739480.2754669

Index Terms

A new modular genetic programming for finding attractive technical patterns in stock markets
1. Applied computing

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Published In

cover image ACM Conferences

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

July 2010

1520 pages

ISBN:9781450300728

DOI:10.1145/1830483

General Chair:
Martin Pelikan
University of Missouri, USA
,
Program Chair:
Jürgen Branke
University of Warwick, Coventry, UK

Copyright © 2010 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

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Published: 07 July 2010

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SIGEVO

GECCO '10: Genetic and Evolutionary Computation Conference

July 7 - 11, 2010

Oregon, Portland, USA

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Cited By

Ha SLee SMoon BTakadama K(2018)Investigation of the latent space of stock market patterns with genetic programmingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205493(1254-1261)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205493
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 SMoon BEsparcia-Alcázar A(2015)Fast Knowledge Discovery in Time Series with GPGPU on Genetic ProgrammingProceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739480.2754669(1159-1166)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739480.2754669

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