Mining better technical trading strategies with genetic algorithms

Ni, J; Zhang, C

Mining better technical trading strategies with genetic algorithms

Ni, J Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Integrating AI and Data Mining - 1st International Workshop Proceedings, AIDM 2006, 2006, pp. 26 - 33
Issue Date:: 2006-12-01

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Field	Value	Language
dc.contributor.author	Ni, J	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2006-12-01	en_US
dc.identifier.citation	Integrating AI and Data Mining - 1st International Workshop Proceedings, AIDM 2006, 2006, pp. 26 - 33	en_US
dc.identifier.isbn	0769527302	en_US
dc.identifier.isbn	9780769527307	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2501
dc.description.abstract	Technical analysis is one of the two main schools of thought in the analysis of security prices. It is widely believed and applied by many professional and amateur traders. However, it is often criticized for lacking scientific rigour or worse, for lacking any basis whatsoever. We propose to explore the feasibility and/orlimitation of technical analysis by the optimization of technical trading strategies over historical stock data with genetic algorithms. This paper presents the optimization problem in detail and discusses the potential problems to be tackled during the optimization. Preliminary experiments show that it can identify the limitations quickly. © 2006 IEEE.	en_US
dc.relation.ispartof	Integrating AI and Data Mining - 1st International Workshop Proceedings, AIDM 2006	en_US
dc.relation.isbasedon	10.1109/AIDM.2006.12	en_US
dc.title	Mining better technical trading strategies with genetic algorithms	en_US
dc.type	Conference Proceeding
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
dc.location.activity	Hobart, Australia	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Technical analysis is one of the two main schools of thought in the analysis of security prices. It is widely believed and applied by many professional and amateur traders. However, it is often criticized for lacking scientific rigour or worse, for lacking any basis whatsoever. We propose to explore the feasibility and/orlimitation of technical analysis by the optimization of technical trading strategies over historical stock data with genetic algorithms. This paper presents the optimization problem in detail and discusses the potential problems to be tackled during the optimization. Preliminary experiments show that it can identify the limitations quickly. © 2006 IEEE.

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http://hdl.handle.net/10453/2501