An efficient implementation of the backtesting of trading strategies

Ni, J; Zhang, C

An efficient implementation of the backtesting of trading strategies

Ni, J Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2005, 3758 LNCS pp. 126 - 131
Issue Date:: 2005-12-01

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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	2005-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2005, 3758 LNCS pp. 126 - 131	en_US
dc.identifier.isbn	3540297693	en_US
dc.identifier.isbn	9783540297697	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/1859
dc.description.abstract	Some trading strategies are becoming more and more complicated and utilize a large amount of data, which makes the backtesting of these strategies very time consuming. This paper presents an efficient implementation of the backtesting of such a trading strategy using a parallel genetic algorithm (PGA) which is fine tuned based on thorough analysis of the trading strategy. The reuse of intermediate results is very important for such backtesting problems. Our implementation can perform the backtesting within a reasonable time range so that the tested trading strategy can be properly deployed in time. © Springer-Verlag Berlin Heidelberg 2005.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0449535
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/11576235_17	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	An efficient implementation of the backtesting of trading strategies	en_US
dc.type	Conference Proceeding
utslib.citation.volume	3758 LNCS	en_US
utslib.for	0806 Information Systems	en_US
dc.location.activity	Nanjing, China	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
pubs.volume	3758 LNCS	en_US

Abstract:

Some trading strategies are becoming more and more complicated and utilize a large amount of data, which makes the backtesting of these strategies very time consuming. This paper presents an efficient implementation of the backtesting of such a trading strategy using a parallel genetic algorithm (PGA) which is fine tuned based on thorough analysis of the trading strategy. The reuse of intermediate results is very important for such backtesting problems. Our implementation can perform the backtesting within a reasonable time range so that the tested trading strategy can be properly deployed in time. © Springer-Verlag Berlin Heidelberg 2005.

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http://hdl.handle.net/10453/1859