A dynamic storage method for stock transaction data

Ni, J; Zhang, C

A dynamic storage method for stock transaction data

Ni, J Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2nd IASTED International Conference on Computational Intelligence, CI 2006, 2006, pp. 338 - 342
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	Proceedings of the 2nd IASTED International Conference on Computational Intelligence, CI 2006, 2006, pp. 338 - 342	en_US
dc.identifier.isbn	0889866023	en_US
dc.identifier.isbn	9780889866027	en_US
dc.identifier.uri	http://hdl.handle.net/10453/1752
dc.description.abstract	Stock transaction data have become very detailed and enormous with the introduction of electronic trading systems. This makes it a problem to store and to access the data in later analyses such as mining useful patterns and backtesting trading strategies. This paper investigates several storage methods in terms of both storage space and access efficiency and then proposes a new dynamic storage method which provides a flexible mechanism to balance between storage space and access efficiency for storing huge intraday transaction data.	en_US
dc.relation.ispartof	Proceedings of the 2nd IASTED International Conference on Computational Intelligence, CI 2006	en_US
dc.title	A dynamic storage method for stock transaction data	en_US
dc.type	Conference Proceeding
utslib.for	080499 Data Format not elsewhere classified	en_US
dc.location.activity	San Francisco, USA	en_US
dc.location.activity	Brisbane, Australia
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	open_access
pubs.publication-status	Published	en_US

Abstract:

Stock transaction data have become very detailed and enormous with the introduction of electronic trading systems. This makes it a problem to store and to access the data in later analyses such as mining useful patterns and backtesting trading strategies. This paper investigates several storage methods in terms of both storage space and access efficiency and then proposes a new dynamic storage method which provides a flexible mechanism to balance between storage space and access efficiency for storing huge intraday transaction data.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/1752