Developing Actionable Trading Strategies for Trading Agents

Cao, L; Luo, C; Zhang, C

Developing Actionable Trading Strategies for Trading Agents

Cao, L

Luo, C Zhang, C

Permalink

Publisher:: IEEE Computer Soc
Publication Type:: Conference Proceeding
Citation:: Proceedings of the IEEE/WIC/ACM International Conference on Intellligent Agent Technology, 2007, pp. 72 - 75
Issue Date:: 2007-01

Closed Access

	Filename	Description	Size
	2007000876.pdf		178.79 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Luo, C	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.editor	Tsau, YTYL	en_US
dc.contributor.editor	Jeffrey, MB	en_US
dc.contributor.editor	Matthias, K	en_US
dc.contributor.editor	Chengqi, Z	en_US
dc.contributor.editor	Andrei, B	en_US
dc.contributor.editor	Howard, H	en_US
dc.date	2007-11-02	en_US
dc.date.issued	2007-01	en_US
dc.identifier.citation	Proceedings of the IEEE/WIC/ACM International Conference on Intellligent Agent Technology, 2007, pp. 72 - 75	en_US
dc.identifier.isbn	0-7695-3027-3	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2367
dc.description.abstract	Trading agents are very useful for developing and backtesting quality trading strategies for actions taking in the real world. However, the existing trading agent research mainly focuses on simulation using artificial data and market models. As a result, the actionable capability of developed trading strategies is often limited. In this paper, we analyze such constraints on developing actionable trading strategies for trading agents. These points are deployed into developing a series of trading strategies for trading agents through optimizing, and enhancing actionable trading strategies. We demonstrate working case studies in large-scale of market data. These approaches and their performance are evaluated from both technical and business perspectives.	en_US
dc.publisher	IEEE Computer Soc	en_US
dc.relation.ispartof	Proceedings of the IEEE/WIC/ACM International Conference on Intellligent Agent Technology	en_US
dc.relation.ispartof	IEEE/WIC/ACM International Conference on Intelligent Agent Technology	en_US
dc.relation.isbasedon	10.1109/IAT.2007.47	en_US
dc.rights	© 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Developing Actionable Trading Strategies for Trading Agents	en_US
dc.type	Conference Proceeding
utslib.location	Los Alamitos	en_US
utslib.location.activity	San Jose	en_US
utslib.for	080110 Simulation and Modelling	en_US
dc.location.activity	San Jose	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 - AAI - Advanced Analytics Institute Research Centre
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.consider-herdc	true	en_US
pubs.place-of-publication	Los Alamitos	en_US
pubs.start-date	2007-11-02	en_US

Abstract:

Trading agents are very useful for developing and backtesting quality trading strategies for actions taking in the real world. However, the existing trading agent research mainly focuses on simulation using artificial data and market models. As a result, the actionable capability of developed trading strategies is often limited. In this paper, we analyze such constraints on developing actionable trading strategies for trading agents. These points are deployed into developing a series of trading strategies for trading agents through optimizing, and enhancing actionable trading strategies. We demonstrate working case studies in large-scale of market data. These approaches and their performance are evaluated from both technical and business perspectives.

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

http://hdl.handle.net/10453/2367