Agent services-driven plug-and-play in F-TRADE

Authors:
Longbing Cao;Jiarui Ni;Jiaqi Wang;Chengqi Zhang
Affiliations:
Faculty of Information Technology, University of Technology Sydney, Australia;Faculty of Information Technology, University of Technology Sydney, Australia;Faculty of Information Technology, University of Technology Sydney, Australia;Faculty of Information Technology, University of Technology Sydney, Australia
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

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Abstract

We have built an agent service-based enterprise infrastructure: F-TRADE With its online connectivity to huge real stock data in global markets, it can be used for online evaluation of trading strategies and data mining algorithms The main functions in the F-TRADE include soft plug-and-play, and back-testing, optimization, integration and evaluation of algorithms In this paper, we'll focus on introducing the intelligent plug-and-play, which is a key system function in the F-TRADE The basic idea for the soft plug-and-play is to build agent services which can support the online plug-in of agents, algorithms and data sources Agent UML-based modeling, role model and agent services for the plug-and-play are discussed With this design, algorithm providers, data source providers, and system module developers of the F-TRADE can expand system functions and resources by online plugging them into the F-TRADE.