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Extending and Evaluating Agent-Based Models of Algorithmic Trading Strategies.

Abstract:

Algorithmic trading (AT) strategies aim at executing large orders discretely, in order to minimize the order's impact, whilst also hiding the traders' intentions. Most AT evaluation methods range from running the AT strategies against historical data (back testing) to evaluating them on simulated markets. The contribution of the work presented in this paper is twofold. First we investigated different types of agent-based market simulations and suggested how to identify the most suitable marke...

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Publication status:
Published

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Publisher copy:
10.1109/ICECCS.2012.18

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author

Contributors

Role:
Editor
Role:
Editor
Role:
Editor
Publisher:
IEEE Publisher's website
Pages:
351-360
Host title:
ICECCS
Publication date:
2012-01-01
DOI:
Source identifiers:
330957
ISBN:
9781467321563
Keywords:
Pubs id:
pubs:330957
UUID:
uuid:a8632ba3-ab5f-40df-a799-f6b715df59ab
Local pid:
pubs:330957
Deposit date:
2013-11-17

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