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Bayesian Optimisation of Gated Bayesian Networks for Algorithmic Trading
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-8678-1164
2015 (English)In: / [ed] John Mark Agosta, Rommel Novaes Carvalho, , 2015, Vol. 1565, 2-11 p.Conference paper (Refereed)
Abstract [en]

Gated Bayesian networks (GBNs) are an extension of Bayesian networks that aim to model systems that have distinct phases. In this paper, we aim to use GBNs to output buy and sell decisions for use in algorithmic trading systems. These systems may have several parameters that require tuning, and assessing the performance of these systems as a function of their parameters cannot be expressed in closed form, and thus requires simulation. Bayesian optimisation has grown in popularity as a means of global optimisation of parameters where the objective function may be costly or a black box. We show how algorithmic trading using GBNs, supported by Bayesian optimisation, can lower risk towards invested capital, while at the same time generating similar or better rewards, compared to the benchmark investment strategy buy-and-hold.

Place, publisher, year, edition, pages , 2015. Vol. 1565, 2-11 p.
, CEUR Workshop Proceedings, ISSN 1613-0073
National Category
Computer Science
URN: urn:nbn:se:liu:diva-120685OAI: diva2:847609
Twelfth UAI Bayesian Modeling Applications Workshop, (BMAW 2015), Amsterdam, The Netherlands, July 16, 2015
Available from: 2015-08-20 Created: 2015-08-20 Last updated: 2016-06-23Bibliographically approved

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