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Deep Probabilistic Modelling of Price Movements for High-Frequency Trading

Lim, YS; Gorse, D; (2020) Deep Probabilistic Modelling of Price Movements for High-Frequency Trading. In: Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN). (In press). Green open access

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Abstract

In this paper we propose a deep recurrent architecture for the probabilistic modelling of high-frequency market prices, important for the risk management of automated trading systems. Our proposed architecture incorporates probabilistic mixture models into deep recurrent neural networks. The resulting deep mixture models simultaneously address several practical challenges important in the development of automated high-frequency trading strategies that were previously neglected in the literature: 1) probabilistic forecasting of the price movements; 2) single objective prediction of both the direction and size of the price movements. We train our models on high-frequency Bitcoin market data and evaluate them against benchmark models obtained from the literature. We show that our model outperforms the benchmark models in both a metric-based test and in a simulated trading scenario.

Type: Proceedings paper
Title: Deep Probabilistic Modelling of Price Movements for High-Frequency Trading
ISBN-13: 9781728169262
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/IJCNN48605.2020.9206995
Publisher version: http://dx.doi.org/10.1109/IJCNN48605.2020.9206995
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Probabilistic logic, Mixture models, Mathematical model, Computer architecture, Data models, Forecasting, Computational modeling
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10115863
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