Deep Learning modeling of Limit Order Book: a comparative perspective

• URL: http://arxiv.org/abs/2007.07319v3
• Date: Sun, 18 Oct 2020 15:44:44 GMT
• Title: Deep Learning modeling of Limit Order Book: a comparative perspective
• Authors: Antonio Briola, Jeremy Turiel, Tomaso Aste
• Abstract summary: The present work addresses theoretical and practical questions in the domain of Deep Learning for High Frequency Trading. State-of-the-art models such as Random models, Logistic Regressions, LSTMs, LSTMs equipped with an Attention mask, CNN-LSTM and Attentions are reviewed and compared on the same tasks. The underlying dimensions of the modeling techniques are investigated to understand whether these are intrinsic to the Limit Order Book's dynamics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The present work addresses theoretical and practical questions in the domain of Deep Learning for High Frequency Trading. State-of-the-art models such as Random models, Logistic Regressions, LSTMs, LSTMs equipped with an Attention mask, CNN-LSTMs and MLPs are reviewed and compared on the same tasks, feature space and dataset, and then clustered according to pairwise similarity and performance metrics. The underlying dimensions of the modeling techniques are hence investigated to understand whether these are intrinsic to the Limit Order Book's dynamics. We observe that the Multilayer Perceptron performs comparably to or better than state-of-the-art CNN-LSTM architectures indicating that dynamic spatial and temporal dimensions are a good approximation of the LOB's dynamics, but not necessarily the true underlying dimensions.

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