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How Robust are Limit Order Book Representations under Data Perturbation?

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arxiv 2110.04752 v1 pith:S7Y4JKKY submitted 2021-10-10 q-fin.TR cs.LG

Yufei Wu , Mahmoud Mahfouz , Daniele Magazzeni , Manuela Veloso This is my paper · ORCID
classification q-fin.TRcs.LG
keywords datarepresentationsbooklearninglimitorderrepresentationanalyse
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The success of machine learning models in the financial domain is highly reliant on the quality of the data representation. In this paper, we focus on the representation of limit order book data and discuss the opportunities and challenges for learning representations of such data. We also experimentally analyse the issues associated with existing representations and present a guideline for future research in this area.

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  1. Exploring Microstructural Dynamics in Cryptocurrency Limit Order Books: Better Inputs Matter More Than Stacking Another Hidden Layer

    cs.LG 2025-06 reject novelty 4.0 of 10

    On one day of BTC/USDT order book data, Savitzky-Golay smoothing and feature choice helped simple models match or beat deeper neural networks.

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