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Liquidity takers behavior representation through a contrastive learning approach

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  • Ruihua Ruan
  • Emmanuel Bacry
  • Jean-Franc{c}ois Muzy

Abstract

Thanks to the access to the labeled orders on the CAC40 data from Euronext, we are able to analyze agents' behaviors in the market based on their placed orders. In this study, we construct a self-supervised learning model using triplet loss to effectively learn the representation of agent market orders. By acquiring this learned representation, various downstream tasks become feasible. In this work, we utilize the K-means clustering algorithm on the learned representation vectors of agent orders to identify distinct behavior types within each cluster.

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  • Ruihua Ruan & Emmanuel Bacry & Jean-Franc{c}ois Muzy, 2023. "Liquidity takers behavior representation through a contrastive learning approach," Papers 2306.05987, arXiv.org, revised Jul 2023.
    • Handle: RePEc:arx:papers:2306.05987
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    References listed on IDEAS

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    1. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," The Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
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