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Liquidity takers behavior representation through a contrastive learning approach

Author

Listed:
  • Ruihua Ruan

    (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Emmanuel Bacry

    (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Jean-François Muzy

    (SPE - Laboratoire Sciences Pour l’Environnement - INEE-CNRS - Institut Ecologie et Environnement - CNRS Ecologie et Environnement - CNRS - Centre National de la Recherche Scientifique - INSIS - CNRS - Institut des Sciences de l'Ingénierie et des Systèmes - CNRS Ingénierie - Università di Corsica Pasquale Paoli [Université de Corse Pascal Paoli] - Partenaires INRAE)

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.

Suggested Citation

  • Ruihua Ruan & Emmanuel Bacry & Jean-François Muzy, 2023. "Liquidity takers behavior representation through a contrastive learning approach," Post-Print hal-04281776, HAL.
    • Handle: RePEc:hal:journl:hal-04281776
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