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Communication Dans Un Congrès Année : 2024

Liquidity takers behavior representation through a contrastive learning approach

Résumé

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.

Dates et versions

hal-04281776 , version 1 (13-11-2023)

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Ruihua Ruan, Emmanuel Bacry, Jean-François Muzy. Liquidity takers behavior representation through a contrastive learning approach. ICAIF-23, Oxford University - University of Michigan, Nov 2023, Brooklyn New York, United States. ⟨hal-04281776⟩
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