  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.
