Agent market orders representation through a contrastive learning approach

Due to the access to the labeled orders on the CAC40 data from Euronext, we are able to analyse agents' behaviours 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 utilise the K-means clustering algorithm on the learned representation vectors of agent orders to identify distinct behaviour types within each cluster.