Liquidity takers behavior representation through a contrastive learning approach
This work addresses the problem of understanding market dynamics for financial analysts and traders, but it is incremental as it applies existing contrastive learning and clustering methods to a specific dataset.
The paper tackled the problem of analyzing agents' behaviors in financial markets by constructing a self-supervised learning model using triplet loss to learn representations of agent orders from labeled CAC40 data, and it utilized K-means clustering on these representations to identify distinct behavior types within clusters.
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.