KANFormer for Predicting Fill Probabilities via Survival Analysis in Limit Order Books
This is an incremental improvement for financial trading systems, offering more accurate and interpretable fill probability predictions.
The paper tackles the problem of predicting time-to-fill for limit orders by introducing KANFormer, a model that combines market- and agent-level information with neural architectures like Transformers and Kolmogorov-Arnold Networks. The results show it outperforms existing works on metrics like C-index and time-dependent AUC using CAC 40 futures data.
This paper introduces KANFormer, a novel deep-learning-based model for predicting the time-to-fill of limit orders by leveraging both market- and agent-level information. KANFormer combines a Dilated Causal Convolutional network with a Transformer encoder, enhanced by Kolmogorov-Arnold Networks (KANs), which improve nonlinear approximation. Unlike existing models that rely solely on a series of snapshots of the limit order book, KANFormer integrates the actions of agents related to LOB dynamics and the position of the order in the queue to more effectively capture patterns related to execution likelihood. We evaluate the model using CAC 40 index futures data with labeled orders. The results show that KANFormer outperforms existing works in both calibration (Right-Censored Log-Likelihood, Integrated Brier Score) and discrimination (C-index, time-dependent AUC). We further analyze feature importance over time using SHAP (SHapley Additive exPlanations). Our results highlight the benefits of combining rich market signals with expressive neural architectures to achieve accurate and interpretabl predictions of fill probabilities.