用深度模型预测订单成交时间，融合市场与交易者行为信息。
KANFormer for Predicting Fill Probabilities via Survival Analysis in Limit Order Books
- 结合扩张因果卷积与Transformer，引入KAN提升非线性拟合能力。
- 在CAC 40期货数据上，校准与区分能力均优于现有方法。
- 通过SHAP分析时变特征重要性，模型兼具准确性与可解释性。
本文提出KANFormer，一种基于深度学习的模型，用于通过生存分析预测限价订单的成交时间。该模型融合扩张因果卷积网络与Transformer编码器，并引入柯尔莫哥洛夫-阿诺德网络（KANs）以增强非线性逼近能力。不同于仅依赖限价订单簿快照的现有方法，KANFormer整合了与订单簿动态相关的交易者行为及订单在队列中的位置信息，更有效捕捉成交可能性模式。我们使用带有标注的CAC 40指数期货数据进行评估。结果表明，KANFormer在校准（右删失对数似然、综合布里尔得分）和区分度（C指数、时变AUC）方面均优于现有工作。进一步通过SHAP分析特征随时间的重要性。结果表明，结合丰富市场信号与表达能力强的神经架构，可实现准确且可解释的成交概率预测。
原文摘要 · Abstract (English)
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.
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