Temporal Kolmogorov-Arnold Networks (T-KAN) for High-Frequency Limit Order Book Forecasting: Efficiency, Interpretability, and Alpha Decay

Abstract:High-Frequency trading (HFT) environments are characterised by large volumes of limit order book (LOB) data, which is notoriously noisy and non-linear. Alpha decay represents a significant challenge, with traditional models such as DeepLOB losing predictive power as the time horizon (k) increases. In this paper, using data from the FI-2010 dataset, we introduce Temporal Kolmogorov-Arnold Networks (T-KAN) to replace the fixed, linear weights of standard LSTMs with learnable B-spline activation functions. This allows the model to learn the 'shape' of market signals as opposed to just their magnitude. This resulted in a 19.1% relative improvement in the F1-score at the k = 100 horizon. The efficacy of T-KAN networks cannot be understated, producing a 132.48% return compared to the -82.76% DeepLOB drawdown under 1.0 bps transaction costs. In addition to this, the T-KAN model proves quite interpretable, with the 'dead-zones' being clearly visible in the splines. The T-KAN architecture is also uniquely optimized for low-latency FPGA implementation via High level Synthesis (HLS). The code for the experiments in this project can be found at this https URL.

8 pages, 5 figures, Proposes T-KAN architecture for HFT. Achieves 19.1% F1-score improvement on FI-2010 and 132.48% return in cost-adjusted this http URL T-KAN architecture for HFT. Achieves 19.1% F1-score improvement on FI-2010 and 132.48% return in cost-adjusted backtests

Machine Learning (cs.LG); Trading and Market Microstructure (q-fin.TR)

arXiv:2601.02310 [cs.LG]

(or arXiv:2601.02310v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2601.02310 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.71706/f0ee1480-22dc-4fc7-a6d4-4807f32ece2d Focus to learn more DOI(s) linking to related resources

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