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

ArXiv ID: 2601.02310 “View on arXiv”

Authors: Ahmad Makinde

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 https://github.com/AhmadMak/Temporal-Kolmogorov-Arnold-Networks-T-KAN-for-High-Frequency-Limit-Order-Book-Forecasting.

Keywords: High-Frequency Trading, Limit Order Book (LOB), Kolmogorov-Arnold Networks (KAN), Alpha Decay, High-Level Synthesis (HLS), Equities

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 9.0/10
• Quadrant: Holy Grail
• Why: The paper introduces advanced mathematical concepts, including the Kolmogorov-Arnold Representation Theorem, B-spline theory, and Cox-de Boor recursion, alongside substantial LaTeX-formatted equations. It demonstrates high empirical rigor by using a specific benchmark dataset (FI-2010), providing backtest-ready results (F1-scores, returns, drawdowns) with transaction costs, and offering a public GitHub repository for code.

  flowchart TD
    Start["Research Goal<br>Address Alpha Decay &amp; Interpretability<br>in High-Frequency LOB Forecasting"] --> Data
    subgraph Data
        A["FI-2010 Dataset<br>High-Frequency Limit Order Book Data"]
    end
    
    Data --> Model
    
    subgraph Model
        B["Temporal KAN (T-KAN)<br>Replace fixed LSTM weights with<br>learnable B-spline activation functions"]
    end
    
    Model --> Proc
    
    subgraph Proc ["Computational Processes"]
        direction LR
        C1["Feature Extraction<br>Market Signal Shapes"]
        C2["FPGA Optimization via HLS<br>Low-Latency Implementation"]
    end
    
    Proc --> Outcome
    
    subgraph Outcome ["Key Findings & Outcomes"]
        F1["F1-Score: +19.1% relative improvement<br>(k = 100 horizon)"]
        Ret["Strategy Return: 132.48%<br>vs. DeepLOB -82.76% (1.0 bps cost)"]
        Int["Interpretability: Visible 'Dead-Zones'<br>in spline activations"]
    end