Hybrid LVQ‐Entropy Model for Robust Algorithmic Trading

This paper proposes a novel entropy-enhanced machine learning framework for algorithmic trading, designed to improve decision reliability and model interpretability under conditions of financial uncertainty. The core methodology integrates a Learning Vector Quantization (LVQ) classifier with a Shannon entropy-based filter that selectively activates trading signals based on their informational certainty. Mathematically, the system is formulated as a composite decision function constrained by entropy: only predictions satisfying H(x_t ) ≤θ are executed, where H(x_t ) is the Shannon entropy of the class probability distribution at time t. The empirical evaluation spans three asset classes—cryptocurrencies, foreign exchange, and equities—demonstrating significant improvements in trade precision, Sharpe ratio, and robustness to market volatility. The entropy filter acts as a regularization mechanism, reducing overfitting and mitigating the propagation of noisy signals. Beyond its empirical success, the model contributes a formalized framework for entropy-regularized decision making, opening avenues for generalized entropic models such as Tsallis and Kaniadakis entropy in future research. This entropy-aware structure bridges statistical learning theory and real-world financial applications by introducing a quantifiable measure of confidence into the machine learning pipeline. The results underscore the model’s potential as a robust, interpretable, and adaptive solution for algorithmic trading in complex and uncertain markets.