Modern financial markets are increasingly shaped by algorithmic trading systems and artificial intelligence techniques that process large volumes of financial data in real time. However, machine learning-based trading systems often suffer from signal instability and excessive sensitivity to market noise, which may lead to overtrading and increased financial risk. In highly volatile environments such as cryptocurrency markets, the reliability of trading signals becomes a critical issue for both portfolio allocation and risk management. This study proposes an entropy-filtered machine learning framework designed to enhance the stability and risk-awareness of algorithmic trading strategies. The proposed approach integrates entropy-based filtering techniques with machine learning classifiers in order to reduce noise in market signals, thereby improving the risk-adjusted stability of trading strategies. Entropy measures are employed as a filtering mechanism that evaluates the informational content of predictive signals and suppresses unreliable model outputs. The empirical analysis is conducted using cryptocurrency market data, where the entropy-filtered framework is applied to trading signal generation and decision making. The results indicate that the proposed approach improves the stability of trading signals and reduces the occurrence of false signals compared to conventional machine learning models. In addition, entropy filtering contributes to a more balanced risk–return profile and enhances the overall robustness of trading strategies. Moreover, entropy filtering contributes to a more balanced risk–return profile and enhances the overall robustness of trading strategies. The findings suggest that entropy-based filtering substantially improves the reliability and risk-awareness of machine learning trading systems, providing a promising direction for the development of more robust AI-driven financial decision frameworks.
Serban et al. (2026) studied this question.