What question did this study set out to answer?

This research aims to explore the effectiveness of fuzzy logic in enhancing high-frequency trading strategies under uncertain market conditions.

July 3, 2026International Journal of Fuzzy Logic and Intelligent Systems0 citationsOpen Access

Harnessing Fuzzy Logic in High-Frequency Trading for Enhanced Market Resilience

AKAbdulsalam KamaraJiangsu University ZLZhen Li KFKadiatu FofanahHarbin Institute of Technology

Key Points

This research aims to explore the effectiveness of fuzzy logic in enhancing high-frequency trading strategies under uncertain market conditions.
Utilized financial data to benchmark a fuzzy-logic-based strategy against traditional HFT approaches
Evaluated optimization tools, including genetic algorithms and neural networks, to refine the trading algorithm
Measured the performance of the fuzzy-logic strategy in volatile market scenarios.
The fuzzy-logic-based trading strategy consistently outperformed traditional HFT methods, especially in volatile markets
The strategy dynamically adapted to market conditions, demonstrating greater resilience
Higher risk-adjusted returns were achieved through the incorporation of optimization techniques.

Abstract

This study investigates how, in the digital age of finance, high-frequency trading (HFT) stands at the forefront of leveraging technology for rapid trading decisions.With the increasing complexity of markets, integrating fuzzy logic into HFT offers a novel approach to address the uncertainties inherent in financial environments.This study adopts a multifaceted approach to assess the efficacy of fuzzy logic in HFT.Using financial data to benchmark a fuzzy-logicbased strategy against traditional HFT approaches, optimization tools were evaluated, and the fuzzy-logic-based trading algorithm was fine-tuned.This bridging of theoretical concepts with application ensures that the findings are relevant and applicable.The results revealed that the fuzzy-logic-based trading strategy exhibited consistent superiority over traditional HFT methods, particularly in volatile market scenarios.By dynamically adapting to market nuances, this strategy exhibits remarkable resilience and adaptability.The inclusion of optimization tools, such as genetic algorithms and neural networks, amplified the strategy's performance, yielding higher risk-adjusted returns.This study provides valuable insights into a potential paradigm shift in algorithmic trading.The demonstrated efficacy of the fuzzy-logic-based strategy, coupled with optimization techniques, indicates a promising avenue for future trading innovations.

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