Abstract
This research paper delves into the intersection of High-Frequency Trading (HFT) and Machine Learning (ML), exploring the significant impact of ML techniques on enhancing the efficiency, accuracy, and profitability of HFT strategies. The paper presents an in-depth examination of the principles, challenges, and opportunities associated with HFT and ML integration. It also discusses various ML approaches applied in HFT, their advantages, limitations, and potential future developments. Through an extensive review of literature and case studies, this paper aims to provide a comprehensive overview of the evolving landscape of HFT driven by ML advancements.
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