Leveraging Deep Learning Techniques in High-Frequency Trading: Computational Opportunities and Mathematical Challenges
DOI:
https://doi.org/10.5281/zenodo.12747424ARK:
https://n2t.net/ark:/40704/AJSM.v2n4a05Keywords:
High-frequency Trading, Deep Learning, Predictive Accuracy, Real-time Data Processing, Risk Management, Automated Strategy Development, LSTM Networks, Reinforcement LearningAbstract
High-frequency trading (HFT) has transformed financial markets by leveraging speed and automation to execute large volumes of transactions within microseconds. The integration of deep learning (DL) algorithms into HFT systems presents new opportunities for enhancing prediction accuracy and trading strategies, uncovering complex patterns in large datasets. This paper explores the synergy between DL and HFT, highlighting the potential benefits and inherent challenges. We conduct a comprehensive review of current literature and case studies to provide a detailed understanding of how DL can revolutionize HFT and the obstacles that must be addressed to fully realize its potential.
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