FINTECH THRUST SEMINAR | Deep Learning in High-Frequency Trading: Opportunities, Challenges, and Real-World Applications

10:00am - 11:30am
W1, 1F, Room 101

Deep Learning in High-Frequency Trading: Opportunities, Challenges, and Real-World Applications

Abstract:
High-frequency trading (HFT) and quantitative finance generate massive streams of market data, making them prime candidates for deep learning methods. In this talk, we will explore how neural networks can capture short-term price movements, model limit order book dynamics, and enhance risk management. We will discuss the challenges unique to HFT - such as ultra-low latency, fleeting alpha signals, and high-dimensional data - and show how recent advances in deep learning address these issues. Real-world case studies will illustrate the practical impact of these techniques, guiding us toward the skills and insights needed to thrive in algorithmic trading and quantitative research.

Event Format
Speakers / Performers:
Dr. Zihao ZHANG
Micro Trading (北京微观博易私募基金管理有限公司)

Dr. Zhang is currently a quantitative researcher at Micro Trading, specializing in predictive modeling and execution strategies. He was previously a postdoctoral researcher at the Oxford-Man Institute, University of Oxford. Dr. Zhang obtained his Ph.D. in Engineering Science from the University of Oxford, where he focused on deep learning and deep reinforcement learning.

 

Language
English
Organizer
Financial Technology Thrust, HKUST(GZ)
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