LEVER : Online Adaptive Sequence Learning Framework for High-Frequency Trading

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

  • Zixuan Yuan
  • Haoyi Zhou
  • Denghui Zhang
  • Hao Liu
  • Nengjun Zhu
  • Hui Xiong

Related Research Unit(s)

Detail(s)

Original languageEnglish
Number of pages13
Journal / PublicationIEEE Transactions on Knowledge and Data Engineering
Online published6 Dec 2023
Publication statusOnline published - 6 Dec 2023

Abstract

Recent years have witnessed the fast development of deep learning techniques in quantitative trading. It still remains unclear how to exploit deep learning techniques to improve high-frequency trading (HFT). Indeed, there are two emerging challenges for the use of deep learning for HFT: (i) how to quantify fast-changing market conditions for tick-level signal prediction; (ii) how to establish a unified trading paradigm for different securities of diverse market conditions and severe signal sparsity. To this end, in this paper, we propose an Online Adaptive Sequence Learning (LEVER) framework, which consists of two distinct components to predict the HFT signals at the tick level for a variety of securities simultaneously. Specifically, we start with a single learner that adopts an encoder-decoder architecture for each security-based HFT signal prediction. In this single learner, an ordered encoder module first captures the variability patterns of the security's price curve by encoding the input indicator sequence from different time ranges. An unordered decoder module then outlines the pivot points of the price curve as support and resistance levels to quantify the market status. Based on the measured market condition, a prediction module further approximates the impacts of upcoming security data as the potential market momentum to detect the tick-level trading signals. To overcome the computational challenges and signal sparsity posed by online HFT for multiple securities, we develop a competitive active-meta learning paradigm to enhance the signal learners' learning efficiency for online implementation. Finally, extensive experiments on real-world stock market data demonstrate the effectiveness of our deployed LEVER for improving the performances of the existing industry method by 0.27 in the Sharpe ratio and by 0.09% in a transaction-based return. © 2023 IEEE.

Research Area(s)

  • Deep Sequence Learning, Active-Meta Learning, High-Frequency Trading, Financial Market

Bibliographic Note

Information for this record is supplemented by the author(s) concerned.