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

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.