An automated portfolio trading system with feature preprocessing and recurrent reinforcement learning

Li，Lin

10.1145/3490354.3494376

2021-11-03

ICAIF 2021 - 2nd ACM International Conference on AI in Finance

We propose a novel portfolio trading system, which contains a feature preprocessing module and a trading module. The feature preprocessing module consists of various data processing operations, while in the trading part, we integrate the portfolio weight rebalance function with the trading algorithm and make the trading system fully automated and suitable for individual investors, holding a handful of stocks. The data preprocessing procedures are applied to remove the white noise in the raw data set and uncover the general pattern underlying the data set before the processed feature set is inputted into the trading algorithm. Our empirical results reveal that the proposed portfolio trading system can efficiently earn high profit and maintain a relatively low drawdown, which clearly outperforms other portfolio trading strategies.

automated trading systems feature preprocessing portfolio trading recurrent reinforcement learning

第一 ; 通讯

英语

EI

20222112154901

Commerce ; Data handling ; Electronic trading ; Financial markets ; Reinforcement learning ; White noise

Data Processing and Image Processing:723.2 ; Artificial Intelligence:723.4 ; Computer Applications:723.5 ; Automatic Control Principles and Applications:731

2-s2.0-85130553335

Scopus

Southern University of Science and Technology,Shenzhen,China

Li，Lin. An automated portfolio trading system with feature preprocessing and recurrent reinforcement learning[C],2021.