Financial Trading with Feature Preprocessing and Recurrent Reinforcement Learning

Li，Lin

10.1109/ISKE54062.2021.9755374

2021

978-1-6654-0554-6

2021 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2021

162-169

26-28 Nov. 2021

Chengdu, China

Financial trading aims to build profitable strategies to make wise investment decisions in the financial market. It has attracted interests in the machine learning community for a long time. This paper proposes to trade financial assets automatically using feature preprocessing skills and Recurrent Reinforcement Learning (RRL) algorithm. The strategy starts from technical indicators extracted from assets' market information. Then these technical indicators are preprocessed by Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) and eventually inputted to the RRL algorithm to do the trading. The extensive empirical evidence shows that the proposed strategy is not only effective and robust in its performance, but also can mitigate the drawbacks underlying the initial trading using RRL.

discrete wavelet transform feature preprocessing financial trading principal component analysis recurrent reinforcement learning

第一 ; 通讯

英语

EI

20221912073434

Commerce ; Discrete wavelet transforms ; Financial markets ; Investments ; Reinforcement learning ; Signal reconstruction

Information Theory and Signal Processing:716.1 ; Artificial Intelligence:723.4 ; Mathematical Transformations:921.3 ; Mathematical Statistics:922.2

2-s2.0-85129368015

Scopus

Southern University of Science and Technology,Shenzhen,China

Li，Lin. Financial Trading with Feature Preprocessing and Recurrent Reinforcement Learning[C],2021:162-169.