Financial Trading with Feature Preprocessing and Recurrent Reinforcement Learning

TL;DR

This paper introduces a novel automated financial trading strategy that combines feature preprocessing techniques like PCA and DWT with Recurrent Reinforcement Learning, demonstrating improved robustness and effectiveness in empirical tests.

Contribution

It presents a new approach integrating feature preprocessing with RRL for financial trading, enhancing performance and robustness over traditional methods.

Findings

Strategy is effective and robust in empirical tests

Preprocessing improves trading performance

Mitigates drawbacks of initial RRL trading approaches

Abstract

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.