An adaptive financial trading strategy based on proximal policy optimization and financial signal representation

Abstract

Trading strategies play a crucial role in financial trading. However, due to the significant amount of noise present in financial signals, traditional trading strategies and those based on price prediction often fail to achieve optimal results in real market conditions. Nowadays, with the pervasive noise in financial signals, developing successful trading strategies to achieve high returns has become one of the most prominent and challenging research areas in modern finance. Therefore, an adaptive financial trading strategy, called proximal policy optimization based on financial signal representation trading strategy (FSRPPO), is proposed. This strategy employs financial signal representation technology (FSR), which combines complete ensemble extreme-point symmetric mode decomposition with adaptive noise (CEEMDAN) and modified rescaled range analysis (MRS), to accurately and robustly represent dynamic market states, enhancing profitability. Additionally, the designed reward function reduces trading frequency to lower costs, while the designed action space increases trading flexibility to reduce risks. The experimental results on real stock data demonstrate the outstanding profitability and good risk avoidance ability of our proposed trading strategy, which means that the proposed model can effectively filter out noise from financial signals, extract valuable information, and provide reliable decision support for investors.