Reinforcement Learning for Stock Price Trading with Keywords in Google Trends

In this paper, we apply the Proximal Policy optimization (PPO) algorithm to train an agent for automated stock trading. In additional to the conventional trading indicators, we also add the strength of keywords obtained from the Google Trends for training the agent. We conduct two experiments to tes...

Full description

Saved in:
Bibliographic Details
Published in2023 9th International Conference on Applied System Innovation (ICASI) pp. 109 - 111
Main Authors You, Shingchern D., Hsiao, Po-Yuan, Tsai, Shengzhe
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.04.2023
Subjects
Difference equations
Google Trends
Internet
PPO
Reinforcement learning
stock trading agent
Symbols
Technological innovation
Training
Online AccessGet full text

Cover

More Information
Summary:In this paper, we apply the Proximal Policy optimization (PPO) algorithm to train an agent for automated stock trading. In additional to the conventional trading indicators, we also add the strength of keywords obtained from the Google Trends for training the agent. We conduct two experiments to test the effectiveness of adding keywords. The first experiment uses general keywords, such as inflation. The second experiment uses stock-specific keywords, such as AAPL for trading apple stock. The experimental results confirm that the proposed approach can improve trading performance.
ISSN:2768-4156
DOI:10.1109/ICASI57738.2023.10179534