{"title":"基于Google趋势关键词的股票价格交易强化学习","authors":"S. D. You, Po-Yuan Hsiao, Shengzhe Tsai","doi":"10.1109/ICASI57738.2023.10179534","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":281254,"journal":{"name":"2023 9th International Conference on Applied System Innovation (ICASI)","volume":"31 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reinforcement Learning for Stock Price Trading with Keywords in Google Trends\",\"authors\":\"S. D. You, Po-Yuan Hsiao, Shengzhe Tsai\",\"doi\":\"10.1109/ICASI57738.2023.10179534\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":281254,\"journal\":{\"name\":\"2023 9th International Conference on Applied System Innovation (ICASI)\",\"volume\":\"31 6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 9th International Conference on Applied System Innovation (ICASI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASI57738.2023.10179534\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 9th International Conference on Applied System Innovation (ICASI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASI57738.2023.10179534","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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 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.
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