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題名 A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions
作者 謝明華
Hsieh, Ming-Hua
Cheng, Li-Chen;Huang, Yu-Hsiang;Wu, Mu-En
貢獻者 風管系
關鍵詞 machine learning; stock trading; decision making; deep learning; reinforcement learning
日期 2021-11
上傳時間 21-Sep-2022 11:07:53 (UTC+8)
摘要 The prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Investors put their money into the financial market, hoping to maximize profits by understanding market trends and designing trading strategies at the entry and exit points. Most studies propose machine learning models to predict stock prices. However, constructing trading strategies is helpful for traders to avoid making mistakes and losing money. We propose an automatic trading framework using LSTM combined with deep Q-learning to determine the trading signal and the size of the trading position. This is more sophisticated than traditional price prediction models. This study used price data from the Taiwan stock market, including daily opening price, closing price, highest price, lowest price, and trading volume. The profitability of the system was evaluated using a combination of different states of different stocks. The profitability of the proposed system was positive after a long period of testing, which means that the system performed well in predicting the rise and fall of stocks.
關聯 Mathematics, Vol.9, No.23, 3094
資料類型 article
DOI https://doi.org/10.3390/math9233094
dc.contributor 風管系
dc.creator (作者) 謝明華
dc.creator (作者) Hsieh, Ming-Hua
dc.creator (作者) Cheng, Li-Chen;Huang, Yu-Hsiang;Wu, Mu-En
dc.date (日期) 2021-11
dc.date.accessioned 21-Sep-2022 11:07:53 (UTC+8)-
dc.date.available 21-Sep-2022 11:07:53 (UTC+8)-
dc.date.issued (上傳時間) 21-Sep-2022 11:07:53 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/142004-
dc.description.abstract (摘要) The prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Investors put their money into the financial market, hoping to maximize profits by understanding market trends and designing trading strategies at the entry and exit points. Most studies propose machine learning models to predict stock prices. However, constructing trading strategies is helpful for traders to avoid making mistakes and losing money. We propose an automatic trading framework using LSTM combined with deep Q-learning to determine the trading signal and the size of the trading position. This is more sophisticated than traditional price prediction models. This study used price data from the Taiwan stock market, including daily opening price, closing price, highest price, lowest price, and trading volume. The profitability of the system was evaluated using a combination of different states of different stocks. The profitability of the proposed system was positive after a long period of testing, which means that the system performed well in predicting the rise and fall of stocks.
dc.format.extent 99 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Mathematics, Vol.9, No.23, 3094
dc.subject (關鍵詞) machine learning; stock trading; decision making; deep learning; reinforcement learning
dc.title (題名) A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.3390/math9233094
dc.doi.uri (DOI) https://doi.org/10.3390/math9233094