政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/142004

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政大機構典藏 > 商學院 > 風險管理與保險學系 > 期刊論文 > Item 140.119/142004

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/142004

Title:	A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions
Authors:	謝明華 Hsieh, Ming-Hua Cheng, Li-Chen;Huang, Yu-Hsiang;Wu, Mu-En
Contributors:	風管系
Keywords:	machine learning;stock trading;decision making;deep learning;reinforcement learning
Date:	2021-11
Issue Date:	2022-09-21 11:07:53 (UTC+8)
Abstract:	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.
Relation:	Mathematics, Vol.9, No.23, 3094
Data Type:	article
DOI 連結:	https://doi.org/10.3390/math9233094
DOI:	10.3390/math9233094
Appears in Collections:	[風險管理與保險學系] 期刊論文

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