现代电子技术2024，Vol.47Issue(6)：154-160,7.DOI:10.16652/j.issn.1004-373x.2024.06.025
基于深度强化学习算法的投资组合策略与自动化交易研究
Research on investment portfolio strategy and automated trading based on deep reinforcement learning algorithm
摘要
Abstract
The problem of investment portfolio strategy is an enduring topic in the financial field,and the application of artificial intelligence techniques in financial markets is an important research direction in the information technology era.Current research is more focused on price prediction of stocks,and less on decision-making problems such as investment portfolio and automated trading.Based on the deep reinforcement learning algorithm,the BiLSTM of deep learning is used to predict the rise and fall of stock prices,and the reinforcement learning agents is used to observe and better assess the current situation,so as to determine one′s own trading actions.Intelligent agents can comparison during automated trading processes by using traditional investment portfolio strategy to establish pre weights for transactions,so as to continuously optimize their strategy choices and generate the optimal investment portfolio strategy at the current time point.10 stocks from the US stock market are selected for experiments.Under real market simulations,the results show that the cumulative return of the model based on deep reinforcement learning algorithm can reach 86.5%.In comparison with other benchmark strategies,it has the highest return and the lowest risk,and has a certain practical value.关键词
投资组合策略/自动化交易/深度强化学习/BiLSTM/深度确定性策略梯度(DDPG)/权重对比Key words
investment portfolio strategy/automated trading/deep reinforcement learning/BiLSTM/DDPG/weighting comparison分类
信息技术与安全科学引用本文复制引用
杨旭,刘家鹏,越瀚,张芹..基于深度强化学习算法的投资组合策略与自动化交易研究[J].现代电子技术,2024,47(6):154-160,7.基金项目
国家社会科学基金项目(18BGL224) （18BGL224）
