A parallel multi-module deep reinforcement learning algorithm for stock trading
In recent years, deep reinforcement learning (DRL) algorithm has been widely used in
algorithmic trading. Many fully automated trading systems or strategies have been built …
algorithmic trading. Many fully automated trading systems or strategies have been built …
A synchronous deep reinforcement learning model for automated multi-stock trading
R AbdelKawy, WM Abdelmoez, A Shoukry - Progress in Artificial …, 2021 - Springer
Automated trading is one of the research areas that has benefited from the recent success of
deep reinforcement learning (DRL) in solving complex decision-making problems. Despite …
deep reinforcement learning (DRL) in solving complex decision-making problems. Despite …
A novel deep reinforcement learning based automated stock trading system using cascaded lstm networks
J Zou, J Lou, B Wang, S Liu - Expert Systems with Applications, 2024 - Elsevier
Abstract Deep Reinforcement Learning (DRL) algorithms have been increasingly used to
construct stock trading strategies, but they often face performance challenges when applied …
construct stock trading strategies, but they often face performance challenges when applied …
Multi-agent deep reinforcement learning algorithm with trend consistency regularization for portfolio management
Financial portfolio management is reallocating the asset into financial products, whose goal
is to maximize the profit under a certain risk. Since AlphaGo debated human professional …
is to maximize the profit under a certain risk. Since AlphaGo debated human professional …
A multi-agent deep reinforcement learning framework for algorithmic trading in financial markets
A Shavandi, M Khedmati - Expert Systems with Applications, 2022 - Elsevier
Algorithmic trading based on machine learning is a developing and promising field of
research. Financial markets have a complex, uncertain, and dynamic nature, making them …
research. Financial markets have a complex, uncertain, and dynamic nature, making them …
A novel deep reinforcement learning framework with BiLSTM-Attention networks for algorithmic trading
Y Huang, X Wan, L Zhang, X Lu - Expert Systems with Applications, 2024 - Elsevier
The financial market, as a complex nonlinear dynamic system frequently influenced by
various factors, such as international investment capital, is very challenging to build trading …
various factors, such as international investment capital, is very challenging to build trading …
A deep reinforcement learning-based decision support system for automated stock market trading
Presently, the volatile and dynamic aspects of stock prices are significant research
challenges for stock markets or any other financial sector to design accurate and profitable …
challenges for stock markets or any other financial sector to design accurate and profitable …
Empirical analysis of automated stock trading using deep reinforcement learning
M Kong, J So - Applied Sciences, 2023 - mdpi.com
There are several automated stock trading programs using reinforcement learning, one of
which is an ensemble strategy. The main idea of the ensemble strategy is to train DRL …
which is an ensemble strategy. The main idea of the ensemble strategy is to train DRL …
Deep reinforcement learning approach for trading automation in the stock market
T Kabbani, E Duman - IEEE Access, 2022 - ieeexplore.ieee.org
Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable
problems. The automation of profit generation in the stock market is possible using DRL, by …
problems. The automation of profit generation in the stock market is possible using DRL, by …
Practical deep reinforcement learning approach for stock trading
Practical Deep Reinforcement Learning Approach for Stock Trading Page 1 Practical Deep
Reinforcement Learning Approach for Stock Trading Zhuoran Xiong, Xiao-Yang Liu, Shan …
Reinforcement Learning Approach for Stock Trading Zhuoran Xiong, Xiao-Yang Liu, Shan …