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 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 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 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 …
Deep robust reinforcement learning for practical algorithmic trading
In algorithmic trading, feature extraction and trading strategy design are two prominent
challenges to acquire long-term profits. However, the previously proposed methods rely …
challenges to acquire long-term profits. However, the previously proposed methods rely …
A multi-agent reinforcement learning framework for optimizing financial trading strategies based on timesnet
Y Huang, C Zhou, K Cui, X Lu - Expert Systems with Applications, 2024 - Elsevier
An increasing number of studies have shown the effectiveness of using deep reinforcement
learning to learn profitable trading strategies from financial market data. However, a single …
learning to learn profitable trading strategies from financial market data. However, a single …
A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning
The adoption of computer-aided stock trading methods is gaining popularity in recent years,
mainly because of their ability to process efficiently past information through machine …
mainly because of their ability to process efficiently past information through machine …
Deep reinforcement learning for automated stock trading: An ensemble strategy
Stock trading strategies play a critical role in investment. However, it is challenging to design
a profitable strategy in a complex and dynamic stock market. In this paper, we propose an …
a profitable strategy in a complex and dynamic stock market. In this paper, we propose an …
Price trailing for financial trading using deep reinforcement learning
A Tsantekidis, N Passalis, AS Toufa… - … on neural networks …, 2020 - ieeexplore.ieee.org
Machine learning methods have recently seen a growing number of applications in financial
trading. Being able to automatically extract patterns from past price data and consistently …
trading. Being able to automatically extract patterns from past price data and consistently …
Practical deep reinforcement learning approach for stock trading
Stock trading strategy plays a crucial role in investment companies. However, it is
challenging to obtain optimal strategy in the complex and dynamic stock market. We explore …
challenging to obtain optimal strategy in the complex and dynamic stock market. We explore …