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 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 …
Adaptive stock trading strategies with deep reinforcement learning methods
The increasing complexity and dynamical property in stock markets are key challenges of
the financial industry, in which inflexible trading strategies designed by experienced …
the financial industry, in which inflexible trading strategies designed by experienced …
Deep reinforcement learning methods for automation forex trading
T Chau, MT Nguyen, DV Ngo… - … on Computing and …, 2022 - ieeexplore.ieee.org
In Forex market, designing effective strategies are a critical role in investment. However, it is
a challenging task due to its inherent characteristics, which include high volatility, trend …
a challenging task due to its inherent characteristics, which include high volatility, trend …
Stock trading strategies based on deep reinforcement learning
Y Li, P Liu, Z Wang - Scientific Programming, 2022 - Wiley Online Library
The purpose of stock market investment is to obtain more profits. In recent years, an
increasing number of researchers have tried to implement stock trading based on machine …
increasing number of researchers have tried to implement stock trading based on machine …
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 …
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 …
Multi-Feature Supervised Reinforcement Learning for Stock Trading
K Fu, Y Yu, B Li - IEEE Access, 2023 - ieeexplore.ieee.org
Deep reinforcement learning (DRL) algorithm is often used to find the best trading strategy in
algorithmic trading. However, the classical DRL model is difficult to achieve rapid …
algorithmic trading. However, the classical DRL model is difficult to achieve rapid …
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 …
Dynamic stock-decision ensemble strategy based on deep reinforcement learning
X Yu, W Wu, X Liao, Y Han - Applied Intelligence, 2023 - Springer
In a complex and changeable stock market, it is very important to design a trading agent that
can benefit investors. In this paper, we propose two stock trading decision-making methods …
can benefit investors. In this paper, we propose two stock trading decision-making methods …