FinRL-Meta: Market environments and benchmarks for data-driven financial reinforcement learning
Finance is a particularly challenging playground for deep reinforcement learning. However,
establishing high-quality market environments and benchmarks for financial reinforcement …
establishing high-quality market environments and benchmarks for financial reinforcement …
Reinforcement learning for quantitative trading
Quantitative trading (QT), which refers to the usage of mathematical models and data-driven
techniques in analyzing the financial market, has been a popular topic in both academia and …
techniques in analyzing the financial market, has been a popular topic in both academia and …
Unveiling the influence of artificial intelligence and machine learning on financial markets: A comprehensive analysis of AI applications in trading, risk management …
This study explores the adoption and impact of artificial intelligence (AI) and machine
learning (ML) in financial markets, utilizing a mixed-methods approach that includes a …
learning (ML) in financial markets, utilizing a mixed-methods approach that includes a …
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 …
[PDF][PDF] A survey of deep learning applications in cryptocurrency
J Zhang, K Cai, J Wen - Iscience, 2024 - cell.com
This study aims to comprehensively review a recently emerging multidisciplinary area
related to the application of deep learning methods in cryptocurrency research. We first …
related to the application of deep learning methods in cryptocurrency research. We first …
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 datasets and market environments for financial reinforcement learning
The financial market is a particularly challenging playground for deep reinforcement
learning due to its unique feature of dynamic datasets. Building high-quality market …
learning due to its unique feature of dynamic datasets. Building high-quality market …
Deep reinforcement learning for financial trading using multi-modal features
Abstract Deep Reinforcement Learning (DRL) has several successful applications in various
fields. One of these fields is financial trading, in which an agent interacts with its environment …
fields. One of these fields is financial trading, in which an agent interacts with its environment …
Deep LSTM and LSTM-Attention Q-learning based reinforcement learning in oil and gas sector prediction
DO Oyewola, SA Akinwunmi… - Knowledge-Based Systems, 2024 - Elsevier
Accurate prediction of stock market trends and movements holds great significance in the
financial industry as it enables investors, traders, and decision-makers to make informed …
financial industry as it enables investors, traders, and decision-makers to make informed …
Deep reinforcement learning for cryptocurrency trading: Practical approach to address backtest overfitting
Designing profitable and reliable trading strategies is challenging in the highly volatile
cryptocurrency market. Existing works applied deep reinforcement learning methods and …
cryptocurrency market. Existing works applied deep reinforcement learning methods and …