Ensemble deep learning: A review
Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …
performance. Currently, deep learning architectures are showing better performance …
[HTML][HTML] Multi-agent reinforcement learning: A review of challenges and applications
In this review, we present an analysis of the most used multi-agent reinforcement learning
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …
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 …
FinRL: A deep reinforcement learning library for automated stock trading in quantitative finance
As deep reinforcement learning (DRL) has been recognized as an effective approach in
quantitative finance, getting hands-on experiences is attractive to beginners. However, to …
quantitative finance, getting hands-on experiences is attractive to beginners. However, to …
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 …
FinRL: Deep reinforcement learning framework to automate trading in quantitative finance
Deep reinforcement learning (DRL) has been envisioned to have a competitive edge in
quantitative finance. However, there is a steep development curve for quantitative traders to …
quantitative finance. However, there is a steep development curve for quantitative traders to …
[HTML][HTML] An overview of machine learning, deep learning, and reinforcement learning-based techniques in quantitative finance: recent progress and challenges
Forecasting the behavior of the stock market is a classic but difficult topic, one that has
attracted the interest of both economists and computer scientists. Over the course of the last …
attracted the interest of both economists and computer scientists. Over the course of the last …
Interpretable reward redistribution in reinforcement learning: A causal approach
A major challenge in reinforcement learning is to determine which state-action pairs are
responsible for future rewards that are delayed. Reward redistribution serves as a solution to …
responsible for future rewards that are delayed. Reward redistribution serves as a solution to …
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 …
Deep reinforcement learning in quantitative algorithmic trading: A review
TV Pricope - arXiv preprint arXiv:2106.00123, 2021 - arxiv.org
Algorithmic stock trading has become a staple in today's financial market, the majority of
trades being now fully automated. Deep Reinforcement Learning (DRL) agents proved to be …
trades being now fully automated. Deep Reinforcement Learning (DRL) agents proved to be …