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 …
Applying artificial intelligence in cryptocurrency markets: A survey
The total capital in cryptocurrency markets is around two trillion dollars in 2022, which is
almost the same as Apple's market capitalisation at the same time. Increasingly …
almost the same as Apple's market capitalisation at the same time. Increasingly …
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 reinforcement learning for trading—A critical survey
A Millea - Data, 2021 - mdpi.com
Deep reinforcement learning (DRL) has achieved significant results in many machine
learning (ML) benchmarks. In this short survey, we provide an overview of DRL applied to …
learning (ML) benchmarks. In this short survey, we provide an overview of DRL applied to …
Deep reinforcement learning for active high frequency trading
We introduce the first end-to-end Deep Reinforcement Learning (DRL) based framework for
active high frequency trading in the stock market. We train DRL agents to trade one unit of …
active high frequency trading in the stock market. We train DRL agents to trade one unit of …
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 …
Using deep reinforcement learning with hierarchical risk parity for portfolio optimization
We devise a hierarchical decision-making architecture for portfolio optimization on multiple
markets. At the highest level a Deep Reinforcement Learning (DRL) agent selects among a …
markets. At the highest level a Deep Reinforcement Learning (DRL) agent selects among a …
Multi-Agent Deep Reinforcement Learning With Progressive Negative Reward for Cryptocurrency Trading
K Kumlungmak, P Vateekul - IEEE Access, 2023 - ieeexplore.ieee.org
Recently, reinforcement learning has been applied to cryptocurrencies to make profitable
trades. However, cryptocurrency trading is a very challenging task due to the volatility of the …
trades. However, cryptocurrency trading is a very challenging task due to the volatility of the …
The design and implementation of a deep reinforcement learning and quantum finance theory-inspired portfolio investment management system
Y Qiu, RK Liu, RST Lee - Expert Systems with Applications, 2024 - Elsevier
Abstract Deep Learning (DL) and Reinforcement Learning (RL) are common machine
learning techniques used in automatic trading, notwithstanding, RL is deficient in portfolio …
learning techniques used in automatic trading, notwithstanding, RL is deficient in portfolio …
Stock market prediction using deep reinforcement learning
AL Awad, SM Elkaffas, MW Fakhr - Applied System Innovation, 2023 - mdpi.com
Stock value prediction and trading, a captivating and complex research domain, continues to
draw heightened attention. Ensuring profitable returns in stock market investments demands …
draw heightened attention. Ensuring profitable returns in stock market investments demands …