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 …

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 …

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 …

An automatic program that generates constant profit from the financial market is lucrative for
every market practitioner. Recent advance in deep reinforcement learning provides a …

It is difficult to automatically produce trading signals based on previous transaction data and
the financial status of assets because of the significant noise and unpredictability of capital …

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 …

Quantitative trading (QT) has been a popular topic in both academia and the financial
industry since the 1970s. In the last decade, deep reinforcement learning (DRL) has …

In algorithmic trading, feature extraction and trading strategy design are two prominent
challenges to acquire long-term profits. However, the previously proposed methods rely …

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 …

The interdisciplinary relationship between machine learning and financial markets has long
been a theme of great interest among both research communities. Recently, reinforcement …