While machine learning's role in financial trading has advanced considerably, algorithmic
transparency and explainability challenges still exist. This research enriches prior studies …

With the development of artificial intelligence, there have been many attempts to incorporate
artificial intelligence into algorithmic trading. In particular, reinforcement learning, which …

The Vietnamese stock market is a challenge for applying algorithmic trading. However, the
advance of Machine Learning, especially Reinforcement Learning, has provided a new …

Deep reinforcement learning (DRL) has revolutionized quantitative finance by achieving
excellent performance without significant manual effort. Whereas we observe that the DRL …

Algorithmic trading is playing an increasingly important role in the financial market,
achieving more efficient trading strategies by replacing human decision-making. Among …

Multiple facets of the financial industry, such as algorithmic trading, have greatly benefited
from their unison with cutting-edge machine learning research in recent years. However …

Data augmentation is a commonly used technique in data science for improving the
robustness and performance of machine learning models. The purpose of the paper is to …

Trend filtering simplifies complex time series data by applying smoothness to filter out noise
while emphasizing proximity to the original data. However, existing trend filtering methods …

In reinforcement Learning (RL), an instant reward signal is generated for each action of the
agent, such that the agent learns to maximize the cumulative reward to obtain the optimal …

As the most important capital market, how formulating a reasonable stock trading strategy to
improve capital return and reduce trading risk has always been the focus of people's …