Numerous deep learning architectures have been developed to accommodate the diversity
of time-series datasets across different domains. In this article, we survey common encoder …

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 …

We adopt Deep Reinforcement Learning algorithms to design trading strategies for
continuous futures contracts. Both discrete and continuous action spaces are considered …

We adopt deep learning models to directly optimise the portfolio Sharpe ratio. The
framework we present circumvents the requirements for forecasting expected returns and …

Numerous machine learning models have been developed to achieve the 'real-life'financial
objective of optimising the risk/return profile of investment strategies. In the current article:(a) …

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 …

Abstract Machine learning (ML) methods are attracting considerable attention among
academics in the field of finance. However, it is commonly perceived that ML has not …

We propose a universal end-to-end framework for portfolio optimization where asset
distributions are directly obtained. The designed framework circumvents the traditional …

Momentum strategies are an important part of alternative investments and are at the heart of
commodity trading advisors (CTAs). These strategies have, however, been found to have …

We introduce Spatio-Temporal Momentum strategies, a class of models that unify both time-
series and cross-sectional momentum strategies by trading assets based on their cross …