Quantum computers are expected to surpass the computational capabilities of classical
computers and have a transformative impact on numerous industry sectors. We present a …

The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …

This open-source book represents our attempt to make deep learning approachable,
teaching readers the concepts, the context, and the code. The entire book is drafted in …

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 …

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 …

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 …

Finance is a particularly challenging playground for deep reinforcement learning. However,
establishing high-quality market environments and benchmarks for financial reinforcement …

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

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 …