An overall view of key problems in algorithmic trading and recent progress

TL;DR

This paper reviews fundamental challenges in algorithmic trading, including optimal execution and price impact, and discusses recent advances using machine learning techniques like deep learning, reinforcement learning, and GANs.

Contribution

It provides a comprehensive overview of key problems and recent progress in algorithmic trading, highlighting the integration of advanced machine learning methods.

Findings

Summarizes core issues in algorithmic trading.

Highlights recent machine learning applications in the field.

Discusses potential future directions for research.

Abstract

We summarize the fundamental issues at stake in algorithmic trading, and the progress made in this field over the last twenty years. We first present the key problems of algorithmic trading, describing the concepts of optimal execution, optimal placement, and price impact. We then discuss the most recent advances in algorithmic trading through the use of Machine Learning, discussing the use of Deep Learning, Reinforcement Learning, and Generative Adversarial Networks.