From Data to Trade: A Machine Learning Approach to Quantitative Trading

72 Pages Posted: 5 Jan 2023

See all articles by Gautier Marti

Gautier Marti

Ecole Polytechnique, Palaiseau

Date Written: December 31, 2022


"Machine learning has revolutionized the field of quantitative trading, enabling traders to develop and implement sophisticated trading strategies that leverage large amounts of data and advanced modeling techniques. In this book, we provide a comprehensive overview of machine learning for quantitative trading, covering the fundamental concepts, techniques, and applications of machine learning in the financial industry.

We start by introducing the key concepts and challenges of machine learning for quantitative trading, including feature engineering, model selection, and backtesting. We then delve into the various machine learning approaches that are commonly used in quantitative trading, including supervised learning, unsupervised learning, and reinforcement learning. We also discuss the challenges and best practices of implementing machine learning models in the live market, including the role of data quality, the importance of risk management, and the need for ongoing model monitoring and validation.

Throughout the book, we provide numerous examples and case studies to illustrate the concepts and techniques discussed, and we also include practical tips and resources to help traders and practitioners get started with machine learning for quantitative trading. This book is an essential resource for anyone looking to gain a deeper understanding of how machine learning is transforming the world of finance."

Book essentially written by ChatGPT.

Keywords: machine learning, quantitative trading, ChatGPT, artificial intelligence

Suggested Citation

Marti, Gautier, From Data to Trade: A Machine Learning Approach to Quantitative Trading (December 31, 2022). Available at SSRN: or

Gautier Marti (Contact Author)

Ecole Polytechnique, Palaiseau ( email )

Route de Saclay
Palaiseau, 91128

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