Anonymous shelf assessment
Machine Learning for Trading
Shelf score 7.5 / 10
On Machine Learning for Trading · Manish Yarats · David Mann · Springer
Published 23 March 2026
This book explores the application of machine learning techniques in trading environments.
Overview
Published in 2019 by Springer, 'Machine Learning for Trading' by Manish Yarats and David Mann focuses on the integration of machine learning methodologies within trading practices. It addresses key areas such as feature engineering, model selection, and execution strategies, making it a practical resource for those involved in quantitative finance.
The book is particularly aimed at traders, quants, and technologists who seek to enhance their trading strategies through the application of machine learning. It provides insights into how machine learning can be effectively utilised in trading scenarios, which is increasingly relevant in today's data-driven financial markets.
While the book offers valuable practical methods for trading applications, it is important to note that the field of machine learning is rapidly evolving, which may affect the longevity of the techniques discussed within.
By area & interest
Target Audience
This book is designed for practitioners in trading, including traders, quantitative analysts, and technologists who are looking to apply machine learning techniques to improve their trading outcomes.
Practical Applications
It emphasises practical methods and applications of machine learning in trading, making it a useful guide for those seeking to implement these techniques in real-world scenarios.
Evolving Field
The rapidly changing landscape of machine learning means that readers should remain aware of ongoing developments in the field, which may impact the relevance of the methods presented.
Basis of this assessment
This assessment is based on the catalogue description and details regarding the book's focus and audience.
Strengths
The book's strengths lie in its practical focus on machine learning applications in trading, providing actionable insights for practitioners in the field.
Limitations
Its limitations stem from the fast-paced evolution of machine learning technologies, which may render some content less applicable over time.
Ideal reader
Ideal readers include ML traders and quantitative analysts who are eager to leverage machine learning techniques to enhance their trading strategies.