PETTER N. KOLM
Professor, Courant Institute of Mathematical Sciences, New York University; Awarded “Quant of the Year” 2021.
A must-have for algorithmic traders and students, this book emphasizes designing trading strategies with QuantConnect. Featuring Python examples and advanced AI/ML models, it offers a clear and accessible presentation ideal for anyone in quantitative finance.
CHRIS BARTLETT
CEO, Algoseek
This comprehensive guide masterfully bridges the gap between AI technology and practical trading applications, offering finance professionals valuable insights for developing robust, data-driven trading strategies.
MICHAEL ROBBINS
Author of "Quantitative Asset Management".
This concise guide provides a gentle introduction with hands-on examples and expert insights into dissecting and evaluating trades from seasoned traders. The code will make otherwise complex or confusing examples clear. It is an excellent springboard for developing your own strategies.
DIMITRI BIANCO
Head of Quant Risk and Research.
The book ties both theory and industry together while providing code, output, and a platform to implement AI models in a trading environment. Cookbook style makes it a great book for those new to machine learning and AI in quantitative finance.
JACQUES JOUBERT
Quant Researcher and Developer, Co-Founder and CEO of Hudson and Thames Quantitative Research
This is the book I wish I had when starting out, it would have saved me years! It offers rare insights and practical tutorials, allowing the next generation of quants to stand on the shoulders of giants.
RAJNEESH SINGH
Director, Amazon SageMaker
As a novice trader myself, I have been looking for ways to apply AI in real world trading scenarios. This book does an excellent job in explaining trading concepts and mapping these to AI concepts to build trading strategies. A must read if you want to use AI for building wealth.
