MACHINE LEARNING IN TRADING

A new book, Machine Learning in Trading, written by Ishan Shah and Rekhit Pachanekar, is an excellent intro to the basics of the most used ML met… See More

A new book, Machine Learning in Trading, written by Ishan Shah and Rekhit Pachanekar, is an excellent intro to the basics of the most used ML methods. Aspiring quants with knowledge of python language that want to broaden their knowledge will find this book very well structured, understandable, and full of practical coding examples.

It will guide you through the most frequently used ML algorithms from all areas - Supervised Learning (Logistic Regression, Bayesian models, Decision Trees, Random Forest) or Unsupervised Learning (Clustering, PCA, NLP, Reinforcement Learning). It's a great book that helps pick the proper Machine Learning method when you want to turn your ideas into trading strategies.

If you are looking for a primer on machine learning for trading, you couldn’t do better than Shah and Pachanekar’s new book. They will take you t… See More

If you are looking for a primer on machine learning for trading, you couldn’t do better than Shah and Pachanekar’s new book. They will take you through the basics all the way up to advanced industry methodology.

It’s a highly practical book, full of step by step instructions, source code and explanations. For those interested in building serious knowledge of the machine learning trading space, I would very much recommend this book.

There are many books on machine learning, but most authors focus on showing off their street creds in mathematics and programming skills instead … See More

There are many books on machine learning, but most authors focus on showing off their street creds in mathematics and programming skills instead of teaching practical skills. True to their educational roots, this book by Ishan Shah and Rekhit Pachanekar of QuantInsti does the opposite.

You will not find fancy newfangled techniques here. You will not find pie-in-the-sky enticements to be the next Jim Simons. What you will find is a solid, practical, step-by-step guide to implement one machine learning program after another, with sample codes and all, and with a special focus on trading applications.

Whether you read the chapters sequentially like a textbook or use it as a dictionary to look up special topics, it is invaluable to the practical trader who needs these skills to survive in the ultra-competitive world of quantitative trading today.

Firstly, the book is well written and accessible to almost everyone. This is a remarkable accomplishment since neither Python nor ML is readily a… See More

Firstly, the book is well written and accessible to almost everyone. This is a remarkable accomplishment since neither Python nor ML is readily available to non-programmers. Secondly, concepts are well defined and enunciated in a clear, concise language. Decent books make the writer look smart, great books make the reader feel smart. I came out of this book feeling intelligent, something rare enough to be mentioned. The code is crisp, intelligible and food for thought.

The transition from theoretical ML to demo trading was smooth. The author was thorough enough to list the classic pitfalls between backtesting and live environment. The purpose was not to roll out a canned strategy but enable the reader to apply concepts learned. This was impeccably executed.

Overall, this book is a good mixture of theoretical concepts and practical code. I learned concepts and snippets of codes.

The following is not a critic of the book, rather an invitation to explore different applications of ML. Price and returns predictions have long dominated the scene. ML can reduce uncertainty, yet randomness cannot be completely eradicated. Simply said, moving a 45% to 49.6% is still a coin toss. ML could however materially improve the trading edge in areas such as risk management, position-sizing, asset allocation, strategy allocation.

A new book, Machine Learning in Trading, written by Ishan Shah and Rekhit Pachanekar, is an excellent intro to the basics of the most used ML met… See More

A new book, Machine Learning in Trading, written by Ishan Shah and Rekhit Pachanekar, is an excellent intro to the basics of the most used ML methods. Aspiring quants with knowledge of python language that want to broaden their knowledge will find this book very well structured, understandable, and full of practical coding examples.

It will guide you through the most frequently used ML algorithms from all areas - Supervised Learning (Logistic Regression, Bayesian models, Decision Trees, Random Forest) or Unsupervised Learning (Clustering, PCA, NLP, Reinforcement Learning). It's a great book that helps pick the proper Machine Learning method when you want to turn your ideas into trading strategies.