Machine Learning Trading Book

MACHINE LEARNING IN TRADING

Step by step implementation of Machine Learning models

Why was this book written?

Machine learning is a vast topic if you look at the various disciplines originating from it. You will also hear buzzwords such as AI, Neural Networks, Deep learning, AI Engineering being associated with machine learning.

Our aim in this book is to demystify these concepts and provide clarity on how machine learning is different from conventional programming. And further, how machine learning can be used to gain an edge in the trading domain. We have structured the book in such a way that initially, you will learn about the various tasks carried out by a machine learning algorithm.

When it is appropriate, you will be introduced to the code which is required to run these tasks. If you are well versed with Python programming, you will be able to breeze through these sections and understand the concepts easily.

What's in this book?

The material presented here is an elementary introduction to the world of machine learning. You can think of it as a book telling you about the foundations of machine learning and how it is applied in real life. From the outset, we believe that only theory is not enough to retain knowledge.

You need to know how you can apply this knowledge in the real world. Thus, our book contains lots of real-world examples, especially in the field of trading. But rest assured that these concepts can be transferred to any other discipline which requires data analysis.

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Frequently Asked Questions

Who should read this?

We think it should also be useful to:


  • Anyone who has heard about machine learning algorithms and is excited to know more.
  • Programmers who would like to expand their horizons and see how machine learning can help them optimize their work.
  • Algo traders who are continuously looking for an edge over the competition.

We do not want the readers of this book to be computer programmers, as we have tried to keep the text simple with lots of real-world examples to illustrate various concepts.

What if I have no experience in programming or Python?

Don’t worry. Python itself is a relatively easier language to understand and program in. For a newcomer in the world of Python, we have created the Python for Trading Basics course on Quantra.


You can go through it as well as the Python handbook for any assistance as you go through the book. Moreover, we have tried to break down the code into bite-sized segments and explain what the code does in plain English. This not only helps you understand faster but will also serve as support later when you start to code and build your own ML algorithm.

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AUTHORS

Ishan Shah
Ishan Shah

Ishan Shah is the AVP and leads the content & research team at Quantra by QuantInsti. Prior to that, he worked with Barclays in the Global Markets team & with Bank of America Merrill Lynch. He has rich experience in financial markets spanning across various asset classes in different roles. He is an expert in data modelling, statistics, machine learning and natural language processing. Strategies on statistical arbitrage is another area of Ishan’s expertise along with using fundamental factors and technical analysis.

Rekhit Pachanekar
Rekhit Pachanekar

Rekhit Pachanekar is a Quant Analyst at QuantInsti. He completed his PGDM from IIM Indore and is a Computer Engineer. He researches equities and fixed-income securities as a part of the content team at Quantra. Away from work, he likes to read up on the outliers in the market and follows Tesla Inc. with keen interest.

Book Reviews

Andreas F. Clenow Andreas F. Clenow
CIO, Acies Asset Management

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.

Dr. Ernest P. Chan Dr. Ernest P. Chan
Founder of PredictNow.ai

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.

Laurent Bernut Laurent Bernut
CEO, Alpha Secure Capital

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.

Radovan Vojtko Radovan Vojtko
CEO, Quantpedia

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.

Andreas F. Clenow Andreas F. Clenow
CIO, Acies Asset Management

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.

Dr. Ernest P. Chan Dr. Ernest P. Chan
Founder of PredictNow.ai

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.

Laurent Bernut Laurent Bernut
CEO, Alpha Secure Capital

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.

Radovan Vojtko Radovan Vojtko
CEO, Quantpedia

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.

About us

QuantInsti®

QuantInsti is one of the pioneer algorithmic trading research and training institutes across the globe. With its educational initiatives, QuantInsti is preparing financial market professionals for the contemporary field of Algorithmic and Quantitative Trading. QuantInsti has also designed education modules and conducted knowledge sessions for/with various exchanges in South and South-East Asia and for leading educational and financial institutions.

Quantra®

Quantra is an e-learning portal by QuantInsti that hosts short and modular self-paced courses on Algorithmic and Quantitative Trading in a highly interactive fashion through machine enabled learning.

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