Machine Learning Trading Book


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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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.



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.

About us


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 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. Qu@antinsti US 1