10 awesome books for Quantitative Trading

What is Quantitative Trading ?

Quantitative trading is the usage of mathematical models or algorithms to create trading strategies and trade them. Quant trading is usually employed by large institutional traders or hedge funds who employ large teams of PhDs and engineers. Historically the quantitative trading field has been very secretive and ideas which work tend to be guarded very closely by the firms but in the last few years the growth of openly available datasets and access to compute i.e ( in the form of GPUs and cloud ) has made quant trading accessible to a larger audience.

Overview of Quantitative Trading

Any quantitative trading system consists of the following steps

• Identifying / Creating a trading strategy
• Backtesting the strategy
• Designing a execution setup / system for your strategy
• Managing risk

Each of the above steps involve lot of research and trial and error to get right.

Quant trading is a complex field and requires careful and detailed study to be successful. The following are 10 such books which can help one get started on their Quant journey.

1 . Quantitative Trading: How to Build Your Own Algorithmic Trading Business by Ernest Chan.

Ernest P. Chan is an award winning quantitative hedge fund manager. He was an researcher in machine learning at IBM. He now runs his own firm and is a well known author who has written multiple books for beginners in quantitative trading. You can find his blogs here.

The above book he covers the basics of quantitative trading for a complete beginner. The objective of the book is to introduce all the main areas of focus involved in quant trading. It talks about only basic and beginner strategies.

2. Machine Trading: Deploying Computer Algorithms to Conquer the Markets by Ernest Chan.

This book too is by Ernest P. Chan. It is a slightly older book from 2017 but the content is still relevant. The focus is not on the end to end quantitative trading process but on intro to strategies in domains such as

• Using Factor Models
• AI for creating strategies
• Options Strategies
• Time series analysis
• Intraday trading

3. Finding Alphas: A Quantitative Approach to Building Trading Strategies by Igor Tulchinsky.

This book is a classic and is a must read for any one seriously exploring to enter the quantitative trading space. Igor Tulchinsky is the founder and CEO of WorldQuant. WorldQuant is one of the most successful quantitative hedge funds. The aim of the book is not to discuss strategies but rather discuss the process of finding strategies. This book is about ‘alpha’ ( this is the jargon for trading strategies ) research and what are the steps and process you should follow to come up with new ideas. Each of the chapters in the book is written by a quant from WorldQuant.

4. Advances in Financial Machine Learning by Marcos Lopez de Prado

This book is by Marcos Lopez de Prado who is also known as “Master of the Robots”. He is currently Professor of Practice at Cornell University’s College of Engineering and before that he was Head of ML at AQR capital ( which is large hedge fund with a focus on using factor models for investing ). This book specifically talks about the challenges and opportunities of applying ML/AI to create trading strategies. This book was one of the first books to talk about all the nuances and challenges in applying ML/AI in quant space such as model overfitting, featurizing and selecting important features, backtesting and evaluating models.

5. Machine Learning for Asset Managers by Marcos Lopez de Prado

This is another book by Marcos Lopez de Prado. The book talks about AI/ML methods in quant with more focus on portfolio construction, feature selection and identifying overfit models.

6. Advances in Active Portfolio Management by Richard Grinold, Ronald Kahn

This book was recommended to me by a friend who works in the quant industry. The focus of this book is not creating strategies but portfolio management. Portfolio management is the process of combining various signals and strategies into a single portfolio with the aim of reducing risk. The book talks about approaches to construct portfolios, optimize cost of trading and minimize risk.

7. 151 Trading Strategies by Zura Kakushadze, Juan Andres Serur

Zura Kakushadze is a researcher in quant finance who has earlier also collaborated with WorldQuant. This is the latest book published by the author. It is a compilation of various strategies across asset classes and it is a source for advanced strategies. It is not a AI/ML focussed book but many of these strategies can be remodelled to use AI/ML. Some of the types of strategies discussed are

• Earnings Strategies
• Options Strategies
• Volatility Strategies
• Momentum / Mean Reversion Strategies
• Index and Forex Strategies.

8. Machine Learning for Asset Management by Emmanuel Jurczenko

This book is focussed on applying ML in the trading space. The initial content is about an intro to ML focussing on the quant trading related concepts. The book talks about

• Creating long/short strategies
• Using News / Sentiment for trades
• Predicting returns using Machine Learning
• Portfolio Optimization using Machine Learning.

9. Python for Algorithmic Trading: From Idea to Cloud Deployment by Yves Hilpisch

This book focusses more on the engineering aspect of things. It mentions briefly about trading strategies. It talks in depth about how to implement all the required processes in python. The topics discussed are

• Handling financial datasets and reading them in python
• How to setup cloud instances and docker for deployment
• How to work with apis and databases
• How to work with conda and virtual environments

10. Machine Learning for Algorithmic Trading by Stefan Jansen

This book takes a hands on approach. It is very extensive in what it covers. Some of the important topics covered are

• Long / Short strategies using Random Forest / NN
• Using News and NLP to enhance strategies
• Using advanced models like CNN/RNNs
• Explore how RL can be used for trading strategies.

Apart from the above the book also talks about using technical indicators, linear models and libraries like Zipline, Alphalens, pyfolio for analysing the outcome of the strategies.

Apart from the above books another important book which is a prerequisite to understand many strategies is

Options, Futures, and Other Derivatives by John Hull & S. Basu

John Hull’s book on Options and derivatives is classic introduction to understanding derivative instruments and no quant’s library is incomplete without this book.