Full recommended reading list

If you want to take luck out of the equation start by reading as many books as possible listed below. A learning curve is essential to (capital) growth. A majority of the books in this list are ones that we have personally read, and thought they provided valuable information.

**This list will be updated regularly, so check back frequently**

Machine Learning & Python

Teaching you how to use high-level libraries like Pandas and Scikit-Learn, the author’s approach is to teach you fundamental data science concepts by having you write simple functions yourself so you can see what is happening “under the hood.”

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet.

By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. 

This book is set-up so that a reader can get an understanding of Machine Learning (ML) step-by-step from the bottom-up.

Covers a broad amount of information needed to get started with algorithmic trading. 

Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full fledged framework for Monte Carlo simulation based derivatives and risk analytics, based on a large, realistic case study.

Explore advanced financial models used by the industry and ways of solving them using Python.

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance.

Clear and intuitive explanations take you deep into the theory and practice of Python machine learning. Bestselling, widely acclaimed Python machine learning book.

This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori.

Statistics

This book gives theory and solved problems for a combined course in probability and mathematical statistics. Can’t hurt to know it.

A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. 

Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods

This book will give you a complete understanding of Bayesian statistics through simple explanations and un-boring examples.

Very clearly written by an authoritative author with 40 years of experience.

Bayesian Methods in Finance provides an overview of the theory of Bayesian methods and explains their real-world applications to financial modeling.

R programming language

Gives you an up-to-date guide to data analysis using the R system for statistical computing.

Machine Learning with R provides a hands-on, readable guide to applying machine learning to real-world problems.

Automated Trading with R explains the broad topic of automated trading, starting with its mathematics and moving to its computation and execution. You can skip chapter 6.

Learn how to use R to turn raw data into insight, knowledge, and understanding.

Good text for aspiring quantitative traders. Financial math and computing concepts are introduced and developed simultaneously. 

The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing.

Algorithmic Trading

Algorithmic Trading with Python discusses modern quant trading methods in Python with a heavy focus on pandas, numpy, and scikit-learn. 

Trading Evolved will guide you all the way, from getting started with the industry standard Python language, to setting up a professional backtesting environment of your own.

Short term traders will be able to use the book to take advantage of historical probabilities, to trade $SPY (SPDR S&P 500 ETF).

Includes numerous quantitative trading strategies and tools for building a high-frequency trading system.

The book focuses on trading strategies and methods, including new insights on the evolution of financial markets, pre-trade models and post-trade analysis, liquidation cost and risk analysis. 

This book gives implementation level detail of how to create and test predictive models using TSSB (free software).