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 & PythonData Science from Scratch: First Principles with 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
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.Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
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.Introduction to Machine Learning with Python: A Guide for Data Scientists
This book is set-up so that a reader can get an understanding of Machine Learning (ML) step-by-step from the bottom-up.Learn Algorithmic Trading: Build and deploy algorithmic trading systems and strategies using Python and advanced data analysis
Covers a broad amount of information needed to get started with algorithmic trading.Python for Finance: Mastering Data-Driven Finance
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.Mastering Python for Finance: Implement advanced state-of-the-art financial statistical applications using Python, 2nd Edition
Explore advanced financial models used by the industry and ways of solving them using Python.Machine Learning in Finance: From Theory to Practice
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.Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition
Clear and intuitive explanations take you deep into the theory and practice of Python machine learning. Bestselling, widely acclaimed Python machine learning book.The Hundred-Page 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.
StatisticsSchaum’s Outline of Probability and Statistics
This book gives theory and solved problems for a combined course in probability and mathematical statistics. Can’t hurt to know it.Statistical Rethinking: A Bayesian Course with Examples in R and STAN (Chapman & Hall/CRC Texts in Statistical Science)
A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data.Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)
Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methodsBayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks
This book will give you a complete understanding of Bayesian statistics through simple explanations and un-boring examples.Statistics
Very clearly written by an authoritative author with 40 years of experience.Bayesian Methods in Finance
Bayesian Methods in Finance provides an overview of the theory of Bayesian methods and explains their real-world applications to financial modeling.
R programming languageA Handbook of Statistical Analyses using R, Third Edition
Gives you an up-to-date guide to data analysis using the R system for statistical computing.Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition
Machine Learning with R provides a hands-on, readable guide to applying machine learning to real-world problems.Automated Trading with R: Quantitative Research and Platform Development
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.R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
Learn how to use R to turn raw data into insight, knowledge, and understanding.Quantitative Trading with R: Understanding Mathematical and Computational Tools from a Quant’s Perspective
Good text for aspiring quantitative traders. Financial math and computing concepts are introduced and developed simultaneously.Machine Learning for Factor Investing: R Version (Chapman and Hall/CRC Financial Mathematics Series)
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 TradingAlgorithmic Trading with Python: Quantitative Methods and Strategy Development
Algorithmic Trading with Python discusses modern quant trading methods in Python with a heavy focus on pandas, numpy, and scikit-learn.Trading Evolved: Anyone can Build Killer Trading Strategies in Python
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.$SPY High Probability Trading Strategies
Short term traders will be able to use the book to take advantage of historical probabilities, to trade $SPY (SPDR S&P 500 ETF).High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems
Includes numerous quantitative trading strategies and tools for building a high-frequency trading system.Algorithmic Trading Methods: Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques
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.Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments: Developing Predictive-Model-Based Trading Systems Using TSSB
This book gives implementation level detail of how to create and test predictive models using TSSB (free software).