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

Data 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* 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

Schaum’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. **

**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

A 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 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: 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 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).**