Quantitative Finance

The Man Who Solved the Market

Gregory Zuckerman

The story of Jim Simons and Renaissance Technologies, the most successful quantitative hedge fund in history. Provides insight into how mathematical and scientific approaches revolutionized investing.

A Man for All Markets

Edward O. Thorp - Foreword by Nassim Nicholas Taleb

The autobiography of the godfather of quantitative investing. Thorp invented card counting, co-invented the first wearable computer with Claude Shannon, developed options pricing formulas before Black-Scholes, and ran Princeton Newport Partners with 29 consecutive profitable years. His journey from Vegas casinos to Wall Street laid the foundation for the entire quant industry.

Beat the Dealer

Edward O. Thorp - 1962, Revised 1966

The book that started it all. Thorp mathematically proved the house advantage in blackjack could be overcome, selling over a million copies and forcing casinos to change their rules. The same analytical approach—finding edge, sizing bets optimally, managing risk—would later transform Wall Street.

Fortune's Formula

William Poundstone - 20th Anniversary Edition 2025

The untold story of the Kelly criterion—the scientific betting system that beat the casinos and Wall Street. Connects Claude Shannon (information theory), John Kelly (optimal betting), and Edward Thorp (practical application). Essential for understanding position sizing and bankroll management in trading.

Flash Boys

Michael Lewis

An investigation into high-frequency trading and market structure. Explores how technology has transformed markets and raises questions about fairness and efficiency.

When Genius Failed

Roger Lowenstein

The story of Long-Term Capital Management's spectacular collapse. A cautionary tale about leverage, model risk, and the limits of quantitative approaches.

More Money Than God

Sebastian Mallaby

A comprehensive history of hedge funds from their origins to the 2008 financial crisis. Covers both quantitative and discretionary strategies across different eras.

An Introduction to Statistical Learning

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

The standard introduction to machine learning for statistics. Covers regression, classification, resampling, and more with applications in R and Python. Free PDF available.

All of Statistics

Larry Wasserman

A concise course in statistical inference. Covers probability, estimation, hypothesis testing, and Bayesian inference at a graduate level.

Probability: Theory and Examples

Rick Durrett

A rigorous introduction to probability theory for those who want a deep mathematical foundation.

Introduction to Probability Models

Sheldon Ross

A classic textbook covering probability and stochastic processes, essential for understanding financial modeling.

Introductory Econometrics: A Modern Approach

Jeffrey Wooldridge

The standard undergraduate/graduate econometrics textbook. Covers regression analysis, time series, and panel data with clear exposition.

Introductory Econometrics for Finance

Chris Brooks

Econometrics tailored specifically for finance applications. Covers asset pricing tests, volatility models, and cointegration.

Using Python for Introductory Econometrics

Florian Heiss

A practical companion that teaches econometrics through Python implementation. Excellent for learning both concepts and code simultaneously.

Options, Futures, and Other Derivatives

John C. Hull - 11th Edition

The definitive textbook on derivatives, used in MBA programs and by practitioners worldwide. Covers options pricing (Black-Scholes-Merton), the Greeks, volatility smiles, exotic options, interest rate derivatives, and credit derivatives. Known simply as "Hull" in the industry.

Option Volatility and Pricing

Sheldon Natenberg - 2nd Edition 2014

The practitioner's bible for options trading. At firms around the world, this is often the first book given to new professional traders. Covers theoretical pricing models, volatility, trading strategies, and risk management from a market maker's perspective.

Dynamic Hedging

Nassim Nicholas Taleb - 1997

The definitive source on derivatives risk from a practitioner's viewpoint. Written by a veteran options trader, it covers managing vanilla and exotic options, the limits of mathematical models, and real-world risk management. Not for beginners—requires solid statistics background.

The Volatility Surface

Jim Gatheral - Foreword by Nassim Taleb

A practitioner's guide to implied volatility and its term structure. Covers the Heston model, SABR, local volatility, and variance swaps. Based on Gatheral's popular course at NYU's Courant Institute. Essential for anyone trading volatility products.

Paul Wilmott on Quantitative Finance

Paul Wilmott - 3 Volume Set, 2nd Edition

The comprehensive reference on derivatives and financial engineering. Covers everything from basic options to exotic derivatives, stochastic calculus, Monte Carlo methods, and fixed income. Written with Wilmott's trademark irreverent style—the Financial Times called him a "cult derivatives lecturer."

Paul Wilmott Introduces Quantitative Finance

A more accessible single-volume introduction adapted from the comprehensive set above. Ideal for university students or self-study. Includes software to visualize key concepts and end-of-chapter exercises.

Python for Finance

Yves Hilpisch

Comprehensive guide to using Python for quantitative finance, covering data analysis, derivatives pricing, and algorithmic trading.

Automate the Boring Stuff with Python

Al Sweigart

A beginner-friendly introduction to Python programming. Free to read online under Creative Commons license.

QuantStart

Free tutorials and articles on quantitative trading, algorithmic strategy development, and backtesting. A great starting point for aspiring quants.