📚 My Quant Finance Library
Food for the brain. Tools for the trade. A collection curated over years in the trenches of markets, models, and machine learning.
"An investment in knowledge pays the best interest."
— Benjamin Franklin
It’s a collection of the books that shaped my thinking—whether I was building trading systems, decoding market structure, or evaluating risk under pressure.
Every title here taught me something special. Something that held up in the chaos of live trading, research deadlines, or strategy pivots. For each book, I’ve added a short note on what it gave me—and why I think it might matter to you, too.
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📘 General Finance Textbooks
Options, Futures and Other Derivatives – John Hull
Still the go-to for a structured introduction to derivatives.
👉 I read this early in my career—it gave me the language and formulas I needed to communicate with traders and risk teams.
The Concepts and Practice of Mathematical Finance – Mark Joshi
A technical yet readable guide to derivative pricing using probability theory.
👉 Joshi writes like a practitioner. You won’t just memorize concepts—you’ll know when they matter.
Paul Wilmott on Quantitative Finance – Paul Wilmott
Big, dense, but surprisingly readable. A wide-ranging text from a legend in quant finance.
👉 When I wanted a sweeping view of the field—math, intuition, and implementation—this book delivered.
💡 Asset Pricing
Asset Pricing – John H. Cochrane
The most rigorous macro-finance asset pricing book out there.
👉 Cochrane changed how I think about consumption-based models. This isn’t bedtime reading, but it’s foundational.
Financial Decisions and Markets – John Y. Campbell
Bridges theoretical asset pricing and real-world investment.
👉 Campbell doesn’t just explain ideas—he helps you think like a researcher.
Asset Pricing and Portfolio Choice Theory – Kerry Back
Combines math and economic reasoning in a clear, consistent way.
👉 If you're building or teaching models, Back’s structure is unbeatable.
📊 Asset Allocation & Risk Premia
Asset Management – Andrew Ang
Factor investing, done academically and practically.
👉 This book helped me move from theoretical models to real implementation.
Introduction to Risk Parity and Budgeting – Thierry Roncalli
Great for learning allocation frameworks beyond traditional mean-variance.
👉 Roncalli was instrumental when I explored alternative risk budgeting techniques.
⚙️ Option Pricing & Stochastic Calculus
Stochastic Calculus for Finance I & II – Steven Shreve
The standard two-volume series every quant should work through.
👉 These books gave me the formal backbone behind Black-Scholes and stochastic models.
Financial Modelling with Jump Processes – Rama Cont & Peter Tankov
Covers jump-diffusion and Lévy models in depth.
👉 When volatility isn’t “nice”—this book shows what to do.
Option Volatility and Pricing – Sheldon Natenberg
A practical classic for traders and quants alike.
👉 Helped me bridge the gap between academic models and live markets.
The Volatility Surface – Jim Gatheral
In-depth, technical, and grounded in market reality.
👉 Gatheral was my entry point into local and stochastic volatility models.
🧮 Mathematical Foundations
Stochastic Differential Equations – Bernt Øksendal
Dense but elegant introduction to SDEs and Itô calculus.
👉 Every time I got stuck in a derivation, Øksendal had the clarity I needed.
Probability – A.N. Shiryaev
Russian-style rigor and depth in probability theory.
👉 My go-to for probability refreshers and conceptual clarity.
🧠 Machine Learning & Data Science
Advances in Financial Machine Learning – Marcos Lopez de Prado
Explores feature engineering, backtesting, and strategy evaluation.
👉 I applied these methods directly when building ML pipelines for trading.
The Elements of Statistical Learning – Hastie, Tibshirani & Friedman
A classic—statistical learning from the ground up.
👉 Still my favorite for understanding model behavior under the hood.
Machine Learning: A Probabilistic Perspective – Kevin Murphy
Thorough, Bayesian, and richly illustrated.
👉 Great when I need to bridge stats and deep learning thinking.
👩💻 Programming & Implementation
Modern Computational Finance – Antoine Savine
Real-time risk, AAD, and implementation with Python.
👉 Savine’s AAD chapter changed how I think about performance bottlenecks.
Applied Computational Economics and Finance – Miranda & Fackler
Strong on numerical methods and dynamic programming.
👉 Useful when moving from theory to simulations and pricing.
🧾 Interview Prep & Career Insight
Quant Job Interview Questions and Answers – Mark Joshi
All the essentials in one place—concise and complete.
👉 I used this book while prepping for quant interviews early on. Still relevant.
🧠 Timeless Market Wisdom
Reminiscences of a Stock Operator – Jesse Livermore
Part fiction, part biography, all insight.
👉 This isn’t a strategy book—it’s psychology, raw and timeless.
Against the Gods – Peter Bernstein
A history of risk and probability through the ages.
👉 A great reminder that finance is just the latest chapter in the human story of uncertainty.
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Enjoy!