Quantitative Finance Reading List

Quantitative finance is a technical and wide-reaching subject. It covers financial markets, time series analysis, risk management, financial engineering, statistics and machine learning.

The following books begin with the absolute basics for each subject area and gradually increase the level of difficulty. You needn't read all of them, but you should certainly study a few in depth.

The lists cover general quant finance, careers guides, interview prep, quant trading, mathematics, statistical analysis and programming in C++, Python and R.

- General Quant Finance
- Interview Preparation
- Quantitative Trading
- Time Series Analysis
- Financial Engineering
- Interest Rate Derivatives
- C++ Programming
- Python
- R

**This section contains classic books on quant/financial markets.**

One area that routinely catches out prospective quants at interview is their lack of basic financial markets knowledge.

It's all well and good being the best mathematician and programmer on the globe, but if you can't tell your stock from your bond, or your bank from your fund, you'll find it a lot harder to pass those HR screenings.

These books also make much better bedtime reading than graduate texts on stochastic calculus...

- Flash Boys: A Wall Street Revolt - Michael Lewis
- The Big Short: Inside the Doomsday Machine - Michael Lewis
- Liar's Poker - Michael Lewis
- When Genius Failed: The Rise and Fall of Long-Term Capital Management - Roger Lowenstein
- More Money Than God: Hedge Funds and the Making of a New Elite - Sebastian Mallaby
- How I Became a Quant: Insights from 25 of Wall Street's Elite - Richard Lindsey, Barry Schachter
- My Life as a Quant: Reflections on Physics and Finance - Emanuel Derman
- Financial Engineering: The Evolution of a Profession - Tanya Beder, Cara Marshall
- The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It - Scott Patterson
- Nerds on Wall Street: Math, Machines and Wired Markets - David Leinweber
- Physicists on Wall Street and Other Essays on Science and Society - Jeremey Bernstein
- The Complete Guide to Capital Markets for Quantitative Professionals - Alex Kuznetsov
- Models.Behaving.Badly.: Why Confusing Illusion with Reality Can Lead to Disaster, on Wall Street and in Life - Emanuel Derman

**Books to help you prepare for quant job interviews.**

On top of needing to be aware of capital markets and how they function, the mathematics of derivatives pricing and quantitative trading methods, being able to program in C++ and possibly Python, you also need to ace that quant interview!

The following books are fantastic resources for getting you prepared. Make sure you study not only the content of the brainteasers, but also try deconstructing how they're put together and what you're really being asked.

- Heard on The Street: Quantitative Questions from Wall Street Job Interviews - Timothy Crack
- Frequently Asked Questions in Quantitative Finance - Paul Wilmott
- Quant Job Interview Questions And Answers - Mark Joshi, Nick Denson, Andrew Downes
- A Practical Guide To Quantitative Finance Interviews - Xinfeng Zhou
- Starting Your Career as a Wall Street Quant: A Practical, No-BS Guide to Getting a Job in Quantitative Finance - Brett Jiu
- Cracking the Coding Interview: 150 Programming Questions and Solutions - Gayle McDowell

**Key books to help you learn quantitative, systematic and algorithmic trading.**

The career paths for quants have shifted recently towards direct quantitative trading and away from derivatives pricing.

Although Black-Scholes theory is still immensely important for hedging and exotic option pricing purposes, it is now necessary to be intimately familiar with systematic trading and the firms that employ it.

It is difficult to get hold of information from funds about their trading strategies (no surprise there!), but these books provide an in-depth overview into how the "black box" operates.

**Successful Algorithmic Trading****- Michael Halls-Moore (our first trading book)****Advanced Algorithmic Trading****- Michael Halls-Moore (our second trading book)**- Quantitative Trading: How to Build Your Own Algorithmic Trading Business - Ernie Chan
- Algorithmic Trading: Winning Strategies and Their Rationale - Ernie Chan
- Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading - Rishi Narang
- The Truth About High-Frequency Trading: What Is It, How Does It Work, and Is It a Problem? - Rishi Narang, Manoj Narang
- Algorithmic and High-Frequency Trading - Álvaro Cartea, Sebastian Jaimungal, José Penalva
- The Science of Algorithmic Trading and Portfolio Management - Robert Kissell
- Algorithmic Trading and DMA: An introduction to direct access trading strategies - Barry Johnson
- Volatility Trading - Euan Sinclair
- Trading and Exchanges: Market Microstructure for Practitioners - Larry Harris

**Key texts to help you learn prediction and forecasting of multivariate time series.**

Time series analysis and financial econometrics are key components of modern algorithmic trading - allowing prediction and forecasting of asset prices.

Time series analysis techniques are widely used in quantitative finance, including asset management and quant hedge funds, for forecasting purposes. Thus if you wish someday to become a skilled quantitative trader it is necessary to have an extensive knowledge of statistical time series analysis and financial econometrics.

The following books will take you from introductory time series and econometrics through to advanced multivariate time series theory at a reasonably comprehensive mathematical level:

- Schaum's Outline of Statistics and Econometrics - Dominick Salvatore, Derrick Reagle
- Introductory Econometrics for Finance - Chris Brooks
- Introduction to Time Series and Forecasting -Peter Brockwell, Richard Davis
- Time Series: Theory and Methods - Peter Brockwell, Richard Davis
- Analysis of Financial Time Series - Ruey Tsay
- Multivariate Time Series Analysis: With R and Financial Applications - Ruey Tsay
- Time Series Analysis - James Douglas Hamilton

**Derivatives pricing via applied stochastic calculus models.**

Derivatives pricing is still a key part of the financial industry, particularly for fixed income and credit asset classes, and relies on theory developed from stochastic calculus.

Although you don't need to read every book below, they are all good. Each provides a different perspective or emphasis on options pricing theory.

If you have your heart set on becoming a derivatives pricing quant, perhaps working in equities, credit, fixed income or foreign exchange, then you should aim to study as many books from the following list as possible:

- Options, Futures, and Other Derivatives - John Hull
- A Primer For The Mathematics Of Financial Engineering - Dan Stefanica
- Solutions Manual - A Primer For The Mathematics Of Financial Engineering - Dan Stefanica
- Paul Wilmott Introduces Quantitative Finance - Paul Wilmott
- Paul Wilmott on Quantitative Finance - Paul Wilmott
- The Concepts and Practice of Mathematical Finance - Mark Joshi
- More Mathematical Finance - Mark Joshi
- Financial Calculus: An Introduction to Derivative Pricing - Martin Baxter, Andrew Rennie
- An Introduction to the Mathematics of Financial Derivatives - Ali Hirsa, Salih Neftci
- Principles of Financial Engineering - Robert Kosowski, Salih Neftci
- Mathematics for Finance: An Introduction to Financial Engineering - Marek Capiski, Tomasz Zastawniak
- Arbitrage Theory in Continuous Time - Tomas Bjork
- The Complete Guide to Option Pricing Formulas - Espen Haug

**Fixed income derivative modelling via advanced mathematical techniques.**

The fixed income derivatives market is the largest global derivatives market, driven largely by investor demand for specific views on interest rates or cashflow requirements.

Modelling of interest rate derivatives requires complex mathematics and necessitates a solid understanding of stochastic calculus techniques. The following texts introduce the main models:

- Interest Rate Models - Theory and Practice: With Smile, Inflation and Credit - Damiano Brigo, Fabio Mercurio
- Interest Rate Modeling - Vol I: Foundations and Vanilla Models - Leif Andersen, Vladimir Piterbarg
- Interest Rate Modeling - Vol II: Term Structure Models - Leif Andersen, Vladimir Piterbarg
- Interest Rate Modeling - Vol III: Products and Risk Management - Leif Andersen, Vladimir Piterbarg
- The SABR/LIBOR Market Model: Pricing, Calibration and Hedging for Complex Interest-Rate Derivatives - Riccardo Rebonato, Kenneth McKay, Richard White
- Discounting, Libor, CVA and Funding: Interest Rate and Credit Pricing - Chris Kenyon, Roland Stamm
- Interest Rate Swaps and Their Derivatives: A Practitioner's Guide - Amir Sadr
- Term-Structure Models: A Graduate Course - Damir Filipovic

**Classic and modern texts on how to become an expert C++ programmer.**

C++ is one of the hardest areas for beginning quants to get to grips with. Since it is such a large programming language, and may in fact be a quants first taste of programming, it can be extremely daunting.

The first six books on the list, if understood properly, would make you a competent C++ programmer. By reading the remainder, you will (eventually) become an expert:

These books are designed for learning the basics and how to utilise the language effectively:

**C++ for Quantitative Finance - Michael Halls-Moore (our C++ book on derivatives pricing)**- Sams Teach Yourself C++ in One Hour a Day - Siddhartha Rao (7th edition, covering C++11)
- C++: A Beginner's Guide - Herbert Schildt
- Accelerated C++: Practical Programming by Example - Andrew Koenig, Barbara Moo
- Effective C++: 55 Specific Ways to Improve Your Programs and Designs - Scott Meyers
- C++ Design Patterns and Derivatives Pricing - Mark Joshi

These books will cover nearly everything a practising quant will likely ever need to learn about C++ itself:

- More Effective C++: 35 New Ways to Improve Your Programs and Designs - Scott Meyers
- Effective STL: 50 Specific Ways to Improve Your Use of the Standard Template Library - Scott Meyers
- Effective Modern C++: 42 Specific Ways to Improve Your Use of C++11 and C++14 - Scott Meyers
- Discovering Modern C++: An Intensive Course for Scientists, Engineers, and Programmers - Peter Gottschling

For those who wish to become the best in their peer group and/or work in high-frequency trading, you will need to know a lot more about the language, including template programming, the ins-and-outs of the STL and Linux programming:

- The C++ Standard Library: A Tutorial and Reference - Nicholai Josuttis
- The C++ Programming Language, 4th Edition - Bjarne Stroustrup
- C++ Concurrency in Action: Practical Multithreading - Anthony Williams
- Optimized C++ - Kurt Guntheroth
- C++ Templates: The Complete Guide - David Vandevoorde, Nicolai Josuttis
- The Linux Programming Interface: A Linux and UNIX System Programming Handbook - Michael Kerrisk
- Advanced Programming in the UNIX Environment, 3rd Edition - W. Richard Stevens, Stephen A. Rago
- Unix Network Programming, Volume 1: The Sockets Networking API (3rd Edition) - W. Richard Stevens, Bill Fenner, Andrew M. Rudoff
- Design Patterns: Elements of Reusable Object-Oriented Software - Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides

**Classic and modern texts on how to become an expert Python programmer.**

In recent years Python has become a staple in the quantitative finance world. I personally know of many funds that employ it as the end-to-end computational infrastructure for carrying out systematic trading.

It is an easy language to learn but it is harder to master, due to the many libraries a quant will use. Regardless of which type of quant you wish to become, I would suggest learning Python, as it is only going to become more widely adopted as time goes on:

These books are designed for learning the basics and how to utilise Python - and its many scientific libraries - effectively:

- Learning Python, 5th Edition - Mark Lutz
- Think Python, 2nd Edition - Allen Downey
- Learn Python the Hard Way, 3rd Edition - Zed Shaw

These books will cover nearly everything a practising quant will likely ever need to learn about programming in Python and using its libraries - particularly with regard to data science, machine learning and quant finance:

- Programming Python, 4th Edition - Mark Lutz
- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython - Wes McKinney
- Data Science from Scratch: First Principles with Python - Joel Grus
- Data Wrangling with Python: Tips and Tools to Make Your Life Easier - Jacqueline Kazil, Katharine Jarmul
- Python for Finance: Analyze Big Financial Data - by Yves Hilpisch
- Effective Python: 59 Specific Ways to Write Better Python - Brett Slatkin
- High Performance Python: Practical Performant Programming for Humans - Micha Gorelick, Ian Ozsvald
- Python 3 Object-Oriented Programming, 2nd Edition - Dusty Phillips
- Python Machine Learning - Sebastian Raschka

**Textbooks on learning the R statistical programming environment.**

R is an advanced statistical programming environment used widely within systematic quant funts and investment banks.

A great way to learn R is to pair the following books with an online course in statistics (which will often make use of R anyway). This will really help you get to grips with the methods of quantitative trading.

In addition numerous books have been written on various statistical topics, often using R as the implementation language:

These books are designed for learning the basics of statistics with R, as related to quantitative finance:

- Introductory Statistics with R, 2nd Edition - Peter Dalgaard
- A Beginner's Guide to R - Alain Zuur, Elena Ieno, Erik Meesters
- R in a Nutshell - Joseph Adler

The following books build on the statistical theory learnt in the aforementioned texts across the fields of time series analysis and machine learning:

- Introductory Time Series with R - Paul Cowpertwait, Andrew Metcalfe
- An Introduction to Applied Multivariate Analysis with R - Brian Everitt, Torsten Hothorn
- R Cookbook - Paul Teetor
- Machine Learning with R, 2nd Edition - Brett Lantz