QUANTITATIVE FINANCE ARTICLES
Welcome to the big list of QuantStart quantitative finance articles!
I've tried to write articles that will benefit YOU on your journey to becoming a professional quantitative analyst.
If you are a complete beginner to the world of quantitative finance, I suggest you take a look at my newbies guide first, then come back here.
Singapore Financial District via Jo@net
When you're ready to learn more advanced material, you can check out the trading, mathematics and programming articles:
- Algorithmic Trading
- The Binomial Model
- Stochastic Calculus
- Numerical PDEs
- C++ Implementation
- Python Implementation
- Book Reviews
I'm constantly adding articles each week, so keep checking back regularly.
If you would like to see articles on any other topic, please feel free to email me at email@example.com.
This is the place to start if you are looking for guidance on how to accelerate your quant career. I've discussed PhDs, MFEs and as well as the different types of quant roles.
- Junior Quant Jobs Beginning a career in Financial Engineering after a PhD
- Understanding How to Become a Quantitative Analyst
- What are the Different Types of Quantitative Analysts?
- What Classes Should You Take To Become a Quantitative Analyst?
- Why Study for a Mathematical Finance PhD?
- My Experiences as a Quantitative Developer in a Hedge Fund
- Which Programming Language Should You Learn To Get A Quant Developer Job?
- Can You Still Become a Quant in Your Thirties?
- Self-Study Plan for Becoming a Quantitative Developer
- Self-Study Plan for Becoming a Quantitative Analyst
- Getting a Job in a Top Tier Quant Hedge Fund
- How to Get a Job at a High Frequency Trading Firm
- Self-Study Plan for Becoming a Quantitative Trader - Part I
- Why a Masters in Finance Won't Make You a Quant Trader
The following lists of books will get you up to speed on how to become a quant. I've broken them down into Maths Finance, C++, Python and Interview Guides. If you want to see in depth book reviews, check out the section below.
- Quant Reading List Derivative Pricing
- Quant Reading List C++ Programming
- Quant Reading List Numerical Methods
- Quant Reading List Python Programming
- 5 Important But Not So Common Books A Quant Should Read Before Applying for a Job
- 5 Top Books for Acing a Quantitative Analyst Interview
- Top 5 Finite Difference Methods books for Quant Analysts
- Top 5 Essential Beginner C++ Books for Financial Engineers
- Quantitative Finance Reading List
- Top 10 Essential Resources for Learning Financial Econometrics
- Free Quantitative Finance Resources
Algorithmic trading is an extremely interesting and growing area, particularly in the hedge fund industry. More funds spring up every year. However, to become a successful algorithmic trader requires a decent background in many topics.
- Beginner's Guide to Quantitative Trading
- How to Identify Algorithmic Trading Strategies
- Successful Backtesting of Algorithmic Trading Strategies - Part I
- Can Algorithmic Traders Still Succeed at the Retail Level?
- Successful Backtesting of Algorithmic Trading Strategies - Part II
- Securities Master Databases for Algorithmic Trading
- Securities Master Database with MySQL and Python
- Sharpe Ratio for Algorithmic Trading Performance Measurement
- Top 5 Essential Beginner Books for Algorithmic Trading
- Interactive Brokers Demo Account Signup Tutorial
- Best Programming Language for Algorithmic Trading Systems?
- Installing a Desktop Algorithmic Trading Research Environment using Ubuntu Linux and Python
- Basics of Statistical Mean Reversion Testing
- My Interview Over At OneStepRemoved.com
- Downloading Historical Futures Data From Quandl
- Self-Study Plan for Becoming a Quantitative Trader - Part II
- Forecasting Financial Time Series - Part I
- Research Backtesting Environments in Python with pandas
- Backtesting a Moving Average Crossover in Python with pandas
- Backtesting a Forecasting Strategy for the S&P500 in Python with pandas
- Continuous Futures Contracts for Backtesting Purposes
- Using Python, IBPy and the Interactive Brokers API to Automate Trades
- Backtesting An Intraday Mean Reversion Pairs Strategy Between SPY And IWM
- Choosing a Platform for Backtesting and Automated Execution
- Event-Driven Backtesting with Python - Part I
- Event-Driven Backtesting with Python - Part II
- Downloading Historical Intraday US Equities From DTN IQFeed with Python
- Event-Driven Backtesting with Python - Part III
- My Talk At The London Financial Python User Group
- Event-Driven Backtesting with Python - Part IV
- Event-Driven Backtesting with Python - Part V
- Event-Driven Backtesting with Python - Part VI
The binomial model is a great way to introduce options pricing. Although the method is rarely used computationally, it provides good intuition on how options pricing works.
- Introduction to Option Pricing with Binomial Trees
- Hedging the sale of a Call Option with a Two-State Tree
- Risk Neutral Pricing of a Call Option with a Two-State Tree
- Replication Pricing of a Call Option with a One-Step Binomial Tree
- Multinomial Trees and Incomplete Markets
- Pricing a Call Option with Two Time-Step Binomial Trees
- Pricing a Call Option with Multi-Step Binomial Trees
- Derivative Pricing with a Normal Model via a Multi-Step Binomial Tree
- Risk Neutral Pricing of a Call Option with Binomial Trees with Non-Zero Interest Rates
You can't do quantitative finance without stochastic calculus. The following articles discuss the relevant stochastic calculus you need to understand the famous Black-Scholes equation derivation.
- Introduction to Stochastic Calculus
- The Markov and Martingale Properties
- Brownian Motion and the Wiener Process
- Stochastic Differential Equations
- Geometric Brownian Motion
- Ito's Lemma
- Deriving the Black-Scholes Equation
The Black-Scholes equation is a partial differential equation (PDE). In order to solve it you can use numerical discretisation techniques such as Finite Difference Methods. The following articles walk you through the basic techniques.
- Derivative Approximation via Finite Difference Methods
- Solving the Diffusion Equation Explicitly
- Crank-Nicholson Implicit Scheme
- Tridiagonal Matrix Solver via Thomas Algorithm
As with stochastic calculus, you really cannot avoid learning C++ for pricing derivatives! Love it or hate it, it is essential. Although the following articles won't teach you how to program from scratch, I will point out intermediate to advanced features that you can impress interviewers with when you apply for that banking role!
- C++ Virtual Destructors: How to Avoid Memory Leaks
- Passing By Reference To Const in C++
- Mathematical Constants in C++
- STL Containers and Auto_ptrs - Why They Don't Mix
- European vanilla option pricing with C++ and analytic formulae
- European vanilla option pricing with C++ via Monte Carlo methods
- Digital option pricing with C++ via Monte Carlo methods
- Double digital option pricing with C++ via Monte Carlo methods
- Tridiagonal Matrix Algorithm ("Thomas Algorithm") in C++
- Matrix Classes in C++ - The Header File
- Matrix Classes in C++ - The Source File
- C++ Standard Template Library Part I - Containers
- Asian option pricing with C++ via Monte Carlo Methods
- Floating Strike Lookback Option Pricing with C++ via Analytic Formulae
- Statistical Distributions in C++
- Function Objects ("Functors") in C++ - Part 1
- Random Number Generation via Linear Congruential Generators in C++
- C++ Explicit Euler Finite Difference Method for Black Scholes
- C++ Standard Template Library Part II - Iterators
- Implied Volatility in C++ using Template Functions and Interval Bisection
- Generating Correlated Asset Paths in C++ via Monte Carlo
- C++ Standard Template Library Part III - Algorithms
- What's New in the C++11 Standard Template Library?
- Eigen Library for Matrix Algebra in C++
- Implied Volatility in C++ using Template Functions and Newton-Raphson
- Heston Stochastic Volatility Model with Euler Discretisation in C++
- Jump-Diffusion Models for European Options Pricing in C++
- Calculating the Greeks with Finite Difference and Monte Carlo Methods in C++
If you are more interested in becoming a quantitative trader in a hedge fund, then Python is something you definitely need to know. End-to-end trading systems are now being built entirely in Python, so I've written some articles to help you get started.
- Options Pricing in Python
- European Vanilla Call-Put Option Pricing with Python
- LU Decomposition in Python and NumPy
- Cholesky Decomposition in Python and NumPy
- QR Decomposition with Python and NumPy
- Jacobi Method in Python and NumPy
I've reviewed some of my favourite Quant Finance books below. If you are interested in any of the following titles then please take a look.
- Paul Wilmott Introduces Quantitative Finance - Paul Wilmott
- The Concepts and Practice of Mathematical Finance - Mark S. Joshi
- Financial Instrument Pricing Using C++ - Daniel J. Duffy
- Introduction to C++ for Financial Engineers: An Object-Oriented Approach - Daniel J. Duffy
- Effective C++: 55 Specific Ways to Improve Your Programs and Designs - Scott Meyers
- Heard on The Street: Quantitative Questions from Wall Street Job Interviews - Timothy Falcon Crack
- Frequently Asked Questions in Quantitative Finance - Paul Wilmott
- Quant Job Interview Questions And Answers - Mark S. 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
- Learning Python: Powerful Object-Oriented Programming - Mark Lutz
- C++ Design Patterns and Derivatives Pricing - Mark S. Joshi
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