Quant Finance Articles

Articles across all topics related to quant finance

If you are a complete beginner to the world of quantitative finance, I suggest you take a look at the Start Here page first then return here for more specific articles.

The following topics are discussed on QuantStart:

Algorithmic Trading

Algorithmic trading is a rapidly growing area, both in the quant fund industry and in the retail trader space. To become a successful algorithmic trader requires a solid background in many topics.

Getting Started with Algorithmic Trading

Building an Algorithmic Trading Infrastructure


Risk and Performance Measurement

Automated Execution

Quantitative Trading Strategies

Talks and Interviews


QSTrader is a freely available open source backtesting and live trading engine, written by members of the QuantStart team and the QuantStart community. These articles track the development of QSTrader from announcement through individual module development:

Forex Trading Diary

Algorithmic forex trading has become a lot easier since many forex brokerages introduced REST-based APIs. Hence, I started a diary in 2015 to track my progress in building a high-frequency open source forex backtesting and live trading engine:

Careers Advice

This is the place to start if you are looking for guidance on how to accelerate your quant career. I've discussed changing careers, PhDs, MFEs and as well as the different types of quant roles.

Life as a Quant



Career Changers

Quant Reading Lists

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.

Statistical Modelling and Machine Learning

The areas of quantitative finance and data science both make heavy use of statistical inference and machine learning.

Bayesian Statistics

Bayesian statistics involves making use of prior information along with available data in order to draw statistical conclusions. It is used heavily in quantitative finance.

Deep Learning

Quantitative finance has now started to make use of deep neural network architectures, so called "Deep Learning" in order to produce trading signals.

Time Series Analysis

The Binomial Model

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.

Stochastic Calculus

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.

Numerical PDE

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.

C++ Implementation

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++ Language

Numerical Methods in C++

Derivatives Pricing with C++

GPU/CUDA Programming in C++

Python Implementation

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.


The following articles relate to Quantstart itself and include updates about progress on certain projects:

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