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.
The following topics are discussed on QuantStart:
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.
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:
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:
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.
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.
The areas of quantitative finance and data science both make heavy use of statistical inference and machine learning.
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.
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 forms the "backbone" of many quantitative finance models that are based on pricing information. The famous ARIMA and GARCH models are the basis for more sophisticated models that are often used in leading quant funds.
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.
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.
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.
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!
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.
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.
The following articles relate to Quantstart itself and include updates about progress on certain projects: