Quantitative finance applies mathematical and statistical models to the pricing of financial instruments in order to manage risk and generate investment returns.
Anybody who works in this field is termed a "quant" and is often employed in investment funds, banks or at home working for themselves.
A career in quant finance is highly intellectually stimulating, very well compensated and brings with it a large array of transferable skills. However, quant finance is not an easy road. Quants need to be adept at mathematical concepts and how to model financial behaviour numerically.
Hence, we have designed these set of resources to teach you everything you need to know, from the ground up, in order to become a highly skilled and effective quant.
There's a lot of mystery surrounding quantitative finance, particularly in relating to how hard it is to break into. We wish to dispel some of these myths but also highlight some difficult truths.
1) Becoming a quant is NOT easy and it takes a lot of hard work.
Nothing in life comes easily. Becoming a skilled quant is no different. Many conventional trading websites and career guides will try to persuade you to think otherwise. Why? Because they're trying to get you to buy their "one crazy trick" or sign up to their expensive university course - and hence make money from false hopes. We're here to tell you the truth: Quant finance is hard. We'll do our best to guide you towards the best resources and help you plan your studies, but we can't make you take action: that's up to you.
2) Becoming a quant does NOT happen overnight.
You can't learn calculus and linear algebra in a day. We don't believe in get-rich-quick schemes, nor do we teach them. Some quants spend four years at university learning mathematics (or a similarly technical degree) and a further three to six years in postgraduate study, all the while working extremely hard to learn quant finance ideas. There is nothing easy about learning these concepts and gaining a job in quant finance. But in relative terms, it is "quick" because you can have an extremely interesting career to look forward to, as well as significantly higher compensation than most other jobs.
3) You do NOT need a PhD in stochastic calculus to become a quant.
While having advanced qualifications is certainly helpful it is NOT a requirement, particularly for roles related to quantitative software development. Most quants make use of undergraduate mathematics and statistics, as well as being good coders. Hence there is no need to toil away for three to six years in grad school for most quant roles.
There's no getting around it - you almost always need to learn college-level Calculus, Linear Algebra and Statistics in order to work in quantitative finance.
The good news is that there are some great online resources for doing so if you have not, or are unable to, go down the traditional university route.
Why are these courses necessary tools for a quant to know?
1) Calculus: Calculus is all about how one quantity changes as another changes. In quant finance, this crops up everywhere. How does a stock price change with time? How does a crude oil future change as the underlying spot price of oil changes? How does the risk of a portfolio change with volatility? Calculus helps us understand these changes in a continuous fashion. Hence it is imperative that we know how to handle change mathematically.
2) Linear Algebra: Many of the quantities we are interested in in finance are vector-valued, that is, they aren't just a single number, but a collection of numbers. A portfolio is a collection of weightings. A regression technique contains a collection of factors. Vector quantities live in vector spaces. Changes to these vector quantities are carried out by matrix transformations. The study of such transformations is known as Linear Algebra. Thus it is clear how important it is in quant finance.
3) Statistics: Much of quantitative finance involves attempting to systematically predict the future from behaviours in the past. The natural domains for this are time series analysis and machine learning, both of which are heavily underpinned by traditional statistical methods, and ultimately probability. Without an understanding of statistics, it is impossible to quantify our predictions and thus manage our portfolios or adjust our trades.
The following guides help you understand the type of mathematics necessary to carry out quant finance:
While having a good grasp of numerical concepts is necessary for becoming a quant, it is not sufficient. All modern quants are proficient in coding.
Why is coding so valuable to quants? Because it allows you to turn a mathematical model into something that can be applied to real data, quickly.
In addition, if you are working for yourself, being able to code allows you to build your own custom trading infrastructure - suited to your trading style - without having to go through anybody else.
Also, it is far easier to set yourself apart via your programming ability than your mathematical ability, as it's much easier to test for in interviews. That means you can stand out from the crowd easier if you learn coding to a high standard.
You may find that you prefer the coding side of quant finance more than the mathematical modelling side, in which case you have the option of becoming a quantitative developer ("quant dev"), which is another intellectually rewarding, and lucrative, career path.
Specialising in coding has the added benefit that if you ever wish to venture onto other career paths, such as data science or tech startup entrepreneurship, you have some readily available skills to get you ahead of the game.
Over the last four years we have written more than 200 articles, released a major open-source trading engine and have authored three books - all to help you learn quant finance more straightforwardly.
The best way to get started is to choose the topic below most suited to your career path, interests or educational level. Click the following links to be taken to your preferred topic area:
Thankyou for taking the time to visit QuantStart.com. We're always interested in hearing your stories, so please get in touch at firstname.lastname@example.org if you have something to share or would like some help with your quantitative finance journey.