**Since I got asked over and over again how to become a quant, I decided to publish a small essay, going through a letter of my blog reader. I myself managed to change to mathematical finance and risk management from pure software development. I like what I am doing but quant jobs are not as sexy as many young professionals believe.**

Dear Vasily,

I hope you are well. I have been meaning to write to you. I regularly visit your blog at and enjoy reading your posts on Quantnet.

As you know, I am doing my second year BS Mathematics(alongside my full time job). This year I would be taking courses in real analysis, classical probability(not measure theoretic), numerical methods and differential equations. I am excited to learn about PDEs, with quant finance in view, implement FDM in C++.

I like to be driven. I absolutely enjoyed proving results in Linear algebra in my 1st year - wrote my own cubic spline generator. I feel, I have gained more maturity to understand abstractions such as a measurable function, or a sigma algebra - the typical setup in quant finance.

This sounds promising because, as one can readily see, a person has already achieved a lot.

I mean, I often got asked by guys and gals who naively think that a quant-job will make them rich but are completely unaware about steep learning curve and complicated math in quantitative finance.

Generally, to get started as a quant one needs a graduate level (contrary to IT, where one can spare university education at all or glorified machine learning, where undergraduate math is sufficient to get started).

Real analysis, classical probability and linear algebra are absolute necessarily prerequisites for mathematical finance. However I would suggest after learning these prerequisites not to dwell into C++ and numerical solutions of PDEs (so far) but rather to understand the measure-theoretic probability and stochastic calculus.

For the former I recommend my notes on measure theory, for the latter a wonderful book Stochastic Calculus for Finance II: Continuous-Time Models by Steven Shreve.

Although the change of measure (Radon-Nikodym theorem) seems to be a highly abstract concept, it has a very plausible interpretation: the market price of risk. And if you want to grasp e.g. the LIBOR Market Modell, you do need to have a working knowledge of Radon-Nikodym theorem in order to switch to a T-Forward measure.

As to numerical solutions of PDEs, well, there are a lot of good libraries. Of course it is helpful to understand how they work but it is impractical to re-invent a wheel. Moreover, what one solves with PDEs (options with early exercise) can usually be solved with the least-square monte-carlo, which is much easier.

C++ is also helpful but (unless you are going to be a quant-developer) you'd better first learn a language for rapid prototyping. Popular languages are Python, R and Matlab.

I aspire to break into a decent quant role in the future. I would like to ask -

1) What are some nice things that would help bolster my resume and become a good quant?

It depends, really đź™‚

First of all one should be more specific, which kind of quants is meant. E.g. I am a model validating quant, research quant and quant-developer, according to Mark Joshi's classification.

But generally, if you want to be closer to a front office (thus closer to money), get your hands dirty with trading and be aware what is going on the markers. Surprisingly, many German quants (I know them in person) do not trade or invest their own money ... and even if they do they often make losses (I really wonder if it is also the case for UK and US quants).

And many mathematical finance students will readily tell you about the nuances of a complicated mathematical model but fail to answer a simple question like what is the current value of the DowDones.

If you want to be a risk-quant (which means less money and more boring but also more secure job), you need to learn the bloated regulatory frameworks. Unfortunately, *regulators have reduced risk managers to box checkers, making sure they take every measure of risk and report it dutifully on extensive forms. It just consumes more and more staff, turning them into accountants and rotting brains*, says former top risk manager at Merrill Lynch.

2) Is it possible to self-learn & become a quant(land a first job)?

For example, learn probability theory(martingales), stochastic calculus and know about industry standard models.

I try to download papers from the net, but many of them are difficult for me to follow, because I am not well versed with probability theory, stochastic processes, or stoch. integrals.

The short answer is: yes, it is possible. Having a working knowledge of the models, discussed in Shreve's book is enough to get started.

As to papers, yes, most of them are very hard-going and 99% are ... bullshit! The problem is not only a bloated education branch (abound with mediocre scientists who need publications for their carrier). Even talented and hard-working scientists often reside in an ivory tower of math. Just an example: once I had a discussion with a professor, who believed that his model is very helpful because "a bank can have a hundred thousand derivatives, so it is very helpful to accelerate the simulation of their prices"... Well, a bank may indeed issue a hundred thousand derivatives but in practice one would never simulate them directly. Rather one should simulate the prices of their underlyings and there are never so many of them.

Thus I generally never read papers without the source code and data. Indeed, according to the manifest of reproducible research *An article about computational science in a scientic publication is not the scholarship itself, it is merely advertising of the scholarship. The actual scholarship is the complete software development environment and the complete set of instructions which generated the figures*.

Additionally, never forget the Reihhard-Rogoff case, the Excel error that changed history.

As a program minimum I would recommend to read the following original papers:

1) **The Pricing of Options and Corporate Liabilities** by Fischer Black and Myron Scholes [link]

2) **The pricing of commodity contracts** (Black76, which originally was *not* intended for pricing the options on interest rate).

3) **AN EQUILIBRIUM CHARACTERIZATION OF THE TERM STRUCTURE** by Oldrich Vasicek [link] (One used to say about this paper that it is far more often cited than read).

4) **Valuing American Options by Simulation: A Simple Least-Squares Approach** by Longstaff and Schwartz [link]

And last but not least: be aware that the golden time of quants is definitely over. Not only in the sense of salaries but also in the sense of mathematical challenges. The market is saturated with models and software for the quantitative finance. Additionally, most of market participants got reluctant to complex financial products after the financial crisis in 2008. So think twice whether you really wanna-be-quant. If you want an easier money and a hotter stuff then current hype is machine learning and artificial intelligence... which likely will also fade out soon :).

FinViz - an advanced stock screener (both for technical and fundamental traders)