This document provides an overview and guide for becoming a Quantitative Developer (QD) in finance. It outlines the key skills needed which include scientific computing, programming, software engineering, database interaction, and numerical algorithms. The traditional route is through an academic background in scientific computing, but other programming backgrounds can also work if fundamental coding skills are developed. The roles involve optimizing trading prototypes, developing trading infrastructure, pricing derivatives, and implementing algorithmic trading strategies. C++ and Python are recommended languages. Interviews will assess database and data analysis skills.

1. QUANT
DEVELOPER
CAREER GUIDE
OCTOBER 2020

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A condensed guide on self-
directed learning to enable the
transition into a QD (Quantitative
Developer) finance role.
Any career in quantitative finance
requires a degree of generalisation
rather than extensive specialisation.
Quantitative Developers are no different.
They must fit into a team of traders,
financial engineers and IT support in
order to help investment banks price
and sell new structured investment
products, and to enable funds to
develop trading infrastructure and
portfolio management systems.

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1. Scientific Computing
2. Programming Skills
3. Software Engineering
Quant Development
Skills Overview
5. Numerical Algorithms
4. Database Interaction
From the many hiring instructions that
we have taken from HF and IB clients we
know that a blend and balance of these
skills are essential in algo trading teams:

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The traditional route into quantitative development is
commonly via an academic background in scientific
computing. The fundamental skills that a "quant dev"
will need are advanced programming skills and
numerical algorithm implementation.
Typically these skills are developed as par for the
course within a graduate school research
environment and learned within physical sciences or
engineering.
Should you have this background then your objective
will be to gain an uptake on the specific products and
numerical algorithms commonly used in quantitative
finance, as your elementary standard of
implementation and programming skills are likely to
be sufficiently evolved.
However, if you lack any form of background in
scientific computing, there are still plenty of
opportunities to become a quantitative developer
leveraging a background in programming. At a
minimum you will need to gain a grounding with
coding algorithms from scratch.
1. Scientific Computing Computational science,
also known as scientific
computing or scientific
computation (SC), is a rapidly
growing field that uses advanced
computing capabilities to
understand and solve complex
problems.

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In this role you will find optimise trading prototypes or
work developing trading infrastructure from scratch.
For bank roles you will be using C++, Java or C# in a
Microsoft/Windows environment.
In hedge funds then you will commonly be translating
MatLab or R into C++ and/or Python. Funds tend to use
Java and C# less, since they're often in a UNIX
environment where C++ and Python make more sense.
We would suggest learning C++ and Python for cross-
sectional capability across different sectors of the
industry.
2. Programming SkillsC++ is a general-purpose
programming language with its
roots in the C language. Even
though Python is also a general-
purpose, it is a high-level
language, meaning that Python
code is easy-to-read and
understand.

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A quant developer must become both a good
programmer and a good software developer.
To become a good software developer it is necessary to
understand how to craft large-scale software projects.
For modern software development this requires using
version control, continuous integration and other agile
practices. contribute to open source software projects
via the internet.
One of the largest quantitative finance projects is the
QuantLib project. Reading through (some of) the source
code on open projects will inform you on how large-
scale C++ software projects are written.
3. Software Engineering Software engineering is the
systematic application of
engineering approaches to the
development of software.

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In a QD interview you will be asked problems relating to
data storage and analysis.
One of the main components in a quant dev's day to day
life is interacting with databases.
If you have never utilised a data storage system, then the
best way to start is by beginning to understand Relational
Database Management Systems (RDBMS) and their
language - Structured Query Language (SQL). Common
RDBMS' include Microsoft SQL Server, Oracle and
MySQL. Other types of data store systems include the so-
called NoSQL data stores, including 10Gen's MongoDB
and Cassandra.
You can learn about RDBMS by installing an open source
version (as you can download them for free. We would
recommend MySQL, as this is a very common database
within hedge funds. SQL Server and Oracle are more likely
to be prevalent within banking. up a certain date/time or
reporting query.
4. Database InteractionA relational database is a digital
database based on the relational
model of data, as proposed by
E. F. Codd in 1970. A software
system used to maintain
relational databases is a relational
database management system.

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Algorithms in quant finance are used to carry out both
instrument pricing and algorithmic trading. Investment
bank derivatives pricing techniques typically are Monte
Carlo Methods and Finite Difference Methods, both rely
on knowledge of probability, statistics, numerical analysis
and partial differential equations. You will need to gain a
good understanding of these methods if you wish to
become an options pricing quant developer in a bank.
For hedge funds, you will likely be implementing trading
infrastructure - either low or high frequency. This will
involve taking an algorithm already coded up in MatLab,
R or Python (or even C++) and then optimising it in a
faster language, such as C++, as well as plugging this
algorithm into a prime brokerage API and executing
trades. The skills required here are quite disparate.
You will need to be able to pull together data from
various sources, put it into the correct context, iterate
over it rapidly and then generate on-demand reports
either in fixed-format (PDF), over the web or as an API
itself. These skills are hard to learn from books directly
and require a few years of software development
experience in the technology industry.
5. Numerical Algorithms Numerical algorithms for high
performance computational
science.

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We’ve developed considerable market knowledge and a large
network of lasting relationships across the UK and pan-
European finance community.
Our quant specialists already have a deep understanding of how
technology is changing these sectors and the opportunities for
candidates at your level.
Consult with us: +44 (0) 207 193 9055.

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www.matrice.co.uk