Quant Developer Career Entry Guide | Matrice.co.uk
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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.
Quant Developer Career Entry Guide | Matrice.co.uk
- 2. 2 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.
- 3. 3 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:
- 4. 4 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.
- 5. 5 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.
- 6. 6 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.
- 7. 7 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.
- 8. 8 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.
- 9. 9 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.