What is computational finance?
Often referred to as ‘financial engineering’, ‘financial mathematics’, or ‘quantitative finance’, computational finance is a branch of applied computer science that uses mathematics, statistics, and programming to handle problems in finance.
Computational finance focuses on the use of practical numerical methods and modeling techniques for asset management and the systems designed for algorithmic or high-frequency trading, rather than just using mathematical proofs or theorems.
Careers in computational finance
Professionals with computational finance skills are typically hired within quant teams in areas such as:
- Quant research and strategies
- Data science and machine learning
- Portfolio management
- Risk management
As financial markets have become increasingly globalized and complex, and as technology has continued to evolve, the demand for people with a computational finance skillset (i.e., advanced skills in math, finance, and programming) has grown significantly.
Find out more about careers in computational finance
If you are interested in starting or progressing a career in computational finance, download The CQF Careers Guide to Quantitative Finance to explore the skills and average salary for six key career paths.
An alternative computational finance online course
Whilst computational finance skills can be gained on the job, one of the best methods to develop the techniques required is to complete further education. There are computational finance online master’s programs available around the world, but they tend to be theoretical rather than practical, expensive, and can take between one and four years to complete the full program. A good alternative to a computational finance master’s is the Certificate in Quantitative Finance (CQF), the world’s largest professional qualification in the field.
Throughout the CQF program, students gain insights on how to build financial models, price assets, construct portfolios, and assess risk through various quantitative techniques. The CQF offers a strong base in Python programming and students learn how to use statistical models, optimization, bootstrapping techniques, and Monte Carlo simulation to address typical problems in evaluating and managing financial assets.
The CQF covers these topics in a cutting-edge syllabus that is updated quarterly in consultation with senior alumni practitioners and faculty to ensure that students learn the skills being used in today’s financial markets. The CQF course modules include:
- Building Blocks of Quantitative Finance
- Quantitative Risk and Return
- Equities and Currencies
- Data Science and Machine Learning I
- Data Science and Machine Learning II
- Fixed Income and Credit
At the end of the program, students choose two advanced electives from a range of options to tailor the program to their interests and career goals. If students are new to quantitative at the start of the program, they can also prepare with online primers in Finance, Mathematics, and Python Programming to ensure their skills are up to scratch ahead of the program start.
The CQF was founded by Dr. Paul Wilmott to bridge the gap between academia and industry. Throughout the program, students are taught both the theory and the practical implementation of techniques and models and how to apply these to real-world scenarios. The CQF faculty is also made up of some of the world’s leading practitioners in quantitative finance who are able to draw on their own industry experience when teaching.
Unlike many top computational finance programs, the CQF is also uniquely flexible. It is delivered online, part-time, allowing students to continue a full-time career alongside the program. The modules can be completed in just six months or, if necessary, the program can be deferred for up to three years at no extra cost.
CQF alumni also typically enter or continue in careers on the same paths as those that have completed a computational finance master’s. However, CQF alumni are able to keep their skills competitive for the rest of their careers through permanent, free access to the Lifelong Learning library which includes a wide range of additional lectures, masterclasses, and the latest CQF program content. They also join a global CQF community of quant professionals.