I have had a long and perhaps atypical journey to the CQF and quant finance. I attended a small private high school in Poland focused on arts, literature, and languages. However, I was always interested in math and I attended some math competitions even then. After graduation, I decided to go into computer science for my university education. The transition went very well.
My first work experience was an internship at CERN - the European Organization for Nuclear Research - one of the world's largest and most respected centers for scientific research. It was a fantastic experience to work in the data analysis for one of the experiments and be surrounded by many talented scientists. I considered additional opportunities at CERN and due to my interest in finance, I landed at the Institute’s pension fund. The CERN Pension Fund at that time was managing about 4bn USD and the investment process was risk driven. Very quickly, I was given a complex and interesting project – to start building a new IT infrastructure for the fund. In the first year, I set up all of the databases that became a foundation for future developments. By the second year we were processing large amounts of data and setting up different reports that went to the portfolio managers and risk manager. We improved the existing infrastructure of the fund, moving from monthly to daily reporting, and we laid the groundwork for an even bigger project, but first I decided that I needed to delve deeper into quant finance.
It gave a nice overview of different tools using quantitative finance and helped me to develop practical things for the fund; everything I was doing in the CQF was actually running in parallel with my efforts to build a quantitative strategy for the pension fund.
I already had an Engineering degree in computer science, MBA from University of Geneva, and a fantastic job at CERN. The Certificate in Quantitative Finance (CQF) was perfect for me, as I was looking for a program to systematize and boost my quant finance skills. Obviously, the CQF program has an excellent course platform, which gives a lot of flexibility, and it was also very demanding. It gave a nice overview of different tools using quantitative finance and helped me to develop practical things for the fund; everything I was doing in the CQF was actually running in parallel with my efforts to build a quantitative strategy for the pension fund. We are based in Switzerland, but the CERN Pension Fund, as an international organization, is not subject to Swiss regulations and has its own guidelines for investment, which give us a lot of freedom – there is an open window for a lot of interesting projects there. I was able to explore how we could use mathematical models to build the quantitative strategy – a strategy that would screen the broad universe of assets and then select assets that provide a robust mix of elements to the portfolio. We were also working on dynamic asset allocation, judging how the markets were behaving and adjusting course as needed. Part of this project entailed building an indicator that would tell us the perceived risk of the market and then building a portfolio optimized for those conditions. This quantitative model has performed very well and I have actually taken on more responsibility, as I was promoted to the Head of Quant IT, focusing on infrastructure and the asset allocation tools for the fund. Over the following years, I have been leading a small team of programmers and jointly we are developing an IT system for quantitative asset management.
Given that CERN is committed to sharing the research, I went through the process of open sourcing large part of this project; it is a Python library, called QF-Lib (www.qf-lib.com), which stands for Quant Finance Library. The code is now available to anyone who wants to try it. In my opinion, it is an institutional grade backtesting system that is both flexible and modular. You can plug your own data, define a strategy in a form of an algorithm, and use the simulator to obtain the results – it gives you a rich set of outputs. The system covers many aspects of engineering a quantitative strategy and it integrates with a number of data providers and brokers. I would invite anyone who is into systematic trading to try this library; it’s like a Swiss Army knife for quant finance and I'm proud to be the originator of the idea and the architect of many of the solutions there.
I would strongly encourage anyone who is seriously interested in quant finance to join the program.
Returning to the CQF, I would strongly encourage anyone who is seriously interested in quant finance to join the program. No matter what you may do in the future, there will be something from CQF that will be useful for you. Personally, I was always interested in building strategies, and I gained deep insights and developed many ideas while studying in this program; these insights and ideas are definitely being put into practice now.