I chose Cambridge for my MBA in part because it’s one of the world’s premier STEM universities and I sought an environment where I could pursue technical projects and entrepreneurship on top of the classic MBA curriculum.
Towards the end of last year, I decided to explore quantitative finance by taking part in the Cambridge Algorithmic Trading Society’s (CUATS) annual trading competition. For the competition I developed an event based strategy utilising my industry experience in pharma and biotech. The competition challenged me to translate theoretical insights into executable code, navigating data preparation, position sizing and risk within the QuantConnect backtesting platform used for the competition. The strategy was more volatile than would be acceptable in a hedge fund setting but achieved somewhat fantastical returns of around a 100% CAGR in out-of-sample testing. This held up during the live trading test too, netting me a 2nd place overall.
This year I continued by joining a CUATS organised team for the global IMC Prosperity 3 competition. IMC Prosperity is one of the largest trading competitions in the world, spanning 15 days over 5 rounds. The competition explores a variety of trading themes, with a particular focus on mean reversion this year. As 1st in the UK, 4th in Europe and 21st out of 12,620 teams globally we delivered a strong inaugural showing for CUATS at Prosperity, just in time before the competition gets revamped for next year’s edition (according to the rumour mill).
One of the draws of an MBA is always the opportunity to connect with and learn from a wide variety of backgrounds and through these quantitative finance ventures I have been able to expand this experience across degree programmes, developing and discarding ideas in late night WhatsApp groups and consolidating piles of code in early mornings to get our submission ready.
I have been quite hooked by the workflow of systematic trading, because it combines creativity with fast-paced, objective feedback. Both during IMC and the CUATS annual trading competition (but especially for IMC) the key to success was to find plausible theories and then to iterate through them efficiently to develop something that actually works. This kind of scientific thinking has always been my preferred approach to solving problems, but in a sense, it is much easier to implement in trading than in general business, because it is easier to generate objective feedback. While during the summer internship I will explore a more traditional MBA role in tech, it is a company known for its fast-paced and data-driven culture, so I hope to utilise a similar workflow there. Cambridge has indeed proven itself as a place with unique opportunities also outside the curriculum, where unusual experiences can be leveraged to provide outsized value.
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