Quantitative trading analyst shares how he applies his background in computer science to his career at DRW.
Can you tell us about how you apply your computer science background to your career?
As a trader, I use many interconnected pieces of software to conduct research, identify trading opportunities, and manage portfolio risk. Having a computer science background allows me to have a richer and more precise dialogue with our technologists when it comes to improving our systems or suggesting new features. My quantitative and computer science background enables me to understand the fundamentals of a problem we’re facing and then quickly prototype and propose a solution.
When did you know that a career in trading was the right path for you?
I have always loved math, and it was in when high school I developed an interest in economics and finance. I organized paper stock trading competitions, watched movies/documentaries, and read books on the subjects. I wasn’t aware of specific jobs at the time, but my interest was certainly piqued. In college, I competed in the MIT and UChicago trading competitions and also took part in extracurriculars that exposed me to quantitative finance. When considering jobs, trading appealed to me most given my love for math, science, competitiveness, and entrepreneurial spirit.
Are there any specific courses in school that helped you prepare for your career?
I took a class called Machine Learning and Large Scale Data Analysis that had a nice balance of rigorous statistical theory and implementation. This course required a solid foundation in probability and statistics (also key for trading) to understand the algorithms, but we also spent several hours a week implementing the algorithms in python to be run on university-provided compute clusters. Similarly, in systematic trading, I find that the work I do daily is interdisciplinary and requires me to understand and improve upon processes that are a blend of theory, market knowledge, and code. I also took classes in math and philosophy that, over time, honed my ability to think logically from first principles. This is invaluable when designing a trading strategy as each of your assumptions may be subject to scrutiny.
What is the biggest lesson you’ve learned from your job and how does that impact the way you approach your work?
Often, it’s best to make a quick yet imperfect (but informed) decision instead of accumulating excessive information. This holds for both trading and working on projects.
What are the 3 most important skills to have to be a successful trader?
Rational decision-making, competitiveness and grit.
What is something you wish you knew about trading in school that you know now?
Within trading, there are many different types of strategies, asset classes and roles that can suit an individual based on their unique skills and personality. For example, you may be a quick decision-maker or take a more methodical, calculated approach to your decisions. For both of these personalities, there is an asset to trade that fits. It’s worth exploring across these dimensions before either deciding that a particular niche is right or discounting trading entirely.
What are the crucial skills you have developed that you would recommend to a computer science major starting their career?
Someone with a computer science background has many tools at their disposal to have a substantial impact on their team and company. The ability to identify places where technology can be applied to create outsized results (i.e. the 80-20 principle) is hard to nurture in school but is invaluable in your professional career. It’s important that you learn enough about their company’s domain so that you can focus their energy on the most valuable problems.
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