BUFC’s Quant Team exists to educate members on computational and mathematical strategies in quantitative finance. We aim to foster and equip our members with valuable quantitative skills that can allow them to succeed in any quantitative field. Members will have the opportunity to learn Python coding and its popular data science libraries. We will also explore various topics in computational finance (e.g., machine learning and Monte Carlo methods). If you are interested in joining or requesting more information, email our VP of Quant, Ryan Nie, at firstname.lastname@example.org with short info about yourself and your experiences with computer science, math, and finance. Ryan will provide a guideline for the application process that primarily consists of gaining the minimum required skill set. Applications are open all year, and no experience is necessary as long as you are ready to learn.
With their new knowledge, teams will develop projects and present their products or findings to the club towards the end of the semester/year. Beginner students will participate in guided projects throughout the semester, and advanced students can take on personal projects with minimal supervision. Sample projects can include, but are not limited to, price predictions, price simulations, quantitative analyses, trading algorithms/bots, etc. We encourage people to take ideas from research papers or any resources they may find online. Our long time goal is to integrate and use our quantitative strategies for a fund and the club’s fund.
Quantitative finance uses mathematics and data to analyze financial markets. Strong mathematical and computational knowledge is needed to be successful in this field. There are several different spaces within quant finance that we will potentially explore: derivative pricing, risk management, statistical arbitrage, modeling, etc. Each area requires extensive use of statistics, finance, computer science, and different types of mathematics.