This project provided students with hands-on experience applying quantitative, data science, and AI techniques to real-world investment problems using live financial datasets. Working in collaborative teams that mirrored professional environments, students gained exposure to investment analysis, quant trading, and AI-driven research. The core objective was to build an AI system that delivered actionable investment intelligence to both retail and institutional investors. Participants onboarded financial data, developed and backtested trading signals using quantitative models, and delivered outputs through AI-generated reports and chatbots.

Designed for students pursuing careers as quantitative analysts, researchers, developers, or data scientists in investment management and trading, the project emphasized a “quantamental” investing approach that combined human judgment with machine learning. Students strengthened their Python programming, financial modeling, and time series analysis skills, while also improving their communication and presentation abilities. Though prior experience with cloud infrastructure or NLP was beneficial, it was not required. The project helped students become more competitive in the job market and offered opportunities to publish their work on leading quant and data science platforms.