As part of the Quantitative Wealth and Investment Management (QWIM) project, a curated literature review and project roadmap were developed to guide student teams in building a robust, data-driven research contribution. The project began with a structured multi-stage literature review process, prioritizing top recommendations and gradually refining the reference list based on abstracts, conclusions, numerical results, and model applicability. Parallel to the literature review, teams were advised to identify and retrieve relevant datasets during the first few weeks.

The project emphasized a collaborative, modular coding framework using Python, with each team member responsible for selecting and implementing a quantitative model aligned with the QWIM theme. Common components such as data retrieval, benchmarking, and interactive visualization were shared across the team. An interactive dashboard, preferably built with Shiny in Python or R, served as a critical tool to demonstrate the comparative strengths of selected models. Additional technical guidance, code templates, and curated tool recommendations (including kedro, polars, and uv) were provided to support implementation and effective teamwork throughout the project.