ETF Dashboard
This project is a dashboard built to keep track of ETF trades at a glance.
ETF Traders
B2B applications, Financial Application, Data Visualization, Dark Mode, Web
2025
This project is a dashboard designed to monitor ETF trades at a glance. It brings together the key metrics: market value, returns, performance, PnL, and more, into a single high-level view, making it easy to parse how a portfolio is performing without bouncing between tools.
While this particular iteration didn’t go into production, it serves as a solid example of clear data visualization and showcases my comfort working with dark-mode interfaces and finance-focused applications.
The data we showcased came from a mix of sources: guidance from the SME, who outlined the key metrics traders rely on; insights from user interviews and watching how they currently track their positions; and a review of industry-standard tools to make sure we weren’t missing any baseline expectations. Together, these inputs shaped the final set of metrics displayed in the dashboard.
The top row focuses on the core numbers traders care about: total ETFs, average return, total market value, and total ownership. Each metric includes a small arrow to show whether it’s moved up or down since the last refresh. The goal here was to surface the essentials in a clean, no-frills way, quick readings the user can scan without needing to interpret anything.
This is followed by an ETF performance chart, daily PnL, and PnL impact table.
One of my favorite parts of the dashboard is the ETF performance chart. It highlights how a book performs over a selected time range, and the chip controls make it easy to compare multiple ETFs side-by-side if needed. It's a compact way to explore trends without adding extra complexity.
The PnL impact table was meant to replace an older, legacy tool, but we also wanted to give users more control to search, sort, and compare their data. After talking with the development team, we decided to base the design on AG Grid: an adaptable, high-performance data-grid library that works across multiple frameworks. Using its built-in features let us offer powerful filtering and customization options while keeping implementation efficient, especially since the team already had an AG Grid license in place.
This iteration never made it to production, and for a good reason. It started out as an early proof-of-concept meant to spark conversation, and the final version that went live took a completely different direction in some pretty unexpected ways. Curious about what actually shipped? Feel free to ask me about it in the interview! :)