FundFlow — FinTech Analytics Dashboard
FundFlow Capital needed a sophisticated analytics dashboard for their portfolio managers to monitor real-time market positions, risk exposure, and fund performance across 200+ investment instruments.
The Problem
Portfolio managers were using a patchwork of Bloomberg terminals, Excel models, and custom Python scripts to monitor positions. Consolidating data took 45 minutes each morning. Risk calculations ran overnight meaning critical decisions were made on stale data. There was no mobile access for managers traveling.
Our Solution
We built a real-time dashboard powered by WebSockets for live price feeds, Python FastAPI for computation-heavy risk calculations, and TimescaleDB for time-series financial data. Custom D3.js visualizations give portfolio managers instant insight into position concentration, VaR, and P&L attribution.
Architecture & Technical Approach
TimescaleDB handles time-series tick data with automatic partitioning. Python FastAPI workers compute risk metrics async and push updates via WebSockets. Next.js frontend uses SWR for non-real-time data and native WebSocket for live feeds. Kubernetes orchestrates auto-scaling during market open hours.
Screenshots
Results & Impact
Processing 2.3M financial transactions per day with sub-100ms latency
Key Learnings
TimescaleDB's continuous aggregates are essential for financial dashboards. WebSocket connection management at scale requires robust reconnection logic and message queuing. D3.js rendering performance requires virtualization for datasets above 10,000 points.