FX Volatility Dashboard Development

I’m building a web-based monitor that ingests Bloomberg FX volatility surface data for 52 currency pairs, scores each surface cross-sectionally, ranks today’s levels against the last two years of history, and then flags the best option structure to trade the signal. I already have a detailed functional document that I’ll share once we start; you will not need terminal access because the feed will arrive through a Bloomberg Server API/BPIPE connection that I control. The stack is set: Python with Django on the back-end. You’ll orchestrate the data pipeline, persist the snapshots, run the analytics layer (basic statistics, z-scores, percentiles, simple optimisation), and expose everything to the front-end in real-time. For live updates I’m open to WebSockets, Django Channels or any approach that keeps the dashboard reactive without constant page refreshes. On the interface side I need clean, customisable dashboards: users should be able to pin preferred currency pairs, tweak look-back windows, and view recommended structures at a glance. A charting library such as Plotly, Highcharts or TradingView JS is fine—pick the one you’re most comfortable with. Because the output influences trading decisions, accuracy and latency matter. You’ll be working closely with me (I’m on the trading desk) so a solid grasp of FX vanilla/vol terminology and comfort with light quantitative work is essential. Deliverables • Django project with modular app structure • Data connector that pulls, normalises and stores Bloomberg surface points in real-time • Analytics engine that generates scores, rankings and trade recommendations on demand • Responsive dashboard with real-time updates and user-driven layout customisation • Brief deployment guide (Docker or similar) plus unit tests covering core calculations Acceptance Criteria 1. A live web instance demonstrates sub-second refresh of new surface data. 2. Scores and rankings match my reference spreadsheet to within 0.01. 3. Dashboard widgets can be added, removed and rearranged via drag-and-drop. 4. Codebase passes all supplied tests and linting checks. If this fits your skill set in Django, quantitative finance and Bloomberg APIs, let’s talk and I’ll forward the full spec. Please quote in Indian Rupee