I need a robust Python backend that exposes live financial data using stock exchange API and feeds three client surfaces: a lightweight HTML web portal, a native iOS app, and a native Android app. The app has to be fast and secure, supporting user authentication out of the box and streaming real-time market prices as JSON. AI APIs will also plug-in and analyze the data giving feedback based on scans. Beyond simply broadcasting numbers, I want to capture usage metrics and run concise data analytics on the traffic and behaviour we see. When any pre-set threshold is crossed, the system must trigger automated alerts—email for the web portal, push notifications for the mobile apps. Mobile users expect immediacy, so both the iOS and Android builds must show: • Real-time data updates pulled directly from the API (WebSockets or Server-Sent Events welcome). • Interactive charts that visualise price movement clearly on small screens. • Push notifications tied into the alert engine described above. Please make the stack explicit in your proposal: the Python framework you prefer (FastAPI, Django Rest Framework, or similar), the charting library you have in mind for the apps, and how you would deploy the solution end-to-end so it scales cleanly under load. I am open to AWS, GCP, or another cloud as long as the path to production is realistic and well documented. Acceptance will hinge on: 1. A Git repo with backend code, Dockerfile, and instructions. 2. A working HTML front end served by the backend. 3. Approved TestFlight build for iOS and an APK (or Play Console track) for Android, both hitting the same staging API. 4. Demonstration of an alert being fired when a price rule is violated. If you have released finance-related apps before, or have benchmarks to show throughput on a live data feed, that will carry extra weight.