Cross-Platform AI Trading Console

I am architecting “GoD Console,” an end-to-end trading platform that has to work seamlessly on both desktop (Windows, macOS, Linux) and mobile (iOS, Android). The system ingests real-time Telegram signals and YouTube live-stream commentary, routes them through an AI instruction-and-memory layer, then drives a strategy engine capable of back-testing, paper trading and live execution through multiple broker APIs.

Core modules that need to be engineered and wired together:
• Signal ingestion: high-throughput Telegram reader and live-caption/ASR pipeline for YouTube streams.
• AI layer: pluggable provider model with automatic fail-over and the option to “bring your own host” so local/offline models can slot in when cloud APIs such as OpenAI, Google Cloud AI or AWS AI are unavailable.
• Strategy, risk and market-data engines that can operate offline, yet sync safely to the cloud when back online.
• Trade lifecycle: back-testing, paper mode and live mode, each feeding unified logs and metrics.
• SaaS wrapper: authentication, subscription billing, admin panel, role-based access.
• Plugin framework so third-party modules can extend data feeds or execution venues without touching core code.
• Security, logging, monitoring and a CI/CD pipeline from the outset.

What I need from you is a detailed project proposal that spells out architecture, technology choices, milestone plan, testing approach and delivery timeline. Please highlight any similar systems you have built, show how you will guarantee data integrity and low-latency execution, and outline how the mobile and desktop clients will share a single code base or communicate with the backend.

I will review proposals primarily on clarity of architecture, risk mitigation strategy and evidence that you can deliver production-grade, secure financial software.