AlphaRexx Trading SaaS

The project needed to be more than a simple signal bot. Users needed plans, activation keys, Telegram access, risk settings, signal history, dashboard visibility, and support/admin workflows. The difficult part was connecting many moving pieces without turning the app into one large, fragile process.

I structured the product around a Django web app, REST-style endpoints, PostgreSQL data models, Redis-backed queues/state, and separate background services for market data, scanning, dispatching, Telegram delivery, monitoring, and expiry handling.

I also worked on admin workflows for users, manual payments, activation keys, support tickets, and operational review. For AI features, I integrated OpenAI-powered assistant flows and a Shariah audit path through a small FastAPI service and Django proxy endpoints.

Key work included authentication and dashboards, subscription checks, activation-key flows, Telegram bot connection, signal records, per-user risk settings, admin review screens, Redis coordination, Docker Compose services, and deployment preparation with Nginx, PostgreSQL, and Redis.

The most important engineering decision was separating real-time responsibilities into smaller services instead of keeping everything inside one Django request cycle or one bot loop. That made the system easier to reason about, debug, and operate.

The result was a complete working SaaS-style platform with product pages, authenticated dashboard, admin operations, signal pipeline, Telegram delivery, AI interaction layer, and deployment structure. It gave the client a practical base to test the business workflow and continue improving the trading logic.