Real-time Trading SaaS Platform

Trading platforms must coordinate multiple moving pieces: rapidly changing market prices, account and permission workflows, automated strategies, and interfaces that need timely updates. The challenge is not only processing data, but keeping systems intelligible and operable as features grow.

I developed React web and React Native applications for the trading platform, including trading-book interfaces, copy-trading workflows, EA strategies, and AI chat. On the backend, I worked on real-time market-data processing, automated monitoring and trading logic, and platform administration and server-health capabilities.

• Market-data ingestion: Aggregated approximately 4,000 symbols using a Meta-platform scraper, Binance WebSocket feeds, Polygon, and Twelve Data fallback.
• Event-driven workflows: Used Apache Kafka with Maxwell change data capture to react to newly opened positions.
• Backend concurrency: Applied Node.js worker threads, Redis coordination, multi-core clustering, and backpressure-aware streams.
• Role-specific application flows: Built workflows for Super Admin, Admin, Super Broker, Broker, and User roles.
• Operational views: Developed server-health monitoring showing load, user counts and available resources, with support for capacity management and user movement between servers.

For streaming systems, data freshness, bounded work queues and recovery behaviors matter as much as headline throughput. My work involved balancing real-time updates with operational visibility and maintainable service boundaries. Exact production latency or uptime figures are not published here because they have not been independently verified.

Implemented functional components across client apps, backend services, integration pipelines, and operations workflows. The market-data service handled approximately 4,000 symbols. This case study documents engineering contributions rather than disclosing proprietary screenshots or source code.