Data Analytics R&D (POC)

Live Stock Price & Financial Risk Terminal

Architected and engineered an end-to-end, event-driven data streaming pipeline designed to process real-time financial market data, compute risk metrics, detect anomalies, and serve dynamic updates to an interactive user interface. Built with a containerized microservices approach orchestrated via Docker, the platform utilizes Apache Kafka to ingest live stock feeds continuously from external market gateways, processing streams asynchronously through dedicated producer and consumer nodes to deliver low-latency telemetry directly to a financial risk visualization dashboard.

Metrics at a Glance:

Key Architectural Achievements:

• Asynchronous Ingestion Pipeline: Developed a multi-threaded TickerData producer engine inside a dedicated Docker container that isolates external market gateway interactions and guarantees stable, continuous stream writes to the messaging cluster.
• Dynamic Sliding Window Calculations: Engineered a robust streaming analytics consumer engine that processes real-time feeds on the fly, applying rolling temporal matrices to track stock price velocities and calculate volatile risk factors.
• Automated Anomaly Alerting: Implemented integrated analytical guardrails to evaluate real-time parameters against incoming data streams, triggering immediate BOLM or PERFIT anomaly notifications into specialized Kafka alert topics when risk thresholds are breached.
• Runtime Configuration Syncing: Designed a hot-swapping configuration data flow utilizing an engine_config topic, allowing business analysts to tune operational thresholds from a separate control center without restarting core processing microservices.
• Decoupled Interactive Dashboard: Built a responsive, web-based layout using Dash/Plotly and a parametric control center, isolating frontend rendering logic completely from data pipelines to ensure zero frame drop under heavy processing loads.