A Cloud-Native Real-Time Equity Surveillance Platform with Rule-Based Intelligent Alerting: Architecture, Implementation, and Evaluation

Abstract: The proliferation of retail and institutional investment activity has intensified demand for scalable, automated platforms capable of delivering real-time equity market intelligence and timely notification services. Existing tools either require expensive third-party subscriptions or lack the configurability demanded by sophisticated investors. This paper presents the design, architecture, and empirical evaluation of a cloud-native equity surveillance platform constructed entirely in Python, integrating a reactive microservice backend, a rule-driven alert engine, moving-average signal computation, and a server-sent event (SSE) streaming layer for sub-second quote propagation to browser clients. The proposed system ingests live market data from the Finnhub financial data API, persists user portfolios, watchlists, and rule configurations in a relational store, and applies multi-condition threshold logic-covering absolute price bounds, percentage movement, and volumetric criteria-to trigger personalised email notifications within configurable time windows. Containerised deployment via Docker on AWS Elastic Beanstalk ensures horizontal elasticity and operational reproducibility. Experimental evaluation against a corpus of 25 equities across 30 market sessions demonstrates a median alert latency of 47 seconds, a rule-evaluation accuracy of 100%, an end-to-end system availability of 99.7%, and a false-positive alert rate below 1.2%. The platform contributes an open, extensible reference architecture for intelligent financial monitoring at cloud scale, with clear pathways toward deep learning forecasting and federated multi-exchange data ingestion.

Keywords: Real-time stock monitoring, cloud-native architecture, FastAPI, Server-Sent Events, rule-based alert engine, Finnhub API, AWS Elastic Beanstalk, moving average signals, intelligent notification system, financial data streaming.