Real-time multithreaded stock monitoring and alert system with enterprise WeChat notification and threshold prediction

With the growing demand for real-time data monitoring in intelligent systems, the integration of multithreading, adaptive threshold prediction, and low-latency alerting has become a core challenge in computer engineering. This paper presents a real-time stock monitoring and alert system that combines a multithreaded architecture, machine learning-based threshold estimation, and enterprise-level messaging. The system concurrently tracks multiple stock data streams, detects abnormal price fluctuations, and dispatches alerts via an enterprise WeChat bot. A Random Forest regression model is embedded to analyze recent high-low price trends and dynamically suggest personalized alert thresholds. The system is designed for deployability, stability, and engineering usability. Experimental evaluations demonstrate high responsiveness, accurate predictions, and robust behavior in volatile network and trading conditions. The proposed architecture provides a practical reference for real-time intelligent monitoring systems in broader application domains beyond finance.