Real-Time Anomaly Detection in Financial Streaming Data

Implement a Python-based real-time anomaly detection system for financial time series data using advanced statistical and machine learning techniques. Develop a solution that combines multiple detection methods including Z-score, DBSCAN, and deep learning autoencoders. The system must handle high-frequency trading data, provide low-latency anomaly identification, and generate both immediate alerts and retrospective analysis with confidence scoring.