High-Frequency Financial Time Series Anomaly Detection

Design a sophisticated Python anomaly detection system for high-frequency financial time series data using advanced statistical and machine learning techniques. Implement multiple detection algorithms including Isolation Forest, Local Outlier Factor, and custom statistical methods. The system should automatically process large datasets, generate real-time alerts, create interactive visualizations with Plotly, and export comprehensive anomaly reports with statistical significance testing.