Anomaly Detection in High-Frequency Financial Trading Data

Develop a sophisticated anomaly detection system for high-frequency trading datasets. Implement multiple detection algorithms including isolation forests, local outlier factor, and custom ensemble methods. Create a pipeline that can process millions of trading events per second, with sub-millisecond latency. Include adaptive thresholding mechanisms that adjust sensitivity based on market volatility and historical trading patterns.