High-Frequency Trading Latency Optimization Pipeline

Create a multi-threaded Python application that simulates and optimizes trading latency for algorithmic strategies. Implement a microservice architecture using asyncio and multiprocessing to process real-time market data streams. The system must be capable of handling 10,000+ transactions per second, with built-in machine learning predictive models for trade execution probability. Include comprehensive logging, performance metrics, and a configurable risk management layer.