Dynamic Portfolio Optimization with Risk Management

Create a sophisticated portfolio optimization framework using Python that implements modern portfolio theory with advanced risk management techniques. Utilize scipy, numpy, and PyPortfolioOpt to develop a system that performs mean-variance optimization, calculates efficient frontiers, implements Black-Litterman model adjustments, and supports multiple asset class allocations. The system should include Monte Carlo simulation capabilities, robust risk metrics calculation, and generate comprehensive investment strategy reports.