This is a Python library for quantitative finance, specifically portfolio optimization and risk management. It implements Conditional Value-at-Risk (CVaR) optimization using Benders decomposition, mean-variance optimization via quadratic programming, and entropy pooling for adjusting scenario probabilities under user-defined views and stress tests. The library also includes fully flexible resampling (FFR) for simulation, Black's model for option pricing, and a set of helper functions for financial statistics. It is designed as a monolithic Python package with a clean API, extensive tests (100% coverage), and 14 Jupyter notebooks that serve as reproducible examples. While the primary character is an application library, it has a strong research and educational orientation, referencing academic papers and providing walkthroughs. The code is well-structured, but domain-specific and depends on cvxopt with GLPK for linear programming, which may limit portability.