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.
This is an investment analysis framework implementing the Turtle Investment Strategy. It provides a modular, multi-agent system for stock analysis that combines quantitative financial analysis, qualitative business assessment, and valuation modeling. The framework collects data from Tushare API (with yfinance fallback), processes financial statements, analyzes annual report PDFs, and generates investment reports. Key features include a two-tier stock screening system, deterministic pre-computation of metrics to prevent LLM calculation errors, and a structured workflow with validation checkpoints. The architecture separates concerns into data collection, processing, analysis, and reporting layers with clear interfaces between components.