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Entropy Pooling in Python with a BSD 3-Clause license.
Portfolio Construction Functions under the Basic Mean_Variance Model, the Factor Model and the Black_Litterman Model.
Enhanced Portfolio Optimization (EPO)
Dynamic adjusted BL portfolio based on GARCH model
End-to-end portfolio optimization (MVO), Risk Parity, Black–Litterman, regime targeting
DRIP Asset Allocation is a collection of model libraries for MPT framework, Black Litterman Strategy Incorporator, Holdings Constraint, and Transaction Costs.
ESG investing web app that takes user inputs to generate personalized equity portfolios and even comparative firm ESG rankings.
Streamlit app to simulate/optimize different portfolio allocations based on mathematical methods.
Asset allocation and portfolio optimization implementations to examine how each one differs and affects the overall portfolio.
McPortfolio: A Model Context Protocol server providing 9 specialized tools for LLM-driven portfolio optimization using natural language, covering mean-variance to machine learning approaches.
Black-Litterman with MVO program for asset allocation (ETF)
Flexible Python library for asset allocation and investor view integration
Dynamic Investing strategy with nowcasting
Portfolio Analyzer is a modular toolkit for advanced portfolio construction, optimization, and risk analytics. It features Black-Litterman blending, robust statistical estimation, Monte Carlo simulation, and interactive Jupyter workflows for quantitative investment research.
Portfolio Management Midterm Project (Team SaigonQuant - K60) - Dr. Nguyen Thi Hoang Anh - FTU2
Index and Factor Construction with Implied Covariance Process
Building a balanced Vanguard ETF portfolio with data-driven optimization—exploring advanced methods, robust backtesting, and an interactive Dash app to pick your optimal mix.
AI-driven bond portfolio optimizer using the Black-Litterman model to blend market equilibrium with subjective views
SiraEdge Portfolio Optimization Module - Complete implementation with 7 optimization models, ML integration, and comprehensive documentation
Production-grade portfolio optimization system implementing 4 quantitative strategies (Mean-Variance, Risk Parity, CVaR, Black-Litterman), backtested over 6 years of real market data, with an interactive dark-theme Streamlit dashboard and full Docker + CI/CD setup.
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