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.

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.

Asset allocation and portfolio optimization implementations to examine how each one differs and affects the overall portfolio.

Flexible Python library for asset allocation and investor view integration

Black-Litterman with MVO program for asset allocation (ETF)

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.

AI-driven bond portfolio optimizer using the Black-Litterman model to blend market equilibrium with subjective views

Index and Factor Construction with Implied Covariance Process

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.

Portfolio Management Midterm Project (Team SaigonQuant - K60) - Dr. Nguyen Thi Hoang Anh - FTU2

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.

End-to-End Python implementation of Ang et al's (2026) Agentic 'Self-Driving Portfolio'. Implements: Black-Litterman equilibrium priors, Grinold-Kroner building blocks, Campbell-Shiller CAPE analysis, Ledoit-Wolf covariance shrinkage, Risk Parity, Hierarchical Risk Parity, and Robust Mean-Variance optimization across 18 asset classes.