Portfolio Construction Functions under the Basic Mean_Variance Model, the Factor Model and the Black_Litterman Model.

Entropy Pooling in Python with a BSD 3-Clause license.

Enhanced Portfolio Optimization (EPO)

Dynamic adjusted BL portfolio based on GARCH model

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.

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

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.

Dynamic Investing strategy with nowcasting

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

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.

Multi-Objective Portfolio Allocation Engine

Flexible Python library for asset allocation and investor view integration

Portfolio Optimization in MATLAB with S&P500 Subset. Efficient Frontier, CAPM, and Black-Litterman implementation using real market data.

LSTM-enhanced Black-Litterman model using NIFTY 50 stocks

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.

Enhanced the Black-Litterman model by incorporating vine-copula models for market equilibrium returns and ensemble machine learning for forecasting asset returns. Used ML model errors to quantify view uncertainty, improving portfolio performance and max drawdown in Taiwan’s stock market.