black-litterman

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

DRIP Asset Allocation is a collection of model libraries for MPT framework, Black Litterman Strategy Incorporator, Holdings Constraint, and Transaction Costs.

End-to-end portfolio optimization (MVO), Risk Parity, Black–Litterman, regime targeting

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.

Flexible Python library for asset allocation and investor view integration

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

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.

Dynamic Investing strategy with nowcasting

Black-Litterman portfolio construction on the EURO STOXX 50 top 15: reverse-optimised equilibrium, Idzorek view-confidence, Theil posterior, max-Sharpe / min-variance / efficient frontier, Streamlit dashboard.

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.

A Python library for advanced quantitative portfolio analysis, optimization, and validation.

An institutional-grade wealth management & portfolio quant engine. Features Mean-Variance Optimization (MVO), Black-Litterman, life-cycle Monte Carlo simulations, and an AI Advisor Agent powered by DeepSeek.

Index and Factor Construction with Implied Covariance Process

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