markowitz

Portfolio optimization with deep learning.

Investment portfolio and stocks analyzing tools for Python with free historical data

Markowitz portfolio optimization on synthetic and real stocks

Markowitzify will implement a variety of portfolio and stock/cryptocurrency analysis methods to optimize portfolios or trading strategies. The two primary classes are "portfolio" and "stonks."

Markowitz portfolio construction on CVXPY — DPP-compliant builders that solve long sequences of related problems without recompiling as assets and factors come and go

critical line algorithm for efficient frontier

Backtesting of different trading strategies by applying different Modern Portfolio Theory (MPT) approaches on long-only ETFs portfolios in Python.

Portfolio Optimization on a Quantum computer.

Interactive Streamlit dashboard for market risk analysis, Markowitz portfolio optimization, and financial planning.

Reproducibility repository for 'Beyond De Prado and Cotton: Hierarchical and Iterative Methods for General Mean-Variance Portfolios' (Wuebben): Python code and result artifacts for HRP-μ, HRP-Σμ, and the CRISP iterative shrinkage solver.

Open-source quantitative finance research engine — Black-Litterman, Ledoit-Wolf shrinkage, Hierarchical Risk Parity, leakage-free CPCV backtesting. Independently reproduces the DeMiguel-Garlappi-Uppal "1/N puzzle": no tested optimization method reliably beats naive equal-weighting once estimation error is properly controlled for.

Quantitative portfolio risk analyzer — VaR, Sharpe, Markowitz optimization, Monte Carlo simulation — Streamlit dashboard

Comparison of Return Forecasting Methods for Markowitz Portfolio Optimization: Historical Mean, AutoARIMA, PatchTST Transformer

An open-source Python module for portfolio optimization and backtesting

Streamlit portfolio analysis and risk assessment dashboard for stock investments.

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.

ML-enhanced portfolio optimizer combining Random Forest return prediction, Ledoit-Wolf covariance shrinkage, and Markowitz mean-variance optimization with walk-forward backtesting. Beats SPY by ~5% CAGR.

Implementación educativa en Python del modelo de Markowitz con simulación de portafolios, Ratio de Sharpe y frontera eficiente usando CVXOPT.

Python Markowitz portfolio optimization. Monte Carlo, efficient frontier.

Python toolkit for portfolio analysis — risk metrics (Sharpe, VaR, CVaR, max drawdown) and Markowitz mean-variance optimization with the full efficient frontier, implemented from scratch with SciPy.