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.

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

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.

An open-source Python module for portfolio optimization and backtesting

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.

Streamlit portfolio analysis and risk assessment dashboard for stock investments.

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.

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

Pipeline de Data Intelligence para análise fundamentalista (B3) e otimização de portfólios via Markowitz, com arquitetura de dados em camadas (Bronze, Silver, Gold).

Python implementation of Modern Portfolio Theory (MPT) with Efficient Frontier construction, Max Sharpe Tangency Portfolio, Capital Market Line (CML), leverage simulation, and real-data backtesting vs SPY.

Markowitz mean-variance portfolio optimization with efficient frontier visualization and Sharpe Ratio maximization using Python.