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

Python financial widgets with okama and Dash (plotly)

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."

Machine Learning Enhanced Multi-Factor Quantitative Trading: A Cross-Sectional Portfolio Optimization Approach with Bias Correction

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.

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

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

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.

An open-source Python module for portfolio optimization and backtesting

Streamlit portfolio analysis and risk assessment dashboard for stock investments.

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

Interactive Modern Portfolio Theory tool with Streamlit UI. Optimizes US and BIST stock portfolios using Markowitz mean-variance analysis.

Reproducible recreation of Markowitz portfolio selection efficient-frontier results

Markowitz + Black-Litterman + Ledoit-Wolf

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.