{"library":"pyportfolioopt","title":"PyPortfolioOpt","description":"PyPortfolioOpt is a financial portfolio optimization library for Python, providing methods for mean-variance optimization, Black-Litterman allocation, and risk parity. Version 1.6.0 supports CVXPY-based solvers and offers both classical and objective-based optimization approaches. Release cadence is irregular, with contributions from the community.","language":"python","status":"active","last_verified":"Mon Apr 27","install":{"commands":["pip install pyportfolioopt"],"cli":null},"imports":["from pypfopt import EfficientFrontier","from pypfopt import risk_models","from pypfopt import expected_returns"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import pandas as pd\nfrom pypfopt import EfficientFrontier, risk_models, expected_returns\n\n# Sample data: prices of 3 assets\ndata = pd.DataFrame({\n    'AAPL': [1.0, 0.9, 0.8, 0.85],\n    'GOOG': [1.0, 1.1, 1.0, 1.05],\n    'MSFT': [1.0, 0.95, 1.1, 1.0]\n})\n\n# Calculate expected returns and covariance matrix\nmu = expected_returns.mean_historical_return(data)\nSigma = risk_models.sample_cov(data)\n\n# Optimize for maximum Sharpe ratio\nef = EfficientFrontier(mu, Sigma)\nweights = ef.max_sharpe()\n\nprint(ef.clean_weights())","lang":"python","description":"Basic mean-variance optimization using historical returns and sample covariance.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}