{"repo":"dcajasn/Riskfolio-Lib","free":true,"listed":false,"github":"https://github.com/dcajasn/Riskfolio-Lib","clone":"git clone https://github.com/dcajasn/Riskfolio-Lib.git","description":"Portfolio Optimization in Python","language":"C++","stars":4444,"topics":["asset-allocation","convex-optimization","cvar-optimization","cvxpy","drawdown-model","duration-matching","efficient-frontier","finance","investment","investment-analysis","portfolio-management","portfolio-optimization","principal-components-regression","quantitative-finance","risk-contribution","risk-factors","risk-parity","sharpe-ratio","stepwise-regression","trading"],"license":"BSD-3-Clause","category":"quant_finance_tool","readme_excerpt":"# Riskfolio-Lib\n\n**Portfolio Optimization in Python, Easy for Everyone.**\n\n\n
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\nBuy Me a Coffee at ko-fi.com\n\n \n \n \n \n \n \n\n[![Star History Chart](https://api.star-history.com/svg?repos=dcajasn/Riskfolio-Lib&type=timeline&legend=top-left)](https://www.star-history.com/#dcajasn/Riskfolio-Lib&type=timeline&legend=top-left)\n\n## Description\n\nRiskfolio-Lib is a library for making __Portfolio Optimization in Python__ made in Peru 🇵🇪. Its objective is to help students, academics and practitioners to build investment portfolios based on mathematically complex models with low effort. It is built on top of\n[CVXPY](https://www.cvxpy.org/) and closely integrated\nwith [Pandas](https://pandas.pydata.org/) data structures.\n\nSome of key functionalities that Riskfolio-Lib offers:\n\n- Mean Risk and Logarithmic Mean Risk (Kelly Criterion) Portfolio Optimization with 4 objective functions:\n\n - Minimum Risk.\n - Maximum Return.\n - Maximum Utility Function.\n - Maximum Risk Adjusted Return Ratio.\n\n- Mean Risk and Logarithmic Mean Risk (Kelly Criterion) Portfolio Optimization with 26 convex risk measures:\n\n **Dispersion Risk Measures:**\n\n - Standard Deviation.\n - Square Root Kurtosis.\n - p-th Root Even Moment of order 2p.\n - Mean Absolute Deviation (MAD).\n - Gini Mean Difference (GMD).\n - Conditional Value at Risk Range.\n - Tail Gini Range.\n - Entropic Value at Risk Range.\n - Relativistic Value at Risk Range.\n - Range.\n  \n \n **Downside Risk Measures:**\n\n - Semi Standard Deviation.\n - Square Root Semi Kurtosis.\n - p-th Root Even Semi Moment of order 2p.\n - First Lower Partial Moment (Omega Ratio).\n - Second Lower Partial Moment (Sortino Ratio).\n - Conditional Value at Risk (CVaR).\n - Tail Gini.\n - Entropic Value at Risk (EVaR).\n - Relativistic Value at Risk (RLVaR).\n - Worst Case Realization (Minimax).\n  \n \n **Drawdown Risk Measures:**\n\n - Average Drawdown for uncompounded cumulative returns.\n - Ulcer Index for uncompounded cumulative returns.\n - Conditional Drawdown at Risk (CDaR) for uncompounded cumulative returns.\n - Entropic Drawdown at Risk (EDaR) for uncompounded cumulative returns.\n - Relativistic Drawdown at Risk (RLDaR) for uncompounded cumulative returns.\n - Maximum Drawdown (Calmar Ratio) for uncompounded cumulative returns.\n\n- Risk Parity Portfolio Optimization with 22 convex risk measures:\n\n **Dispersion Risk Measures:**\n\n - Standard Deviation.\n - Square Root Kurtosis.\n - p-th Root Even Moment of order 2p.\n - Mean Absolute Deviation (MAD).\n - Gini Mean Difference (GMD).\n - Conditional Value at Risk Range.\n - Tail Gini Range.\n - Entropic Value at Risk Range.\n - Relativistic Value at Risk Range.\n  \n\n **Downside Risk Measures:**\n\n - Semi Standard Deviation.\n - Square Root Semi Kurtosis.\n - p-th Root Even Semi Moment of order 2p.\n - First Lower Partial Moment (Omega Ratio)\n - Second Lower Partial Moment (Sortino Ratio)\n - Conditional Value at Risk (CVaR).\n - Tail Gini.\n - Entropic Value at Risk (EVaR).\n - Relativistic Value at Risk (RLVaR).\n  \n \n **Drawdown Risk Measures:**\n\n - Ulcer Index for uncompounded cumulative returns.\n - Conditional Drawdown at Risk (CDaR) for uncompounded cumulative returns.\n - Entropic Drawdown at Risk (EDaR) for uncompounded cumulative returns.\n - Relativistic Drawdown at Risk (RLDaR) for uncompounded cumulative returns.\n\n- Hierarchical Clustering Portfolio Optimization: Hierarchical Risk Parity (HRP) and Hierarchical Equal Risk Contribution (HERC) with 37 risk measures using naive risk parity:\n\n **Dispersion Risk Measures:**\n\n - Standard Deviation.\n - Variance.\n - Square Root Kurtosis.\n - 2p-th Root of Even Moment of Order 2p.\n - Mean Absolute Deviation (MAD).\n - Gini Mean Difference (GMD).\n - Value at Risk Range.\n - Conditional Value at Risk Range.\n - Tail Gini Range.\n - Entropic Value at Risk Range.\n - Relativistic Value at Risk Range.\n - Range.\n  \n \n **Downside Risk Measures:**\n\n - Semi Standard Deviation.\n - Fourth Root Semi Kurtosis.\n - 2p-th Root Even Semi Moment of order 2p.\n - First Lower Partial Moment (Omega Ratio).\n - Second Lower Partial Moment (Sortino Ratio).\n - Value at Risk (VaR).\n - Conditional Value at Risk (CVaR).\n - Tail Gini.\n - Entropic Value at Risk (EVaR).\n - Relativistic Value at Risk (RLVaR).\n - Worst Case Realization (Minimax).\n  \n \n **Drawdown Risk Measures:**\n\n - Average Drawdown for compounded and uncompounded cumulative returns.\n - Ulcer Index for compounded and uncompounded cumulative returns.\n - Drawdown at Risk (DaR) for compounded and uncompounded cumulative returns.\n - Conditional Drawdown at Risk (CDaR) for compounded and uncompounded cumulative returns.\n - Entropic Drawdown at Risk (EDaR) for compounded and uncompounded cumulative returns.\n - Relativistic Drawdown at Risk (RLDaR) for compounded and uncompounded cumulative returns.\n - Maximum Drawdown (Calmar Ratio) for compounded and uncompounded cumulative returns.\n\n- Nested Clustered Optimization (NCO) with four objective functions and the available risk measures to each objective:\n\n - Minimum Risk.\n - Maximum Return.\n - Maximum Utility Function.\n - Equal Risk Contribution.\n\n- Worst Case Mean Variance Portfolio Optimization.\n- Relaxed Risk Parity Portfolio Optimization.\n- Ordered Weighted Averaging (OWA) Portfolio Optimization.\n- Mean-Variance-Skewness-Kurtosis (MVSK) Portfolio Optimization (Semidefinite Relaxation).\n- Portfolio optimization with Black Litterman model.\n- Portfolio optimization with Risk Factors model.\n- Portfolio optimization with Black Litterman Bayesian model.\n- Portfolio optimization with Augmented Black Litterman model.\n- Portfolio optimization with Entropy Pooling model.\n- Portfolio optimization with constraints on tracking error and turnover.\n- Portfolio optimization with short positions and leveraged portfolios.\n- Portfolio optimization with constraints on maximum number of assets and number of effective assets.\n- Portfolio optimization with constraints based on graph information.\n- Portfolio optimization with inequality constraints on risk contributions for variance.\n- Portfolio optimization with inequality constraints on factor risk contributions for variance.\n- Portfolio optimization with integer constraints such as Cardinality on Assets and Categories, Mutually Exclusive and Join Investment.\n- Tools to build efficient frontier for 24 convex risk measures.\n- Tools to build linear constraints on assets, asset classes and risk factors.\n- Tools to build views on assets and asset classes.\n- Tools to build views on risk factors.\n- Tools to build risk contribution constraints per asset classes.\n- Tools to build risk contribution constraints per risk factor using explicit risk factors and principal components.\n- Tools to build bounds constraints for Hierarchical Clustering Portfolios.\n- Tools to calculate risk measures.\n- Tools to calculate risk contributions per asset.\n- Tools to calculate risk contributions per risk factor.\n- Tools to calculate uncertainty sets for mean vector and covariance matrix.\n- Tools to calculate assets clusters based on codependence metrics.\n- Tools to estimate loadings matrix (Stepwise Regression and Principal Components Regression).\n- Tools to visualizing portfolio properties and risk measures.\n- Tools to build reports on Jupyter Notebook and Excel. \n- Option to use commercial optimization solver such as MOSEK or GUROBI for large scale problems.\n\n\n## Documentation\n\nOnline documentation is available at [Documentation](https","default_branch":"master","files":2101,"tree":[".gitattributes",".github/FUNDING.yml",".github/workflows/build.yml",".github/workflows/docs.yml",".gitignore",".gitmodules",".readthedocs.yaml","AUTHORS.rst","CHANGELOG.rst","LICENSE.txt","MANIFEST.in","README.md","_config.yml","continuous_integration/install_dependencies.sh","docs/Makefile","docs/make.bat","docs/requirements.txt","docs/source/Makefile","docs/source/_static/Button.png","docs/source/_static/Button2.png","docs/source/_static/Riskfolio-social-1200x627.png","docs/source/_static/Riskfolio.ico","docs/source/_static/Riskfolio.png","docs/source/_static/custom.css","docs/source/_templates/base.html","docs/source/authors/index.rst","docs/source/book/book.rst","docs/source/conf.py","docs/source/course/course.rst","docs/source/images/.DS_Store","docs/source/images/Area_Frontier.png","docs/source/images/Assets_Clusters.png","docs/source/images/Assets_Clusters_Network.png","docs/source/images/Assets_Clusters_Network_Allocation.png","docs/source/images/Assets_Dendrogram.png","docs/source/images/Assets_Network.png","docs/source/images/Assets_Network_Allocation.png","docs/source/images/AxB.png","docs/source/images/AxB_int.png","docs/source/images/Bar_Chart.png","docs/source/images/BrinAttr.png","docs/source/images/BrinAttr_Plot.png","docs/source/images/Centrality_df.png","docs/source/images/Clusters_matrix_df.png","docs/source/images/Connection_df.png","docs/source/images/Constraints.png","docs/source/images/Constraints2.png","docs/source/images/Constraints_int.png","docs/source/images/Contents-1.png","docs/source/images/Contents-2.png","docs/source/images/Contents-3.png","docs/source/images/Contents-4.png","docs/source/images/Contents-5.png","docs/source/images/Contents-6.png","docs/source/images/CxD.png","docs/source/images/CxD_int.png","docs/source/images/Drawdown.png","docs/source/images/EP_Views.png","docs/source/images/ExF_int.png","docs/source/images/Excel.png","docs/source/images/HRPConstraints.png","docs/source/images/HRP_Bounds.png","docs/source/images/Histogram.png","docs/source/images/Images-XL/Annual_Pay.png","docs/source/images/Images-XL/Installation_1.png","docs/source/images/Images-XL/Installation_2.png","docs/source/images/Images-XL/Installation_3.png","docs/source/images/Images-XL/Monthly_Pay.png","docs/source/images/MSV_Frontier.png","docs/source/images/MVSC1.png","docs/source/images/MVSC2.png","docs/source/images/P_eqxQ_eq.png","docs/source/images/P_fxQ_f.png","docs/source/images/Pie_Chart.png","docs/source/images/Port_Series.png","docs/source/images/Port_Table.png","docs/source/images/PxQ.png","docs/source/images/Range.png","docs/source/images/Report_1.png","docs/source/images/Report_2.png","docs/source/images/Report_3.png","docs/source/images/Report_4.png","docs/source/images/Risk_Con.png","docs/source/images/Risk_Con_PC.png","docs/source/images/Risk_Con_RF.png","docs/source/images/Views.png","docs/source/images/clusters_df.png","docs/source/images/factorsviews.png","docs/source/index.rst","docs/source/riskfoliolib/LICENSE.txt","docs/source/riskfoliolib/auxiliary.rst","docs/source/riskfoliolib/biblio.bib","docs/source/riskfoliolib/changelog.rst","docs/source/riskfoliolib/constraints.rst","docs/source/riskfoliolib/contributing.rst","docs/source/riskfoliolib/examples.rst","docs/source/riskfoliolib/hcportfolio.rst","docs/source/riskfoliolib/index.rst","docs/source/riskfoliolib/install.rst","docs/source/riskfoliolib/license.rst","docs/source/riskfoliolib/parameters.rst","docs/source/riskfoliolib/plot.rst","docs/source/riskfoliolib/portfolio.rst","docs/source/riskfoliolib/reports.rst","docs/source/riskfoliolib/risk.rst","docs/source/riskfolioxl/LICENSE-XL.txt","docs/source/riskfolioxl/excel.rst","docs/source/riskfolioxl/index.rst","docs/source/riskfolioxl/licensexl.rst","docs/source/robots.txt","examples/.DS_Store","examples/Assets.xlsx","examples/Excel.png","examples/Fig1.png","examples/Fig2.png","examples/Fig3.png","examples/KeyRates.xlsx","examples/README.md","examples/Tutorial 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from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}