Metadata-Version: 2.1
Name: Riskfolio-Lib
Version: 0.0.7
Summary: Portfolio Optimization and Quantitative Strategic Asset Allocation in Python
Home-page: https://github.com/dcajasn/Riskfolio-Lib
Author: Dany Cajas
Author-email: dany.cajas.n@uni.pe
Maintainer: Dany Cajas
Maintainer-email: dany.cajas.n@uni.pe
License: BSD (3-clause)
Download-URL: https://github.com/dcajasn/Riskfolio-Lib.git
Project-URL: Documentation, https://riskfolio-lib.readthedocs.io/en/latest/
Project-URL: Issues, https://github.com/dcajasn/Riskfolio-Lib/issues
Project-URL: Personal website, http://financioneroncios.wordpress.com
Description: # Riskfolio-Lib
        
        **Quantitative Strategic Asset Allocation, Easy for Everyone.**
        
        <div class="row">
        <img src="https://raw.githubusercontent.com/dcajasn/Riskfolio-Lib/master/docs/source/images/MSV_Frontier.png" height="200">
        <img src="https://raw.githubusercontent.com/dcajasn/Riskfolio-Lib/master/docs/source/images/Pie_Chart.png" height="200">
        </div>
        
        <a href='https://ko-fi.com/B0B833SXD' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://cdn.ko-fi.com/cdn/kofi1.png?v=2' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
        
        ## Description
        
        Riskfolio-Lib is a library for making quantitative strategic asset allocation
        or portfolio optimization in Python made in Peru &#x1F1F5;&#x1F1EA;. It is built on top of
        [cvxpy](https://www.cvxpy.org/) and closely integrated
        with [pandas](https://pandas.pydata.org/) data structures.
        
        Some of key functionalities that Riskfolio-Lib offers:
        
        * Portfolio optimization with 4 objective functions:
        
            * Minimum Risk.
            * Maximum Return.
            * Maximum Utility Function.
            * Maximum Risk Adjusted Return Ratio.
        
        * Portfolio optimization with 11 convex risk measures:
        
            * Standard Deviation.
            * Semi Standard Deviation.
            * Mean Absolute Deviation (MAD).
            * First Lower Partial Moment (Omega Ratio)
            * Second Lower Partial Moment (Sortino Ratio)
            * Conditional Value at Risk (CVaR).
            * Worst Case Realization (Minimax Model)
            * Maximum Drawdown (Calmar Ratio)
            * Average Drawdown
            * Conditional Drawdown at Risk (CDaR).
            * Ulcer Index.
        
        * Risk Parity Portfolio optimization with 8 convex risk measures:
        
            * Standard Deviation.
            * Semi Standard Deviation.
            * Mean Absolute Deviation (MAD).
            * First Lower Partial Moment (Omega Ratio)
            * Second Lower Partial Moment (Sortino Ratio)
            * Conditional Value at Risk (CVaR).
            * Conditional Drawdown at Risk (CDaR).
            * Ulcer Index.
        
        * Worst Case Mean Variance Portfolio optimization.
        * Portfolio optimization with Black Litterman model.
        * Portfolio optimization with Risk Factors model.
        * Portfolio optimization with constraints on tracking error and turnover.
        * Portfolio optimization with short positions and leveraged portfolios.
        * Tools for build efficient frontier for 11 risk measures.
        * Tools for build linear constraints on assets, asset classes and risk factors.
        * Tools for build views on assets and asset classes.
        * Tools for calculate risk measures.
        * Tools for calculate risk contributions per asset.
        * Tools for calculate uncertainty sets for mean vector and covariance matrix.
        * Tools for estimate loadings matrix (Stepwise Regression and Principal Components Regression).
        * Tools for visualizing portfolio properties and risk measures.
        
        
        ## Documentation
        
        Online documentation is available at [Documentation](https://riskfolio-lib.readthedocs.io/en/latest/).
        
        The docs include a [tutorial](https://riskfolio-lib.readthedocs.io/en/latest/examples.html)
        with examples that shows the capacities of Riskfolio-Lib.
        
        
        ## Dependencies
        
        Riskfolio-Lib supports Python 3.7+.
        
        Installation requires:
        * [numpy](http://www.numpy.org/) >= 1.17.0
        * [scipy](https://www.scipy.org/) >= 1.1.0
        * [pandas](https://pandas.pydata.org/) >= 1.0.0
        * [matplotlib](https://matplotlib.org/) >= 3.3.0
        * [cvxpy](https://www.cvxpy.org/) >= 1.0.15
        * [scikit-learn](https://scikit-learn.org/stable/) >= 0.22.0
        * [statsmodels](https://www.statsmodels.org/) >= 0.10.1
        * [arch](https://bashtage.github.io/arch/) >= 4.15
        
        
        ## Installation
        
        The latest stable release (and older versions) can be installed from PyPI:
        
            pip install riskfolio-lib
        
         
        ## Development
        
        Riskfolio-Lib development takes place on Github: https://github.com/dcajasn/Riskfolio-Lib
        
        ## RoadMap
        
        The plan for this module is to add more functions that will be very useful
        to asset managers.
        
        * Mean Entropic Risk Optimization Portfolios.
        * Add functions to estimate Duration, Convexity, Key Rate Durations and Convexities of bonds without embedded options (for loadings matrix).
        * Add more functions based on suggestion of users.
Keywords: finance,portfolio,optimization,quant,asset,allocation,investing
Platform: UNKNOWN
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Office/Business :: Financial :: Investment
Classifier: Topic :: Office/Business :: Financial
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Operating System :: Microsoft
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Requires-Python: >=3.7
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
