Metadata-Version: 2.1
Name: pyPortfolioAnalysis
Version: 1.0.2
Summary: Portfolio Analysis, methods for portfolio optimization
Home-page: https://github.com/anuragagrawaal/pyPortfolioAnalysis
Author: Anurag Agrawal
Author-email: agrawalanurag1999@gmail.com
License: GPL3
Description: 
        # pyPortfolioAnalysis
        
        pyPortfolioAnalysis is a Python library for numeric method for portfolio optimisation.
        
        ## Installation
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install [pyPortfolioAnalysis](https://pypi.org/project/pyPortfolioAnalysis/).
        
        Documentation is available as docstring, [HTML](https://pyportfolioanalysis.readthedocs.io/en/latest/index.html) or [Text](https://github.com/anuragagrawaal/pyPortfolioAnalysis/blob/main/docs/doc_pyPortfolioAnalysis.txt)
        
        
        
        ```bash
        pip install pyPortfolioAnalysis
        ```
        
        ## Usage
        
        ```python
        from pyPortfolioAnalysis import *
        import pandas as pd
        #Sample portfolio optimisation
        import pandas_datareader as pdr
        aapl = pdr.get_data_yahoo('AAPL')
        msft = pdr.get_data_yahoo('MSFT')
        tsla = pdr.get_data_yahoo('TSLA')
        uber = pdr.get_data_yahoo('UBER')
        amzn = pdr.get_data_yahoo('AMZN')
        port = pd.DataFrame({'aapl': pd.DataFrame.reset_index(aapl).iloc[:,6], 'msft':pd.DataFrame.reset_index(msft).iloc[:,6],
                           'tsla': pd.DataFrame.reset_index(tsla).iloc[:,6], 'uber': pd.DataFrame.reset_index(uber).iloc[:,6],
                            'amzn': pd.DataFrame.reset_index(amzn).iloc[:,6]})
        port_ret = port.pct_change().dropna()
        p1 = portfolio_spec(assets = ['AAPL', 'MSFT', 'TSLA', 'UBER', 'AMZN'])
        add_constraint(p1, 'long_only')
        add_constraint(p1, 'full_investment')
        add_objective(p1, kind='return', name = 'mean', target = 0.002)
        add_objective(p1, kind='risk', name = 'std', target = .018)
        p1.port_summary()
        constraints = get_constraints(p1)
        p1.port_summary()
        
        optimize_portfolio(port_ret, p1, optimize_method = 'DEoptim', disp = False)
        ```
        
        ## Contributing
        Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
        
        Please make sure to update tests as appropriate.
        
        ## Authors
        Anurag Agrawal
        
        ## Contributors
        Saloni Mangla
        
        ## License
        [GPL3](https://choosealicense.com/licenses/gpl-3.0/)
        
        ## References
        Brian G. Peterson and Peter Carl (2018). PortfolioAnalytics: Portfolio Analysis, Including Numerical Methods for Optimization of Portfolios. R package version 1.1.0. https://CRAN.R-project.org/package=PortfolioAnalytics
        
        Boudt, Kris and Lu, Wanbo and Peeters, Benedict, Higher Order Comoments of Multifactor Models and Asset Allocation (June 16, 2014). Available at SSRN: http://ssrn.com/abstract=2409603 or http://dx.doi.org/10.2139/ssrn.2409603
        
        Chriss, Neil A and Almgren, Robert, Portfolios from Sorts (April 27, 2005). Available at SSRN: http://ssrn.com/abstract=720041 or http://dx.doi.org/10.2139/ssrn.720041
        
        Meucci, Attilio, The Black-Litterman Approach: Original Model and Extensions (August 1, 2008). Shorter version in, THE ENCYCLOPEDIA OF QUANTITATIVE FINANCE, Wiley, 2010. Avail- able at SSRN: http://ssrn.com/abstract=1117574 or http://dx.doi.org/10.2139/ssrn.1117574
        
        Meucci, Attilio, Fully Flexible Views: Theory and Practice (August 8, 2008). Fully Flexible Views: Theory and Practice, Risk, Vol. 21, No. 10, pp. 97-102, October 2008. Available at SSRN: http://ssrn.com/abstract=1213325
        
        Scherer, Bernd and Martin, Doug, Modern Portfolio Optimization. Springer. 2005.
        
        Shaw, William Thornton, Portfolio Optimization for VAR, CVaR, Omega and Utility with General Return Distributions: A Monte Carlo Approach for Long-Only and Bounded Short Portfolios with Optional Robustness and a Simplified Approach to Covariance Matching (June 1, 2011). Available at SSRN: http://ssrn.com/abstract=1856476 or http://dx.doi.org/10.2139/ssrn.1856476
        
        [![Downloads](https://pepy.tech/badge/pyportfolioanalysis)](https://pepy.tech/project/pyportfolioanalysis)
        
Platform: UNKNOWN
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
