pyPortfolioAnalysis

pyPortfolioAnalysis is a Python library for numeric method for portfolio optimisation.

Installation

Use the package manager pip to install pyPortfolioAnalysis.

Documentation is available as docstring, HTML or Text

pip install pyPortfolioAnalysis

Usage

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

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