pyPortfolioAnalysis 0.0.900

Portfolio Analysis, methods for portfolio optimization

pyPortfolioAnalysis

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

Installation

Use the package manager pip to install foobar.

pip install pyPortfolioAnalysis

Usage

import pyPortfolioAnalysis
#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]})
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(R, 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