pynance

v1.0.0 Release: May 11, 2022

See CHANGELOG.md for details

pynance creates various financial models and relevant graphics. This project is a work-in-progress and does not promise any results. See docs for a comprehensive list of functions and functionality.

Important Information

This project currently draws requested stock data through Yahoo!Finance and uses their adjusted close prices for necessary computations

Time period for data and time horizon may be adjusted within the code but is currently not accessible from main functionality

Installation

pynance may be installed via the repo:

git clone https://github.com/mqandil/pynance
cd pynance
pip install -e .

This is the most up-to-date version of pynance

Portfolio Optimizer

portfolio_optimizer optimizes portfolios of selected stock data using Markowitz's Modern Portfolio Theory. Portfolios can currently be optimized by maximum sharpe ratio or minimum standard deviation. A portfolio's capital allocation line may also be determined.

Maximum Sharpe Ratio Portfolio

Retreive a dataframe of expected returns and standard deviation, or a dataframe or pie chart including stock portfolio weights with max_sharpe_portfolio().

>>> from pynance import portfolio_optimizer as po
>>> ticker_list = ['MSFT', 'PG', 'HLI']
>>> portfolio = po.PortfolioCalculations(ticker_list)
>>> risk_return = portfolio.max_sharpe_portfolio('rr')
>>> print(risk_return)

                   Max Sharpe Portfolio
Expected Return                  27.03%
Standard Deviation               14.28%

>>> max_sharpe_df = portfolio.max_sharpe_portfolio('df')
>>> print(max_sharpe_df.head(3))

      Portfolio Weight
MSFT            54.42%
PG              36.44%
HLI              9.13% 
[3 rows x 1 column]

Minimum Variance Portfolio

Retreive a pie chart including stock portfolio weights and a chart of portfolio weights for chosen stocks with min_std_portfolio().

>>> from pynance import portfolio_optimizer as po
>>> ticker_list = ['MSFT', 'PG', 'HLI']
>>> portfolio = po.PortfolioCalculations(ticker_list)
>>> risk_return = portfolio.min_var_portfolio('rr')
>>> print(risk_return)

                   Min Var Portfolio
Expected Return               22.76%
Standard Deviation            13.17%

>>> min_var_df = portfolio.min_var_portfolio('df')
>>> print(min_var_df.head(3))

      Portfolio Weight
MSFT            29.23%
PG              57.19%
HLI             13.58% 
[3 rows x 1 column]

Efficient Frontier

Return Scatterplot of Annualized Expected Returns and Standard Deviations for Optimized Portfolios with efficient_frontier()

>>> from pynance import portfolio_optimizer as po
>>> ticker_list = ['MSFT', 'PG', 'HLI']
>>> portfolio = po.PortfolioCalculations(ticker_list)
>>> fig = portfolio.efficient_frontier()
>>> fig.show()

Expected Returns and Standard Deviation Error

Return Continuous Error Bars (Standard Deviation) by Portfolio ID with expected_return_range()

>>> from pynance import portfolio_optimizer as po
>>> ticker_list = ['MSFT', 'PG', 'HLI']
>>> portfolio = po.PortfolioCalculations(ticker_list)
>>> fig = portfolio.expected_return_range()
>>> fig.show()

Final Capital Allocation

Returns Capital Allocation for selected Portfolio with Portfolio_ID argument with capital_allocation()

>>> from pynance import portfolio_optimizer as po
>>> ticker_list = ['MSFT', 'PG', 'HLI']
>>> portfolio_ID = 56
>>> portfolio = po.PortfolioCalculations(ticker_list)
>>> portfolio_56_allocation = portfolio.capital_allocation(portfolio_ID)
>>> print(portfolio_56_allocation.head(3))

      Portfolio Weight
MSFT            37.48%
PG              50.38%
HLI             12.13%
[3 rows x 1 column]

Valuation Virtuoso

valuation_virtuoso creates discounted cash flow models for selected publicly-traded firms and returns models, charts, and an estimated stock price valuation.

Coming Soon...