PyVest is a Python library that provides tools for investment analysis.

PyVest can be used to easily create graphs of risk-return trade-off. Given a set of risky and/or non-risky assets, the following objects can be represented on a two-dimensional graph of the expected return vs the standard deviation:

• Feasible portfolios
• Minimum variance portfolio (MVP)
• Efficient frontier
• Tangency portfolio
• Capital allocation line (CAL)
• Optimal portfolio of an investor
• Indifference curves of an investor

Import the class InvestmentUniverse:

from pyvest import InvestmentUniverse

Define the names of the assets:

assets = ['KO', 'MSFT']

Define the expected returns corresponding to each asset:

mu = [8, 14]

Define the variance-covariance matrix of the assets:

cov = [[3**2, 0],
       [0, 6**2]]

Construct the InvestmentUniverse corresponding to those assets:

investment_universe = InvestmentUniverse(assets, mu, cov)

Calculate the feasible portfolios:

investment_universe.calculate_feasible_portfolios()

Calculate the MVP:

investment_universe.calculate_mvp()

Calculate the efficient frontier:

investment_universe.calculate_efficient_frontier()

Plot the risk-return trade-off graph of the investment universe:

investment_universe.plot()

The risky assets are defined as above:

from pyvest import InvestmentUniverse

assets = ['KO', 'MSFT']
mu = [8, 14]
cov = [[3**2, 0],
       [0, 6**2]]

A risk-free asset of 2% is added to the investment universe:

investment_universe_with_r_f = InvestmentUniverse(assets, mu, cov, r_f=2)

The feasible portfolios, the MVP and the efficient frontier are calculated as above:

investment_universe_with_r_f.calculate_feasible_portfolios()
investment_universe_with_r_f.calculate_mvp()
investment_universe_with_r_f.calculate_efficient_frontier()

Calculate the tangency portfolio:

investment_universe_with_r_f.calculate_tangency_portfolio()

Calculate the CAL:

investment_universe_with_r_f.calculate_cal()

Plot the risk-return trade-off graph of the investment universe with a risk-free asset:

investment_universe_with_r_f.plot()