| Name | pyvest JSON |
| Version |
0.0.6
JSON |
| download |
| home_page | None |
| Summary | PyVest is a Python library that provides tools for investment analysis. |
| upload_time | 2024-11-03 18:30:27 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | MIT |
| keywords |
finance
investment
portfolio theory
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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No Travis.
|
| coveralls test coverage |
No coveralls.
|
# PyVest
PyVest is a Python library that provides tools for investment
analysis.
## Risk-return trade-off graph
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
### Example 1: No risk-free asset
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()
### Example 2: With a risk-free asset
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()
Raw data
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"description": "# PyVest\r\n\r\nPyVest is a Python library that provides tools for investment \r\nanalysis.\r\n\r\n## Risk-return trade-off graph\r\n\r\nPyVest can be used to easily create graphs of risk-return trade-off. \r\nGiven a set of risky and/or non-risky assets, the following objects \r\ncan be represented on a two-dimensional graph of the expected return \r\nvs the standard deviation:\r\n\r\n- Feasible portfolios\r\n- Minimum variance portfolio (MVP)\r\n- Efficient frontier\r\n- Tangency portfolio\r\n- Capital allocation line (CAL)\r\n- Optimal portfolio of an investor\r\n- Indifference curves of an investor\r\n\r\n\r\n### Example 1: No risk-free asset\r\n\r\nImport the class InvestmentUniverse:\r\n\r\n from pyvest import InvestmentUniverse\r\n\r\nDefine the names of the assets:\r\n\r\n assets = ['KO', 'MSFT']\r\n\r\nDefine the expected returns corresponding to each asset:\r\n\r\n mu = [8, 14]\r\n\r\nDefine the variance-covariance matrix of the assets:\r\n\r\n cov = [[3**2, 0],\r\n [0, 6**2]]\r\n\r\nConstruct the InvestmentUniverse corresponding to those assets:\r\n\r\n investment_universe = InvestmentUniverse(assets, mu, cov)\r\n\r\nCalculate the feasible portfolios:\r\n\r\n investment_universe.calculate_feasible_portfolios()\r\n\r\nCalculate the MVP:\r\n\r\n investment_universe.calculate_mvp()\r\n\r\nCalculate the efficient frontier:\r\n\r\n investment_universe.calculate_efficient_frontier()\r\n\r\nPlot the risk-return trade-off graph of the investment universe:\r\n\r\n investment_universe.plot()\r\n\r\n### Example 2: With a risk-free asset\r\n\r\nThe risky assets are defined as above:\r\n\r\n from pyvest import InvestmentUniverse\r\n\r\n assets = ['KO', 'MSFT']\r\n mu = [8, 14]\r\n cov = [[3**2, 0],\r\n [0, 6**2]]\r\n\r\nA risk-free asset of 2% is added to the investment universe:\r\n\r\n investment_universe_with_r_f = InvestmentUniverse(assets, mu, cov, r_f=2)\r\n\r\nThe feasible portfolios, the MVP and the efficient frontier are \r\ncalculated as above:\r\n\r\n investment_universe_with_r_f.calculate_feasible_portfolios()\r\n investment_universe_with_r_f.calculate_mvp()\r\n investment_universe_with_r_f.calculate_efficient_frontier()\r\n\r\nCalculate the tangency portfolio:\r\n\r\n investment_universe_with_r_f.calculate_tangency_portfolio()\r\n\r\nCalculate the CAL:\r\n\r\n investment_universe_with_r_f.calculate_cal()\r\n\r\nPlot the risk-return trade-off graph of the investment universe with a \r\nrisk-free asset:\r\n\r\n investment_universe_with_r_f.plot()\r\n",
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