OpenBB/website/content/sdk/reference/portfolio/po/ef.md

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ef	The page highlights the use and functioning of the efficient frontier model in portfolio optimization. It includes explanations about parameters used, type of returns, and examples. Furthermore, it details the process for displaying the efficient frontier chart.	Efficient Frontier Portfolio Optimization Financial Models Risk Measure Portfolio Simulation Stock Portfolio Data Visualization Financial Software

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Get Efficient Frontier

Source Code: [link]

openbb.portfolio.po.ef(portfolio_engine: portfolio_optimization.po_engine.PoEngine = None, symbols: List[str] = None, kwargs: Any)

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Parameters

Name	Type	Description	Default	Optional
portfolio_engine	PoEngine	Portfolio optimization engine, by default None Use portfolio.po.load to load a portfolio engine	None	True
symbols	List[str]	List of symbols, by default None	None	True
interval	str	Interval to get data, by default '3y'	None	True
start_date	str	If not using interval, start date string (YYYY-MM-DD), by default ""	None	True
end_date	str	If not using interval, end date string (YYYY-MM-DD). If empty use last weekday, by default ""	None	True
log_returns	bool	If True use log returns, else arithmetic returns, by default False	None	True
freq	str	Frequency of returns, by default 'D'. Options: 'D' for daily, 'W' for weekly, 'M' for monthly	None	True
maxnan	float	Maximum percentage of NaNs allowed in the data, by default 0.05	None	True
threshold	float	Value used to replace outliers that are higher than threshold, by default 0.0	None	True
method	str	Method used to fill nan values, by default 'time' For more information see interpolate <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.interpolate.html>__.	None	True
value	float	Amount to allocate to portfolio in long positions, by default 1.0	None	True
value_short	float	Amount to allocate to portfolio in short positions, by default 0.0	None	True
risk_measure	str	The risk measure used to optimize the portfolio, by default 'MV' Possible values are: - 'MV': Standard Deviation. - 'MAD': Mean Absolute Deviation. - 'MSV': Semi Standard Deviation. - 'FLPM': First Lower Partial Moment (Omega Ratio). - 'SLPM': Second Lower Partial Moment (Sortino Ratio). - 'CVaR': Conditional Value at Risk. - 'EVaR': Entropic Value at Risk. - 'WR': Worst Realization. - 'ADD': Average Drawdown of uncompounded cumulative returns. - 'UCI': Ulcer Index of uncompounded cumulative returns. - 'CDaR': Conditional Drawdown at Risk of uncompounded cumulative returns. - 'EDaR': Entropic Drawdown at Risk of uncompounded cumulative returns. - 'MDD': Maximum Drawdown of uncompounded cumulative returns.	None	True
risk_free_rate	float	Risk free rate, annualized. Used for 'FLPM' and 'SLPM' and Sharpe objective function, by default 0.0	None	True
alpha	float	Significance level of VaR, CVaR, EDaR, DaR, CDaR, EDaR, Tail Gini of losses, by default 0.05	None	True
n_portfolios	int	Number of portfolios to simulate, by default 100	None	True
seed	int	Seed used to generate random portfolios, by default 123	None	True

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Returns

Type	Description
Tuple[	pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, Optional[pd.DataFrame], NDArray[floating], NDArray[floating], rp.Portfolio,

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Examples

from openbb_terminal.sdk import openbb
frontier = openbb.portfolio.po.ef(symbols=["AAPL", "MSFT", "AMZN"])

from openbb_terminal.sdk import openbb
p = openbb.portfolio.po.load(symbols_file_path="~/openbb_terminal/miscellaneous/portfolio_examples/allocation/60_40_Portfolio.xlsx")
frontier = openbb.portfolio.po.ef(portfolio_engine=p)

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Display efficient frontier

Source Code: [link]

openbb.portfolio.po.ef_chart(portfolio_engine: portfolio_optimization.po_engine.PoEngine = None, symbols: List[str] = None, kwargs: Any)

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Parameters

Name	Type	Description	Default	Optional
portfolio_engine	PoEngine	Portfolio optimization engine, by default None Use portfolio.po.load to load a portfolio engine	None	True
symbols	List[str]	List of symbols, by default None	None	True
interval	str	Interval to get data, by default '3y'	None	True
start_date	str	If not using interval, start date string (YYYY-MM-DD), by default ""	None	True
end_date	str	If not using interval, end date string (YYYY-MM-DD). If empty use last weekday, by default ""	None	True
log_returns	bool	If True use log returns, else arithmetic returns, by default False	None	True
freq	str	Frequency of returns, by default 'D'. Options: 'D' for daily, 'W' for weekly, 'M' for monthly	None	True
maxnan	float	Maximum percentage of NaNs allowed in the data, by default 0.05	None	True
threshold	float	Value used to replace outliers that are higher than threshold, by default 0.0	None	True
method	str	Method used to fill nan values, by default 'time' For more information see interpolate <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.interpolate.html>__.	None	True
value	float	Amount to allocate to portfolio in long positions, by default 1.0	None	True
value_short	float	Amount to allocate to portfolio in short positions, by default 0.0	None	True
risk_measure	str	The risk measure used to optimize the portfolio, by default 'MV' Possible values are: - 'MV': Standard Deviation. - 'MAD': Mean Absolute Deviation. - 'MSV': Semi Standard Deviation. - 'FLPM': First Lower Partial Moment (Omega Ratio). - 'SLPM': Second Lower Partial Moment (Sortino Ratio). - 'CVaR': Conditional Value at Risk. - 'EVaR': Entropic Value at Risk. - 'WR': Worst Realization. - 'ADD': Average Drawdown of uncompounded cumulative returns. - 'UCI': Ulcer Index of uncompounded cumulative returns. - 'CDaR': Conditional Drawdown at Risk of uncompounded cumulative returns. - 'EDaR': Entropic Drawdown at Risk of uncompounded cumulative returns. - 'MDD': Maximum Drawdown of uncompounded cumulative returns.	None	True
risk_free_rate	float	Risk free rate, annualized. Used for 'FLPM' and 'SLPM' and Sharpe objective function, by default 0.0	None	True
alpha	float	Significance level of VaR, CVaR, EDaR, DaR, CDaR, EDaR, Tail Gini of losses, by default 0.05	None	True
n_portfolios	int	Number of portfolios to simulate, by default 100	None	True
seed	int	Seed used to generate random portfolios, by default 123	None	True

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Returns

This function does not return anything

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Examples

from openbb_terminal.sdk import openbb
frontier = openbb.portfolio.po.ef_chart(symbols=["AAPL", "MSFT", "AMZN"])

from openbb_terminal.sdk import openbb
p = openbb.portfolio.po.load(symbols_file_path="openbb_terminal/miscellaneous/portfolio_examples/allocation/60_40_Portfolio.xlsx")
frontier = openbb.portfolio.po.ef_chart(portfolio_engine=p)

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