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

title, description, keywords

title	description	keywords
riskparity	Documentation page on how to optimize portfolio with Risk Parity using the risk budgeting approach in the OpenBB Terminal Python package. It includes parameters specifications, return values, and usage examples.	Risk Parity Portfolio Optimization Risk Budgeting Approach Portfolio Engine Investment Strategies

import HeadTitle from '@site/src/components/General/HeadTitle.tsx';

Optimize with Risk Parity using the risk budgeting approach

Source Code: [link]

openbb.portfolio.po.riskparity(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
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
target_return	float	Constraint on minimum level of portfolio's return, by default -1.0	None	True
mean	str	The method used to estimate the expected returns, by default 'hist' Possible values are: - 'hist': use historical estimates. - 'ewma1': use ewma with adjust=True. For more information see EWM <https://pandas.pydata.org/pandas-docs/stable/user_guide/window.html#exponentially-weighted-window>. - 'ewma2': use ewma with adjust=False. For more information see EWM <https://pandas.pydata.org/pandas-docs/stable/user_guide/window.html#exponentially-weighted-window>.	None	True
covariance	str	The method used to estimate the covariance matrix, by default 'hist' Possible values are: - 'hist': use historical estimates. - 'ewma1': use ewma with adjust=True. For more information see EWM <https://pandas.pydata.org/pandas-docs/stable/user_guide/window.html#exponentially-weighted-window>. - 'ewma2': use ewma with adjust=False. For more information see EWM <https://pandas.pydata.org/pandas-docs/stable/user_guide/window.html#exponentially-weighted-window>. - 'ledoit': use the Ledoit and Wolf Shrinkage method. - 'oas': use the Oracle Approximation Shrinkage method. - 'shrunk': use the basic Shrunk Covariance method. - 'gl': use the basic Graphical Lasso Covariance method. - 'jlogo': use the j-LoGo Covariance method. For more information see: a-jLogo. - 'fixed': denoise using fixed method. For more information see chapter 2 of a-MLforAM. - 'spectral': denoise using spectral method. For more information see chapter 2 of a-MLforAM. - 'shrink': denoise using shrink method. For more information see chapter 2 of a-MLforAM.	None	True
d_ewma	float	The smoothing factor of ewma methods, by default 0.94	None	True
risk_cont	List[str]	The vector of risk contribution per asset, by default 1/n (number of assets)	None	True

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Returns

Type	Description
Tuple[pd.DataFrame, Dict]	Tuple with weights and performance dictionary

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Examples

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

(        value
 AAPL  0.33201
 MSFT  0.34953
 AMZN  0.31846,
 {'Return': 0.19946644069106015,
  'Volatility': 0.29074583524705444,
  'Sharpe ratio': 0.6860508956957826})

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

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