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

title, description, keywords

title	description	keywords
herc	The page provides detailed documentation for the Hierarchical Equal Risk Contribution (HERC) method in the OpenBB Terminal's portfolio optimization module. The method is used for the optimized allocation of resources in a portfolio to minimize risk. The page includes a detailed explanation of input parameters, return types, and code examples.	HERC Method Portfolio Optimization Risk Management Hierarchical Equal Risk Contribution Financial Modelling

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

Optimize with Hierarchical Equal Risk Contribution (HERC) method.

Source Code: [link]

openbb.portfolio.po.herc(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
objective	str	Objective function of the optimization model, by default 'MinRisk' Possible values are: - 'MinRisk': Minimize the selected risk measure. - 'Utility': Maximize the risk averse utility function. - 'Sharpe': Maximize the risk adjusted return ratio based on the selected risk measure. - 'MaxRet': Maximize the expected return of the portfolio.	None	True
risk_measure	str	The risk measure used to optimize the portfolio, by default 'MV' If model is 'NCO', the risk measures available depends on the objective function. Possible values are: - 'MV': Variance. - 'MAD': Mean Absolute Deviation. - 'MSV': Semi Standard Deviation. - 'FLPM': First Lower Partial Moment (Omega Ratio). - 'SLPM': Second Lower Partial Moment (Sortino Ratio). - 'VaR': Value at Risk. - 'CVaR': Conditional Value at Risk. - 'TG': Tail Gini. - 'EVaR': Entropic Value at Risk. - 'WR': Worst Realization (Minimax). - 'RG': Range of returns. - 'CVRG': CVaR range of returns. - 'TGRG': Tail Gini range of returns. - 'MDD': Maximum Drawdown of uncompounded cumulative returns (Calmar Ratio). - 'ADD': Average Drawdown of uncompounded cumulative returns. - 'DaR': Drawdown at Risk of uncompounded cumulative returns. - 'CDaR': Conditional Drawdown at Risk of uncompounded cumulative returns. - 'EDaR': Entropic Drawdown at Risk of uncompounded cumulative returns. - 'UCI': Ulcer Index of uncompounded cumulative returns. - 'MDD_Rel': Maximum Drawdown of compounded cumulative returns (Calmar Ratio). - 'ADD_Rel': Average Drawdown of compounded cumulative returns. - 'DaR_Rel': Drawdown at Risk of compounded cumulative returns. - 'CDaR_Rel': Conditional Drawdown at Risk of compounded cumulative returns. - 'EDaR_Rel': Entropic Drawdown at Risk of compounded cumulative returns. - 'UCI_Rel': Ulcer Index of compounded 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
risk_aversion	float	Risk aversion factor of the 'Utility' objective function, by default 1.0	None	True
alpha	float	Significance level of VaR, CVaR, EDaR, DaR, CDaR, EDaR, Tail Gini of losses, by default 0.05	None	True
a_sim	float	Number of CVaRs used to approximate Tail Gini of losses, by default 100	None	True
beta	float	Significance level of CVaR and Tail Gini of gains. If None it duplicates alpha value, by default None	None	True
b_sim	float	Number of CVaRs used to approximate Tail Gini of gains. If None it duplicates a_sim value, by default None	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
codependence	str	The codependence or similarity matrix used to build the distance metric and clusters. The default is 'pearson'. Possible values are: - 'pearson': pearson correlation matrix. Distance formula: .. math:: D_{i,j} = \sqrt{0.5(1-\rho^{pearson}{i,j})} - 'spearman': spearman correlation matrix. Distance formula: .. math:: D{i,j} = \sqrt{0.5(1-\rho^{spearman}{i,j})} - 'abs_pearson': absolute value pearson correlation matrix. Distance formula: .. math:: D{i,j} = \sqrt{(1-	\rho^{pearson}_{i,j}	)} - 'abs_spearman': absolute value spearman correlation matrix. Distance formula: .. math:: D_{i,j} = \sqrt{(1-
linkage	str	Linkage method of hierarchical clustering. For more information see linkage <https://docs.scipy.org/doc/scipy/reference/generated/scipy.<br/>cluster.hierarchy.linkage.html?highlight=linkage#scipy.cluster.hierarchy.linkage>__. The default is 'single'. Possible values are: - 'single'. - 'complete'. - 'average'. - 'weighted'. - 'centroid'. - 'median'. - 'ward'. - 'dbht': Direct Bubble Hierarchical Tree.	None	True
k	int	Number of clusters. This value is took instead of the optimal number of clusters calculated with the two difference gap statistic, by default None	None	True
max_k	int	Max number of clusters used by the two difference gap statistic to find the optimal number of clusters, by default 10	None	True
bins_info	str	Number of bins used to calculate variation of information, by default 'KN'. Possible values are: - 'KN': Knuth's choice method. For more information see knuth_bin_width <https://docs.astropy.org/en/stable/api/astropy.stats.knuth_bin_width.html>. - 'FD': Freedman–Diaconis' choice method. For more information see freedman_bin_width <https://docs.astropy.org/en/stable/api/astropy.stats.freedman_bin_width.html>. - 'SC': Scotts' choice method. For more information see scott_bin_width <https://docs.astropy.org/en/stable/api/astropy.stats.scott_bin_width.html>__. - 'HGR': Hacine-Gharbi and Ravier' choice method.	None	True
alpha_tail	float	Significance level for lower tail dependence index, by default 0.05	None	True
leaf_order	bool	Indicates if the cluster are ordered so that the distance between successive leaves is minimal, by default True	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.herc(symbols=["AAPL", "MSFT", "AMZN"])

(        value
 AAPL  0.17521
 MSFT  0.19846
 AMZN  0.62633,
 {'Return': 0.16899696275924703,
  'Volatility': 0.34337400112782096,
  'Sharpe ratio': 0.49216586638525933})

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.herc(portfolio_engine=p)

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