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

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title	description	keywords
herc	The page contains details about using HERC (Hierarchical Equal Risk Contribution) for building a portfolio. It covers its usage, parameters, and a clear step-by-step example.	Hierarchical Equal Risk Contribution Portfolio HERC Risk Measurement SEO improvement Financial trading Financial portfolio Risk management Codependence Covariance Risk measure optimization

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Builds a hierarchical equal risk contribution portfolio

Usage

herc [-cd {pearson,spearman,abs_pearson,abs_spearman,distance,mutual_info,tail}] [-cv {hist,ewma1,ewma2,ledoit,oas,shrunk,gl,jlogo,fixed,spectral,shrink}] [-rm RISK-MEASURE] [-as CVAR_SIMULATIONS_LOSSES] [-b CVAR_SIGNIFICANCE] [-bs CVAR_SIMULATIONS_GAINS] [-lk LINKAGE] [-k AMOUNT_CLUSTERS] [-mk MAX_CLUSTERS] [-bi {KN,FD,SC,HGR}] [-at ALPHA_TAIL] [-lo LEAF_ORDER] [-de SMOOTHING_FACTOR_EWMA] [-mt METHOD] [-ct CATEGORIES] [-p PERIOD] [-s START_PERIOD] [-e END_PERIOD] [-lr] [--freq {d,w,m}] [-mn MAX_NAN] [-th THRESHOLD_VALUE] [-r RISK_FREE] [-a SIGNIFICANCE_LEVEL] [-v LONG_ALLOCATION] [--name NAME]

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Parameters

Name	Description	Default	Optional	Choices
co_dependence	The codependence or similarity matrix used to build the distance metric and clusters. Possible values are: 'pearson': pearson correlation matrix 'spearman': spearman correlation matrix 'abs_pearson': absolute value of pearson correlation matrix 'abs_spearman': absolute value of spearman correlation matrix 'distance': distance correlation matrix 'mutual_info': mutual information codependence matrix 'tail': tail index codependence matrix	pearson	True	pearson, spearman, abs_pearson, abs_spearman, distance, mutual_info, tail
covariance	Method used to estimate covariance matrix. Possible values are 'hist': historical method 'ewma1': exponential weighted moving average with adjust=True 'ewma2': exponential weighted moving average with adjust=False 'ledoit': Ledoit and Wolf shrinkage method 'oas': oracle shrinkage method 'shrunk': scikit-learn shrunk method 'gl': graphical lasso method 'jlogo': j-logo covariance 'fixed': takes average of eigenvalues above max Marchenko Pastour limit 'spectral': makes zero eigenvalues above max Marchenko Pastour limit 'shrink': Lopez de Prado's book shrinkage method	hist	True	hist, ewma1, ewma2, ledoit, oas, shrunk, gl, jlogo, fixed, spectral, shrink
risk_measure	Risk measure used to optimize the portfolio. Possible values are: 'MV' : Variance 'MAD' : Mean Absolute Deviation 'GMD' : Gini Mean Difference 'MSV' : Semi Variance (Variance of negative returns) 'FLPM' : First Lower Partial Moment 'SLPM' : Second Lower Partial Moment 'VaR' : Value at Risk 'CVaR' : Conditional Value at Risk 'TG' : Tail Gini 'EVaR' : Entropic Value at Risk 'WR' : Worst Realization 'RG' : Range 'CVRG' : CVaR Range 'TGRG' : Tail Gini Range 'ADD' : Average Drawdown of uncompounded returns 'UCI' : Ulcer Index of uncompounded returns 'DaR' : Drawdown at Risk of uncompounded returns 'CDaR' : Conditional Drawdown at Risk of uncompounded returns 'EDaR' : Entropic Drawdown at Risk of uncompounded returns 'MDD' : Maximum Drawdown of uncompounded returns 'ADD_Rel' : Average Drawdown of compounded returns 'UCI_Rel' : Ulcer Index of compounded returns 'DaR_Rel' : Drawdown at Risk of compounded returns 'CDaR_Rel' : Conditional Drawdown at Risk of compounded returns 'EDaR_Rel' : Entropic Drawdown at Risk of compounded returns 'MDD_Rel' : Maximum Drawdown of compounded returns	MV	True	MV, MAD, GMD, MSV, VaR, CVaR, TG, EVaR, RG, CVRG, TGRG, WR, FLPM, SLPM, MDD, ADD, DaR, CDaR, EDaR, UCI, MDD_Rel, ADD_Rel, DaR_Rel, CDaR_Rel, EDaR_Rel, UCI_Rel
cvar_simulations_losses	Number of CVaRs used to approximate Tail Gini of losses. The default is 100	100	True	None
cvar_significance	Significance level of CVaR and Tail Gini of gains. If empty it duplicates alpha	None	True	None
cvar_simulations_gains	Number of CVaRs used to approximate Tail Gini of gains. If empty it duplicates a_sim value	None	True	None
linkage	Linkage method of hierarchical clustering	single	True	single, complete, average, weighted, centroid, median, ward, dbht
amount_clusters	Number of clusters specified in advance	None	True	None
max_clusters	Max number of clusters used by the two difference gap statistic to find the optimal number of clusters. If k is empty this value is used	10	True	None
amount_bins	Number of bins used to calculate the variation of information	KN	True	KN, FD, SC, HGR
alpha_tail	Significance level for lower tail dependence index, only used when when codependence value is 'tail'	0.05	True	None
leaf_order	Indicates if the cluster are ordered so that the distance between successive leaves is minimal	True	True	None
smoothing_factor_ewma	Smoothing factor for ewma estimators	0.94	True	None
nan_fill_method	Method used to fill nan values in time series, by default time. Possible values are: 'linear': linear interpolation 'time': linear interpolation based on time index 'nearest': use nearest value to replace nan values 'zero': spline of zeroth order 'slinear': spline of first order 'quadratic': spline of second order 'cubic': spline of third order 'barycentric': builds a polynomial that pass for all points	time	True	linear, time, nearest, zero, slinear, quadratic, cubic, barycentric
categories	Show selected categories	ASSET_CLASS, COUNTRY, SECTOR, INDUSTRY	True	None
historic_period	Period to get yfinance data from. Possible frequency strings are: 'd': means days, for example '252d' means 252 days 'w': means weeks, for example '52w' means 52 weeks 'mo': means months, for example '12mo' means 12 months 'y': means years, for example '1y' means 1 year 'ytd': downloads data from beginning of year to today 'max': downloads all data available for each asset	3y	True	1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max
start_period	Start date to get yfinance data from. Must be in 'YYYY-MM-DD' format		True	None
end_period	End date to get yfinance data from. Must be in 'YYYY-MM-DD' format		True	None
log_returns	If use logarithmic or arithmetic returns to calculate returns	False	True	None
return_frequency	Frequency used to calculate returns. Possible values are: 'd': for daily returns 'w': for weekly returns 'm': for monthly returns	d	True	d, w, m
max_nan	Max percentage of nan values accepted per asset to be considered in the optimization process	0.05	True	None
threshold_value	Value used to replace outliers that are higher to threshold in absolute value	0.3	True	None
risk_free	Risk-free rate of borrowing/lending. The period of the risk-free rate must be annual	0.02924	True	None
significance_level	Significance level of CVaR, EVaR, CDaR and EDaR	0.05	True	None
long_allocation	Amount to allocate to portfolio	1	True	None
name	Save portfolio with personalized or default name	HERC_0	True	None

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Examples

2022 Apr 05, 14:40 (🦋) /portfolio/po/ $ herc

 [3 Years] Hierarchical equal risk contribution portfolio using pearson
codependence,single linkage and volatility as risk measure

     Weights
┏━━━━━━┳━━━━━━━━━┓
┃      ┃ Value   ┃
┡━━━━━━╇━━━━━━━━━┩
│ AAPL │ 10.78 % │
├──────┼─────────┤
│ AMZN │ 12.64 % │
├──────┼─────────┤
│ BA   │  9.12 % │
├──────┼─────────┤
│ FB   │  8.08 % │
├──────┼─────────┤
│ MSFT │ 12.86 % │
├──────┼─────────┤
│ T    │ 43.83 % │
├──────┼─────────┤
│ TSLA │  2.66 % │
└──────┴─────────┘

Annual (by 252) expected return: 18.09%
Annual (by √252) volatility: 24.19%
Sharpe ratio: 0.7401

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