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title	description	keywords
hrp	This technical documentation page provides comprehensive instructions for using 'hrp', a Python command for creating a hierarchical risk parity portfolio. It includes detailed information on usage, parameters, and examples for building custom portfolios optimized for specific risk parameters. Tools like codependence matrix, covariance matrix, CVaR, Tail Gini and hierarchical clustering methodologies are explored in depth.	hrp hierarchical risk parity portfolio portfolio optimization risk management codependence matrix covariance matrix risk measure CVaR Tail Gini hierarchical clustering

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Builds a hierarchical risk parity portfolio

Usage

hrp [-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	_HRP0	True	None

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Examples

2022 Apr 05, 14:20 (🦋) /portfolio/po/ $ hrp

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

     Weights
┏━━━━━━┳━━━━━━━━━┓
┃      ┃ Value   ┃
┡━━━━━━╇━━━━━━━━━┩
│ AAPL │ 13.74 % │
├──────┼─────────┤
│ AMZN │ 17.97 % │
├──────┼─────────┤
│ BA   │  5.74 % │
├──────┼─────────┤
│ FB   │ 10.29 % │
├──────┼─────────┤
│ MSFT │ 18.28 % │
├──────┼─────────┤
│ T    │ 27.57 % │
├──────┼─────────┤
│ TSLA │  6.37 % │
└──────┴─────────┘

Annual (by 252) expected return: 28.03%
Annual (by √252) volatility: 25.35%
Sharpe ratio: 1.0982

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