OpenBB/assets/js/b122ea69.e258a253.js

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Function",source:"@site/content/sdk/reference/portfolio/po/herc.md",sourceDirName:"reference/portfolio/po",slug:"/reference/portfolio/po/herc",permalink:"/sdk/reference/portfolio/po/herc",draft:!1,editUrl:"https://github.com/OpenBB-finance/OpenBBTerminal/edit/main/website/content/sdk/reference/portfolio/po/herc.md",tags:[],version:"current",frontMatter:{title:"herc",description:"OpenBB SDK Function"},sidebar:"tutorialSidebar",previous:{title:"get_properties",permalink:"/sdk/reference/portfolio/po/get_properties"},next:{title:"hrp",permalink:"/sdk/reference/portfolio/po/hrp"}},u={},p=[{value:"Parameters",id:"parameters",level:2},{value:"Returns",id:"returns",level:2},{value:"Examples",id:"examples",level:2}],d={toc:p};function m(t){let{components:e,...n}=t;return(0,r.kt)("wrapper",(0,a.Z)({},d,n,{components:e,mdxType:"MDXLayout"}),(0,r.kt)("h1",{id:"herc"},"herc"),(0,r.kt)("p",null,"Optimize with Hierarchical Equal Risk Contribution (HERC) method."),(0,r.kt)("p",null,"Source Code: [",(0,r.kt)("a",{parentName:"p",href:"https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/portfolio/portfolio_optimization/po_model.py#L1793"},"link"),"]"),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python",metastring:"wordwrap",wordwrap:!0},"openbb.portfolio.po.herc(portfolio_engine: portfolio_optimization.po_engine.PoEngine = None, kwargs: Any)\n")),(0,r.kt)("hr",null),(0,r.kt)("h2",{id:"parameters"},"Parameters"),(0,r.kt)("table",null,(0,r.kt)("thead",{parentName:"table"},(0,r.kt)("tr",{parentName:"thead"},(0,r.kt)("th",{parentName:"tr",align:null},"Name"),(0,r.kt)("th",{parentName:"tr",align:null},"Type"),(0,r.kt)("th",{parentName:"tr",align:null},"Description"),(0,r.kt)("th",{parentName:"tr",align:null},"Default"),(0,r.kt)("th",{parentName:"tr",align:null},"Optional"))),(0,r.kt)("tbody",{parentName:"table"},(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"portfolio_engine"),(0,r.kt)("td",{parentName:"tr",align:null},"PoEngine"),(0,r.kt)("td",{parentName:"tr",align:null},"Portfolio optimization engine, by default None",(0,r.kt)("br",null),"Use ",(0,r.kt)("inlineCode",{parentName:"td"},"portfolio.po.load")," to load a portfolio engine"),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"interval"),(0,r.kt)("td",{parentName:"tr",align:null},"str"),(0,r.kt)("td",{parentName:"tr",align:null},"Interval to get data, by default '3y'"),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"start_date"),(0,r.kt)("td",{parentName:"tr",align:null},"str"),(0,r.kt)("td",{parentName:"tr",align:null},'If not using interval, start date string (YYYY-MM-DD), by default ""'),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"end_date"),(0,r.kt)("td",{parentName:"tr",align:null},"str"),(0,r.kt)("td",{parentName:"tr",align:null},'If not using interval, end date string (YYYY-MM-DD). If empty use last weekday, by default ""'),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"log_returns"),(0,r.kt)("td",{parentName:"tr",align:null},"bool"),(0,r.kt)("td",{parentName:"tr",align:null},"If True use log returns, else arithmetic returns, by default False"),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"freq"),(0,r.kt)("td",{parentName:"tr",align:null},"str"),(0,r.kt)("td",{parentName:"tr",align:null},"Frequency of returns, by default 'D'. Options: 'D' for daily, 'W' for weekly, 'M' for monthly"),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"maxnan"),(0,r.kt)("td",{parentName:"tr",align:null},"float"),(0,r.kt)("td",{parentName:"tr",align:null},"Maximum percentage of NaNs allowed in the data, by default 0.05"),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"threshold"),(0,r.kt)("td",{parentName:"tr",align:null},"float"),(0,r.kt)("td",{parentName:"tr",align:null},"Value used to replace outliers that are higher than threshold, by default 0.0"),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"method"),(0,r.kt)("td",{parentName:"tr",align:null},"str"),(0,r.kt)("td",{parentName:"tr",align:null},"Method used to fill nan values, by default 'time'",(0,r.kt)("br",null),"For more information see ",(0,r.kt)("inlineCode",{parentName:"td"},"interpolate <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.interpolate.html>"),"__."),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"value"),(0,r.kt)("td",{parentName:"tr",align:null},"float"),(0,r.kt)("td",{parentName:"tr",align:null},"Amount to allocate to portfolio in long positions, by default 1.0"),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"objective"),(0,r.kt)("td",{parentName:"tr",align:null},"str"),(0,r.kt)("td",{parentName:"tr",align:null},"Objective function of the optimization model, by default 'MinRisk'",(0,r.kt)("br",null),"Possible values are:",(0,r.kt)("br",null),(0,r.kt)("br",null),"- 'MinRisk': Minimize the selected risk measure.",(0,r.kt)("br",null),"- 'Utility': Maximize the risk averse utility function.",(0,r.kt)("br",null),"- 'Sharpe': Maximize the risk adjusted return ratio based on the selected risk measure.",(0,r.kt)("br",null),"- 'MaxRet': Maximize the expected return of the portfolio."),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"risk_measure"),(0,r.kt)("td",{parentName:"tr",align:null},"str"),(0,r.kt)("td",{parentName:"tr",align:null},"The risk measure used to optimize the portfolio, by default 'MV'",(0,r.kt)("br",null),"If model is 'NCO', the risk measures available depends on the objective function.",(0,r.kt)("br",null),"Possible values are:",(0,r.kt)("br",null),(0,r.kt)("br",null),"- 'MV': Variance.",(0,r.kt)("br",null),"- 'MAD': Mean Absolute Deviation.",(0,r.kt)("br",null),"- 'MSV': Semi Standard Deviation.",(0,r.kt)("br",null),"- 'FLPM': First Lower Partial Moment (Omega Ratio).",(0,r.kt)("br",null),"- 'SLPM': Second Lower Partial Moment (Sortino Ratio).",(0,r.kt)("br",null),"- 'VaR': Value at Risk.",(0,r.kt)("br",null),"- 'CVaR': Conditional Value at Risk.",(0,r.kt)("br",null),"- 'TG': Tail Gini.",(0,r.kt)("br",null),"- 'EVaR': Entropic Value at Risk.",(0,r.kt)("br",null),"- 'WR': Worst Realization (Minimax).",(0,r.kt)("br",null),"- 'RG': Range of returns.",(0,r.kt)("br",null),"- 'CVRG': CVaR range of returns.",(0,r.kt)("br",null),"- 'TGRG': Tail Gini range of returns.",(0,r.kt)("br",null),"- 'MDD': Maximum Drawdown of uncompounded cumulative returns (Calmar Ratio).",(0,r.kt)("br",null),"- 'ADD': Average Drawdown of uncompounded cumulative returns.",(0,r.kt)("br",null),"- 'DaR': Drawdown at Risk of uncompounded cumulative returns.",(0,r.kt)("br",null),"- 'CDaR': Conditional Drawdown at Risk of uncompounded cumulative returns.",(0,r.kt)("br",null),"- 'EDaR': Entropic Drawdown at Risk of uncompounded cumulative returns.",(0,r.kt)("br",null),"- 'UCI': Ulcer Index of uncompounded cumulative returns.",(0,r.kt)("br",null),"- 'MDD_Rel': Maximum Drawdown of compounded cumulative returns (Calmar Ratio).",(0,r.kt)("br",null),"- 'ADD_Rel': Average Drawdown of compounded cumulative returns.",(0,r.kt)("br",null),"- 'DaR_Rel': Drawdown at Risk of compounded cumulative returns.",(0,r.kt)("br",null),"- 'CDaR_Rel': Conditional Drawdown at Risk of compounded cumulative returns.",(0,r.kt)("br",null),"- 'EDaR_Rel': Entropic Drawdown at Risk of compounded cumulative returns.",(0,r.kt)("br",null),"- 'UCI_Rel': Ulcer Index of compounded cumulative returns."),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"risk_free_rate"),(0,r.kt)("td",{parentName:"tr",align:null},"float"),(0,r.kt)("td",{parentName:"tr",align:null},"Risk free rate, annualized. Used for 'FLPM' and 'SLPM' and Sharpe objective function, by default 0.0"),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"risk_aversion"),(0,r.kt)("td",{parentName:"tr",align:null},"float"),(0,r.kt)("td",{parentName:"tr",align:null},"Risk aversion factor of the 'Utility' objective function, by default 1.0"),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"alpha"),(0,r.kt)("td",{parentName:"tr",align:null},"float"),(0,r.kt)("td",{parentName:"tr",align:null},"Significance level of VaR, CVaR, EDaR, DaR, CDaR, EDaR, Tail Gini of losses, by default 0.05"),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"a_sim"),(0,r.kt)("td",{parentName:"tr",align:null},"float"),(0,r.kt)("td",{parentName:"tr",align:null},"Number of CVaRs used to approximate Tail Gini of losses, by default 100"),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"beta"),(0,r.kt)("td",{parentName:"tr",align:null},"float"),(0,r.kt)("td",{parentName:"tr",align:null},"Significance level of CVaR and Tail Gini of gains. If None it duplicates alpha value, by default None"),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"b_sim"),(0,r.kt)("td",{parentName:"tr",align:null},"float"),(0,r.kt)("td",{parentName:"tr",align:null},"Number of CVaRs used to approximate Tail Gini of gains. If None it duplicates a_sim value, by default None"),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"covariance"),(0,r.kt)("td",{parentName:"tr",align:null},"str"),(0,r.kt)("td",{parentName:"tr",align:null},"The method used to estimate the covariance matrix, by default 'hist'",(0,r.kt)("br",null),"Possible values are:",(0,r.kt)("br",null),(0,r.kt)("br",null),"- 'hist': use historical estimates.",(0,r.kt)("br",null),"- 'ewma1': use ewma with adjust=True. For more information see ",(0,r.kt)("inlineCode",{parentName:"td"},"EWM <https://pandas.pydata.org/pandas-docs/stable/user_guide/window.html#exponentially-weighted-window>"),(0,r.kt)("strong",{parentName:"td"},".",(0,r.kt)("br",null),"- 'ewma2': use ewma with adjust=False. For more information see ",(0,r.kt)("inlineCode",{parentName:"strong"},"EWM <https://pandas.pydata.org/pandas-docs/stable/user_guide/window.html#exponentially-weighted-window>")),".",(0,r.kt)("br",null),"- 'ledoit': use the Ledoit and Wolf Shrinkage method.",(0,r.kt)("br",null),"- 'oas': use the Oracle Approximation Shrinkage method.",(0,r.kt)("br",null),"- 'shrunk': use the basic Shrunk Covariance method.",(0,r.kt)("br",null),"- 'gl': use the basic Graphical Lasso Covariance method.",(0,r.kt)("br",null),"- 'jlogo': use the j-LoGo Covariance method. For more information see: ",(0,r.kt)("inlineCode",{parentName:"td"},"a-jLogo"),".",(0,r.kt)("br",null),"- 'fixed': denoise using fixed method. For more information see chapter 2 of ",(0,r.kt)("inlineCode",{parentName:"td"},"a-MLforAM"),".",(0,r.kt)("br",null),"- 'spectral': denoise using spectral method. For more information see chapter 2 of ",(0,r.kt)("inlineCode",{parentName:"td"},"a-MLforAM"),".",(0,r.kt)("br",null),"- 'shrink': denoise using shrink method. For more information see chapter 2 of ",(0,r.kt)("inlineCode",{parentName:"td"},"a-MLforAM"),"."),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"d_ewma"),(0,r.kt)("td",{parentName:"tr",align:null},"float"),(0,r.kt)("td",{parentName:"tr",align:null},"The smoothing factor of ewma methods, by default 0.94"),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"codependence"),(0,r.kt)("td",{parentName:"tr",align:null},"str"),(0,r.kt)("td",{parentName:"tr",align:null},"The codependence or similarity matrix used to build the distance",(0,r.kt)("br",null),"metric and clusters. The default is 'pearson'. Possible values are:",(0,r.kt)("br",null),(0,r.kt)("br",null),"- 'pearson': pearson correlation matrix. Distance formula:",(0,r.kt)("br",null),"    .. math:: D",(0,r.kt)("em",{parentName:"td"},"{i,j} = \\sqrt{0.5(1-\\rho^{pearson}"),"{i,j})}",(0,r.kt)("br",null),"- 'spearman': spearman correlation matrix. Distance formula:",(0,r.kt)("br",null),"    .. math:: D",(0,r.kt)("em",{parentName:"td"},"{i,j} = \\sqrt{0.5(1-\\rho^{spearman}"),"{i,j})}",(0,r.kt)("br",null),"- 'abs",(0,r.kt)("em",{parentName:"td"},"pearson': absolute value pearson correlation matrix. Distance formula:",(0,r.kt)("br",null),"    .. math:: D"),"{i,j} = \\sqrt{(1-"),(0,r.kt)("td",{parentName:"tr",align:null},"\\rho^{pearson}_{i,j}"),(0,r.kt)("td",{parentName:"tr",align:null},")}",(0,r.kt)("br",null),"- 'abs",(0,r.kt)("em",{parentName:"td"},"spearman': absolute value spearman correlation matrix. Distance formula:",(0,r.kt)("br",null),"    .. math:: D"),"{i,j} = \\sqrt{(1-")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"linkage"),(0,r.kt)("td",{parentName:"tr",align:null},"str"),(0,r.kt)("td",{parentName:"tr",align:null},"Linkage method of hierarchical clustering. For more information see ",(0,r.kt)("inlineCode",{parentName:"td"},"linkage <https://docs.scipy.org/doc/scipy/reference/generated/scipy.<br/>cluster.hierarchy.linkage.html?highlight=linkage#scipy.cluster.hierarchy.linkage>"),"__.",(0,r.kt)("br",null),"The default is 'single'. Possible values are:",(0,r.kt)("br",null),(0,r.kt)("br",null),"- 'single'.",(0,r.kt)("br",null),"- 'complete'.",(0,r.kt)("br",null),"- 'average'.",(0,r.kt)("br",null),"- 'weighted'.",(0,r.kt)("br",null),"- 'centroid'.",(0,r.kt)("br",null),"- 'median'.",(0,r.kt)("br",null),"- 'ward'.",(0,r.kt)("br",null),"- 'dbht': Direct Bubble Hierarchical Tree."),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"k"),(0,r.kt)("td",{parentName:"tr",align:null},"int"),(0,r.kt)("td",{parentName:"tr",align:null},"Number of clusters. This value is took instead of the optimal number",(0,r.kt)("br",null),"of clusters calculated with the two difference gap statistic, by default None"),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"max_k"),(0,r.kt)("td",{parentName:"tr",align:null},"int"),(0,r.kt)("td",{parentName:"tr",align:null},"Max number of clusters used by the two difference gap statistic",(0,r.kt)("br",null),"to find the optimal number of clusters, by default 10"),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"bins_info"),(0,r.kt)("td",{parentName:"tr",align:null},"str"),(0,r.kt)("td",{parentName:"tr",align:null},"Number of bins used to calculate variation of information, by default 'KN'.",(0,r.kt)("br",null),"Possible values are:",(0,r.kt)("br",null),(0,r.kt)("br",null),"- 'KN': Knuth's choice method. For more information see ",(0,r.kt)("inlineCode",{parentName:"td"},"knuth_bin_width <https://docs.astropy.org/en/stable/api/astropy.stats.knuth_bin_width.html>"),(0,r.kt)("strong",{parentName:"td"},".",(0,r.kt)("br",null),"- 'FD': Freedman\u2013Diaconis' choice method. For more information see ",(0,r.kt)("inlineCode",{parentName:"strong"},"freedman_bin_width <https://docs.astropy.org/en/stable/api/astropy.stats.freedman_bin_width.html>")),".",(0,r.kt)("br",null),"- 'SC': Scotts' choice method. For more information see ",(0,r.kt)("inlineCode",{parentName:"td"},"scott_bin_width <https://docs.astropy.org/en/stable/api/astropy.stats.scott_bin_width.html>"),"__.",(0,r.kt)("br",null),"- 'HGR': Hacine-Gharbi and Ravier' choice method."),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"alpha_tail"),(0,r.kt)("td",{parentName:"tr",align:null},"float"),(0,r.kt)("td",{parentName:"tr",align:null},"Significance level for lower tail dependence index, by default 0.05"),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"leaf_order"),(0,r.kt)("td",{parentName:"tr",align:null},"bool"),(0,r.kt)("td",{parentName:"tr",align:null},"Indicates if the cluster are ordered so that the distance between",(0,r.kt)("br",null),"successive leaves is minimal, by default True"),(0,r.kt)("td",{parentName:"tr",align:null},"None"),(0,r.kt)("td",{parentName:"tr",align:null},"True")))),(0,r.kt)("hr",null),(0,r.kt)("h2",{id:"returns"},"Returns"),(0,r.kt)("table",null,(0,r.kt)("thead",{parentName:"table"},(0,r.kt)("tr",{parentName:"thead"},(0,r.kt)("th",{parentName:"tr",align:null},"Type"),(0,r.kt)("th",{parentName:"tr",align:null},"Description"))),(0,r.kt)("tbody",{parentName:"table"},(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"Tuple","[pd.DataFrame, Dict]"),(0,r.kt)("td",{parentName:"tr",align:null},"Tuple with weights and performance dictionary")))),(0,r.kt)("hr",null),(0,r.kt)("h2",{id:"examples"},"Examples"),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},"from openbb_terminal.sdk import openbb\nd = {\n")),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre"},'"SECTOR": {\n                "AAPL": "INFORMATION TECHNOLOGY",\n                "MSFT": "INFORMATION TECHNOLOGY",\n                "AMZN": "CONSUMER DISCRETIONARY",\n            },\n            "CURRENCY": {\n                "AAPL": "USD",\n                "MSFT": "USD",\n                "AMZN": "USD",\n            },\n            "CURRENT_INVESTED_AMOUNT": {\n                "AAPL": "100000.0",\n                "MSFT": "200000.0",\n                "AMZN": "300000.0",\n            },\n        }\n')),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},"p = openbb.portfolio.po.load(symbols_categories=d)\nweights, performance = openbb.portfolio.po.herc(portfolio_engine=p)\n")),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from openbb_terminal.sdk import openbb\np = openbb.portfolio.po.load(symbols_file_path="~/openbb_terminal/miscellaneous/portfolio_examples/allocation/60_40_Portfolio.xlsx")\nweights, performance = openbb.portfolio.po.herc(portfolio_engine=p)\n')),(0,r.kt)("hr",null))}m.isMDXComponent=!0}}]);