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diff --git a/website/content/sdk/reference/portfolio/po/hcp.mdx b/website/content/sdk/reference/portfolio/po/hcp.mdx new file mode 100644 index 00000000000..d2f372f85d4 --- /dev/null +++ b/website/content/sdk/reference/portfolio/po/hcp.mdx @@ -0,0 +1,76 @@ +--- +title: hcp +description: + The page provides details on hierarchical clustering based portfolios + (HCP) in the OpenBBTerminal, a python tool for advanced investment strategies. It + describes parameters for portfolio optimization including risk measures, covariance + estimations, and clustering techniques. The return outcomes include portfolio weights + and stock returns. +keywords: + - portfolio optimization + - hierarchical clustering + - stock returns + - Hierarchical Risk Parity + - Nested Clustered Optimization + - covariance matrix + - risk measures + - stocks + - portfolio management +--- + +import HeadTitle from "@site/src/components/General/HeadTitle.tsx"; + +<HeadTitle title="portfolio.po.hcp - Reference | OpenBB SDK Docs" /> + +Builds hierarchical clustering based portfolios + +Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/portfolio/portfolio_optimization/optimizer_model.py#L1903)] + +```python +openbb.portfolio.po.hcp(symbols: List[str], kwargs: Any) +``` + +--- + +## Parameters + +| Name | Type | Description | Default | Optional | +| -------------- | --------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------- | -------- | +| symbols | List[str] | List of portfolio stocks | None | False | +| interval | str | interval to get stock data, by default "3mo" | None | True | +| start_date | str | If not using interval, start date string (YYYY-MM-DD) | None | True | +| end_date | str | If not using interval, end date string (YYYY-MM-DD). If empty use last<br/>weekday. | None | True | +| log_returns | bool | If True calculate log returns, else arithmetic returns. Default value<br/>is False | None | True | +| freq | str | The frequency used to calculate returns. Default value is 'D'. Possible<br/>values are:<br/><br/>- 'D' for daily returns.<br/>- 'W' for weekly returns.<br/>- 'M' for monthly returns. | None | True | +| maxnan | float | Max percentage of nan values accepted per asset to be included in<br/>returns. | None | True | +| threshold | float | Value used to replace outliers that are higher to threshold. | None | True | +| method | str | Method used to fill nan values. Default value is 'time'. For more information see `interpolate <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.interpolate.html>`\_\_. | None | True | +| model | str | The hierarchical cluster portfolio model used for optimize the<br/>portfolio. The default is 'HRP'. Possible values are:<br/><br/>- 'HRP': Hierarchical Risk Parity.<br/>- 'HERC': Hierarchical Equal Risk Contribution.<br/>- 'NCO': Nested Clustered Optimization. | None | True | +| codependence | str | The codependence or similarity matrix used to build the distance<br/>metric and clusters. The default is 'pearson'. Possible values are:<br/><br/>- 'pearson': pearson correlation matrix. Distance formula:<br/> .. math:: $D*{i,j} = \sqrt{0.5(1-\rho^{pearson}*{i,j})}$<br/>- 'spearman': spearman correlation matrix. Distance formula:<br/> .. math:: $D*{i,j} = \sqrt{0.5(1-\rho^{spearman}*{i,j})}$<br/>- 'abs*pearson': absolute value pearson correlation matrix. Distance formula:<br/> .. math:: $D*{i,j} = \sqrt{(1-\|\rho^{pearson}\_{i,j}\|)}$<br/>- 'abs*spearman': absolute value spearman correlation matrix. Distance formula:<br/> .. math:: $D*{i,j} = \sqrt{(1-\|\rho^{spearman}\_{i,j}\|)}$<br/>- 'distance': distance correlation matrix. Distance formula:<br/> .. math:: $D*{i,j} = \sqrt{(1-\rho^{distance}*{i,j})}$<br/>- 'mutual*info': mutual information matrix. Distance used is variation information matrix.<br/>- 'tail': lower tail dependence index matrix. Dissimilarity formula:<br/> .. math:: $D*{i,j} = -\log{\lambda\_{i,j}}$. | None | True | +| covariance | str | The method used to estimate the covariance matrix:<br/>The default is 'hist'. Possible values are:<br/><br/>- 'hist': use historical estimates.<br/>- '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>`**.<br/>- '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>`**.<br/>- 'ledoit': use the Ledoit and Wolf Shrinkage method.<br/>- 'oas': use the Oracle Approximation Shrinkage method.<br/>- 'shrunk': use the basic Shrunk Covariance method.<br/>- 'gl': use the basic Graphical Lasso Covariance method.<br/>- 'jlogo': use the j-LoGo Covariance method. For more information see: `c-jLogo`.<br/>- 'fixed': denoise using fixed method. For more information see chapter 2 of `c-MLforAM`.<br/>- 'spectral': denoise using spectral method. For more information see chapter 2 of `c-MLforAM`.<br/>- 'shrink': denoise using shrink method. For more information see chapter 2 of `c-MLforAM`. | None | True | +| objective | str | Objective function used by the NCO model.<br/>The default is 'MinRisk'. Possible values are:<br/><br/>- 'MinRisk': Minimize the selected risk measure.<br/>- 'Utility': Maximize the risk averse utility function.<br/>- 'Sharpe': Maximize the risk adjusted return ratio based on the selected risk measure.<br/>- 'ERC': Equally risk contribution portfolio of the selected risk measure. | None | True | +| risk_measure | str | The risk measure used to optimize the portfolio. If model is 'NCO',<br/>the risk measures available depends on the objective function.<br/>The default is 'MV'. Possible values are:<br/><br/>- 'MV': Variance.<br/>- 'MAD': Mean Absolute Deviation.<br/>- 'MSV': Semi Standard Deviation.<br/>- 'FLPM': First Lower Partial Moment (Omega Ratio).<br/>- 'SLPM': Second Lower Partial Moment (Sortino Ratio).<br/>- 'VaR': Value at Risk.<br/>- 'CVaR': Conditional Value at Risk.<br/>- 'TG': Tail Gini.<br/>- 'EVaR': Entropic Value at Risk.<br/>- 'WR': Worst Realization (Minimax).<br/>- 'RG': Range of returns.<br/>- 'CVRG': CVaR range of returns.<br/>- 'TGRG': Tail Gini range of returns.<br/>- 'MDD': Maximum Drawdown of uncompounded cumulative returns (Calmar Ratio).<br/>- 'ADD': Average Drawdown of uncompounded cumulative returns.<br/>- 'DaR': Drawdown at Risk of uncompounded cumulative returns.<br/>- 'CDaR': Conditional Drawdown at Risk of uncompounded cumulative returns.<br/>- 'EDaR': Entropic Drawdown at Risk of uncompounded cumulative returns.<br/>- 'UCI': Ulcer Index of uncompounded cumulative returns.<br/>- 'MDD_Rel': Maximum Drawdown of compounded cumulative returns (Calmar Ratio).<br/>- 'ADD_Rel': Average Drawdown of compounded cumulative returns.<br/>- 'DaR_Rel': Drawdown at Risk of compounded cumulative returns.<br/>- 'CDaR_Rel': Conditional Drawdown at Risk of compounded cumulative returns.<br/>- 'EDaR_Rel': Entropic Drawdown at Risk of compounded cumulative returns.<br/>- 'UCI_Rel': Ulcer Index of compounded cumulative returns. | None | True | +| risk_free_rate | float | Risk free rate, must be in annual frequency.<br/>Used for 'FLPM' and 'SLPM'. The default is 0. | None | True | +| risk_aversion | float | Risk aversion factor of the 'Utility' objective function.<br/>The default is 1. | None | True | +| alpha | float | Significance level of VaR, CVaR, EDaR, DaR, CDaR, EDaR, Tail Gini of losses.<br/>The default is 0.05. | None | True | +| a_sim | float | Number of CVaRs used to approximate Tail Gini of losses. The default is 100. | None | True | +| beta | float | Significance level of CVaR and Tail Gini of gains. If None it duplicates alpha value.<br/>The default is None. | None | True | +| b_sim | float | Number of CVaRs used to approximate Tail Gini of gains. If None it duplicates a_sim value.<br/>The default is None. | None | True | +| 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>`\_\_.<br/>The default is 'single'. Possible values are:<br/><br/>- 'single'.<br/>- 'complete'.<br/>- 'average'.<br/>- 'weighted'.<br/>- 'centroid'.<br/>- 'median'.<br/>- 'ward'.<br/>- 'dbht': Direct Bubble Hierarchical Tree. | None | True | +| k | int | Number of clusters. This value is took instead of the optimal number<br/>of clusters calculated with the two difference gap statistic.<br/>The default is None. | None | True | +| max_k | int | Max number of clusters used by the two difference gap statistic<br/>to find the optimal number of clusters. The default is 10. | None | True | +| bins_info | str | Number of bins used to calculate variation of information. The default<br/>value is 'KN'. Possible values are:<br/><br/>- '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>`**.<br/>- '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>`**.<br/>- 'SC': Scotts' choice method. For more information see `scott_bin_width <https://docs.astropy.org/en/stable/api/astropy.stats.scott_bin_width.html>`\_\_.<br/>- 'HGR': Hacine-Gharbi and Ravier' choice method. | None | True | +| alpha_tail | float | Significance level for lower tail dependence index. The default is 0.05. |