hcp
Builds hierarchical clustering based portfolios
Source Code: [link]
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 weekday. | None | True |
| log_returns | bool | If True calculate log returns, else arithmetic returns. Default value is False | None | True |
| freq | str | The frequency used to calculate returns. Default value is 'D'. Possible values are: - 'D' for daily returns. - 'W' for weekly returns. - 'M' for monthly returns. | None | True |
| maxnan | float | Max percentage of nan values accepted per asset to be included in 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 portfolio. The default is 'HRP'. Possible values are: - 'HRP': Hierarchical Risk Parity. - 'HERC': Hierarchical Equal Risk Contribution. - 'NCO': Nested Clustered Optimization. | 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})} - 'abspearson': absolute value pearson correlation matrix. Distance formula: .. math:: D{i,j} = \sqrt{(1- | \rho^{pearson}_{i,j} | )} - 'absspearman': absolute value spearman correlation matrix. Distance formula: .. math:: D{i,j} = \sqrt{(1- |
| covariance | str | The method used to estimate the covariance matrix: The default is '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: c-jLogo.- 'fixed': denoise using fixed method. For more information see chapter 2 of c-MLforAM.- 'spectral': denoise using spectral method. For more information see chapter 2 of c-MLforAM.- '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. The default is '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. - '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', the risk measures available depends on the objective function. The default is 'MV'. 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, must be in annual frequency. Used for 'FLPM' and 'SLPM'. The default is 0. | None | True |
| risk_aversion | float | Risk aversion factor of the 'Utility' objective function. The default is 1. | None | True |
| alpha | float | Significance level of VaR, CVaR, EDaR, DaR, CDaR, EDaR, Tail Gini of losses. 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. 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. 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>__.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. The default is None. | None | True |
| max_k | int | Max number of clusters used by the two difference gap statistic 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 value is '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. The default is 0.05. | None | True |
| leaf_order | bool | Indicates if the cluster are ordered so that the distance between successive leaves is minimal. The default is True. | None | True |
| d_ewma | float | The smoothing factor of ewma methods. The default is 0.94. | None | True |
| value | float | Amount of money to allocate. The default is 1. | None | True |
Returns
| Type | Description |
|---|---|
| Tuple[Optional[dict], pd.DataFrame] | Dictionary of portfolio weights, DataFrame of stock returns. |