> ## Documentation Index > Fetch the complete documentation index at: https://systematica.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Neptune Ai > systematica.portfolio.analyzers.neptune_ai ## `NeptuneAnalyzer` ```python theme={null} NeptuneAnalyzer( project: str = 'systematica', api_token: str = None, ) ``` A class for analyzing Neptune runs and fetching experiment data. Method generated by attrs for class NeptuneAnalyzer. ### Static methods #### `combine_trials` ```python theme={null} combine_trials( trials: Dict[str, Any], metrics: List[str] = None, ) ‑> pandas.core.frame.DataFrame ``` Combine trials into a `pd.DataFrame` object. **Parameters**: | Name | Type | Default | Description | | -------- | -------------- | ------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `trials` | `tp.Kwargs` | `--` | Dictionary containing trial data. | | `metric` | `tp.List[str]` | `None` | List of metrics to fall back to if values is not in the trials. If `None`, a single 'metrics' column with NaN is added when 'values' is missing. Defaults to `None`. | **Returns**: | Type | Description | | -------------- | ----------------------------------------- | | `pd.DataFrame` | DataFrame containing combined trial data. | ### Instance variables * `api_token: str`: Neptune API token, e.g. "your\_api\_token". * `project: str`: Neptune project name, e.g. "user\_name/project\_name". ### Methods #### `run_context` ```python theme={null} run_context( self, **kwargs, ) ``` Context manager to initialize and stop a Neptune run. **Parameters**: | Name | Type | Default | Description | | -------- | ----------- | ------- | ----------------------------------------------------------------------------- | | `kwargs` | `tp.Kwargs` | `--` | Additional arguments to pass to `neptune.init_run` (e.g., `with_id`, `mode`). | **Yields**: | Type | Description | | ------------- | ----------------------------------- | | `neptune.Run` | The initialized Neptune run object. | #### `clear_cache` ```python theme={null} clear_cache( self, ) ``` Clear the cache for `_fetch_neptune_metadata`. #### `best_metrics` ```python theme={null} best_metrics( self, run_ids: str | List[str], ) ‑> pandas.core.frame.DataFrame ``` Fetch best metrics from Neptune runs. **Parameters**: | Name | Type | Default | Description | | --------- | ----- | ------- | ----------------------------------------- | | `run_ids` | `str` | `--` | The ID(s) of the Neptune run(s) to fetch. | **Returns**: | Type | Description | | -------------- | -------------------------------------------------------- | | `pd.DataFrame` | DataFrame with metrics as index and run\_ids as columns. | #### `best_params` ```python theme={null} best_params( self, run_ids: str | List[str], ) ‑> pandas.core.frame.DataFrame ``` Fetch best parameters from Neptune runs. **Parameters**: | Name | Type | Default | Description | | --------- | ----- | ------- | ----------------------------------------- | | `run_ids` | `str` | `--` | The ID(s) of the Neptune run(s) to fetch. | **Returns**: | Type | Description | | -------------- | ----------------------------------------------------------- | | `pd.DataFrame` | DataFrame with parameters as index and run\_ids as columns. | #### `trial_params` ```python theme={null} trial_params( self, run_id: str, ) ‑> pandas.core.frame.DataFrame ``` Fetch trial parameters from Neptune runs. Metadata is queried with the below: * `metadata_tag=trials/trials/0/distributions` (Primary) * `metadata_tag=trials/trials` (Fallback) **Parameters**: | Name | Type | Default | Description | | -------- | ----- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | **Returns**: | Type | Description | | -------------- | ----------------------------------------------------------------- | | `pd.DataFrame` | `pd.DataFrame` with parameters as index and `run_ids` as columns. | #### `get_best_trials` ```python theme={null} get_best_trials( self, run_ids: str | List[str], ) ‑> pandas.core.frame.DataFrame ``` Fetch the best trials from Neptune runs. **Parameters**: | Name | Type | Default | Description | | --------- | ----- | ------- | ----------------------------------------- | | `run_ids` | `str` | `--` | The ID(s) of the Neptune run(s) to fetch. | **Returns**: | Type | Description | | -------------- | --------------------------------------------- | | `pd.DataFrame` | Trial data, including 'run\_id' and 'number'. | #### `get_all_trials` ```python theme={null} get_all_trials( self, run_ids: str | List[str], is_file: bool = True, ) ‑> pandas.core.frame.DataFrame ``` Fetch all trials from Neptune runs. **Parameters**: | Name | Type | Default | Description | | --------- | ------ | ------- | ------------------------------------------------------------------------------------------------------------------------------------ | | `run_ids` | `str` | `--` | The ID(s) of the Neptune run(s) to fetch. | | `is_file` | `bool` | `True` | Flag to fetch compressed file trials through 'trials/compressed\_trials' if True. Fetch 'trials/trials' otherwise. Defaults to True. | **Returns**: | Type | Description | | -------------- | ------------------------------------------------- | | `pd.DataFrame` | All trial data, including 'run\_id' and 'number'. | #### `metrics` ```python theme={null} metrics( self, run_id: str, ) ‑> List[str] ``` List metric names from a Neptune run. **Parameters**: | Name | Type | Default | Description | | -------- | ----- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | **Returns**: | Type | Description | | ------------- | --------------------- | | `list of str` | List of metric names. | #### `params` ```python theme={null} params( self, run_id: str, ) ‑> List[str] ``` List parameter names from a Neptune run. **Parameters**: | Name | Type | Default | Description | | -------- | ----- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | **Returns**: | Type | Description | | ------------- | ------------------------ | | `list of str` | List of parameter names. | #### `all_params` ```python theme={null} all_params( self, run_id: str, ) ‑> List[str] ``` List parameter names from a Neptune run. **Parameters**: | Name | Type | Default | Description | | -------- | ----- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | **Returns**: | Type | Description | | ------------- | ------------------------ | | `list of str` | List of parameter names. | #### `run_composer` ```python theme={null} run_composer( self, run_id: str, trial_selector: str | int | systematica.portfolio.trial_selectors.base.BaseTrialSelector = None, loader: vectorbtpro.data.base.Data | Callable = None, loader_kwargs: Dict[str, Any] = None, column_stack: bool = False, group_by: bool = True, ) ‑> systematica.portfolio.analyzers.base.PortfolioAnalyzer ``` Run portfolio analytics from `run_id`. New generation logging is supported. **Parameters**: | Name | Type | Default | Description | | ---------------- | ----------- | ------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | | `trial_selector` | `str` | `None` | Custom parameter selection. If None, retrieve `best/params` from neptune. if int, retrieve trial number. If `BaseTrialSelector`, retrieve params based on algorithm. Defaults to None. | | `loader` | `vbt.Data` | `None` | Data loader instance. If None, defaults to `load_clean_data`. Defaults to None. | | `loader_kwargs` | `tp.Kwargs` | `None` | Additional loader parameters. Note that timeframe, start and end are already fetched from the neptune pipeline field. Defaults to None. | | `column_stack` | `bool` | `False` | Compute `vbt.PF.column_stack` to combine portfolio when several trial numbers are passed. Default to False. | | `group_by` | `bool` | `True` | Group strategy when `column_stack` is True. If False, compute individual backtests. Combined all strategy into a single run otherwise. Defaults to True. | **Returns**: | Type | Description | | ------------------- | ----------------------------------------- | | `PortfolioAnalyzer` | Class with inherited `PortfolioAnalizer`. | #### `all_combination` ```python theme={null} all_combination( self, run_id: str, ) ‑> List[str] ``` Generate combinations of metric and parameter names. **Parameters**: | Name | Type | Default | Description | | -------- | ----- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | **Returns**: | Type | Description | | --------------- | ------------------------------- | | `list of tuple` | List of (metric, param) tuples. | #### `param_combination` ```python theme={null} param_combination( self, run_id: str, ) ‑> List[str] ``` Generate permutations of parameter names. **Parameters**: | Name | Type | Default | Description | | -------- | ----- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | **Returns**: | Type | Description | | --------------- | ------------------------------- | | `list of tuple` | List of (metric, param) tuples. | #### `optuna_study` ```python theme={null} optuna_study( self, run_id: str, ) ‑> optuna.study.study.Study ``` Loads Optuna study from an existing Neptune run. Loading mechanics depend on the study storage type used during the run: * If the study used `InMemoryStorage`, it will be loaded from the logged pickled study object. * If the study used database storage, it will be loaded from the logged database URL. To resume an existing run, you need the run ID. It is stored in the run's `sys/id` field. **Parameters**: | Name | Type | Default | Description | | -------- | ----- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | **Returns**: | Type | Description | | -------------- | ------------------------ | | `optuna.Study` | The Optuna study object. | #### `optuna_trials_dataframe` ```python theme={null} optuna_trials_dataframe( self, run_id: str, ) ‑> pandas.core.frame.DataFrame ``` Export optuna trials as a pandas DataFrame. The DataFrame provides various features to analyze studies. It is also useful to draw a histogram of objective values and to export trials as a CSV file. If there are no trials, an empty `pd.DataFrame` is returned. **Parameters**: | Name | Type | Default | Description | | -------- | ----- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | **Returns**: | Type | Description | | -------------- | ------------------------ | | `pd.DataFrame` | The Optuna study object. | #### `optuna_best_trials` ```python theme={null} optuna_best_trials( self, run_id: str, ) ‑> pandas.core.frame.DataFrame ``` Export optuna best trials as a pandas DataFrame. **Parameters**: | Name | Type | Default | Description | | -------- | ----- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | **Returns**: | Type | Description | | -------------- | ------------------------ | | `pd.DataFrame` | The Optuna study object. | #### `stats` ```python theme={null} stats( self, run_id: str, ) ‑> pandas.core.series.Series ``` Compute statistics from a Neptune run. **Parameters**: | Name | Type | Default | Description | | -------- | ----- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | **Returns**: | Type | Description | | ----------- | ------------------------- | | `pd.Series` | Placeholder return value. | #### `plot_pareto_front` ```python theme={null} plot_pareto_front( self, run_id: str, metrics: List[str] = None, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot pareto front. **Parameters**: | Name | Type | Default | Description | | --------- | -------------- | ------- | ----------------------------------------------------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | | `metrics` | `tp.List[str]` | `None` | Metrics to display. If `None`, All metrics are displayed. Defaults to `None`. | **Returns**: | Type | Description | | ------------------ | ------------------- | | `vbt.FigureWidget` | The generated plot. | #### `plot_contour` ```python theme={null} plot_contour( self, run_id: str, params: List[str], ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot the parameter relationship as contour plot in a study. **Parameters**: | Name | Type | Default | Description | | -------- | -------------- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | | `params` | `tp.List[str]` | `--` | Parameter list to visualize. | **Returns**: | Type | Description | | ------------------ | ------------------- | | `vbt.FigureWidget` | The generated plot. | #### `plot_param_importances` ```python theme={null} plot_param_importances( self, run_id: str, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot parameter importance. An importance evaluator object that specifies which algorithm to base the importance assessment on, defaults to `FanovaImportanceEvaluator`. **Parameters**: | Name | Type | Default | Description | | -------- | ----- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | **Returns**: | Type | Description | | ------------------ | ------------------- | | `vbt.FigureWidget` | The generated plot. | #### `plot_edf` ```python theme={null} plot_edf( self, run_id: str, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot the objective value EDF (empirical distribution function) of a study. **Parameters**: | Name | Type | Default | Description | | -------- | ----- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | **Returns**: | Type | Description | | ------------------ | ------------------- | | `vbt.FigureWidget` | The generated plot. | #### `plot_optimization_history` ```python theme={null} plot_optimization_history( self, run_id: str, error_bar: bool = False, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot optimization history of all trials in a study. **Parameters**: | Name | Type | Default | Description | | ----------- | ------ | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | | `error_bar` | `bool` | `False` | A flag to show the error bar. | **Returns**: | Type | Description | | ------------------ | ------------------- | | `vbt.FigureWidget` | The generated plot. | #### `plot_parallel_coordinate` ```python theme={null} plot_parallel_coordinate( self, run_id: str, metric: str, params: List[str] = None, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot the high-dimensional parameter relationships in a study. **Parameters**: | Name | Type | Default | Description | | -------- | -------------- | ------- | -------------------------------------------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | | `metric` | `str` | `--` | Target metric. | | `params` | `tp.List[str]` | `None` | Parameter list to visualize. if None, the default is all parameters. | **Returns**: | Type | Description | | ------------------ | ------------------- | | `vbt.FigureWidget` | The generated plot. | #### `plot_rank` ```python theme={null} plot_rank( self, run_id: str, params: List[str] = None, n_columns: int = 4, **layout_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot parameter relations as scatter plots with colors indicating ranks of target value. Trials missing the specified parameters will not be plotted. **Parameters**: | Name | Type | Default | Description | | ----------- | -------------- | ------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | | `params` | `tp.List[str]` | `None` | Parameter list to visualize. if None, the default is all parameters. | | `n_columns` | `int` | `4` | When n\_params is more than 3, the number of columns in the grid. The total number of subplots will be split into `n_columns` columns. layout\_kwargs, tp.Kwargs Additional layout parameters. | **Returns**: | Type | Description | | ------------------ | ------------------- | | `vbt.FigureWidget` | The generated plot. | #### `plot_slice` ```python theme={null} plot_slice( self, run_id: str, params: List[str] = None, n_columns: int = 4, **layout_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot the parameter relationship as a slice plot in a study. Trials missing the specified parameters will not be plotted. The slice plot is useful for visualizing the relationship between hyperparameters and the objective function values. It can help you: * Identify Parameter Sensitivity: Shows which hyperparameters have a strong influence on performance. * Detect Bad Regions: Highlights ranges where performance is consistently poor. * Spot Non-linear Patterns: You might notice trends. * Debug Optimization: If you see no pattern or strange clumping, it might indicate: * A bad search space. * Problems with the objective function. * Guide Future Searches: Helps you refine the search space for better performance in future runs. **Parameters**: | Name | Type | Default | Description | | --------------- | -------------- | ------- | -------------------------------------------------------------------------------------------------------------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | | `params` | `tp.List[str]` | `None` | Parameter list to visualize. If None, the default is all parameters. | | `n_columns` | `int` | `4` | When n\_params is more than 3, the number of columns in the grid. The total number of subplots will be split into `n_columns` columns. | | `layout_kwargs` | `tp.Kwargs` | `--` | Additional layout parameters. | **Returns**: | Type | Description | | ------------------ | ------------------- | | `vbt.FigureWidget` | The generated plot. | #### `plot_hypervolume_history` ```python theme={null} plot_hypervolume_history( self, run_id: str, reference_point: Sequence[float] = None, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot hypervolume history of all trials in a study. This function is only applicable for multi-objective optimization studies. It computes the hypervolume of the Pareto front at each trial and plots the hypervolume history. The hypervolume is a measure of the volume of the dominated region in the objective space, defined by the reference point. The higher the hypervolume, the better the Pareto front. Study must be multi-objective. For single-objective optimization, please use `plot_optimization_history` instead. **Parameters**: | Name | Type | Default | Description | | ----------------- | -------------------- | ------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | | `reference_point` | `tp.Sequence[float]` | `None` | A reference point to use for hypervolume computation. The dimension of the reference point must be the same as the number of objectives. If None, takes the min of each metrics to ensure the reference point is lower than all Pareto solutions and defines the dominated region and create measurable volume. Important note, the default value implied all objectives are minimized. Optuna transforms the objective values and the reference point using `study.directions`. Defaults to `None`. | **Returns**: | Type | Description | | ------------------ | ------------------- | | `vbt.FigureWidget` | The generated plot. | #### `plot_timeline` ```python theme={null} plot_timeline( self, run_id: str, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot the timeline of a study. **Parameters**: | Name | Type | Default | Description | | -------- | ----- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | **Returns**: | Type | Description | | ------------------ | ------------------- | | `vbt.FigureWidget` | The generated plot. | #### `plot_intermediate_values` ```python theme={null} plot_intermediate_values( self, run_id: str, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot intermediate values of all trials in a study. **Parameters**: | Name | Type | Default | Description | | -------- | ----- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | **Returns**: | Type | Description | | ------------------ | ------------------- | | `vbt.FigureWidget` | The generated plot. | #### `plot_terminator_improvement` ```python theme={null} plot_terminator_improvement( self, run_id: str, plot_error: bool = False, min_n_trials: int = 20, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot the potentials for future objective improvement. This function does not support multi-objective optimization study. This function visualizes the objective improvement potentials, evaluated with `improvement_evaluator`. It helps to determine whether we should continue the optimization or not. You can also plot the error evaluated with `error_evaluator` if the `plot_error` argument is set to True. Note that this function may take some time to compute the improvement potentials. The `improvement_evaluator` defaults to `RegretBoundEvaluator` and the `error_evaluator` to `CrossValidationErrorEvaluator`. **Parameters**: | Name | Type | Default | Description | | -------------- | ------ | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | | `plot_error` | `bool` | `False` | A flag to show the error. If it is set to `True`, errors evaluated by error\_evaluator are also plotted as line graph. Defaults to `False`. | | `min_n_trials` | `int` | `20` | The minimum number of trials before termination is considered. Terminator improvements for trials below this value are shown in a lighter color. Defaults to `20`. | **Returns**: | Type | Description | | ------------------ | ------------------- | | `vbt.FigureWidget` | The generated plot. | #### `plot_params_correlation` ```python theme={null} plot_params_correlation( self, run_id, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot parameters correlation matrix. **Parameters**: | Name | Type | Default | Description | | -------- | ----- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | **Returns**: | Type | Description | | ------------------ | ------------------- | | `vbt.FigureWidget` | The generated plot. | #### `plot_metrics_correlation` ```python theme={null} plot_metrics_correlation( self, run_id, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot metrics correlation matrix. **Parameters**: | Name | Type | Default | Description | | -------- | ----- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | **Returns**: | Type | Description | | ------------------ | ------------------- | | `vbt.FigureWidget` | The generated plot. | #### `plot_metrics` ```python theme={null} plot_metrics( self, run_id: str, **layout_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot a histogram matrix of metrics for a Neptune run. **Parameters**: | Name | Type | Default | Description | | --------------- | ----------- | ------- | ----------------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | | `layout_kwargs` | `tp.Kwargs` | `--` | Additional layout arguments for the plot. | **Returns**: | Type | Description | | ------------------ | ------------------- | | `vbt.FigureWidget` | The generated plot. | #### `plot_params` ```python theme={null} plot_params( self, run_id: str, n_columns: int = 3, **layout_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot a scatter matrix of parameters for a Neptune run. **Parameters**: | Name | Type | Default | Description | | --------------- | ----------- | ------- | ------------------------------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | | `n_columns` | `int` | `3` | Number of columns in the scatter matrix. Defaults to 3. | | `layout_kwargs` | `tp.Kwargs` | `--` | Additional layout arguments for the plot. | **Returns**: | Type | Description | | ------------------ | ------------------- | | `vbt.FigureWidget` | The generated plot. | #### `plot_density_heatmap` ```python theme={null} plot_density_heatmap( self, run_id: str, metric: str, param: str, **kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot a density heatmap for a metric and parameter. **Parameters**: | Name | Type | Default | Description | | -------- | ----------- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | | `metric` | `str` | `--` | The metric to plot. | | `param` | `str` | `--` | The parameter to plot. | | `kwargs` | `tp.Kwargs` | `--` | Additional arguments for the plot. | **Returns**: | Type | Description | | ------------------ | ------------------- | | `vbt.FigureWidget` | The generated plot. | #### `plot_density_contour` ```python theme={null} plot_density_contour( self, run_id: str, metric: str, param: str, colorscale: List[str | Tuple[float]] = None, **kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot a density contour for a metric and parameter. **Parameters**: | Name | Type | Default | Description | | ------------ | ----------- | ------- | ----------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | | `metric` | `str` | `--` | The metric to plot. | | `param` | `str` | `--` | The parameter to plot. | | `colorscale` | `list` | `--` | The colorscale for the plot. | | `kwargs` | `tp.Kwargs` | `--` | Additional arguments for the plot. | **Returns**: | Type | Description | | ------------------ | ------------------- | | `vbt.FigureWidget` | The generated plot. | #### `plot_frontier` ```python theme={null} plot_frontier( self, run_id: str, x: str, y: str, list_trial_selectors: List[systematica.portfolio.trial_selectors.base.BaseTrialSelector] = None, remove_negative_values: bool = False, **layout_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot an efficient frontier. This function plots the efficient frontier of a study, which is a graphical representation of the trade-off between risk and return. The efficient frontier is a curve that shows the optimal risk-return combinations for a given set of trials. The points on the curve represent the best possible trade-offs between risk and return, while points below the curve are suboptimal: * x is generally a risk metric (e.g., volatility), while y is a performance measure (e.g., returns). * The risk-adjusted optimized point is calculated as: risk\_adj = (returns - risk\_free\_rate) / risks **Parameters**: | Name | Type | Default | Description | | ---------------------- | ---------------------------- | ------- | -------------------------------------------------------------- | | `run_id` | `str` | `--` | The ID of the Neptune run to fetch. | | `x` | `str` | `--` | X axis. Must be a metric from neptune run. | | `y` | `str` | `--` | Y axis. Must be a metric from neptune run. | | `list_trial_selectors` | `tp.List[BaseTrialSelector]` | `None` | Trial selector points added to the charts. Defaults to `None`. | | `risk_free_rate` | `float` | `0.0` | Risk-free rate for volatility calculation. Defaults to 0.0. | | `layout_kwargs` | `tp.Kwargs` | `--` | Additional layout parameters. | **Returns**: | Type | Description | | ------------------ | ----------------------------------------------- | | `vbt.FigureWidget` | The generated efficient frontier plot with CML. |