> ## 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. # Custom Plots > systematica.generic.custom_plots ## `plot_allocation` ```python theme={null} plot_allocation( data: vectorbtpro.data.base.Data, signals: ~Signals, fig: vectorbtpro.utils.figure.FigureWidget = None, ) ``` Plot asset allocation based on trading signals. **Parameters**: | Name | Type | Default | Description | | --------- | ------------------ | ------- | ---------------------------------------------- | | `data` | `vbt.Data` | `--` | Data object containing asset prices. | | `signals` | `SignalsT` | `--` | Object containing trading signals. | | `fig` | `vbt.FigureWidget` | `None` | Existing figure to update. Defaults to `None`. | **Returns**: | Type | Description | | ------------------ | -------------------------------------- | | `vbt.FigureWidget` | Figure containing the allocation plot. | ## `plot_signal_crossover` ```python theme={null} plot_signal_crossover( model_output: pandas.core.series.Series, upper_crossed: pandas.core.series.Series, lower_crossed: pandas.core.series.Series, upper: float, lower: float, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot signal crossover points with upper and lower bounds. **Parameters**: | Name | Type | Default | Description | | --------------- | ----------- | ------- | -------------------------------------------- | | `model_output` | `pd.Series` | `--` | Model output series. | | `upper_crossed` | `pd.Series` | `--` | Series indicating upper boundary crossovers. | | `lower_crossed` | `pd.Series` | `--` | Series indicating lower boundary crossovers. | | `upper` | `float` | `--` | Upper threshold value. | | `lower` | `float` | `--` | Lower threshold value. | **Returns**: | Type | Description | | ------------------ | ------------------------------------- | | `vbt.FigureWidget` | Figure containing the crossover plot. | ## `plot_signal_vlines` ```python theme={null} plot_signal_vlines( df: pandas.core.frame.DataFrame, symbol: str, signals: ~Signals, **plot_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot long and short entry signals as vertical lines. **Parameters**: | Name | Type | Default | Description | | ------------- | -------------- | ------- | ------------------------------------------ | | `df` | `pd.DataFrame` | `--` | Dataframe containing price data. | | `symbol` | `str` | `--` | Symbol for which to plot signals. | | `signals` | `Signals` | `--` | Signal object containing entry points. | | `plot_kwargs` | `tp.Kwargs` | `--` | Additional keyword arguments for plotting. | **Returns**: | Type | Description | | ------------------ | ------------------------------------------------------ | | `vbt.FigureWidget` | A figure with entry signals plotted as vertical lines. | ## `plot_performance_matrix` ```python theme={null} plot_performance_matrix( pf: Any, **plot_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Create a performance matrix heatmap. This function generates a heatmap of performance metrics for the given portfolio by resampling returns data to a monthly frequency. The matrix displays monthly returns by year, with an additional column for the total annual return. **Parameters**: | Name | Type | Default | Description | | ------------- | ----------- | ------- | ------------------------------------------------------------ | | `pf` | `tp.Any` | `--` | The portfolio object containing return data. | | `plot_kwargs` | `tp.Kwargs` | `--` | Additional keyword arguments passed to the heatmap function. | **Returns**: | Type | Description | | ------------------ | ------------------------------------------------------------------ | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing the performance matrix heatmap. | **Examples**: ```python theme={null} >>> pf = ... # Initialize your portfolio object >>> fig = plot_performance_matrix(pf) >>> fig.show() ``` ## `plot_smas` ```python theme={null} plot_smas( close: pandas.core.frame.DataFrame, timeperiods: list = None, length: int = None, **plot_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot Simple Moving Averages (SMAs). This function calculates and plots SMAs for the specified time periods. **Parameters**: | Name | Type | Default | Description | | ------------- | ---------------- | ------- | ---------------------------------------------------------------------------- | | `close` | `pd.DataFrame` | `--` | DataFrame containing closing prices. | | `timeperiods` | `list, optional` | `--` | List of time periods for SMAs. Defaults to `[20, 50, 100, 200]`. | | `length` | `int, optional` | `--` | Number of data points to include in the plot. Defaults to `None` (all data). | | `plot_kwargs` | `tp.Kwargs` | `--` | Additional keyword arguments for customizing the plot. | **Returns**: | Type | Description | | ------------------ | ------------------------------------------------- | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing the SMA plots. | ## `plot_bbands` ```python theme={null} plot_bbands( close: pandas.core.frame.DataFrame, length: int = None, **plot_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot Bollinger Bands. This function calculates and plots Bollinger Bands for the given data. **Parameters**: | Name | Type | Default | Description | | ------------- | -------------- | ------- | ---------------------------------------------------------------------------- | | `close` | `pd.DataFrame` | `--` | DataFrame containing closing prices. | | `length` | `int` | `None` | Number of data points to include in the plot. Defaults to `None` (all data). | | `plot_kwargs` | `tp.Kwargs` | `--` | Additional keyword arguments for customizing the plot. | **Returns**: | Type | Description | | ------------------ | ------------------------------------------------------------ | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing the Bollinger Bands plot. | ## `plot_slope_range` ```python theme={null} plot_slope_range( close: pandas.core.frame.DataFrame, window: int = 50, length: int = None, **plot_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot slope ranges for a moving average. This function identifies and visualizes steep slopes in a moving average. **Parameters**: | Name | Type | Default | Description | | ------------- | -------------- | ------- | ---------------------------------------------------------------------------- | | `close` | `pd.DataFrame` | `--` | DataFrame containing closing prices. | | `window` | `int` | `50` | Window size for the moving average. Defaults to `50`. | | `length` | `int` | `None` | Number of data points to include in the plot. Defaults to `None` (all data). | | `plot_kwargs` | `tp.Kwargs` | `--` | Additional keyword arguments for customizing the plot. | **Returns**: | Type | Description | | ------------------ | -------------------------------------------------------- | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing the slope range plot. | ## `plot_pattern` ```python theme={null} plot_pattern( close: pandas.core.frame.DataFrame, pattern: Union(annotations=(, ), resolved=True) = None, window: int = 50, max_window: int = 120, min_similarity: float = 0.8, length: int = None, **plot_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot pattern matches in price data. This function identifies and visualizes occurrences of a specified pattern in the price data. **Parameters**: | Name | Type | Default | Description | | ---------------- | -------------- | ------- | -------------------------------------------------------------------------- | | `close` | `pd.DataFrame` | `--` | DataFrame containing closing prices. | | `pattern` | `list` | `--` | or vbt.Param, default None Pattern to search for. Defaults None. | | `window` | `int` | `50` | Minimum window size for pattern matching. Defaults to 50. | | `max_window` | `int` | `120` | Maximum window size for pattern matching. Defaults to 120. | | `min_similarity` | `float` | `0.8` | Minimum similarity threshold for pattern matching. Defaults to 0.8. | | `length` | `int` | `None` | Number of data points to include in the plot. Defaults to None (all data). | | `plot_kwargs` | `tp.Kwargs` | `--` | Additional keyword arguments for customizing the plot. | **Returns**: | Type | Description | | ------------------ | ---------------------------------------------------------- | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing the pattern match plot. | ## `RegressionLine` ```python theme={null} RegressionLine( data: pandas.core.frame.DataFrame | numpy.ndarray = None, x_labels: Sequence[Hashable] = None, trace_names: str | Sequence[str | None] | None = None, trace_kwargs: Dict[str, Any] | Sequence[Dict[str, Any] | None] | None = None, add_trace_kwargs: Dict[str, Any] | None = None, make_figure_kwargs: Dict[str, Any] | None = None, fig: plotly.basedatatypes.BaseFigure = None, use_gl: bool = None, **layout_kwargs, ) ``` Create a regression line plot. Inherites from `vbt.Scatter`. **Parameters**: | Name | Type | Default | Description | | -------------------- | ----------------------- | ------- | ----------------------------------------------------------------------------------------------------------------------- | | `data` | `pd.DataFrame` | `--` | Data in any format that can be converted to NumPy. Must be of shape (`x_labels`, `trace_names`). | | `x_labels` | `tp.Labels` | `None` | Labels for the x-axis. Must be of shape (`data.shape[0]`,). | | `trace_names` | `tp.TraceNames` | `None` | Trace names, corresponding to columns in pandas. Must be of shape (`data.shape[1]`,). | | `trace_kwargs` | `tp.KwargsLikeSequence` | `None` | Keyword arguments passed to `plotly.graph_objects.Scatter`. Can be specified per trace as a sequence of dicts. | | `add_trace_kwargs` | `tp.KwargsLike` | `None` | Keyword arguments passed to `add_trace`. | | `make_figure_kwargs` | `tp.KwargsLike` | `None` | Keyword arguments passed to `vectorbtpro.utils.figure.make_figure`. | | `fig` | `tp.BaseFigure` | `None` | Figure to add traces to. | | `use_gl` | `bool` | `None` | Whether to use WebGL for rendering. If `None`, it will be set to `True` if the data is large enough, otherwise `False`. | | `layout_kwargs` | `tp.Kwargs` | `--` | Keyword arguments for layout. | ### Ancestors * `vectorbtpro.generic.plotting.Scatter` * `vectorbtpro.generic.plotting.TraceType` * `vectorbtpro.utils.config.Configured` * `vectorbtpro.utils.config.HasSettings` * `vectorbtpro.utils.caching.Cacheable` * `vectorbtpro.utils.checks.Comparable` * `vectorbtpro.utils.pickling.Pickleable` * `vectorbtpro.utils.formatting.Prettified` * `vectorbtpro.utils.chaining.Chainable` * `vectorbtpro.generic.plotting.TraceUpdater` * `vectorbtpro.utils.base.Base` ## `TargetPortfolio` ```python theme={null} TargetPortfolio( data: pandas.core.series.Series = None, trace_names: str | Sequence[str | None] | None = None, trace_kwargs: Dict[str, Any] | Sequence[Dict[str, Any] | None] | None = None, add_trace_kwargs: Dict[str, Any] | None = None, make_figure_kwargs: Dict[str, Any] | None = None, fig: plotly.basedatatypes.BaseFigure = None, use_gl: bool = None, **layout_kwargs, ) ``` Create a target portfolio plot. Inherites from `vbt.Scatter`. **Parameters**: | Name | Type | Default | Description | | -------------------- | ----------------------- | ------- | ----------------------------------------------------------------------------------------------------------------------- | | `data` | `pd.Series` | `None` | Data in any format that can be converted to pandas Series. Must be of shape (`trace_names`,). | | `trace_names` | `tp.TraceNames` | `None` | Trace names, corresponding to columns in pandas. Must be of shape (`data.shape[0]`,). | | `trace_kwargs` | `tp.KwargsLikeSequence` | `None` | Keyword arguments passed to `plotly.graph_objects.Scatter`. Can be specified per trace as a sequence of dicts. | | `add_trace_kwargs` | `tp.KwargsLike` | `None` | Keyword arguments passed to `add_trace`. | | `make_figure_kwargs` | `tp.KwargsLike` | `None` | Keyword arguments passed to `vectorbtpro.utils.figure.make_figure`. | | `fig` | `tp.BaseFigure` | `None` | Figure to add traces to. | | `use_gl` | `bool` | `None` | Whether to use WebGL for rendering. If `None`, it will be set to `True` if the data is large enough, otherwise `False`. | | `layout_kwargs` | `tp.Kwargs` | `--` | Keyword arguments for layout. | ### Ancestors * `vectorbtpro.generic.plotting.Scatter` * `vectorbtpro.generic.plotting.TraceType` * `vectorbtpro.utils.config.Configured` * `vectorbtpro.utils.config.HasSettings` * `vectorbtpro.utils.caching.Cacheable` * `vectorbtpro.utils.checks.Comparable` * `vectorbtpro.utils.pickling.Pickleable` * `vectorbtpro.utils.formatting.Prettified` * `vectorbtpro.utils.chaining.Chainable` * `vectorbtpro.generic.plotting.TraceUpdater` * `vectorbtpro.utils.base.Base` ## `Frontier` ```python theme={null} Frontier( data: pandas.core.series.Series = None, trace_names: str | Sequence[str | None] | None = None, trace_kwargs: Dict[str, Any] | Sequence[Dict[str, Any] | None] | None = None, add_trace_kwargs: Dict[str, Any] | None = None, make_figure_kwargs: Dict[str, Any] | None = None, fig: plotly.basedatatypes.BaseFigure = None, use_gl: bool = None, **layout_kwargs, ) ``` Create a frontier plot. Inherits from `vbt.Scatter`. **Parameters**: | Name | Type | Default | Description | | -------------------- | ----------------------- | ------- | ----------------------------------------------------------------------------------------------------------------------- | | `data` | `pd.Series` | `None` | Data in any format that can be converted to pandas Series. Must be of shape (`trace_names`,). | | `trace_names` | `tp.TraceNames` | `None` | Trace names, corresponding to columns in pandas. Must be of shape (`data.shape[0]`,). | | `trace_kwargs` | `tp.KwargsLikeSequence` | `None` | Keyword arguments passed to `plotly.graph_objects.Scatter`. Can be specified per trace as a sequence of dicts. | | `add_trace_kwargs` | `tp.KwargsLike` | `None` | Keyword arguments passed to `add_trace`. | | `make_figure_kwargs` | `tp.KwargsLike` | `None` | Keyword arguments passed to `vectorbtpro.utils.figure.make_figure`. | | `fig` | `tp.BaseFigure` | `None` | Figure to add traces to. | | `use_gl` | `bool` | `None` | Whether to use WebGL for rendering. If `None`, it will be set to `True` if the data is large enough, otherwise `False`. | | `layout_kwargs` | `tp.Kwargs` | `--` | Keyword arguments for layout. | ### Ancestors * `vectorbtpro.generic.plotting.Scatter` * `vectorbtpro.generic.plotting.TraceType` * `vectorbtpro.utils.config.Configured` * `vectorbtpro.utils.config.HasSettings` * `vectorbtpro.utils.caching.Cacheable` * `vectorbtpro.utils.checks.Comparable` * `vectorbtpro.utils.pickling.Pickleable` * `vectorbtpro.utils.formatting.Prettified` * `vectorbtpro.utils.chaining.Chainable` * `vectorbtpro.generic.plotting.TraceUpdater` * `vectorbtpro.utils.base.Base` ## `Pie` ```python theme={null} Pie( data: pandas.core.frame.DataFrame | numpy.ndarray = None, rp_kwargs: Dict[str, Any] = None, trace_names: str | Sequence[str | None] | None = None, trace_kwargs: Dict[str, Any] | Sequence[Dict[str, Any] | None] | None = None, add_trace_kwargs: Dict[str, Any] | None = None, make_figure_kwargs: Dict[str, Any] | None = None, fig: plotly.basedatatypes.BaseFigure = None, **layout_kwargs, ) ``` Create a bar plot. Inherits from `vbt.Configured` and `vbt.TraceUpdater`. **Parameters**: | Name | Type | Default | Description | | -------------------- | ----------------------- | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `data` | `tp.Array` | `None` | Data in any format that can be converted to NumPy. Must be of shape (`trace_names`). | | `rp_kwargs` | `tp.Kwargs` | `None` | Keyword arguments for `vectorbtpro.portfolio.utils.rp`. If `data` is not None, it will be used to compute the portfolio. If `data` is None, it will be used to compute the portfolio from `trace_names`. | | `trace_names` | `tp.TraceNames` | `None` | Trace names, corresponding to columns in pandas. Must be of shape (`data.shape[0]`,) if `data` is not None. If `data` is None, it must be of shape (`trace_names`,). If `trace_names` is a string, it will be used as the name for all traces. | | `trace_kwargs` | `tp.KwargsLikeSequence` | `None` | Keyword arguments passed to `plotly.graph_objects.Pie`. Can be specified per trace as a sequence of dicts. If `data` is not None, it will be used to compute the portfolio. If `data` is None, it will be used to compute the portfolio from `trace_names`. | | `add_trace_kwargs` | `tp.KwargsLike` | `None` | Keyword arguments passed to `add_trace`. If `data` is not None, it will be used to compute the portfolio. If `data` is None, it will be used to compute the portfolio from `trace_names`. | | `make_figure_kwargs` | `tp.KwargsLike` | `None` | Keyword arguments passed to `vectorbtpro.utils.figure.make_figure`. If `data` is not None, it will be used to compute the portfolio. If `data` is None, it will be used to compute the portfolio from `trace_names`. | | `fig` | `tp.BaseFigure` | `None` | Figure to add traces to. If `None`, a new figure will be created. If `data` is not None, it will be used to compute the portfolio. If `data` is None, it will be used to compute the portfolio from `trace_names`. | | `layout_kwargs` | `tp.Kwargs` | `None` | Keyword arguments for layout. If `data` is not None, it will be used to compute the portfolio. If `data` is None, it will be used to compute the portfolio from `trace_names`. | **Examples**: ```python theme={null} >>> import numpy as np >>> import systematica as sma >>> pie = sma.Pie( ... data=np.array([0.06, 0.02, 0.05, 0.09]), ... trace_names=['SPY', 'TLT', 'XLF', 'XLE'], ... ) >>> pie.fig.show() ``` ### Ancestors * `vectorbtpro.utils.config.Configured` * `vectorbtpro.utils.config.HasSettings` * `vectorbtpro.utils.caching.Cacheable` * `vectorbtpro.utils.checks.Comparable` * `vectorbtpro.utils.pickling.Pickleable` * `vectorbtpro.utils.formatting.Prettified` * `vectorbtpro.utils.chaining.Chainable` * `vectorbtpro.generic.plotting.TraceUpdater` * `vectorbtpro.utils.base.Base` ### Static methods #### `update_trace` ```python theme={null} update_trace( trace: plotly.basedatatypes.BaseTraceType, data: numpy.ndarray, ) ``` Update the trace with new data. **Parameters**: | Name | Type | Default | Description | | ------- | --------------- | ------- | ------------------------------------------------------------------------------------- | | `trace` | `BaseTraceType` | `--` | Trace to update. | | `data` | `tp.Array1d` | `--` | Data in any format that can be converted to NumPy. Must be of shape (`trace_names`,). | ### Methods #### `update` ```python theme={null} update( self, data: numpy.ndarray, ) ``` Update the plot with new data. **Parameters**: | Name | Type | Default | Description | | ------ | -------------- | ------- | ------------------------------------------------------------------------------------- | | `data` | `tp.ArrayLike` | `--` | Data in any format that can be converted to NumPy. Must be of shape (`trace_names`,). |