> ## 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. # Plotting > systematica.utils.plotting ## `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_signals` ```python theme={null} plot_signals( data: vectorbtpro.data.base.Data, symbol: str, signals: ~Signals, fig: vectorbtpro.utils.figure.FigureWidget = None, **plot_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot trading signals on a price chart. This function overlays trading signals (long/short entries and exits) on a price chart for a given asset symbol. It uses the provided data and signals to generate the plot. **Parameters**: | Name | Type | Default | Description | | ------------- | ------------------ | ------- | ------------------------------------------------------------------------------------- | | `data` | `vbt.Data` | `--` | A data object containing asset prices and other relevant information. | | `symbol` | `str` | `--` | The asset symbol to plot. | | `signals` | `SignalsT` | `--` | An object containing trading signals (long/short entries and exits). | | `fig` | `vbt.FigureWidget` | `None` | An existing figure to update. If `None`, a new figure is created. Defaults to `None`. | | `plot_kwargs` | `tp.Kwargs` | `--` | Additional keyword arguments for customizing the plot. | **Returns**: | Type | Description | | ------------------ | ---------------------------------------------------- | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing the signals plot. | **Examples**: ```python theme={null} >>> fig = plot_signals("AAPL", data, signals) >>> fig.show() ``` ## `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_dashboard` ```python theme={null} plot_dashboard( data: vectorbtpro.data.base.Data, s1: str, s2: str, pf: vectorbtpro.portfolio.base.Portfolio, model_output: numpy.ndarray, showlegend: bool = False, **layout_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Generate a dashboard visualization for a pair of assets. **Parameters**: | Name | Type | Default | Description | | --------------- | --------------- | ------- | -------------------------------------------------------- | | `data` | `vbt.Data` | `--` | Market data. | | `s1` | `str` | `--` | First asset symbol. | | `s2` | `str` | `--` | Second asset symbol. | | `pf` | `vbt.Portfolio` | `--` | Portfolio object. | | `model_output` | `tp.Array` | `--` | Model output data. | | `showlegend` | `bool` | `False` | Show legend. Defaults to `False`. | | `layout_kwargs` | `tp.Kwargs` | `--` | Additional keyword arguments for customizing the layout. | **Returns**: | Type | Description | | ------------------ | ------------------------------------------------------------------------- | | `vbt.FigureWidget` | A dashboard figure displaying asset data, portfolio returns, and signals. | ## `plot_pie_chart` ```python theme={null} plot_pie_chart( *args, **kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot a pie chart. This function creates a pie chart using the `Pie` class from `systematica.utils.custom_plots`. **Parameters**: | Name | Type | Default | Description | | -------- | ----------- | ------- | ----------------------------------------------- | | `args` | `tp.Args` | `--` | Positional arguments passed to the `Pie` class. | | `kwargs` | `tp.Kwargs` | `--` | Keyword arguments passed to the `Pie` class. | **Returns**: | Type | Description | | ------------------ | ------------------------------------------------- | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing the pie chart. | ## `plot_allocations` ```python theme={null} plot_allocations( pfo: vectorbtpro.portfolio.pfopt.base.PortfolioOptimizer, market_returns: pandas.core.frame.DataFrame, categories: pandas.core.frame.DataFrame = None, group_by_category: bool = False, column: Hashable = None, dropna: str = 'head', line_shape: str = 'hv', plot_rb_dates: bool = None, trace_kwargs: Dict[str, Any] | Sequence[Dict[str, Any] | None] | None = None, add_shape_kwargs: Dict[str, Any] | None = None, add_trace_kwargs: Dict[str, Any] | None = None, fig: plotly.basedatatypes.BaseFigure = None, **layout_kwargs, ) ‑> plotly.basedatatypes.BaseFigure ``` Plot portfolio allocations over time. This function visualizes portfolio allocations as an area plot. It supports grouping by categories and marking rebalancing dates. **Parameters**: | Name | Type | Default | Description | | ------------------- | ------------------------ | -------- | -------------------------------------------------------------------------------- | | `pfo` | `vbt.PortfolioOptimizer` | `--` | Portfolio optimizer object containing allocation data. | | `market_returns` | `pd.DataFrame` | `--` | DataFrame containing market returns. | | `categories` | `pd.DataFrame` | `None` | DataFrame containing category information for assets. Defaults to `None`. | | `group_by_category` | `bool` | `False` | Whether to group allocations by category. Defaults to `False`. | | `column` | `tp.Label` | `None` | Column to select from the portfolio optimizer. Defaults to `None`. | | `dropna` | `str` | `"head"` | How to handle NaN values in allocations. Defaults to `head`. | | `line_shape` | `str` | `"hv"` | Shape of the lines in the area plot. Defaults to `hv`. | | `plot_rb_dates` | `bool` | `None` | Whether to plot rebalancing dates. Defaults to `None`. | | `trace_kwargs` | `tp.KwargsLikeSequence` | `None` | Additional keyword arguments for traces. Defaults to `None`. | | `add_shape_kwargs` | `tp.KwargsLike` | `None` | Additional keyword arguments for shapes. Defaults to `None`. | | `add_trace_kwargs` | `tp.KwargsLike` | `None` | Additional keyword arguments for adding traces. Defaults to `None`. | | `fig` | `tp.BaseFigure` | `None` | Existing figure to update. If None, a new figure is created. Defaults to `None`. | | `layout_kwargs` | `tp.Kwargs` | `--` | Additional layout arguments for the figure. | **Returns**: | Type | Description | | --------------- | ---------------------------------------------------- | | `tp.BaseFigure` | A Plotly BaseFigure containing the allocations plot. | ## `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_performance_reset` ```python theme={null} plot_performance_reset( data: pandas.core.series.Series | pandas.core.frame.DataFrame, by: str = 'M', fig: vectorbtpro.utils.figure.FigureWidget = None, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot performance reset by month or year. This function calculates and plots the percentage change in performance reset at the start of each month or year. **Parameters**: | Name | Type | Default | Description | | ------ | ------------------ | ------- | -------------------------------------------------------------------------------- | | `data` | `tp.SeriesFrame` | `--` | Time series data of performance values. | | `by` | `str` | `M` | Reset frequency: `M` for monthly or `Y` for yearly. Defaults to `M`. | | `fig` | `vbt.FigureWidget` | `None` | Existing figure to update. If None, a new figure is created. Defaults to `None`. | **Returns**: | Type | Description | | ------------------ | -------------------------------------------------------------- | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing the performance reset plot. | ## `plot_performance_rebase` ```python theme={null} plot_performance_rebase( df: pandas.core.series.Series | pandas.core.frame.DataFrame, fig: vectorbtpro.utils.figure.FigureWidget = None, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot rebased performance. This function rebases the performance data to a starting value of 1 and plots it. **Parameters**: | Name | Type | Default | Description | | ----- | ------------------ | ------- | -------------------------------------------------------------------------------- | | `df` | `tp.SeriesFrame` | `--` | Time series data of performance values. | | `fig` | `vbt.FigureWidget` | `None` | Existing figure to update. If None, a new figure is created. Defaults to `None`. | **Returns**: | Type | Description | | ------------------ | ---------------------------------------------------------------- | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing the rebased performance plot. | ## `plot_cash` ```python theme={null} plot_cash( pf: Any, **plot_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot cash-related metrics for a portfolio. This function creates subplots for cash, cash flow, gross exposure, and net exposure. **Parameters**: | Name | Type | Default | Description | | ------------- | ----------- | ------- | ------------------------------------------------------ | | `pf` | `tp.Any` | `--` | Portfolio object containing cash-related data. | | `plot_kwargs` | `tp.Kwargs` | `--` | Additional keyword arguments for customizing the plot. | **Returns**: | Type | Description | | ------------------ | ---------------------------------------------------------- | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing the cash-related plots. | ## `plot_excursions` ```python theme={null} plot_excursions( pf: Any, expanding: bool = True, **plot_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot trade excursions for a portfolio. This function visualizes Maximum Adverse Excursion (MAE) and Maximum Favorable Excursion (MFE) for each trade in the portfolio. **Parameters**: | Name | Type | Default | Description | | ------------- | ----------- | ------- | ------------------------------------------------------ | | `pf` | `tp.Any` | `--` | Portfolio object containing trade data. | | `expanding` | `bool` | `True` | Whether to plot expanding MAE/MFE. Defaults to `True`. | | `plot_kwargs` | `tp.Kwargs` | `--` | Additional keyword arguments for customizing the plot. | **Returns**: | Type | Description | | ------------------ | ------------------------------------------------------- | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing the excursion plots. | ## `plot_all_trades` ```python theme={null} plot_all_trades( pf: Any, **layout_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Create a plot of all trades in the portfolio. The function generates subplots for all trades within the specified portfolio. **Parameters**: | Name | Type | Default | Description | | --------------- | ----------- | ------- | ------------------------------------------------------ | | `port` | `tp.Any` | `--` | The portfolio object containing trade data. | | `layout_kwargs` | `tp.Kwargs` | `--` | Additional keyword arguments for layout configuration. | **Returns**: | Type | Description | | ------------------ | ---------------------------------------------------- | | `vbt.FigureWidget` | A figure widget containing the plots for all trades. | **Examples**: ```python theme={null} >>> port = ... # Initialize your portfolio object >>> fig = plot_all_trades(port) >>> fig.show() ``` ## `plot_drawdown` ```python theme={null} plot_drawdown( pf: Any, **plot_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Create a drawdown plot for the portfolio. This function visualizes the drawdowns and underwater metrics of the portfolio. **Parameters**: | Name | Type | Default | Description | | ------------- | ----------- | ------- | ------------------------------------------------------ | | `port` | `tp.Any` | `--` | The portfolio object containing drawdown data. | | `plot_kwargs` | `tp.Kwargs` | `--` | Additional keyword arguments for customizing the plot. | **Returns**: | Type | Description | | ------------------ | ---------------------------------------------- | | `vbt.FigureWidget` | A figure widget containing the drawdown plots. | **Examples**: ```python theme={null} >>> pf = ... # Initialize your portfolio object >>> fig = plot_drawdown(pf) >>> fig.show() ``` ## `plot_trades` ```python theme={null} plot_trades( pf: Any, symbol: str, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot trades and asset flow for a specific symbol. This function creates subplots for trades, asset flow, and asset holdings. **Parameters**: | Name | Type | Default | Description | | -------- | -------- | ------- | ----------------------------------------------- | | `pf` | `tp.Any` | `--` | Portfolio object containing trade data. | | `symbol` | `str` | `--` | Symbol for which to plot trades and asset flow. | **Returns**: | Type | Description | | ------------------ | ------------------------------------------------------------------- | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing the trades and asset flow plots. | ## `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. | ## `plot_frontier` ```python theme={null} plot_frontier( target_portfolio: pandas.core.frame.DataFrame, efficient_frontier: pandas.core.frame.DataFrame = None, portfolio_name: str = 'Target Portfolio', marker_color: str = 'red', marker_size: int = 20, make_figure_kwargs: Dict[str, Any] | None = None, fig: plotly.basedatatypes.BaseFigure = None, showscale: bool = True, **layout_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot Frontier. **Parameters**: | Name | Type | Default | Description | | -------------------- | ---------------- | ----------- | ------------------------------------------------------------- | | `target_portfolio` | `pd.DataFrame` | `--` | Target\_portfolio. | | `efficient_frontier` | `pd.DataFrame` | `None` | Efficient frontier. If `None`, the frontier is not displayed. | | `portfolio_name` | `str` | `Target...` | Portfolio name. Defaults to `Target Portfolio`. | | `marker_color` | `str` | `red` | Color of the portfolio star shaped marker. Defaults to `red`. | | `marker_size` | `int` | `20` | Size of the portfolio star shaped marker. Default to `20`. | | `make_figure_kwargs` | `tp.KwargsLike` | `None` | Additional figure arguments. Defaults to `None`. | | `fig` | `ttp.BaseFigure` | `None` | Figure. Defaults to None. | | `showscale` | `bool` | `True` | Show scale. Defaults to `True`. | | `layout_kwargs` | `tp.Kwargs` | `--` | Additional layout arguments for the figure. | **Returns**: | Type | Description | | ------------------ | ------------------------------------------------- | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing frontier plot. | ## `plot_scatter_matrix` ```python theme={null} plot_scatter_matrix( df: pandas.core.frame.DataFrame, metrics: List[str], params: List[str], n_columns: int = 3, **layout_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot trial. **Parameters**: | Name | Type | Default | Description | | --------------- | -------------- | ------- | ----------------------------------------------- | | `df` | `pd.DataFrame` | `--` | Trial dataframe. | | `metrics` | `tp.List[str]` | `--` | Metric columns. | | `params` | `str` | `--` | Param columns. | | `n_columns` | `int` | `3` | Number of columns in the grid. Defaults to `3`. | | `layout_kwargs` | `tp.Kwargs` | `--` | Additional layout arguments for the figure. | **Returns**: | Type | Description | | ------------------ | ---------------------------------------------- | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing the trials. | ## `plot_histogram_matrix` ```python theme={null} plot_histogram_matrix( df: pandas.core.frame.DataFrame, metrics: List[str], **layout_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot trial. **Parameters**: | Name | Type | Default | Description | | --------------- | -------------- | ------- | ------------------------------------------- | | `df` | `pd.DataFrame` | `--` | Trial dataframe. | | `metrics` | `tp.List[str]` | `--` | Metric columns. | | `layout_kwargs` | `tp.Kwargs` | `--` | Additional layout arguments for the figure. | **Returns**: | Type | Description | | ------------------ | ---------------------------------------------- | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing the trials. | ## `plot_density_heatmap` ```python theme={null} plot_density_heatmap( df: pandas.core.frame.DataFrame, metric: str, param: str, **kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot density heatmap. **Parameters**: | Name | Type | Default | Description | | -------- | -------------- | ------- | ------------------------------------------- | | `df` | `pd.DataFrame` | `--` | Trial dataframe. | | `metric` | `str` | `--` | Metric column. | | `param` | `str` | `--` | Param column. | | `kwargs` | `tp.Kwargs` | `--` | Additional layout arguments for the figure. | **Returns**: | Type | Description | | ------------------ | ---------------------------------------------- | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing the trials. | ## `plot_density_contour` ```python theme={null} plot_density_contour( df: pandas.core.frame.DataFrame, metric: str, param: str, colorscale: List[str | Tuple[float]], **kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot density contour. **Parameters**: | Name | Type | Default | Description | | -------- | -------------- | ------- | ------------------------------------------- | | `df` | `pd.DataFrame` | `--` | Trial dataframe. | | `metric` | `str` | `--` | Metric column. | | `param` | `str` | `--` | Param column. | | `kwargs` | `tp.Kwargs` | `--` | Additional layout arguments for the figure. | **Returns**: | Type | Description | | ------------------ | ---------------------------------------------- | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing the trials. | ## `plot_range_breakout_signals` ```python theme={null} plot_range_breakout_signals( data: vectorbtpro.data.base.Data, symbol: str, training_window: str = '2h', by: str = 'D', freq: str = 'hours', tol: float = 0.5, use_close: bool = True, **layout_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot range breakout. **Parameters**: | Name | Type | Default | Description | | ----------------- | ----------- | ------- | -------------------------------------------------------------------------------------------------------- | | `data` | `vbt.Data` | `--` | A data object containing asset prices and other relevant information. | | `symbol` | `str` | `--` | The asset symbol to plot. | | `training_window` | `str` | `2h` | The split size of the training window used in `splitter.from_grouper`. Defaults to `2h` | | `by` | `str` | `D` | Split data temporarily. Defaults to `D` for daily. | | `freq` | `str` | `hours` | Split data with `by`. Defaults to `hours`. | | `tol` | `float` | `0.5` | Range tolerance to trigger a long/short signal such as `trigger = tol * range_values`. Defauts to `0.5`. | | `use_close` | `bool` | `True` | Use close or open price for signals. Defaults to `True` (close). | | `layout_kwargs` | `tp.Kwargs` | `--` | Additional layout arguments for the figure. | **Returns**: | Type | Description | | ------------------ | ---------------------------------------------- | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing the trials. | ## `plot_range_breakout_model_output` ```python theme={null} plot_range_breakout_model_output( data: vectorbtpro.data.base.Data, symbol: str, training_window: str = '2h', by: str = 'D', freq: str = 'hours', tol: float = 0.5, use_close: bool = True, **layout_kwargs, ) ‑> vectorbtpro.utils.figure.FigureWidget ``` Plot range breakout indicator. **Parameters**: | Name | Type | Default | Description | | ----------------- | ----------- | ------- | -------------------------------------------------------------------------------------------------------- | | `data` | `vbt.Data` | `--` | A data object containing asset prices and other relevant information. | | `symbol` | `str` | `--` | The asset symbol to plot. | | `training_window` | `str` | `2h` | The split size of the training window used in `splitter.from_grouper`. Defaults to `2h`. | | `by` | `str` | `D` | Split data temporarily. Defaults to `D` for daily. | | `freq` | `str` | `hours` | Split data with `by`. Defaults to `hours`. | | `tol` | `float` | `0.5` | Range tolerance to trigger a long/short signal such as `trigger = tol * range_values`. Defauts to `0.5`. | | `use_close` | `bool` | `True` | Use close or open price for signals. Defaults to `True` (close). | | `layout_kwargs` | `tp.Kwargs` | `--` | Additional layout arguments for the figure. | **Returns**: | Type | Description | | ------------------ | ---------------------------------------------- | | `vbt.FigureWidget` | A Plotly `FigureWidget` containing the trials. |