Custom Plots - Systematica - Blockforce Capital

`plot_allocation`

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`

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`

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`

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:

>>> pf = ...  # Initialize your portfolio object
>>> fig = plot_performance_matrix(pf)
>>> fig.show()

`plot_smas`

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`

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`

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`

plot_pattern(
    close: pandas.core.frame.DataFrame,
    pattern: Union(annotations=(<class 'vectorbtpro.utils.params.Param'>, <class 'list'>), 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`

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`

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`

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`

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:

>>> 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`

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`

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`,).

​plot_allocation

​plot_signal_crossover

​plot_signal_vlines

​plot_performance_matrix

​plot_smas

​plot_bbands

​plot_slope_range

​plot_pattern

​RegressionLine

​Ancestors

​TargetPortfolio

​Ancestors

​Frontier

​Ancestors

​Pie

​Ancestors

​Static methods

​update_trace

​Methods

​update

`plot_allocation`

`plot_signal_crossover`

`plot_signal_vlines`

`plot_performance_matrix`

`plot_smas`

`plot_bbands`

`plot_slope_range`

`plot_pattern`

`RegressionLine`

Ancestors

`TargetPortfolio`

Ancestors

`Frontier`

Ancestors

`Pie`

Ancestors

Static methods

`update_trace`

Methods

`update`