Custom Plots - Systematica - Blockforce Capital

`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`,).

Overview

​RegressionLine

​Ancestors

​TargetPortfolio

​Ancestors

​Frontier

​Ancestors

​Pie

​Ancestors

​Static methods

​update_trace

​Methods

​update

`RegressionLine`

Ancestors

`TargetPortfolio`

Ancestors

`Frontier`

Ancestors

`Pie`

Ancestors

Static methods

`update_trace`

Methods

`update`