Plotting - 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_signals`

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:

>>> fig = plot_signals("AAPL", data, signals)
>>> fig.show()

`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_dashboard`

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`

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`

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`

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_performance_reset`

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`

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`

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`

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`

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:

>>> port = ...  # Initialize your portfolio object
>>> fig = plot_all_trades(port)
>>> fig.show()

`plot_drawdown`

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:

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

`plot_trades`

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`

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.

`plot_frontier`

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`

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`

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`

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`

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`

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`

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.

Overview

​plot_allocation

​plot_signal_crossover

​plot_signals

​plot_signal_vlines

​plot_dashboard

​plot_pie_chart

​plot_allocations

​plot_performance_matrix

​plot_performance_reset

​plot_performance_rebase

​plot_cash

​plot_excursions

​plot_all_trades

​plot_drawdown

​plot_trades

​plot_smas

​plot_bbands

​plot_slope_range

​plot_pattern

​plot_frontier

​plot_scatter_matrix

​plot_histogram_matrix

​plot_density_heatmap

​plot_density_contour

​plot_range_breakout_signals

​plot_range_breakout_model_output

`plot_allocation`

`plot_signal_crossover`

`plot_signals`

`plot_signal_vlines`

`plot_dashboard`

`plot_pie_chart`

`plot_allocations`

`plot_performance_matrix`

`plot_performance_reset`

`plot_performance_rebase`

`plot_cash`

`plot_excursions`

`plot_all_trades`

`plot_drawdown`

`plot_trades`

`plot_smas`

`plot_bbands`

`plot_slope_range`

`plot_pattern`

`plot_frontier`

`plot_scatter_matrix`

`plot_histogram_matrix`

`plot_density_heatmap`

`plot_density_contour`

`plot_range_breakout_signals`

`plot_range_breakout_model_output`