Analyzer

systematica.portfolio.analyzer

PortfolioAnalyzer

PortfolioAnalyzer(
    data: vectorbtpro.data.base.Data,
    s1: str,
    s2: str,
    vbt: vectorbtpro.portfolio.base.Portfolio,
    model_output: pandas.core.frame.DataFrame | numpy.ndarray = None,
    window: int = None,
    minp: int = None,
)

A class representing a portfolio analyzer: Analyzes post-simulation backtest and inherites from vbt.Portfolio.

Method generated by attrs for class PortfolioAnalyzer.

Class variables

• data: vectorbtpro.data.base.Data: Data object.

• s1: str: First symbol.

• s2: str: Second symbol.

• vbt: vectorbtpro.portfolio.base.Portfolio: VectorBT PRO Portfolio object.

• model_output: pandas.core.frame.DataFrame | numpy.ndarray: Model output used in the strategy. Defaults to None.

• window: int: Rolling window used in the model. Defaults to None.

• minp: int: Minimum number of observations required in the model. Defaults to None.

Instance variables

• freq: pandas._libs.tslibs.timedeltas.Timedelta: Get the frequency of the data.

• column_wrapper: List[str]: Get the column wrapper for the portfolio. If MultiIndex (where group_by is False), Get the first level only.

• categories: pandas.core.frame.DataFrame | None: Retrieve asset categories.

• drawdowns_readable: Get the drawdowns of the portfolio.

• pfo: vectorbtpro.portfolio.pfopt.base.PortfolioOptimizer: Pick allocations from an already filled array.

• pfo_allocations: pandas.core.frame.DataFrame: Get the portfolio allocations.

• latest_allocation: pandas.core.frame.DataFrame: Get the latest allocation of the portfolio.

• current_holdings: Get the current holdings of the portfolio.

• pfo_describe: pandas.core.frame.DataFrame: Get descriptive statistics of portfolio allocations.

• average_allocation: Get the average allocation of the portfolio.

• allocation_records_readable: Get readable allocation records.

• latest_allocation_records: Get the latest allocation records.

Methods

get_signals

get_signals(
    self,
    start: str | float | int | complex | bool | object | numpy.generic | Sequence[str | float | int | complex | bool | object | numpy.generic] | Sequence[Sequence[Any]] | vectorbtpro._typing.SupportsArrayT | numpy.ndarray = None,
    end: str | float | int | complex | bool | object | numpy.generic | Sequence[str | float | int | complex | bool | object | numpy.generic] | Sequence[Sequence[Any]] | vectorbtpro._typing.SupportsArrayT | numpy.ndarray = None,
    group_by: bool = False,
) ‑> systematica.signals.base.Signals

Return signals for long and short entries and exits.

Compute boolean signals indicating the occurrence of long entry orders, long exit orders, short entry orders, and short exit orders.

Signals are computed per group if grouping is enabled. Pass group_by=False to disable grouping.

Parameters:

Name	Type	Default	Description
start	ArrayLike	None	Start index of the simulation range.
end	ArrayLike	None	End index of the simulation range.
group_by	bool	False	Grouping specification.

Returns:

Type	Description
Signals	systematica Signals namedtuple class.

get_market_returns

get_market_returns(
    self,
) ‑> pandas.core.frame.DataFrame

Calculate market returns without NaNs.

Returns:

Type	Description
pd.DataFrame	DataFrame of market returns.

get_pf_metrics

get_pf_metrics(
    self,
    metrics: str | List[str],
    to_dict: bool = False,
    to_numpy: bool = False,
    **kwargs,
) ‑> pandas.core.frame.DataFrame

Get performance metrics from a backtested portfolio.

Parameters:

Name	Type	Default	Description
metrics	str	--	or list of str A single metric key or a list of metric keys to look up in the registry.
to_dict	bool	False	Change frame format output to dict type if set to False. Raw pd.DataFrame object data otherwise. Frames are retrieve when group_by is False in the vbt.PF configuration and/or when rolling calculation is triggered. Defaults to False.
to_numpy	bool	False	If False, returns a pandas DataFrame. Otherwise a numpy array.
kwargs	tp.Kwargs	--	Additional key-word arguments passed to Metric.run_pf_metric.

Returns:

Type	Description
pd.DataFrame	DataFrame containing performance metrics.
Example
-------
With a pf object:
````python`
>>> model.get_performance_output(pd, metrics='sharpe_ratio')
`````
With a parameterized pf object:
````python`
>>> opt_results = model.get_performance_output(pd, metrics='sharpe_ratio')
>>> opt_results['sharpe_ratio'].vbt.histplot().show()
`````

get_rolling_metrics

get_rolling_metrics(
    self,
    metrics: str | List[str],
    window: int = None,
    minp: int = None,
    to_numpy: bool = False,
    **kwargs,
) ‑> pandas.core.frame.DataFrame | numpy.ndarray

Get rolling metrics.

Parameters:

Name	Type	Default	Description
metrics	str	--	Metric to compute
window	int	None	Rolling window length. The default is None.
minp	int	None	Mininum periods. The default is None.
to_numpy	bool	False	Output numpy array if True. The default is False.
kwargs	tp.Kwargs	--	Additional key-word arguments passed to Metric.run_rolling_metric.

Raises:

Type	Description
ValueError	if parameter window is to be specified..

Returns:

Type	Description
pd.DataFrame | tp.Array	Rolling metrics.

all_stats

all_stats(
    self,
    start: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    end: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    portfolio: Dict[str, Any] = None,
    drawdowns: Dict[str, Any] = None,
) ‑> pandas.core.series.Series

Get all statistics of the portfolio.

Parameters:

Name	Type	Default	Description
start	str	None	The start date. Defaults to None.
end	str	None	The end date. Defaults to None.
portfolio	tp.Kwargs	None	Addtional arguments for vbt.stats. Defaults to None.
drawdowns	tp.Kwargs	None	Addtional arguments for vbt.drawdowns.stats. Defaults to None.

Returns:

Type	Description
pd.Series	All portfolio, returns, orders, trades and drawdowns statistics.

plot_rolling_metrics

plot_rolling_metrics(
    self,
    metrics: str | List[str],
    window: int,
    minp: int = None,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot rolling metrics.

Parameters:

Name	Type	Default	Description
metrics	str	--	Metric to compute
window	int	None	Rolling window length. The default is None.
minp	int	None	Mininum periods. The default is None.
layout_kwargs	tp.Kwargs	--	Additional layout keyword arguments passed to vbt.FigureWidget.plot.

Returns:

Type	Description
vbt.FigureWidget	Figure widget of the rolling metric.

plot_equity_curve

plot_equity_curve(
    self,
    start: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    end: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    bm_returns: bool = False,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the equity curve of the portfolio.

Parameters:

Name	Type	Default	Description
start	str	None	The start date. Defaults to None.
end	str	None	The end date. Defaults to None.
bm_returns	bool	False	Whether to include benchmark returns. Defaults to False.
layout_kwargs	tp.Kwargs	--	Additional layout keyword arguments.

Returns:

Type	Description
vbt.FigureWidget	Figure widget of the equity curve.

plot_performance_pnl

plot_performance_pnl(
    self,
    symbol: str,
    start: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    end: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    group_by: bool = False,
    pct_scale=False,
    marker_size_range=(7, 14),
    opacity_range=(0.75, 0.9),
    closed_trace_kwargs=None,
    closed_profit_trace_kwargs=None,
    closed_loss_trace_kwargs=None,
    open_trace_kwargs=None,
    hline_shape_kwargs=None,
    add_trace_kwargs=None,
    xref='x',
    yref='y',
    fig=None,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the performance PnL of the portfolio.

Parameters:

Name	Type	Default	Description
symbol	str	--	Symbol to plot.
start	str	None	The start date. Defaults to None.
end	str	None	The end date. Defaults to None.
group_by	bool	False	Whether to group by. Defaults to False.
pct_scale	bool	False	Whether to scale by percentage. Defaults to False.
marker_size_range	tuple	“(7,`	14)` Range of marker sizes. Defaults to (7, 14).
opacity_range	tuple	“(0.75,`	0.9)` Range of opacity. Defaults to (0.75, 0.9).
closed_trace_kwargs	dict	None	Keyword arguments for closed trace.
closed_profit_trace_kwargs	dict	None	Keyword arguments for closed profit trace.
closed_loss_trace_kwargs	dict	None	Keyword arguments for closed loss trace.
open_trace_kwargs	dict	None	Keyword arguments for open trace.
hline_shape_kwargs	dict	None	Keyword arguments for horizontal line shape.
add_trace_kwargs	dict	None	Keyword arguments for adding trace.
xref	str	x	X reference. Defaults to x.
yref	str	y	Y reference. Defaults to y.
fig	vbt.FigureWidget	None	Existing figure widget.
layout_kwargs	tp.Kwargs	--	Additional layout keyword arguments.

Returns:

Type	Description
vbt.FigureWidget	Figure widget of the performance PnL.

plot_running_edge_ratio

plot_running_edge_ratio(
    self,
    start: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    end: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    volatility: bool = None,
    entry_price_open: bool = False,
    exit_price_close: bool = False,
    max_duration: int = None,
    incl_shorter: bool = False,
    group_by: str = None,
    jitted: bool = None,
    xref: str = 'x',
    yref: str = 'y',
    hline_shape_kwargs: Dict[str, Any] = None,
    **kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the running edge ratio of the portfolio.

Parameters:

Name	Type	Default	Description
start	str	None	The start date. Defaults to None.
end	str	None	The end date. Defaults to None.
volatility	float	None	Volatility value.
entry_price_open	bool	False	Whether to use entry price open. Defaults to False.
exit_price_close	bool	False	Whether to use exit price close. Defaults to False.
max_duration	int	None	Maximum duration.
incl_shorter	bool	False	Whether to include shorter durations. Defaults to False.
group_by	str	None	Group by value.
jitted	bool	None	Whether to use jitted mode. Defaults to None.
xref	str	x	X reference. Defaults to x.
yref	str	y	Y reference. Defaults to y.
hline_shape_kwargs	dict	None	Keyword arguments for horizontal line shape.
kwargs	tp.Kwargs	--	Additional keyword arguments.

Returns:

Type	Description
vbt.FigureWidget	Figure widget of the running edge ratio.

plot_performance_reset

plot_performance_reset(
    self,
    start: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    end: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    by: str = 'Y',
    add_close: bool = True,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the performance reset of the portfolio.

Parameters:

Name	Type	Default	Description
start	str	None	The start date. Defaults to None.
end	str	None	The end date. Defaults to None.
by	str	Y	Reset by value. Defaults to Y.
add_close	bool	True	Whether to add close prices. Defaults to True.

Returns:

Type	Description
vbt.FigureWidget	Figure widget of the performance reset.

plot_performance_rebase

plot_performance_rebase(
    self,
    start: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    end: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    add_close: bool = True,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the performance rebase of the portfolio.

Parameters:

Name	Type	Default	Description
start	str	None	The start date. Defaults to None.
end	str	None	The end date. Defaults to None.
add_close	bool	True	Whether to add close prices. Defaults to True.

Returns:

Type	Description
vbt.FigureWidget	Figure widget of the performance rebase.

plot_performance_matrix

plot_performance_matrix(
    self,
    start: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    end: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    **plot_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the performance matrix of the portfolio.

Parameters:

Name	Type	Default	Description
start	str	None	The start date. Defaults to None.
end	str	None	The end date. Defaults to None.
plot_kwargs	tp.Kwargs	--	Additional plot keyword arguments.

Returns:

Type	Description
vbt.FigureWidget	Figure widget of the performance matrix.

plot_drawdown_and_underwater

plot_drawdown_and_underwater(
    self,
    start: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    end: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    pct_scale: bool = False,
    **plot_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the drawdowns of the portfolio.

Parameters:

Name	Type	Default	Description
start	str	None	The start date. Defaults to None.
end	str	None	The end date. Defaults to None.
pct_scale	bool	s	to False Percentage scale. Defaults to False.
plot_kwargs	tp.Kwargs	--	Additional plot keyword arguments.

Returns:

Type	Description
vbt.FigureWidget	Figure widget of the drawdowns.

plot_allocation_report

plot_allocation_report(
    self,
    start: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    end: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    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,
    group_by_category: bool = False,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the allocations of the portfolio.

Parameters:

Name	Type	Default	Description
start	str	None	The start date. Defaults to None.
end	str	None	The end date. Defaults to None.
column	tp.Label	None	Column to plot.
dropna	str	head	Drop NaN values. Defaults to head.
line_shape	str	hv	Line shape. Defaults to hv.
plot_rb_dates	bool	None	Whether to plot rebalancing dates.
trace_kwargs	dict	None	Keyword arguments for trace.
add_shape_kwargs	dict	None	Keyword arguments for adding shape.
add_trace_kwargs	dict	None	Keyword arguments for adding trace.
fig	vbt.FigureWidget	None	Existing figure widget.
group_by_category	bool	False	Whether to group by category. Defaults to False.
layout_kwargs	tp.Kwargs	--	Additional layout keyword arguments.

Returns:

Type	Description
vbt.FigureWidget	Figure widget of the allocations.

plot_pie_allocation

plot_pie_allocation(
    self,
    rp_kwargs: Dict[str, Any] | 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: ForwardRef('plotly.basedatatypes.BaseFigure') | None = None,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the pie chart of the latest allocation.

Parameters:

Name	Type	Default	Description
rp_kwargs	dict	None	Keyword arguments for radial plot.
trace_kwargs	dict	None	Keyword arguments for trace.
add_trace_kwargs	dict	None	Keyword arguments for adding trace.
make_figure_kwargs	dict	None	Keyword arguments for making the figure.
fig	vbt.FigureWidget	None	Existing figure widget.
layout_kwargs	tp.Kwargs	--	Additional layout keyword arguments.

Returns:

Type	Description
vbt.FigureWidget	Figure widget of the pie chart.

plot_average_allocation

plot_average_allocation(
    self,
    rp_kwargs: Dict[str, Any] | 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: ForwardRef('plotly.basedatatypes.BaseFigure') | None = None,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the pie chart of the average allocation.

Parameters:

Name	Type	Default	Description
rp_kwargs	dict	None	Keyword arguments for radial plot.
trace_kwargs	dict	None	Keyword arguments for trace.
add_trace_kwargs	dict	None	Keyword arguments for adding trace.
make_figure_kwargs	dict	None	Keyword arguments for making the figure.
fig	vbt.FigureWidget	None	Existing figure widget.
layout_kwargs	tp.Kwargs	--	Additional layout keyword arguments.

Returns:

Type	Description
vbt.FigureWidget	Figure widget of the pie chart.

plot_cash_report

plot_cash_report(
    self,
    start: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    end: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    **plot_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the cash balance of the portfolio.

Parameters:

Name	Type	Default	Description
start	str	None	The start date. Defaults to None.
end	str	None	The end date. Defaults to None.
plot_kwargs	tp.Kwargs	--	Additional plot keyword arguments.

Returns:

Type	Description
vbt.FigureWidget	Figure widget of the cash balance.

plot_excursions

plot_excursions(
    self,
    start: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    end: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    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
start	str	None	The start date. Defaults to None.
end	str	None	The end date. Defaults to None.
expanding	bool	True	Whether to plot expanding MAE/MFE. Defaults to True.
plot_kwargs	tp.Kwargs	--	Additional plot keyword arguments.

Returns:

Type	Description
vbt.FigureWidget	Figure widget of the expanding excursions.

plot_trades_by_symbol

plot_trades_by_symbol(
    self,
    symbol: str,
    start: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    end: str | pandas._libs.tslibs.timestamps.Timestamp = None,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the trades of the portfolio by symbol.

Parameters:

Name	Type	Default	Description
symbol	str	--	Symbol to plot.
start	str	None	The start date. Defaults to None.
end	str	None	The end date. Defaults to None.

Returns:

Type	Description
vbt.FigureWidget	Figure widget of the trades by symbol.

plot_all_trades

plot_all_trades(
    self,
    start: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    end: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot all trades of the portfolio.

Parameters:

Name	Type	Default	Description
start	str	None	The start date. Defaults to None.
end	str	None	The end date. Defaults to None.
layout_kwargs	tp.Kwargs	--	Additional layout keyword arguments.

Returns:

Type	Description
vbt.FigureWidget	Figure widget of all trades.

plot_dashboard

plot_dashboard(
    self,
    start: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    end: str | pandas._libs.tslibs.timestamps.Timestamp = None,
    showlegend: bool = False,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the dashboard of the portfolio.

Parameters:

Name	Type	Default	Description
start	str	None	The start date. Defaults to None.
end	pd.Timestamp	None	The end date. Defaults to None.
showlegend	bool	False	Show legend. Defaults to False.
layout_kwargs	tp.Kwargs	--	Additional layout keyword arguments.

Returns:

Type	Description
vbt.FigureWidget	Figure widget of the dashboard.