Arbitrage Index

`ArbitrageIndexCV`

ArbitrageIndexCV(
    id: str = None,
    loader_or_data: Callable = <function load_clean_data>,
    timeframe: str = '1d',
    start: str = None,
    end: str = None,
    s1: str = 'BTCUSDT',
    s2: str = 'ETHUSDT',
    suffix: str = 'h5',
    use_close: bool = True,
    freq: str = None,
    signal_model: str = None,
    long_entries: float = nan,
    long_exits: float = nan,
    short_entries: float = nan,
    short_exits: float = nan,
    clean: bool = True,
    select_symbols: str | int | List[str | int] = None,
    portfolio_config: Dict[str, Any] = _Nothing.NOTHING,
    model_params: Dict[str, Any] = _Nothing.NOTHING,
    metrics: str | List[str] = None,
    metrics_kwargs: Dict[str, Any] = _Nothing.NOTHING,
    use_rolling: bool = False,
    storage: str | optuna.storages._base.BaseStorage = None,
    pruner: optuna.pruners._base.BasePruner = <optuna.pruners._successive_halving.SuccessiveHalvingPruner object>,
    sampler: optuna.samplers._base.BaseSampler = <optuna.samplers._tpe.sampler.TPESampler object>,
    study_name: str = None,
    direction: str | List[str] = 'maximize',
    load_if_exists: bool = False,
    n_trials: int = 100,
    n_completed_trials: int = None,
    timeout: float = None,
    n_jobs: int = 1,
    catch: Iterable[Type[Exception]] | Type[Exception] = _Nothing.NOTHING,
    callbacks: Iterable[Callable[[optuna.study.study.Study, optuna.trial._frozen.FrozenTrial], None]] | None = None,
    gc_after_trial: bool = False,
    show_progress_bar: bool = False,
    verbose: bool = True,
    tracker: Type = None,
    model: Any = None,
    preprocess_func: Callable = None,
)

Breakdown analysis for arbitrage index models, extending WalkForwardCV. Includes methods to compute Kendall’s Tau, determine the best copula, and extract copula parameters. Method generated by attrs for class WalkForwardCV.

Ancestors

systematica.walk_forward.cv.WalkForwardCV
systematica.walk_forward.base.BaseWalkForward
abc.ABC
systematica.generic.base.Model
systematica.generic.base.Feature

Methods

`get_kendall_tau`

get_kendall_tau(
    self,
    key: str = 'train',
    raw_output: bool = True,
    set_datetime_index: bool = True,
) ‑> pandas.core.series.Series

Compute Kendall’s Tau correlation coefficient in each split. Parameters:

Name	Type	Default	Description
`key`	`str`	`train`	If `train`, extract train-set. If `test`, extract test-set. Defaults to `train`.
`raw_output`	`bool`	`True`	If `True`, returns raw results. Default is `True`, which returns processed results.
`set_datetime_index`	`bool`	`True`	If `True`, set end-test datetime index. Ignored otherwise. Defaults to `True`.

Returns:

Type	Description
`pd.Series`	Series containing Kendall’s Tau values.

`get_ecdf`

get_ecdf(
    self,
    key: str = 'test',
    raw_output: bool = True,
    set_datetime_index: bool = True,
) ‑> pandas.core.frame.DataFrame

Compute ECDF object in each set. Retrieves processed data splits based on the cross-validation splitter. Parameters:

Name	Type	Default	Description
`key`	`str`	`train`	If `train`, extract train-set. If `test`, extract test-set. Defaults to `train`.
`raw_output`	`bool`	`True`	If `True`, returns raw results. Default is `True`, which returns processed results.
`set_datetime_index`	`bool`	`True`	If `True`, set end-test datetime index. Ignored otherwise. Defaults to `True`.

Returns:

Type	Description
`pd.DataFrame`	DataFrame containing ECDF objects, indexed by the test set index.

`get_copula`

get_copula(
    self,
    key: str = 'train',
    raw_output: bool = True,
    set_datetime_index: bool = True,
) ‑> pandas.core.series.Series

Identify the best-fitting copula. Parameters:

Name	Type	Default	Description
`key`	`str`	`train`	If `train`, extract train-set. If `test`, extract test-set. Defaults to `train`.
`raw_output`	`bool`	`True`	If `True`, returns raw results. Default is `True`, which returns processed results.
`set_datetime_index`	`bool`	`True`	If `True`, set end-test datetime index. Ignored otherwise. Defaults to `True`.

Returns:

Type	Description
`pd.Series`	Series containing the names of the best-fitting copula models.

`get_dependence`

get_dependence(
    self,
    key: str = 'train',
    raw_output: bool = True,
    set_datetime_index: bool = True,
) ‑> pandas.core.series.Series

Compute the dependence parameter) for the best-fitting copula in each set. Parameters:

Name	Type	Default	Description
`key`	`str`	`train`	If `train`, extract train-set. If `test`, extract test-set. Defaults to `train`.
`raw_output`	`bool`	`True`	If `True`, returns raw results. Default is `True`, which returns processed results.
`set_datetime_index`	`bool`	`True`	If `True`, set end-test datetime index. Ignored otherwise. Defaults to `True`.

Returns:

Type	Description
`pd.Series`	Series containing the dependence parameter.

`iter_transform_output`

iter_transform_output(
    self,
    key: str = 'train',
    apply_transform: bool = False,
    reset_stacked_by_set: bool = False,
    to_numpy: bool = False,
) ‑> pandas.core.frame.DataFrame | numpy.ndarray

Get model output, either transformed or as partial derivatives. Parameters:

Name	Type	Default	Description
`key`	`str`	`train`	If `train`, extract train-set. If `test`, extract test-set. Defaults to `train`.
`apply_transform`	`bool`	`False`	If `True`, applies the model transformation to the output. Defaults to `False`.
`reset_stacked_by_set`	`bool`	`False`	If `True`, resets the index stacked by set. Defaults to `False`.
`to_numpy`	`bool`	`False`	If `True`, returns a NumPy array instead of a DataFrame. Defaults to `False`.

Returns:

Type	Description
`pd.DataFrame`	The model output.

`get_marginals`

get_marginals(
    self,
    key: str = 'train',
    reset_stacked_by_set: bool = False,
    to_numpy: bool = False,
) ‑> pandas.core.frame.DataFrame | numpy.ndarray

Get model marginals. Parameters:

Name	Type	Default	Description
`key`	`str`	`train`	If `train`, extract train-set. If `test`, extract test-set. Defaults to `train`.
`reset_stacked_by_set`	`bool`	`False`	If `True`, resets the index stacked by set. Defaults to `False`.
`to_numpy`	`bool`	`False`	If `True`, returns a NumPy array instead of a DataFrame. Defaults to `False`.

Returns:

Type	Description
`pd.DataFrame`	The model marginals.

`get_partial_derivative`

get_partial_derivative(
    self,
    key: str = 'train',
    reset_stacked_by_set: bool = False,
    to_numpy: bool = False,
) ‑> pandas.core.frame.DataFrame | numpy.ndarray

Get model partial derivatives. Parameters:

Name	Type	Default	Description
`key`	`str`	`train`	If `train`, extract train-set. If `test`, extract test-set. Defaults to `train`.
`reset_stacked_by_set`	`bool`	`False`	If `True`, resets the index stacked by set. Defaults to `False`.
`to_numpy`	`bool`	`False`	If `True`, returns a NumPy array instead of a DataFrame. Defaults to `False`.

Returns:

Type	Description
`pd.DataFrame`	The model partial derivatives.

`get_transform`

get_transform(
    self,
    key: str = 'train',
    reset_stacked_by_set: bool = False,
    to_numpy: bool = False,
) ‑> pandas.core.frame.DataFrame | numpy.ndarray

Get model output after applying the transformation. Parameters:

Name	Type	Default	Description
`key`	`str`	`train`	If `train`, extract train-set. If `test`, extract test-set. Defaults to `train`.
`reset_stacked_by_set`	`bool`	`False`	If `True`, resets the index stacked by set. Defaults to `False`.
`to_numpy`	`bool`	`False`	If `True`, returns a NumPy array instead of a DataFrame. Defaults to `False`.

Returns:

Type	Description
`pd.DataFrame`	The model output after applying the transformation.

`get_all_metrics`

get_all_metrics(
    self,
    key: str,
    level_names: List[str] = None,
    reset_stacked_by_set: bool = False,
    to_numpy: bool = False,
) ‑> Dict[str, pandas.core.frame.DataFrame]

Get all metrics in a single iteration through splits. This method performs one iteration through all walk-forward splits, computing all required metrics simultaneously. This avoids redundant copula fitting that occurs when calling get_kendall_tau(), get_dependence(), get_copula(), get_transform(), etc. separately. Parameters:

Name	Type	Default	Description
`key`	`str`	`--`	Dataset to use, either ‘train’ or ‘test’.
`level_names`	`tp.List[str, str], default` None“	`--`	Names for the levels in the resulting hierarchical index. if `None` defaults to `['field', 'symbol']`. Defaults to None
`reset_stacked_by_set`	`bool`	`False`	If `True`, resets the index stacked by set. Defaults to `False`.
`to_numpy`	`bool`	`False`	If `True`, returns a NumPy array instead of a DataFrame. Defaults to `False`.

Returns:

Type	Description
`pd.DataFrame`	All metrics

Notes: This is the performance-critical method. By computing all metrics in one pass, we reduce complexity from O(6n) to O(n) where n is the number of splits.

`run_portfolio`

run_portfolio(
    self,
    key: str = 'train',
    validate_model: bool = True,
) ‑> vectorbtpro.portfolio.base.Portfolio

Generate a vectobtpro portfolio object. Parameters:

Name	Type	Default	Description
`key`	`str, optional`	`--`	Dataset to use, either ‘train’ or ‘test’. Default is ‘train’.
`validate_model`	`bool, optional`	`--`	If True, validate model parameters before processing. Default is True.

Returns:

Type	Description
`vbt.Portfolio`	A vbt portfolio object.

`run_report`

run_report(
    self,
    key: str = 'train',
    validate_model: bool = True,
) ‑> pandas.core.frame.DataFrame

Generate a report DataFrame with model inputs, outputs, and metrics. This optimized version computes all metrics in a single pass through the walk-forward splits, reducing redundant copula fitting. Parameters:

Name	Type	Default	Description
`key`	`str`	`'train'`	Dataset to use, either ‘train’ or ‘test’.
`validate_model`	`bool`	`True`	If True, validate model parameters before processing.

Returns:

Type	Description
`pd.DataFrame`	A DataFrame containing model metrics, signals, and trade history, all aligned on a common index.

Notes: Performance optimization: This method performs a single iteration through all splits, computing all metrics simultaneously to avoid redundant copula fitting operations.

`plot_kendall_tau`

plot_kendall_tau(
    self,
    key: str = 'train',
    fig: vectorbtpro.utils.figure.FigureWidget = None,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot Kendall’s Tau values. Parameters:

Name	Type	Default	Description
`key`	`str`	`train`	If `train`, extract train-set. If `test`, extract test-set. Defaults to `train`.
`fig`	`vbt.FigureWidget`	`None`	Existing figure to update, by default `None`.
`layout_kwargs`	`tp.Kwargs`	`--`	Additional layout keyword arguments for customization.

Returns:

Type	Description
`vbt.FigureWidget`	The figure displaying Kendall’s Tau values.

`plot_copula`

plot_copula(
    self,
    key: str = 'train',
    fig: vectorbtpro.utils.figure.FigureWidget = None,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the best-fitting copula models. Parameters:

Name	Type	Default	Description
`key`	`str`	`train`	If `train`, extract train-set. If `test`, extract test-set. Defaults to `train`.
`fig`	`vbt.FigureWidget`	`None`	Existing figure to update, by default `None`.
`layout_kwargs`	`tp.Kwargs`	`--`	Additional layout keyword arguments for customization.

Returns:

Type	Description
`vbt.FigureWidget`	The figure displaying the best-fitting copula models.

`plot_dependence`

plot_dependence(
    self,
    key: str = 'train',
    fig: vectorbtpro.utils.figure.FigureWidget = None,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the dependence parameter values. Parameters:

Name	Type	Default	Description
`key`	`str`	`train`	If `train`, extract train-set. If `test`, extract test-set. Defaults to `train`.
`fig`	`vbt.FigureWidget`	`None`	Existing figure to update, by default `None`.
`layout_kwargs`	`tp.Kwargs`	`--`	Additional layout keyword arguments for customization.

Returns:

Type	Description
`vbt.FigureWidget`	The figure displaying dependence values.

`plot_tail_concentration`

plot_tail_concentration(
    self,
    key: str = 'train',
    set_datetime_index: bool = True,
    title: str | None = None,
    smoothing: float | None = 1.3,
) ‑> pandas.core.series.Series

Container with copula tail concentration figure object. Parameters:

Name	Type	Default	Description
`key`	`str`	`train`	If `train`, extract train-set. If `test`, extract test-set. Defaults to `train`.
`set_datetime_index`	`bool`	`True`	It `True`, set output datetime index. Note that it is indexed by the test set index inferred from `get_best_copula`. Defaults to `True`.
`title`	`str`	`None`	The title for the plot. If not provided, a default title based on the fitted copula’s representation is used.
`smoothing`	`float`	`1.3`	Smoothing parameter for the spline line shape. If provided, the curves will be smoothed using a spline interpolation.

Returns:

Type	Description
`pd.Series`	Copula ail concentration figures.

`plot_heatmap`

plot_heatmap(
    self,
    key: str = 'train',
    set_datetime_index: bool = True,
    title: str | None = None,
) ‑> pandas.core.series.Series

Container with copula heatmap figure object. Parameters:

Name	Type	Default	Description
`key`	`str`	`train`	If `train`, extract train-set. If `test`, extract test-set. Defaults to `train`.
`set_datetime_index`	`bool`	`True`	It `True`, set output datetime index. Note that it is indexed by the test set index inferred from `get_best_copula`. Defaults to `True`.
`title`	`str`	`None`	The title for the plot. If not provided, a default title based on the fitted copula’s representation is used.

Returns:

Type	Description
`pd.Series`	Copula heatmap figures.

`plot_surface`

plot_surface(
    self,
    key: str = True,
    set_datetime_index: bool = True,
    title: str | None = None,
) ‑> pandas.core.series.Series

Container with copula surface figure object. Parameters:

Name	Type	Default	Description
`key`	`str`	`train`	If `train`, extract train-set. If `test`, extract test-set. Defaults to `train`.
`set_datetime_index`	`bool`	`True`	It `True`, set output datetime index. Note that it is indexed by the test set index inferred from `get_best_copula`. Defaults to `True`.
`title`	`str`	`None`	The title for the plot. If not provided, a default title based on the fitted copula’s representation is used.

Returns:

Type	Description
`pd.Series`	Copula surface figures.

`plot_contour`

plot_contour(
    self,
    key: str = 'train',
    set_datetime_index: bool = True,
    show_latest_point: bool = False,
    alpha: float = 0.5,
    opacity: float = 0.2,
    title: str | None = None,
    showlegend: bool = True,
    fig: plotly.basedatatypes.BaseFigure = None,
    **layout_kwargs,
) ‑> pandas.core.series.Series

Container with copula contour figure object. Parameters:

Name	Type	Default	Description
`key`	`str`	`train`	If `train`, extract train-set. If `test`, extract test-set. Defaults to `train`.
`set_datetime_index`	`bool`	`True`	It `True`, set output datetime index. Note that it is indexed by the test set index inferred from `get_best_copula`. Defaults to `True`.
`show_latest_point`	`bool, Default` True“	`--`	If True, show latest values as red dot. Defaults to True.
`alpha`	`float`	`0.5`	Contour alpha size. Defaults to `0.5`.
`opacity`	`float`	`0.2`	Point opacity. Defaults to `0.2`.
`title`	`str`	`None`	The title for the plot. If not provided, a default title based on the fitted copula’s representation is used.
`showlegend`	`bool`	`True`	Show legend. Defaults to `True`.
`fig`	`tp.BaseFigure`	`None`	Existing figure to update. If None, a new figure is created. Defaults to `None`.
`layout_kwargs`	`tp.Kwargs`	`--`	Additional keyword arguments for customizing the layout.

Returns:

Type	Description
`pd.Series`	Copula surface figures.

`plot_all`

plot_all(
    self,
    key: str = 'train',
    in_outputs: str | List[str] | None = None,
    select_signals: Literal[{'short_exits', 'short_entries', 'long_entries', 'long_exits'}] = None,
    set_datetime_index: bool = True,
    show_latest_point: bool = False,
    show_contour: bool = True,
    alpha: float = 0.5,
    opacity: float = 0.2,
    title: str | None = None,
    showlegend: bool = True,
    fig: plotly.basedatatypes.BaseFigure = None,
    **layout_kwargs,
) ‑> pandas.core.series.Series

Container with copula contour, marginal and signals figure object. Parameters:

Name	Type	Default	Description
`key`	`str`	`train`	If `train`, extract train-set. If `test`, extract test-set. Defaults to `train`.
`in_outputs`	`None`	`--`	\| str \| tp.List[str], default `None` Report data. Defaults to `None`.
`select_signals`	`tp.Literal[VALID_SIGNALS]`	`None`	Filter `Signals` by a signal (e.g. `long_entries`). If None, It is ignored. Defaults to `None`.
`set_datetime_index`	`bool`	`True`	It `True`, set output datetime index. Note that it is indexed by the test set index inferred from `get_best_copula`. Defaults to `True`.
`show_contour`	`bool`	`True`	Show contour if `True`. Defaults to `True`.
`show_latest_point`	`bool, Default` True“	`--`	If True, show latest values as red dot. Defaults to True.
`alpha`	`float`	`0.5`	Contour alpha size. Defaults to `0.5`.
`opacity`	`float`	`0.2`	Point opacity. Defaults to `0.2`.
`title`	`str`	`None`	The title for the plot. If not provided, a default title based on the fitted copula’s representation is used.
`showlegend`	`bool`	`True`	Show legend. Defaults to `True`.
`fig`	`tp.BaseFigure`	`None`	Existing figure to update. If None, a new figure is created. Defaults to `None`.
`layout_kwargs`	`tp.Kwargs`	`--`	Additional keyword arguments for customizing the layout.

Returns:

Type	Description
`pd.Series`	Copula surface figures.

`ArbitrageIndexRolling`

ArbitrageIndexRolling(
    id: str = None,
    loader_or_data: Callable = <function load_clean_data>,
    timeframe: str = '1d',
    start: str = None,
    end: str = None,
    s1: str = 'BTCUSDT',
    s2: str = 'ETHUSDT',
    suffix: str = 'h5',
    use_close: bool = True,
    freq: str = None,
    signal_model: str = None,
    long_entries: float = nan,
    long_exits: float = nan,
    short_entries: float = nan,
    short_exits: float = nan,
    clean: bool = True,
    select_symbols: str | int | List[str | int] = None,
    portfolio_config: Dict[str, Any] = _Nothing.NOTHING,
    model_params: Dict[str, Any] = _Nothing.NOTHING,
    metrics: str | List[str] = None,
    metrics_kwargs: Dict[str, Any] = _Nothing.NOTHING,
    use_rolling: bool = False,
    storage: str | optuna.storages._base.BaseStorage = None,
    pruner: optuna.pruners._base.BasePruner = <optuna.pruners._successive_halving.SuccessiveHalvingPruner object>,
    sampler: optuna.samplers._base.BaseSampler = <optuna.samplers._tpe.sampler.TPESampler object>,
    study_name: str = None,
    direction: str | List[str] = 'maximize',
    load_if_exists: bool = False,
    n_trials: int = 100,
    n_completed_trials: int = None,
    timeout: float = None,
    n_jobs: int = 1,
    catch: Iterable[Type[Exception]] | Type[Exception] = _Nothing.NOTHING,
    callbacks: Iterable[Callable[[optuna.study.study.Study, optuna.trial._frozen.FrozenTrial], None]] | None = None,
    gc_after_trial: bool = False,
    show_progress_bar: bool = False,
    verbose: bool = True,
    tracker: Type = None,
    model: Any = None,
    preprocess_func: Callable = None,
)

Breakdown analysis for arbitrage index models, extending BaseRollingWalkForward. Includes methods to compute Kendall’s Tau, determine the best copula, and extract copula parameters. Method generated by attrs for class ArbitrageIndexRolling.

Ancestors

systematica.walk_forward.rolling.RollingWalkForward
systematica.walk_forward.base.BaseWalkForward
abc.ABC
systematica.generic.base.Model
systematica.generic.base.Feature

Overview

Analytics

Models

Reports

Signal Models

Signals

`ArbitrageIndexCV`

Ancestors

Methods

`get_kendall_tau`

`get_ecdf`

`get_copula`

`get_dependence`

`iter_transform_output`

`get_marginals`

`get_partial_derivative`

`get_transform`

`get_all_metrics`

`run_portfolio`

`run_report`

`plot_kendall_tau`

`plot_copula`

`plot_dependence`

`plot_tail_concentration`

`plot_heatmap`

`plot_surface`

`plot_contour`

`plot_all`

`ArbitrageIndexRolling`

Ancestors

​ArbitrageIndexCV

​Ancestors

​Methods

​get_kendall_tau

​get_ecdf

​get_copula

​get_dependence

​iter_transform_output

​get_marginals

​get_partial_derivative

​get_transform

​get_all_metrics

​run_portfolio

​run_report

​plot_kendall_tau

​plot_copula

​plot_dependence

​plot_tail_concentration

​plot_heatmap

​plot_surface

​plot_contour

​plot_all

​ArbitrageIndexRolling

​Ancestors

`ArbitrageIndexCV`

Ancestors

Methods

`get_kendall_tau`

`get_ecdf`

`get_copula`

`get_dependence`

`iter_transform_output`

`get_marginals`

`get_partial_derivative`

`get_transform`

`get_all_metrics`

`run_portfolio`

`run_report`

`plot_kendall_tau`

`plot_copula`

`plot_dependence`

`plot_tail_concentration`

`plot_heatmap`

`plot_surface`

`plot_contour`

`plot_all`

`ArbitrageIndexRolling`

Ancestors