Rolling

systematica.walk_forward.rolling

RollingWalkForward

RollingWalkForward(
    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,
)

Analyzes a statistical arbitrage model using rolling metrics.

Method generated by attrs for class RollingWalkForward.

Ancestors

• systematica.walk_forward.base.BaseWalkForward
• abc.ABC
• systematica.generic.base.Model
• systematica.generic.base.Feature

Descendants

• systematica.api.analytics.arbitrage_index.ArbitrageIndexRolling
• systematica.api.analytics.meta_model.MetaModelRolling
• systematica.api.analytics.momentum.AverageMomentumFactorRolling
• systematica.api.analytics.momentum.MomentumFactorRolling
• systematica.api.analytics.ou_process.OUProcessRolling
• systematica.api.analytics.volatility.VolatilityFactorRolling
• systematica.api.analytics.volume_profile.VolumeProfileFactorRolling

Static methods

check_is_valid

check_is_valid(
    model: Any,
)

Validates that the model has the necessary parameters for the strategy.

Specifically, the model must have either:

• Both window and minp attributes, or
• Both train_window and splitter (set to *_rolling).

Raises:

Type	Description
ValueError	If the model does not have either window and minp or train_window and splitter=="*_rolling").

Instance variables

• window: int: Window size.

• minp: int | None: Minimum number of observations required.

• all_portfolio_metrics: List[str]: Get available rolling metrics.

Methods

get_portfolio_metrics

get_portfolio_metrics(
    self,
    metrics: str | List[str] = None,
    metrics_kwargs: Dict[str, Any] = None,
    to_numpy: bool = False,
    validate_model: bool = True,
    portfolio_config: Dict[str, Any] = None,
) ‑> pandas.core.frame.DataFrame | numpy.ndarray

Computes portfolio performance metrics on a rolling basis.

Parameters:

Name	Type	Default	Description
metrics	str	--	| tp.List[str], default None Portfolio performance metrics to compute. If None, uses the default metrics.
metrics_kwargs	tp.Kwargs	None	Additional keyword arguments for the metrics. Defaults to None.
to_numpy	bool	False	If True, returns a np.ndarray. Default is False, returning a pd.DataFrame.
validate_model	bool	True	Check if model parameters are valid. Defaults to True.
portfolio_config	tp.Kwargs	None	Additional configuration for the portfolio simulation. Defaults to None.

Returns:

Type	Description
pd.DataFrame | tp.Array	DataFrame or array containing portfolio performance metrics.