Skip to content

aether_quant.experts

Expert dataset definitions.

Expert dataset definitions.

ExpertDatasetSummary dataclass

One expert's training slice: its name, what it covers and how many rows it holds.

backtest_rows instance-attribute 

backtest_rows: int

date_end instance-attribute 

date_end: str | None

date_start instance-attribute 

date_start: str | None

description instance-attribute 

description: str

expert instance-attribute 

expert: str

filter instance-attribute 

filter: dict

positive_target_rate instance-attribute 

positive_target_rate: float | None

rows instance-attribute 

rows: int

tickers instance-attribute 

tickers: list[str]

train_rows instance-attribute 

train_rows: int

validation_rows instance-attribute 

validation_rows: int

to_dict 

to_dict() -> dict[str, Any]

annotate_dataset_with_regimes 

annotate_dataset_with_regimes(dataset: DataFrame, config: dict[str, Any] | None = None) -> pd.DataFrame

Return a copy of the dataset with V2 regime columns added.

build_expert_dataset_manifest 

build_expert_dataset_manifest(dataset: DataFrame, dataset_manifest: dict[str, Any], config: dict[str, Any] | None = None, expert_definitions: dict[str, Any] | None = None) -> tuple[pd.DataFrame, dict]

The manifest of each expert's slice of the training dataset (bullish, bearish, sideways, volatility).