realized_volatility_compare
Compute realised volatility under every requested estimator, side by side.
Examples
from openbb import obb
obb.technical.realized_volatility_compare(data=[{'date': '2023-01-02', 'open': 110.0, 'high': 120.0, 'low': 100.0, 'close': 115.0, 'volume': 10000.0}, {'date': '2023-01-03', 'open': 165.0, 'high': 180.0, 'low': 150.0, 'close': 172.5, 'volume': 15000.0}, {'date': '2023-01-04', 'open': 146.67, 'high': 160.0, 'low': 133.33, 'close': 153.33, 'volume': 13333.33}, {'date': '2023-01-05', 'open': 137.5, 'high': 150.0, 'low': 125.0, 'close': 143.75, 'volume': 12500.0}, {'date': '2023-01-06', 'open': 132.0, 'high': 144.0, 'low': 120.0, 'close': 138.0, 'volume': 12000.0}])
Parameters
- standard
data: Data | DataFrame | Series | dict | list | list[DataFrame] | list[Data] | list[Series] | ndarray
Input OHLC price series — every column required.
models: list[Literal['std', 'parkinson', 'garman_klass', 'hodges_tompkins', 'rogers_satchell', 'yang_zhang']]
Estimators to compute. Defaults to all six.
index: str
Default: date
Index column name in data, by default "date".
window: int
Default: 30
Rolling window length in bars, by default 30.
trading_periods: int
Annualisation factor. When unset, resolves to 365 if is_crypto is True, otherwise 252.
is_crypto: bool
Default: False
When True and trading_periods is unset, annualise over 365 instead of 252, by default False.
clean: bool
Default: True
Drop rows where ANY requested model has not yet warmed up.
Returns
results: list[RealizedVolatilityCompareData]
Serializable results.
provider: str
Provider name.
warnings: Optional[list[Warning_]]
List of warnings.
chart: Optional[Chart]
Chart object.
extra: dict[str, Any]
Extra info.
Data
- standard
date: date | datetime | str
Observation date.
std: float
Close-to-close standard-deviation volatility.
parkinson: float
Parkinson range-based volatility.
garman_klass: float
Garman-Klass OHLC volatility.
hodges_tompkins: float
Bias-corrected close-to-close volatility.
rogers_satchell: float
Rogers-Satchell drift-invariant volatility.
yang_zhang: float
Yang-Zhang minimum-variance volatility.