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Version: v5

realized_volatility

Calculate annualised rolling realised volatility under a chosen estimator.

Examples​

from openbb import obb
# Yang-Zhang realised volatility on daily SPY.
data = obb.equity.price.historical(symbol='SPY', start_date='2022-01-01', provider='yfinance').results
rv = obb.technical.realized_volatility(data=data, model='yang_zhang', window=30)
obb.technical.realized_volatility(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}], model='parkinson')

Parameters​

data: Data | DataFrame | Series | dict | list | list[DataFrame] | list[Data] | list[Series] | ndarray
Input OHLC(V) price series.

model: str
Default: yang_zhang
Volatility estimator. yang_zhang is the lowest-error blend; parkinson and rogers_satchell are range-based and drift-invariant; hodges_tompkins is bias-corrected close-to-close.

index: str
Default: date
Index column name in data.

window: int
Default: 30
Rolling window length in bars.

trading_periods: int
Annualisation factor. None resolves to 365 when is_crypto is true, otherwise 252.

is_crypto: bool
Default: False
When true and trading_periods is unset, annualise over 365 instead of 252.

clean: bool
Default: True
Drop the leading warm-up rows where the rolling window is incomplete.

Returns​

results: list[RealizedVolatilityData]

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​

date: date | datetime | str
Observation date.

volatility: float
Annualised realised volatility per the chosen model over the trailing window bars. None during warm-up when clean=False.

model: str
Echoed on every row so the response is self-describing.

window: int
Window used to produce this row.

trading_periods: int
Annualisation factor used to produce this row.

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