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Version: v5

ultimate_oscillator

Calculate Larry Williams' Ultimate Oscillator across three timeframes.

Examples​

from openbb import obb
# Ultimate Oscillator on daily TSLA.
data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').results
uo = obb.technical.ultimate_oscillator(data=data)
obb.technical.ultimate_oscillator(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​

data: Data | DataFrame | Series | dict | list | list[DataFrame] | list[Data] | list[Series] | ndarray
OHLC price series.

index: str
Default: date
Index column name in data, by default "date".

fast: int
Default: 7
Short-period lookback.

medium: int
Default: 14
Medium-period lookback.

slow: int
Default: 28
Long-period lookback.

fast_weight: float
Default: 4.0
Weight applied to the fast period.

medium_weight: float
Default: 2.0
Weight applied to the medium period.

slow_weight: float
Default: 1.0
Weight applied to the slow period.

Returns​

results: list[UltimateOscillatorData]

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.

ultimate_oscillator: float
Weighted blend of buying pressure over three timeframes (0–100).

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