choppiness

Calculate the Choppiness Index, a trending-vs-sideways regime classifier.

Examples

from openbb import obb
# Choppiness(14) on daily TSLA.
data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').results
chop = obb.technical.choppiness(data=data, length=14)
obb.technical.choppiness(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}], length=2)

Parameters

• standard

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".

length: int
Default: 14
Choppiness lookback window.

atr_length: int
Default: 1
ATR window used inside choppiness.

scalar: float
Default: 100.0
Output magnification factor.

---

Returns

results: list[ChoppinessData]

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.

choppiness: float
Choppiness Index — high values mean sideways action.