clenow

Calculate Clenow Volatility-Adjusted Momentum on log prices.

Examples

from openbb import obb
# Clenow momentum on TSLA over the trailing 90 days.
data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='yfinance').results
out = obb.technical.clenow(data=data, period=90)
obb.technical.clenow(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}], period=2)

Parameters

• standard

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

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

target: str
Default: close
Column to regress.

period: int
Default: 90
Lookback window for the log-price regression.

---

Returns

results: list[ClenowData]

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.

predicted: float
Best-fit log-linear value at this date.

r2: float
R-squared of the regression fit on log prices.

coefficient: float
Annualised slope of the log-price regression.

annualized_coefficient: float
coefficient * r2 - Clenow's momentum factor.