quantile
Calculate the rolling quantile of a target column within a given window size at a specified quantile percentage.
Quantiles are points dividing the range of a probability distribution into intervals with equal probabilities, or dividing the sample in the same way. This function is useful for understanding the distribution of data within a specified window, allowing for analysis of trends, identification of outliers, and assessment of risk.
Examples
from openbb import obb
# Get Rolling Quantile.
stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()
returns = stock_data["close"].pct_change().dropna()
obb.quantitative.rolling.quantile(data=returns, target="close", window=252, quantile_pct=0.25)
obb.quantitative.rolling.quantile(data=returns, target="close", window=252, quantile_pct=0.75)
obb.quantitative.rolling.quantile(target='close', window=2, data=[{'date': '2023-01-02', 'close': 0.05}, {'date': '2023-01-03', 'close': 0.08}, {'date': '2023-01-04', 'close': 0.07}, {'date': '2023-01-05', 'close': 0.06}, {'date': '2023-01-06', 'close': 0.06}])
Parameters
- standard
| Name | Type | Description | Default | Optional |
|---|---|---|---|---|
| data | List[Data] | The time series data as a list of data points. | False | |
| target | str | The name of the column for which to calculate the quantile. | False | |
| window | PositiveInt | The number of observations used for calculating the rolling measure. | False | |
| quantile_pct | NonNegativeFloat, optional | The quantile percentage to calculate (e.g., 0.5 for median), default is 0.5. | False | |
| index | str, optional | The name of the index column, default is "date". | False |
Returns
OBBject
List