mean
Calculate the rolling mean (average) of a target column within a given window size.
The rolling mean is a simple moving average that calculates the average of a target variable over a specified window. This function is widely used in financial analysis to smooth short-term fluctuations and highlight longer-term trends or cycles in time series data.
Example
from openbb import obb
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.mean(data=returns, target="close", window=252)
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 mean. | False | |
| window | int | The number of observations used for calculating the rolling measure. | 21 | False |
| index | str | The name of the index column, default is "date". | date | False |
Returns
OBBject
results : List[Data]
An object containing the rolling mean values.