mean
Calculate the rolling 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.
Examples
from openbb import obb
# Get Rolling Mean.
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)
obb.quantitative.rolling.mean(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 mean. | False | |
| window | PositiveInt | The number of observations used for calculating the rolling measure. | False | |
| index | str, optional | The name of the index column, default is "date". | False |
Returns
OBBject
List