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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​

data: list[openbb_core.provider.abstract.data.Data]

The time series data as a list of data points.

• Optional: False

target: str

The name of the column for which to calculate the mean.

• Optional: False

window: int

The number of observations used for calculating the rolling measure.

• Default: 21

• Optional: True

index: str

The name of the index column, default is 'date'.

• Default: date

• Optional: True

Returns​

results: list[Data]

Serializable results.

provider: None

Provider name.

warnings: Optional[list[Warning_]]

list of warnings.

chart: Optional[Chart]

Chart object.

extra: dict[str, Any]

Extra info.

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