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ols_regression_summary

Perform Ordinary Least Squares (OLS) regression.

This returns the summary object from statsmodels.

Examples​

from openbb import obb
# Perform Ordinary Least Squares (OLS) regression and return the summary.
stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()
obb.econometrics.ols_regression_summary(data=stock_data, y_column="close", x_columns=["open", "high", "low"])
obb.econometrics.ols_regression_summary(y_column='close', x_columns='['open', 'high', 'low']', 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}]')

Parameters​

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

Input dataset.

• Optional: False

y_column: str

Target column.

• Optional: False

x_columns: list[str]

list of columns to use as exogenous variables.

• Optional: False

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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