ols_regression

Perform Ordinary Least Squares (OLS) regression.

OLS regression is a fundamental statistical method to explore and model the relationship between a dependent variable and one or more independent variables. By fitting the best possible linear equation to the data, it helps uncover how changes in the independent variables are associated with changes in the dependent variable. This returns the model and results objects from statsmodels library.

Examples

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

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Parameters

• standard

Name	Type	Description	Default	Optional
data	List[Data]	Input dataset.		False
y_column	str	Target column.		False
x_columns	List[str]	List of columns to use as exogenous variables.		False

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Returns

OBBject
    Dict