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}])
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 |
Returns
OBBject
results : Dict
OBBject with the results being model and results objects.