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Version: v5

correlation_matrix

Get the correlation matrix of an input dataset.

The correlation matrix shows how strongly the variables in the dataset move in relation to one another. Scores range from -1 (perfect negative correlation) through 0 (no correlation) to 1 (perfect positive correlation).

Examples​

from openbb import obb
# Get the correlation matrix of a dataset.
stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()
obb.econometrics.correlation_matrix(data=stock_data)
obb.econometrics.correlation_matrix(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: Data | DataFrame | Series | dict | list | list[DataFrame] | list[Data] | list[Series] | ndarray
Input dataset.

method: str
Default: pearson
Correlation method: 'pearson' (standard), 'kendall' (Kendall Tau), or 'spearman' (rank correlation).

min_periods: int
Default: 1
Minimum number of observations required per pair of columns to compute a correlation.

chart: bool
Default: False
Whether to create a chart or not, by default False.

Returns​

results: list[CorrelationMatrixData]

Serializable results.

provider: str

Provider name.

warnings: Optional[list[Warning_]]

List of warnings.

chart: Optional[Chart]

Chart object.

extra: dict[str, Any]

Extra info.

Data​

comp_to: str
The variable each correlation in the row is measured against.

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