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correlation_matrix

Get the correlation matrix of an input dataset.

The correlation matrix provides a view of how different variables in your dataset relate to one another. By quantifying the degree to which variables move in relation to each other, this matrix can help identify patterns, trends, and potential areas for deeper analysis. The correlation score ranges from -1 to 1, with -1 indicating a perfect negative correlation, 0 indicating no correlation, and 1 indicating a 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: ForwardRef('Data') | ForwardRef('DataFrame') | ForwardRef('Series') | ForwardRef('ndarray') | dict | list
Input dataset.

method: Literal['pearson', 'kendall', 'spearman']
Default: pearson
Method to use for correlation calculation. Default is "pearson". pearson : standard correlation coefficient kendall : Kendall Tau correlation coefficient spearman : Spearman rank correlation

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

Returns​

results: list[Data]

Serializable results.

provider: str

Provider name.

warnings: Optional[list[Warning_]]

list of warnings.

chart: Optional[Chart]

Chart object.

extra: dict[str, Any]

Extra info.

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