autocorrelation

Compute autocorrelation (ACF) and partial autocorrelation (PACF) with bands.

Examples

from openbb import obb
obb.technical.autocorrelation(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

data: Data | DataFrame | Series | dict | list | list[DataFrame] | list[Data] | list[Series] | ndarray
Input series.

index: str
Default: date
Index column name in data.

target: str
Default: close
Column to evaluate.

use_returns: bool
Default: True
Difference and percent-change before computing.

max_lag: int
Default: 40
Largest lag to compute.

method: str
Default: both
Which functions to compute.

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Returns

results: list[AutocorrelationData]

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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Data

• standard

lag: int
Lag in bars (0 is the series with itself, by definition 1.0 for ACF).

acf: float
Sample autocorrelation at this lag, or None when ACF was skipped.

pacf: float
Sample partial autocorrelation at this lag, or None when PACF was skipped.

acf_confidence_lower: float
Lower bound of the 95% confidence band for the ACF estimate.

acf_confidence_upper: float
Upper bound of the 95% confidence band for the ACF estimate.

significant: bool
True when the ACF estimate at this lag (other than lag 0) lies outside the 95% confidence band.