ewm_var

ewm_var(source, com=None, span=None, halflife=None, alpha=None, min_periods=0, adjust=True, ignore_na=False, bias=False, name=None, metadata=None)

Return pandas exponentially weighted variances over time.

Parameters

source : Panel | Graph

Input numeric Panel or single-output Graph. com : float | None, default None

Center-of-mass decay parameter. Supply exactly one decay parameter. span : float | None, default None

Span decay parameter. Supply exactly one decay parameter. halflife : float | None, default None

Half-life decay parameter. Supply exactly one decay parameter. alpha : float | None, default None

Smoothing or regularization parameter, depending on the operation. min_periods : int, default 0

Minimum number of observations required to produce a value. adjust : bool, default True

Whether to divide by the decaying adjustment factor. ignore_na : bool, default False

Whether missing values are ignored when calculating weights. bias : bool, default False

Whether to use the biased exponentially weighted variance calculation. name : str | None, default None

Optional graph-node name. A generated name is used when omitted. metadata : Mapping[str, Any] | None, default None

Optional metadata stored on the graph node.

Returns

Graph

Lazy single-output graph. Call .compute() to materialize a Panel.

Examples

import pandas as pd

from bagelquant_core import Domain, Panel
from bagelquant_core.transformer import ewm_var

domain = Domain(calendar=pd.to_datetime(["2024-01-02", "2024-01-03", "2024-01-04"]), universe=["a", "b"])
source = Panel.from_domain(pd.DataFrame({"a": [1.0, 2.0, 4.0], "b": [2.0, 3.0, 8.0]}, index=domain.sessions), domain)

result = ewm_var(source, span=2).compute().data
print(result)

Notes

Rows represent time and columns represent assets.

Rolling calculations run independently down each asset column.