vol_scale

vol_scale(frame, volatility, name=None, metadata=None)

Scale values by volatility.

Parameters

frame : Panel | Graph: Input numeric Panel or single-output Graph. volatility : Panel | Graph; Volatility Panel or single-output Graph used as the divisor. 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.composer import vol_scale

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

result = vol_scale(left, right).compute().data
print(result)

Notes

Inputs are aligned by index and columns before the operation runs.