kelly_rank_boxcox

kelly_rank_boxcox(source, window, lambda_=0, name=None, metadata=None)

Rank to positive values, apply Box-Cox, then estimate Kelly.

Parameters

source : Panel | Graph

Input numeric Panel or single-output Graph. window : int

Positive trailing-window length in rows. lambda_ : float, default 0

Box-Cox lambda parameter. Use 0 for the logarithmic limit. 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 kelly_rank_boxcox

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 = kelly_rank_boxcox(source, window=2).compute().data
print(result)

Notes

Rows represent time and columns represent assets.