xpectral 1.0.1

Quant Research Library

Xpectral

A quantitative research library that extends Polars and Pandas DataFrames with charting and quant analytics.

Modules

• xpectral.charts — Fluent Bokeh visualization via df.bokeh.line(...), df.bokeh.scatter(...), etc.
• xpectral.quant — Financial metrics (returns, volatility, beta) via pl.col(...).quant.returns()
• xpectral.data — Market data from the Polygon/Massive API with caching and rate limiting

xpectral.charts

import xpectral  # registers the accessors
from xpectral import PandasDataFrame
from xpectral import PolarsDataFrame

df: PolarsDataFrame = pl.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
fig = df.bokeh(title="Example", width=600, height=400)
fig.line(x="x", y="y")

pd_df: PandasDataFrame = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
pd_fig = pd_df.bokeh(title="Example", width=600, height=400)
pd_fig.line(x="x", y="y")

Annotate sample DataFrames with PolarsDataFrame or PandasDataFrame when you want the editor (pyright) to resolve the df.bokeh(...) parameters and chained accessor methods. Annotation is neccessary for type. hinting as accessors are not discovered dinamically.

Install

uv sync