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RTS Kalman Smoother (C++ × Python)

The package provides a high-performance Rauch–Tung–Striebel Kalman smoother for the local level model. This model is a canonical choice for asset prices and similar data (random walk latent state).
It smooths each asset (column) independently with global process/observation variances (Q, R) shared across assets.

Features

  • Handles NaN observations (prediction-only step when missing)
  • Returns smoothed states as the same object type
  • Diffuse initialization

Install (from PyPI)

pip install rts_smoother

Install (from source)

pip install -U pip build twine
pip install -e .

Usage

from rts_smoother import smooth

# Provide Q and R manually
df_smoothed = smooth(df, Q=1e-4, R=1e-3)

Model

  • State: x_t = x_{t-1} + w_t, w_t ~ N(0, Q)
  • Obs: y_t = x_t + v_t, v_t ~ N(0, R)

Notes

  • Built with scikit-build-core and pybind11.
  • The compiled extension lives at rts_smoother/_core.* inside the wheel.

Metadata

Release files for rts-smoother 0.1.0

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Source distribution for rts-smoother 0.1.0
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Table of built distributions (wheels) for rts-smoother 0.1.0
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rts_smoother-0.1.0-cp314-cp314-win_amd64.whl CPython 3.14 CPython 3.14 Windows x86-64 Details
rts_smoother-0.1.0-cp314-cp314-macosx_15_0_arm64.whl CPython 3.14 CPython 3.14 macOS 15.0+ ARM64 Details

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