Roll Rate Analysis

Roll rate analysis is a credit-risk technique used to define the target variable when building Application or Behavioural scorecards. It's an iterative process — this package parametrises the moving parts so each iteration is a few lines of code rather than a fresh notebook.

The library has zero pandas dependency: inputs and outputs are Polars frames.

Installation

From PyPI:

uv add roll-rate-analysis        # uv projects
pip install roll-rate-analysis   # plain pip

Requires Python 3.10 or newer.

What's in the box

Two classes, one method each:

Both expose compute() (full transition matrix) and reduce() (roll_down / stable / roll_up summary). Both return polars DataFrames whose first column (from_state) holds the row label.

Quick start

from roll_rate_analysis import MOMRollRateTable

table = MOMRollRateTable(
    "data/jan.csv",
    "data/feb.csv",
    unique_key_col="id",
    delinquency_col="delq",
    max_delq=6,
)

table.compute()    # polars.DataFrame, full transition matrix
table.reduce()     # polars.DataFrame, roll_down / stable / roll_up percentages

In-memory polars frames work too:

import polars as pl
from roll_rate_analysis import SnapshotRollRateTable

snap = pl.read_csv("data/snap.csv")
obs = [pl.scan_csv(p) for p in ["data/obs1.csv", "data/obs2.csv"]]
perf = [pl.scan_csv(p) for p in ["data/perf1.csv", "data/perf2.csv"]]

table = SnapshotRollRateTable(
    snap, obs, perf,
    unique_key_col="id",
    delinquency_col="delq",
    detailed=True,
    granularity=2,
)
table.compute()

See the notebooks under examples/ for end-to-end walkthroughs.

Development

This project uses uv. Clone and bootstrap with:

git clone https://github.com/alexliap/roll_rate_analysis.git
cd roll_rate_analysis
uv sync --dev

Run the test suite, linter, and formatter:

uv run pytest
uv run ruff check .
uv run ruff format .

Pre-commit hooks (ruff + standard checks) keep the tree clean:

uv run pre-commit install
uv run pre-commit run --all-files

License

MIT — see LICENSE.