wfvkit 0.1.7

Walk-forward validation utilities for time-series ML: splits, purge/embargo, and evaluation helpers.

wfv-toolkit (wfvkit)

A tiny Python toolkit for walk-forward validation of time-ordered data with purge + embargo utilities to reduce label leakage (useful for trading/finance ML and any temporal prediction setup).

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What you get

• Naive time split (baseline): naive_time_split

• Walk-forward splits (rolling windows): walk_forward_splits

• Leakage guards

  • purge_overlap(train_idx, test_idx) — removes training indices that overlap the test window
  • embargo_after(test_idx, embargo) — blocks samples immediately after the test window

• A runnable example: examples/demo_naive_vs_purged.py

• Tests: pytest

The core idea is common in financial ML: if labels use a forward horizon, nearby samples can “bleed” information between train/test. Purge and embargo help.

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Install

Option A: from PyPI (recommended)

pip install wfvkit

Option B: editable install (development)

python -m venv .venv
.\.venv\Scripts\python.exe -m pip install -U pip
.\.venv\Scripts\python.exe -m pip install -e ".[dev]"

Option C: install from GitHub tag

pip install "git+https://github.com/Mohsentinal/wfv-toolkit.git@v0.1.3"

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Quickstart

Minimal example

import datetime as dt

from wfvkit import (
    naive_time_split,
    walk_forward_splits,
    purge_overlap,
    embargo_after,
)

# 10 timestamps (toy example)
times = [dt.datetime(2025, 1, 1, 0, 0) + dt.timedelta(minutes=i) for i in range(10)]

# 1) naive split: pass an index cutoff OR a datetime cutoff
train_idx, test_idx = naive_time_split(times, train_end=6)
train_idx2, test_idx2 = naive_time_split(times, train_end=times[6])

print("naive_idx:", train_idx, test_idx)
print("naive_dt :", train_idx2, test_idx2)

# 2) walk-forward splits (rolling windows)
splits = list(walk_forward_splits(times, train_size=5, test_size=2, step=2, embargo=1))
print("splits:", splits)

# 3) purge + embargo helpers
tr, te = splits[0]
print("purged:", purge_overlap(tr, te))
print("embargo:", sorted(embargo_after(te, embargo=1)))

Run tests

.\.venv\Scripts\python.exe -m pytest -q

Run the demo

.\.venv\Scripts\python.exe examples\demo_naive_vs_purged.py

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Usage

Import the public API

from wfvkit import (
    naive_time_split,
    walk_forward_splits,
    purge_overlap,
    embargo_after,
)

Naive split (baseline)

import datetime as dt

times = [dt.datetime(2025, 1, 1) + dt.timedelta(minutes=i) for i in range(10)]

# Cut by index
train_idx, test_idx = naive_time_split(times, train_end=6)

# Or cut by datetime
train_idx2, test_idx2 = naive_time_split(times, train_end=times[6])

Walk-forward splits + purge + embargo

import datetime as dt

times = [dt.datetime(2025, 1, 1) + dt.timedelta(minutes=i) for i in range(50)]

for train_idx, test_idx in walk_forward_splits(
    times,
    train_size=20,
    test_size=5,
    step=5,
    embargo=2,
):
    train_purged = purge_overlap(train_idx, test_idx)
    embargo_idx = embargo_after(test_idx, embargo=2)

    # Fit on `train_purged`, evaluate on `test_idx`,
    # and avoid using indices in `embargo_idx` for training.

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Concepts (plain English)

Purge

If a sample in train overlaps the test interval, it can leak information. Purging removes those overlapping training indices.

Embargo

Even after the test window ends, samples immediately after can still be contaminated if labels depend on future horizons. Embargo blocks a small number of samples after the test window.

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Project layout

wfv-toolkit/
  src/wfvkit/
    __init__.py
    splits.py
    leakage.py
    metrics.py
    evaluate.py
  tests/
  examples/

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Roadmap (next nice upgrades)

• Add purged k-fold / combinatorial purged CV
• Add utilities for event-based labels (start/end times per sample)
• Add richer evaluation helpers (rolling metrics and robustness checks)
• Provide a small CLI (optional)

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License

MIT (see LICENSE).