ml4t-stationarity-tests SKILL.md skillA
ml4t-stationarity-tests from ml4t/skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add ml4t/skills/ml4t-stationarity-tests@git:20260901.c415df0Pins git:20260901.c415df0 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- ml4t/skills
- Path
validation/stationarity-tests/SKILL.md- Versions recorded
- 2
- Latest version
- git:20260901.c415df0, committed Sep 1, 2026
- First version recorded
- May 28, 2026
- Size
- 4,418 bytes
- Repository stars
- 41
- License
- Apache-2.0
Test whether time-series features are stationary using ADF and KPSS tests. Use when selecting or transforming features to verify stationarity assumptions for predictive models.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Stationarity Tests
Feeding non-stationary features (raw prices, trending volume) into ML models produces spurious correlations that vanish out of sample. Test stationarity first, transform if needed.
## The Problem
Raw asset prices are non-stationary: their mean and variance change over time. A regression of one non-stationary series on another can produce high R-squared and significant t-statistics even when there is no causal relationship (spurious regression). In ML4T, this means a model trained on raw prices will appear to predict well in-sample but fail live. Returns and most financial ratios are stationary. The gap between "looks predictive" and "is predictive" often comes down to stationarity.
## The Pattern
### WRONG
```python
import numpy as np
from sklearn.linear_model import Ridge
# Model raw prices - non-stationary, spurious correlation
X = price_features # Trending upward
y = forward_prices # Also trending upward
model = Ridge().fit(X, y)
print(f"R-squared: {model.score(X, y):.3f}") # High but meaningless
```
### CORRECT
```python
import numpy as np
from statsmodels.tsa.stattools import adfuller, kpss
def test_stationarity(series):
…Read the whole file at its exact version.
How to install
mdr add ml4t/skills/ml4t-stationarity-tests@sha256:b8a981de627af514The line at the top of this page pins the label git:20260901.c415df0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_4cwva25eeoi6xyx4)
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Versions
The registry has recorded 2 versions of this file, between May 28, 2026 and Sep 1, 2026, about one every 96 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260901.c415df0 latest | 2026-09-01 | c415df0 | 4,418 B | A | view · diff |
| git:20260528.303089e | 2026-05-28 | 303089e | 4,424 B | A | view |
Audit of the latest version
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Source
ml4t/skills · 41 stars · license Apache-2.0 · pushed 2026-11-08 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_4cwva25eeoi6xyx4 GET https://markdownregistry.com/api/v1/resolve?ref=ml4t/skills/ml4t-stationarity-tests GET https://markdownregistry.com/api/v1/blob/b8a981de627af514eba66765328536da6dcbc399b07f0e28349cc3bcdcedd651
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