ml4t-sensitivity-analysis skillA
ml4t-sensitivity-analysis is agent-read markdown (skill) from ml4t/skills: Test strategy robustness to parameter variation and detect overfitting cliffs. Use when validating that performance is stable across parameter perturbations..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# Parameter Sensitivity Analysis
A strategy optimized to Sharpe 2.0 at lookback=21 that drops to 0.3 at lookback=20 or lookback=22 is not a strategy - it is a curve fit. Sensitivity analysis sweeps parameters to verify that performance is stable across a neighborhood, not balanced on a knife edge.
## The Problem
Single-parameter backtests find the best setting. But the best setting may be a statistical fluke - one data point away from failure. If small perturbations in entry threshold, lookback period, or position sizing cause large performance swings, the parameters are overfit. You need to see the performance surface, not just its peak.
## The Pattern
### WRONG
```python
import numpy as np
# Optimize one parameter, report the best - classic overfitting
best_sharpe, best_lookback = -np.inf, None
for lookback in range(5, 60):
ret = run_strategy(prices, lookback=lookback)
sr = ret.mean() / ret.std() * np.sqrt(252)
if sr > best_sharpe:
best_sharpe, best_lookback = sr, lookback
print(f"Best: lookback={best_lookback}, Sharpe={best_sharpe:.2f}") # overstated
```
### CORRECT
```python
import itertools
import numpy as np
import polars as pl
…Read the whole file at its exact version.
How to install
mdr add ml4t/skills/ml4t-sensitivity-analysis@git:20260901.85fc79bmdr add ml4t/skills/ml4t-sensitivity-analysis@sha256:a90f8df8edc8555aPin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_oamfhphhqxuw66ml)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260901.85fc79b latest | 2026-09-01 | 85fc79b | 4,987 B | A | view · diff |
| git:20260901.c415df0 | 2026-09-01 | c415df0 | 4,920 B | A | view · diff |
| git:20260528.303089e | 2026-05-28 | 303089e | 4,932 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (4987 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
ml4t/skills · 19 stars · license Apache-2.0 · pushed 2026-09-24 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_oamfhphhqxuw66ml GET https://markdownregistry.com/api/v1/resolve?ref=ml4t/skills/ml4t-sensitivity-analysis GET https://markdownregistry.com/api/v1/blob/a90f8df8edc8555a7643b9dfc5354f784322d18c77bce7b11626003f689e2fb7
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