ml4t-meta-labels skillA
ml4t-meta-labels is agent-read markdown (skill) from ml4t/skills: Secondary model predicts whether a primary signal will be profitable. Use when sizing positions or filtering low-conviction trades from a base alpha model..
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
# Meta-Labels
A momentum signal fires 1,000 times per year but only 40% are profitable. Instead of discarding the signal, train a second model to predict *which* of those 1,000 trades will work.
## The Problem
Raw trading signals typically have acceptable recall (they catch most real moves) but poor precision (many false positives). Tuning the primary model to improve precision degrades recall. Meta-labeling decouples the two: the primary model generates candidates, the meta-model filters them.
## The Pattern
### WRONG
```python
from sklearn.ensemble import GradientBoostingClassifier
# Use raw signal directly - many false positives passed through
signal = primary_model.predict(X) # 1=buy, -1=sell, 0=hold
positions = signal # Every signal becomes a trade
```
### CORRECT
```python
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import TimeSeriesSplit
def purged(split, horizon):
"""Drop training rows whose label window reaches into the test fold."""
for train, test in split:
yield train[train < test[0] - horizon], test
def oof_signal(X, y, splits):
…Read the whole file at its exact version.
How to install
mdr add ml4t/skills/ml4t-meta-labels@git:20260901.830d7a8mdr add ml4t/skills/ml4t-meta-labels@sha256:f00353e862a4ac5dPin 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_yqrbsq73skyiiujc)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260901.830d7a8 latest | 2026-09-01 | 830d7a8 | 4,828 B | A | view · diff |
| git:20260901.f84f825 | 2026-09-01 | f84f825 | 4,799 B | A | view · diff |
| git:20260901.32123e7 | 2026-09-01 | 32123e7 | 4,764 B | A | view · diff |
| git:20260901.c415df0 | 2026-09-01 | c415df0 | 4,507 B | A | view · diff |
| git:20260528.303089e | 2026-05-28 | 303089e | 4,519 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 (4828 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_yqrbsq73skyiiujc GET https://markdownregistry.com/api/v1/resolve?ref=ml4t/skills/ml4t-meta-labels GET https://markdownregistry.com/api/v1/blob/f00353e862a4ac5dde397e610d279f193c478aa79dfe872ca14a9a29540fa696
Your agent does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.