ml4t-information-coefficient skillA
ml4t-information-coefficient is agent-read markdown (skill) from ml4t/skills: Measure predictive signal quality with IC, Rank IC, and IC_IR. Use when evaluating whether a feature has predictive power for returns..
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
# Information Coefficient
IC is the correlation between a predicted signal and realized returns. It is the primary metric for judging whether a signal has predictive power before building a full backtest.
## The Problem
Reporting a single IC value (or worse, the best IC from many trials) tells you almost nothing. IC varies over time and across market regimes. A signal with mean IC = 0.04 and std = 0.02 (IC_IR = 2.0) is far more valuable than one with mean IC = 0.08 and std = 0.10 (IC_IR = 0.8). Without time-series statistics and proper standard errors, you cannot distinguish a real signal from noise.
## The Pattern
### WRONG
```python
from scipy.stats import spearmanr
# Single pooled IC - hides time variation, inflates significance
ic, pval = spearmanr(all_predictions.flatten(), all_returns.flatten())
print(f"IC = {ic:.4f}, p = {pval:.4f}")
```
### CORRECT
```python
import numpy as np
from scipy.stats import spearmanr
# Cross-sectional IC per period
ic_series = []
for t in timestamps:
mask = dates == t
if mask.sum() >= 10:
ic, _ = spearmanr(predictions[mask], returns[mask])
ic_series.append(ic)
ic_series = np.array(ic_series)
…Read the whole file at its exact version.
How to install
mdr add ml4t/skills/ml4t-information-coefficient@git:20260901.c415df0mdr add ml4t/skills/ml4t-information-coefficient@sha256:ed56593e3c4e6590Pin 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_bgz7xzvikksao65z)
1 badge views in 30 days
Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260901.c415df0 latest | 2026-09-01 | c415df0 | 4,152 B | A | view · diff |
| git:20260528.303089e | 2026-05-28 | 303089e | 4,212 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 (4152 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_bgz7xzvikksao65z GET https://markdownregistry.com/api/v1/resolve?ref=ml4t/skills/ml4t-information-coefficient GET https://markdownregistry.com/api/v1/blob/ed56593e3c4e6590a627833196e80145e19f5aad627a864cceebb8718abd9243
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.