ml4t-latent-factors skillA
ml4t-latent-factors is agent-read markdown (skill) from ml4t/skills: Extract latent factors from return panels using PCA, IPCA, or autoencoders with proper noise diagnostics. Use when reducing dimensionality or discovering risk structure..
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
# Latent Factor Extraction PCA explains 80% of return variance - but variance is not alpha. The first principal component captures market beta, which earns the equity premium, not a tradeable edge. Confusing variance-explained with pricing power is the central mistake. ## The Problem With 400+ published return predictors, hand-picking factors invites overfitting. Latent factor methods (PCA, autoencoders) extract structure directly from data. But three failure modes undermine them: 1. **Variance != pricing** - high-variance factors may capture idiosyncratic noise, not compensated risk. 2. **Eigenvector instability** - when assets (N) approach time periods (T), sample covariance is dominated by noise. Marchenko-Pastur theory gives the noise boundary. 3. **Full-sample PCA is leakage** - fitting PCA on the complete panel, then testing on a held-out period, leaks the covariance structure of the test period into training. ## The Pattern ### WRONG ```python from sklearn.decomposition import PCA import numpy as np # Fit PCA on FULL return panel, then use factors for prediction pca = PCA(n_components=5) factors = pca.fit_transform(returns_panel) # Full-sample fit = leakage …
Read the whole file at its exact version.
How to install
mdr add ml4t/skills/ml4t-latent-factors@git:20260901.32123e7mdr add ml4t/skills/ml4t-latent-factors@sha256:71de3c06913bb767Pin 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_2bdsxwqq7k5fnjr2)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260901.32123e7 latest | 2026-09-01 | 32123e7 | 5,400 B | A | view · diff |
| git:20260901.cf90cdd | 2026-09-01 | cf90cdd | 5,494 B | A | view · diff |
| git:20260901.c415df0 | 2026-09-01 | c415df0 | 4,989 B | A | view · diff |
| git:20260528.303089e | 2026-05-28 | 303089e | 5,025 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 (5400 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_2bdsxwqq7k5fnjr2 GET https://markdownregistry.com/api/v1/resolve?ref=ml4t/skills/ml4t-latent-factors GET https://markdownregistry.com/api/v1/blob/71de3c06913bb767867c9a05e91c7d257cc84e30b18c86e188fd269ec0f926c1
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