ml4t-regime-awareness skillA
ml4t-regime-awareness is agent-read markdown (skill) from ml4t/skills: Market regimes as conditioning features for risk scaling, not timing signals. Use when incorporating regime detection into strategy logic..
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
# Regime Awareness
Markets alternate between regimes (low/high volatility, trending/mean-reverting, risk-on/risk-off). Regime detection for diagnostics and risk scaling is reliable. Regime detection for market timing is not.
## The Problem
Regime-switching models promise to predict when to be in or out of the market. In practice, regime transitions are identified with high confidence only after they have already occurred. A model that correctly labels the March 2020 crash as "crisis" does so 2-4 weeks late, after the drawdown has already happened. Trading on regime predictions produces whipsaw losses and underperforms a regime-conditioned but always-invested approach.
The correct use of regimes is as a conditioning feature: scale risk, adjust position sizes, and evaluate strategy performance per regime - but stay invested.
## The Pattern
### WRONG
```python
# Regime-timing: go to cash when model predicts "bear"
def generate_signal(data, regime_model):
regime = regime_model.predict(data)
if regime == "bear":
return 0.0 # exit market entirely
else:
return model.predict(data) # normal signal
```
### CORRECT
```python
import numpy as np
…Read the whole file at its exact version.
How to install
mdr add ml4t/skills/ml4t-regime-awareness@git:20260901.c415df0mdr add ml4t/skills/ml4t-regime-awareness@sha256:460a78e9ffab166bPin 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_bhg72zwzvce5ilqw)
1 badge views in 30 days
Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260901.c415df0 latest | 2026-09-01 | c415df0 | 4,380 B | A | view · diff |
| git:20260528.303089e | 2026-05-28 | 303089e | 4,384 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 (4380 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_bhg72zwzvce5ilqw GET https://markdownregistry.com/api/v1/resolve?ref=ml4t/skills/ml4t-regime-awareness GET https://markdownregistry.com/api/v1/blob/460a78e9ffab166b7a07fc5607056555d3cf259482aaa5d1b7f2201b91ddf8d0
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.