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SKEW

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SKEW

What it is

SKEW β€” registry key skew.

Classifier metric. See the bands table below for the band-by-band reading.

Data-pyramid tier

T1 β€” Signal. This metric sits at the signal layer of the platform’s six-tier data pyramid (T0 raw inputs β†’ T5 actionable read). The tier reflects how far the value is from a raw measurement β€” not how strongly it is validated. Abstraction and validation are separate axes: a higher tier is not a claim of stronger evidence.

Horizon & validation

No validated skill horizon is on file for this metric β€” read it as context / a data carrier, not a validated edge. Stamps are added only when a gated research verdict lands.

Source

Source module: vol_structure
Data source: back_fillable

Fetched directly from app/sources/vol_structure.py; see source code for the upstream API call and any provider-specific handling.

How it’s computed

Raw CBOE SKEW Index close from market-data fallback ticker ^SKEW. SKEW is a CBOE measure of the implied volatility of OTM S&P 500 puts relative to ATM options β€” a market-implied probability of a 2Οƒ+ tail move over the next 30 days. Typical range 100–170; 100 means tail risk is priced in line with a lognormal distribution, higher means a fatter left tail. Fetched in vol_structure (_fetch_skew), moved there from macro_indicators (refactor C3) so it refreshes on the hot profile. The classifier bands (Benign / Normal / Firm / High / Extreme tail risk) are applied downstream via signal_defs.band_for('skew', value).

Where it surfaces

Bands / thresholds

Classifier direction: lower_is_better.

Range Label Dot Implication Points
β€” Benign favorable BULLISH 3
β€” Normal leaning NEUTRAL 2
β€” Firm neutral NEUTRAL 2
β€” High cautionary BEARISH 1
β€” Extreme tail risk adverse BEARISH 0

Health-score / alignment role

Release cadence

See also