Definitions
In plain terms
It measures ratio-like separation, so equal percentage differences are comparable at different quote levels.
Technical
d(i,j)=|log(p_i)-log(p_j)| for finite positive prices.
Scope
The transform requires a consistent positive price basis and does not remove all market-regime differences.
Formula
d(i,j) = abs(log(p_i) - log(p_j))d(i,j)=|\log p_i-\log p_j|| Symbol | Meaning | Unit |
|---|---|---|
p_i | positive pivot price | price |
Output unit: log-price
Examples
- A reviewer computes Log-Price Distance on a small deterministic fixture and checks timing, units, and boundary behavior before publication.
Common misconceptions
- The transform requires a consistent positive price basis and does not remove all market-regime differences.
Concept relationships
Where this concept is used
Tutorials planned
These catalogued topics use this concept, but their complete build has not shipped yet.
- D08-F01-A03 Important
Evidence and governance
- numpy.log NumPy · first party technical publication
Supports: preferred label, short definition, technical definition, formula
Limits: A log transform requires positive finite prices and does not by itself define a financial level.
- sklearn.cluster.DBSCAN scikit-learn · first party technical publication
Supports: preferred label, short definition, technical definition, formula
Limits: The package implements deterministic one-dimensional DBSCAN directly for Python/TypeScript parity.
- Pine Script pivot techniques TradingView · official platform documentation
Supports: preferred label, short definition, technical definition, formula
Limits: The package freezes its own equality, separation, and OHLC policies rather than claiming one universal pivot definition.
- Reviewed by
- fintech-builder-batch-011
- Last reviewed
- 2026-08-09
- Next review
- 2027-08-09
- Record status
- evidence reviewed
This record is evidence-reviewed and readable, but not yet promoted to published — it is served noindex,follow and excluded from the sitemap.
