Learning scores
How to Interpret Scores
QQ assigns a 0–100 score to each dimension (Fundamentals, Tokenomics, On-chain, TA, Macro) and a global QQ Score per project. These scores are designed to be read as a relative measure of quality versus valuation.
General reading:
| Range | Label | Meaning |
|---|---|---|
| 🔵 80 – 100 | very cheap | high score vs valuation → strongly undervalued |
| 🟢 60 – 80 | cheap | good score vs valuation → undervalued |
| 🟡 40 – 60 | fair value | score and valuation aligned |
| 🟠 20 – 40 | expensive | low score vs valuation → overvalued |
| 🔴 0 – 20 | very expensive | very low score vs valuation → extremely overvalued |
What “Cheap / Expensive” Means in QQ Omega
In QQ Omega, “cheap” and “expensive” emerge from a relative valuation process that compares an asset’s current valuation against the quality implied by the scores produced by each swarm, then aggregates those views into a single QQ score that reflects how undervalued or overvalued the asset appears relative to its scored quality.
For example, an asset may fall into the “cheap zone” when it exhibits strong fundamentals, on-chain signals, tokenomics, and structural indicators while its valuation does not yet reflect that quality. Conversely, an asset may fall into the “expensive zone” when quality signals are weak but valuation remains elevated, often driven by narratives, hype, or short-term speculation.
QQ highlights these mismatches so you can decide whether to accumulate in favorable conditions or take profit / reduce risk when conditions appear stretched.
How the score engine is built
Each QQ Score sits at the top of a four-level scores pipeline: it is a layered aggregation of agents / data insight. At the base, components capture atomic sub-signals. These roll up into factors, the first business-readable signal inside a dimension. Factors then aggregate into dimension scores (the 5 swarms: Fundamentals, Tokenomics, On-chain, TA, Macro). Dimension scores are weighted per timeframe to produce the final QQ Score (4 per asset: Daily, Weekly, Monthly, Yearly).
This layered structure is what makes QQ scores traceable: when a score moves, you can drill down to the dimension that drove it, the factor inside that dimension, and the component signal that triggered the change.