Introducing Diagnostics: Why Ten Factor Groups

Diagnostics is the entry point to the Vortex Research Suite — a per-asset structural-health diagnostic that scores any supported coin across ten factor groups and aggregates them into three top-level composites: Structural Quality (SQS), Fragility (FS), and the dampening-adjusted overall score (AOS). The shape of the module is opinionated, and this post explains why.

The ten factor groups are trend quality, volatility, downside volatility, drawdown, liquidity, mcap-to-FDV, supply pressure, relative strength, holder concentration, and market position. They were chosen against two constraints. First: every group must measure something a sophisticated buyer expects an asset-quality diagnostic to cover, and that has a defensible mathematical definition. Five groups would have left obvious gaps (no concentration measurement, no supply pressure). Second: groups must be empirically distinct enough that aggregating them isn't double-counting. Twenty groups would have meant several pairs measuring the same thing — volatility and drawdown already share variance, and we lean on the cross-factor correlation insights pass to deflate joint weights when correlation is high.

Each factor produces a 0–100 composite where high reads as healthy. The composition into SQS, FS, and AOS does three things at once. Data-quality weighting: every factor reports a data_quality in 0–1 — the fraction of expected metrics with usable input. A factor that could only compute two of its four metrics is correspondingly down-weighted; factors with data_quality of zero are skipped. This stops thin-data assets from being scored as if everything was measured. Inversion for fragility: each factor's quality composite is mirror-imaged to produce its fragility contribution, so the same metric (drawdown) contributes to both SQS and FS with sign reversal. Dampening: AOS multiplies SQS by (1 − dampening · FS / 100), so a high-quality but very-fragile asset scores meaningfully below a high-quality, low-fragility one without collapsing to zero.

What the module deliberately does not do is forecast prices. Every output is a measurement of past behaviour — a property of the price, supply, holder, and market-microstructure series at the moment the diagnostic ran. The fragility-adjusted overall score is the closest the module comes to a single recommendation, and even that is a measurement of the asset's current structural state, not a forecast of where it's going next. This boundary is the most important design constraint of Diagnostics.

The full operational reference — every factor definition, the regime-detection logic, the bootstrap-confidence-interval methodology, and the cross-factor correlation deflation — lives on the methodology page at /methodology, with a deeper per-module treatment under /docs/diagnostics.