GhostGauge — Brand Card (v1.1)

This document defines GhostGauge's public narrative (what it is, who it's for), naming conventions (G-Score vs GRS), band labels, tone, and ready-to-use copy. It exists so UI text, social posts, and docs stay consistent. Keep this file in sync with live config and methodology versions.

GhostGauge — Brand Card (v1.1)

A short, practical guide to what GhostGauge is, how it speaks, and how to present it to people with zero context.

Brand at a glance

• What it is: A daily, transparent, factor-weighted market risk dashboard for Bitcoin. It compresses diverse data into a single 0–100 G-Score (higher = higher risk) with full driver transparency.

• Who it's for: Hedge-fund PMs, quants, sophisticated retail, and market-curious readers who want signal over noise.

• Why it exists: To help people frame risk, not predict price. Decision support—clear, repeatable, explainable.

• Where it lives: GhostGauge at ghostgauge.com; analysis voice from GrayGhost (author of the TWIMM newsletter).

• How it feels: Professional, analytical, modern UI—occasional, tasteful noir accents (mainly in social/newsletter, not the app).

Brand architecture

• Platform / Product: GhostGauge The destination and tool (dashboard, drivers, methodology, history/CSV, BTC⇄Gold, Sats per Dollar, Alerts).

• Author / Persona: GrayGhost Byline & narrative lens in newsletter/social. On-site copy stays sober; persona is a light flourish.

• Metric (formal): GrayGhost Risk Score (GRS v3) Methods, whitepaper, code comments, API/docs.

• Metric (everyday): G-Score Headlines, UI chips, social posts, casual usage.

One-liner & elevator pitch

• One-liner: GhostGauge turns market chaos into a single, transparent G-Score so you can calibrate risk at a glance.

• 30-second pitch: GhostGauge blends liquidity, momentum, term structure, macro, and social/attention into a 0–100 G-Score (higher = higher risk). Every input is sourced, time-stamped, normalized, and blended with outlier control and EWMA smoothing. No black boxes—click through to see the drivers and download the history.

Positioning & proof

• Positioning: Transparent risk telemetry for crypto + macro.

• Promise: Signals, not hype. Methods before marketing.

• Proof points: o Five-pillar model with published inputs & weights (35/25/20/10/10). o Winsorized z-scores → logistic 0–100; stale data auto-excluded with weight re-normalization. o Factor History CSVs updated daily; Provenance with source notes, schema tripwires, and fallbacks. o ETF Flows via robust parser (21-day sum) with staleness & outlier guards. o Optional small adjustments: cycle residual & spike detector—capped and disclosed. o Clear risk bands and plain-English playbook.

Audience & use cases

• PMs/Quants: Fast regime check; portfolio guardrails; risk-on/risk-off framing.

• Sophisticated retail: Sanity check against headlines & influencer noise.

• Media/Creators: Reliable daily artifact to cite/embed.

• You (GrayGhost): Anchor for weekly commentary and cross-asset context.

Messaging pillars (what we talk about)

  1. Transparency — clear inputs, weights, freshness.
  2. Discipline — stable methodology; versioned updates.
  3. Context — show drivers, not just a number.
  4. Restraint — risk framing ≠ trade signals.
  5. Macro-aware — liquidity and cross-market data matter.

Voice & tone

• Primary: Crisp, neutral, specific. Short sentences. No predictions.

• Persona seasoning (optional): A brief noir-tinged line in social/newsletter—not in the app chrome.

• Always include: UTC timestamp and a path to methodology.

Ready-to-use lines

• "Signals, not hype." • "One score, five pillars, zero mystery." • "Transparent risk for macro + crypto."

Naming & notation

Public names

• G-Score (BTC) / Bitcoin G-Score • (Future-ready) G-Score (ETH) • GhostGauge: product/site • XAU Lens: BTC↔Gold module • Sats Lens: Satoshis per Dollar module • Alerts: zero-cross & band-change notices

Formal references

• GRS v3 — GrayGhost Risk Score, versioned methodology. • Modules: Drivers, Methodology, History, XAU Lens, Sats Lens, Alerts.

Risk bands (display + copy — align with app defaults)

• 0–15 Aggressive Buying • 15–35 Regular DCA Buying • 35–55 Hold / Neutral • 55–70 Begin Scaling Out • 70–85 Increase Selling • 85–100 Maximum Selling

(Note: these bands are configurable in app config; keep brand copy in sync with the live config and use live config as main source)

Slugs / API / data keys (examples)

• gscore_btc, grs_version=3 • pillar_liquidity, pillar_momentum, pillar_leverage, pillar_macro, pillar_social

Do / Don't (naming)

• Do: Use G-Score in UI/social; use GRS in docs/methods. • Do: Qualify asset explicitly (e.g., "Bitcoin G-Score"). • Don't: Call it an "index" or imply trade signals. • Don't: Use sensational qualifiers ("warning!", "guaranteed!", etc.).

Headline / subhead templates

Dashboard H1

Today's Bitcoin G-Score: {value}

Dashboard subhead / tooltip

The GrayGhost Risk Score (GRS v3) blends five pillars into a transparent 0–100 risk measure.

Drivers section

Drivers — Liquidity · Momentum · Term Structure · Macro · Social

Band legend (match live config)

0–15 Aggressive Buying · 15–35 Regular DCA Buying · 35–55 Hold/Neutral · 55–70 Begin Scaling Out · 70–85 Increase Selling · 85–100 Maximum Selling

Methodology CTA

Methodology (GRS v3) — inputs, normalization, weights, and staleness handling

SEO title

GhostGauge — Bitcoin G-Score (0–100 multi-factor market risk)

SEO description

Daily, transparent, factor-weighted risk for BTC. Liquidity, momentum, term structure, macro, social. Signals, not hype.

Social & newsletter patterns

Crossing-band alert (X/Twitter, from @grayghost)

Bitcoin G-Score just crossed 70 — High risk. Drivers + chart on GhostGauge. ghostgauge.com

Weekly wrap (TWIMM)

W/W Δ: 58 → 66. Liquidity led; term structure cooled. Full breakdown in Drivers. ghostgauge.com

Multi-asset tease (future)

ETH G-Score: 48 (Hold/Neutral). Compare BTC/ETH drivers on GhostGauge.

Persona-seasoned alt (sparingly)

"The street's loud. Signals aren't. BTC G-Score 72 — High."

How the metric works (brief public summary)

• Inputs → Pillars: Liquidity/Flows (35%), Momentum/Valuation (25%), Term Structure/Leverage (20%), Macro (10%), Social/Attention (10%). • Normalization: Winsorize tails → z-score vs history → apply direction (invert where "more = less risk") → logistic 0–100. • Smoothing: EWMA with configurable half-life; stale data excluded with weight re-normalization. • Transparency: Every input sourced, timestamped, and downloadable as CSV.