TokenEQ is a research platform built on FLOWnomics v2.0 — a multi-class crypto valuation framework extended from Blair (2025). It answers three questions for every token:
Network utility ≠ token value capture. LINK secures $100B+ in DeFi — VCR 8%. XRP processes $15B/yr in ODL — VCR 25%. BTC has fixed supply and $120B in ETF AUM — VCR 90%. Every deep-dive separates these explicitly. The gap between these two numbers is often the most analytically useful insight.
The platform is built on the principle that a wrong number produced precisely is worse than no number at all. Every score comes with a confidence band. Every data source is labelled. Every known limitation is published. Competitors hide their methodology. We publish ours.
Every token is evaluated across five dimensions. The weights applied to each dimension depend on the token's class — a payment token is judged differently from a governance token or a store of value. This is the core of FLOWnomics v2.0: class-specific computation that produces economically meaningful numbers instead of applying one formula to all assets.
| Dimension | What it measures | Data source | Confidence |
|---|---|---|---|
| F₁ Face Value | Raw market signal — price, market cap, 24h volume. The observable reality. | CoinGecko API — live on every page load | High |
| F₂ Flow Value | How much real economic value flows through the network. ASV ÷ Velocity = implied price. The core FLOWnomics formula. | CoinGecko volume proxy (Phase 1) → Token Terminal Pro (Phase 2) | Medium (Phase 1) |
| F₃ Finality | How final and fast is settlement? RTGS scores highest. Probabilistic PoS is fast but not deterministic. | Token registry — protocol facts. Rarely changes. | High |
| F₄ Friction | Barriers to real-world adoption. TAM capture %, order book depth (PSC), leakage risk. | Research-derived quarterly. Phase 4 → Nansen live. | Estimated (Phase 1) |
| F₅ Form | Supply mechanics and governance health. Inflation, unlock risk, staking, treasury overhang. | Research-derived quarterly. Phase 3 → Messari live. | Estimated (Phase 1) |
The same metric matters differently for different token types. A payment token's value comes primarily from settlement utility (Flow, Finality). A governance token's value comes primarily from protocol revenue and tokenomics (Flow, Form). Applying one weight set to all classes produces misleading scores.
| Class | Face | Flow | Finality | Friction | Form |
|---|---|---|---|---|---|
| Payment | 15% | 35% | 25% | 15% | 10% |
| Infrastructure | 15% | 25% | 20% | 15% | 25% |
| Governance | 15% | 20% | 10% | 20% | 35% |
| Store of Value | 15% | 20% | 15% | 20% | 30% |
| Hybrid (ADA 60/40) | 13% | 23% | 16% | 17% | 31% |
FLOWnomics v1.0 (Blair 2025) established FLOW = ASV ÷ Velocity for payment tokens. This formula is precise and auditable for tokens whose value comes from facilitating economic settlement. Applied without modification to other token classes, it produces mathematically valid but economically meaningless numbers.
v2.0 preserves the v1.0 formula for payment tokens and defines class-specific computation methods for every other class. The output format is identical (a score 0–100 with confidence label) but the derivation is correct for each class's actual value mechanics.
| Class | Flow Value model | Key variable | Examples |
|---|---|---|---|
| Payment | ASV × QAF × SPF ÷ Velocity | ODL/verified settlement volume | XRP, XLM, XDC |
| Infrastructure | (ASV × QAF + Fees) × SPF ÷ Velocity | DEX volume quality + protocol fees | SOL, ETH, AVAX |
| Oracle | TVE × Fee_Rate × (1 − Sell_Rate) | Operator sell rate, Reserve growth | LINK |
| Governance | Revenue × Fee_Capture_Rate ÷ Discount | Fee switch status (0 if off) | UNI, AAVE |
| Store of Value | Monetary Premium Score | Gold penetration rate, ETF AUM | BTC |
| Hybrid | Weighted composite of above | Class composition weights | BNB, ADA |
The Value Capture Ratio (VCR) quantifies the gap between what a network does and what the token captures from that activity. It is one of the most important metrics in the framework — and one of the most commonly ignored in crypto analysis.
VCR = % of total transaction demand that requires open-market acquisition of the token to complete the transaction.
This definition makes VCR observable, debatable with data, and directly tied to price pressure mechanics. It is not "% of value captured" — an abstract concept that cannot be verified. Instead it anchors to a specific, measurable question: does this transaction require someone to buy the token on the open market?
| Class | Mechanical VCR definition | Example |
|---|---|---|
| Payment | % of settlement transactions where token must be acquired on open market to complete the transfer | XRP: ODL volume / total RippleNet = ~0.7% raw → VCR 0.25 after RLUSD competition |
| Infrastructure | % of transactions where gas must be acquired vs covered by pre-existing holdings or abstracted away | SOL: VCR ~0.55 — all txns require SOL gas, discounted by 3.9%/yr inflation diluting non-stakers |
| Oracle | % of fee revenue that accumulates to Reserve/stakers rather than being immediately sold by operators | LINK: VCR 0.08 — operators sell ~90% of earned fees immediately |
| Governance | % of protocol revenue flowing to token via burn/buyback requiring open-market acquisition | UNI: VCR ~0.15 — fee switch active, 15% of LP fees burned via protocol mechanism |
| Store of Value | % of institutional demand requiring open-market acquisition | BTC: VCR 0.90 — fixed supply means all new demand must buy from existing holders |
Critical implementation rule: VCR is always computed before the Flow Value score, from independent data sources. It must never be derived from the Flow Value score it subsequently adjusts. The execution order in the scoring engine enforces this independence — circularity would make both numbers meaningless.
Market Price ÷ VCR-adjusted implied price. Measures how much future thesis the market price embeds relative to what the token actually captures today.
| Range | Label | Meaning |
|---|---|---|
| < 2× | Utility-driven | Price close to current captured utility. Limited speculative premium. |
| 2–10× | Balanced | Market pricing near-term growth alongside current utility. |
| 10–50× | Narrative premium | Significant portion of price reflects future thesis, not current utility. |
| > 50× | Deep thesis | Price is almost entirely a bet on future scenarios. |
XRP Thesis Premium ~220×. LINK ~26×. BTC ~1.6×. UNI ~40× (post fee-switch). These numbers are not verdicts — they describe the character of the investment thesis, not its quality.
A core principle of TokenEQ v2.1: prices must be computed, not asserted. Prior to v2.1, all scenario price ranges were editorial opinions ("$3.50–$4.50 for XRP bull"). These are now replaced with model-derived outputs for bull and wildcard scenarios.
These remain reality-anchored — narrative descriptions tied to current observable data. Bear describes what goes wrong at current trajectory. Base describes the current trajectory continuing. No model computation needed because these scenarios don't depend on a structural constraint being met.
These are model-derived — computed from the mechanics of the thesis. Each scenario answers: "What would this token need to be worth if this specific trigger fired?" The price comes from the formula. The formula comes from the right model for the token class. The key variable and sensitivity table are shown explicitly so the model can be debated.
Derived from the square-root law of market impact — an empirical regularity validated across equities, futures, and crypto. Asks: what price does this asset need to be worth for the largest transaction to clear without unacceptable slippage? The key variable is the vol/mcap turnover ratio (0.3%–1.5%) which encodes the execution architecture: lower turnover = more institutional holding = higher required price.
Standard DCF-equivalent for smart contract platforms. Projects scenario fee revenue based on scenario TVL/volume, applies staker capture rate, and solves for price at a target yield or earnings multiple. Cross-checked against P/F multiple (implied market cap ÷ fee revenue — should fall within 15–40× for defensible scenarios).
LINK's Reserve must be large enough to credibly backstop the contracts it secures. At scenario TVE and coverage ratio, a required reserve size can be derived. Divided by staked supply, this gives the implied price. Sensitivity shown across 3%, 5%, and 10% coverage ratios. Secondary check: CCIP fee revenue capitalised at 20×.
Governance token value is conditional on fee capture — what fraction of protocol revenue flows to token holders via burn or buyback. UNI uses a burn mechanism (supply reduction). AAVE uses direct buybacks (demand creation). Both are modelled as revenue × capture × multiple ÷ supply, with sensitivity across 15×, 25×, and 40× multiples.
BTC's value derives from monetary premium — the premium society places on a verifiably scarce, censorship-resistant store of purchasing power. Gold ($22.6T market cap) is the benchmark. Scenarios describe penetration rates from 5%–50%. A quality factor (0.82) adjusts for crypto-specific attributes relative to gold. Current BTC price implies ~7.3% gold penetration.
Some tokens perform meaningfully different economic functions simultaneously. ADA is 60% infrastructure gas token + 40% governance token. BNB is 40% payment + 35% SoV + 25% governance. Each constituent is modelled independently using its own class model, then weighted and summed.
Some tokens don't have a structural constraint that bounds their price under the thesis scenario. LTC's thesis is 'digital silver / Bitcoin alternative' — not institutional settlement. ZBCN is streaming payroll — not institutional settlement. Applying Model A to these would produce a number, but not a meaningful one. Model G explicitly labels the absence of a structural model and states the narrative driver instead.
All scenario model outputs carry Estimated confidence. Inputs are analyst-derived. The formula is correct; the inputs are debatable. TokenEQ publishes both so the debate can happen on the inputs rather than on whether there's a derivation at all.
Model A (Liquidity Sizing) is only valid if the asset is the binding constraint at final settlement — meaning open-market acquisition of the asset is required to complete the transaction. This is not true of all payment tokens, and it's partially threatened for tokens where competing alternatives exist.
| Level | Meaning | Model A status | Example |
|---|---|---|---|
| High | Asset is the required settlement asset with no viable alternative | Fully valid | XRP in pure ODL corridors |
| Medium | Asset required for key flows but competing alternatives exist | Valid with VCR discount applied | XRP today (RLUSD threatens) |
| Low | Asset can be bypassed at settlement by stablecoins or abstraction | Does not apply — price anchors to utility fee level | LTC, most non-payment tokens |
| N/A | Not a settlement token | Not applicable | UNI, BTC, AAVE |
If Settlement Dependency is Low or N/A for a payment token, Model A's wildcard prices cannot be taken at face value. The price instead gravitates toward the current-utility implied price from FLOWnomics. This is displayed explicitly on the Token Capture Rate signal card for payment tokens.
The OTC objection addressed: Some argue that OTC execution, pre-positioned corridors, and bilateral netting eliminate the need for public market depth — and therefore eliminate the price constraint. This argument misses a critical dependency: OTC desks need XRP inventory. LPs need capital. Pre-positioned corridors need the underlying asset to be worth enough to fund the working capital. You cannot have deep OTC infrastructure in a shallow asset. Execution layer sophistication is a downstream consequence of price depth, not an alternative to it.
Assets don't stay in one pricing regime forever. Ethereum in 2019 was priced purely on fee revenue and gas utility. Ethereum in 2025 has $14B in ETF AUM, institutional staking products, and a staking yield (3.3%) approaching the risk-free rate — signals that monetary premium is forming alongside utility premium.
The Monetary Premium Index (0–100) measures how far an asset has transitioned from utility/cash-flow pricing toward monetary premium/reserve asset pricing.
| Score | Label | Dominant pricing model | Example |
|---|---|---|---|
| 0–15 | Pure utility | Model B (fee revenue) | AVAX today (~12%) |
| 15–35 | Utility-dominant | Model B with emerging premium | SOL (~15%) |
| 35–60 | Emerging premium | Blend of Model B and E | ETH (~35%) |
| 60–80 | Monetary transition | Model E becoming dominant | — |
| 80–100 | Monetary asset | Model E (monetary premium) | BTC (~95%) |
When an asset's Monetary Premium Index crosses ~60, pricing shifts from Model B (fee revenue capitalisation) toward Model E (monetary premium vs gold). Threshold signals:
This matters for scenario analysis. If ETH crosses the Model Transition Threshold, applying Model B to wildcard scenarios understates the implied price — the correct model is Model E (gold penetration equivalent for ETH's role as productive monetary reserve). TokenEQ displays the current threshold progress on every infrastructure token's Adoption Stage card.
Each token is assigned one of nine verdicts based on its TokenEQ Score and Maturity Index. These criteria are explicit and public — there are no editorial overrides.
| Verdict | Score | Maturity | Meaning |
|---|---|---|---|
| Blue chip | ≥72 | ≥70 | Mature, fundamentally confirmed |
| Hidden gem | ≥68 | ≤50 | Strong fundamentals, undiscovered |
| Compounding | ≥65 | 40–65 | Growing, asymmetric upside remains |
| Priced in | ≥60 | ≥75 | Market has fully discovered value |
| Speculative potential | 55–67 | ≤40 | Early, unproven but plausible thesis |
| Developing | 50–64 | 30–55 | Infrastructure real, unproven at scale |
| Hype cycle | <55 | >60 | Price exceeds verifiable fundamentals |
| Unproven | <50 | <30 | Insufficient track record |
| Declining utility | ↓ 3Q+ | >60 | Activity falling, market cap elevated |
Required disclaimer on every verdict badge: "This classification reflects TokenEQ Score and Maturity Index only. It is not a recommendation to buy, sell, or hold."
Displaying a score of 67 as a single integer implies the model can distinguish 67 from 66. It cannot. All scores display as ranges.
| Score range | Band | Display example | Interpretation |
|---|---|---|---|
| ≥65, High confidence | ±5 | 62–72 | Above average — inputs are High or Medium confidence |
| 50–64 | ±7 | 55–69 | Moderate — wider band reflects more uncertainty |
| <50 or any Estimated input | ±10 | 35–55 | Weak — significant estimation involved |
| Tier | Meaning | Examples |
|---|---|---|
| High | On-chain verified or live API data | Price, volume, market cap, finality facts |
| Medium | Company-reported or third-party research | ODL volume (Ripple), TVL estimates (DeFiLlama) |
| Estimated | Model-derived or analyst estimate | QAF, VCR, scenario model outputs, treasury overhang |
| What | Source | Update frequency | Phase |
|---|---|---|---|
| Price, market cap, volume, supply | CoinGecko free API | Live on every page load | Phase 1 — live now |
| 30-day price chart | CoinGecko free API | Live on every page load | Phase 1 — live now |
| PSC / order book depth | CoinGecko tickers?depth=true | Live on every page load | Phase 1 — live now |
| ATH drawdown | CoinGecko free API | Live on every page load | Phase 1 — live now |
| Flow Value (ASV) | Volume proxy (Phase 1) → Token Terminal Pro (Phase 2) | Daily / quarterly | Phase 2 — on revenue |
| Form score inputs | Quarterly research (Phase 1) → Messari (Phase 3) | Quarterly | Phase 3 — on revenue |
| Friction score inputs | Quarterly research (Phase 1) → Nansen (Phase 4) | Quarterly | Phase 4 — on revenue |
| VCR, settlement dependency | Quarterly research | Quarterly | Phase 1 — manual |
| Scenario model inputs | Quarterly research | Quarterly | Phase 1 — manual |
FLOWnomics v2.0 closed 14 gaps from v1.0. Twelve gaps remain. We publish them because transparency is the product.
Moody's became the standard not because it was most accurate — but because it was most transparent. TokenEQ publishes its criteria, confidence bands, data sources, model inputs, and all 12 known gaps. Competitors hide their methodology. We publish ours. This is the product.