Market Microstructure Index (MMI)
Version v1.7-MMI · Published March 2026
The IntLiq Market Microstructure Index (MMI) is a technical quantitative indicator designed to measure the operational accessibility of tokenised Real World Assets (RWAs).
Unlike traditional sentiment-based ratings, the MMI is a deterministic, subtractive penalty model that evaluates structural liquidity, redemption SLAs, and secondary market anti-fragility.
Scoring Components (Base 100)
| Component | Weight | Description |
|---|---|---|
| Structural AUM | 30% | Absolute fund size providing baseline redemption depth. |
| Redemption SLA | 25% | Primary market liquidity terms (T+0 to T+2). |
| Market Depth | 20% | Secondary volume adjusted for venue diversity and slippage. |
| Issuer Resilience | 15% | Institutional track record and regulatory compliance. |
| Jurisdiction | 10% | Legal clarity and investor protection frameworks. |
Anti-Manipulation Penalties
The MMI applies 5 subtractive penalty layers to detect and discount artificial or fragile liquidity. The detection framework is documented below. Exact penalty thresholds, scaling functions, and interaction rules are proprietary and calibrated against historical incident data.
1. Triangulated NAV Proof (TNP)
Cross-references primary NAV against secondary market prices from Chainlink, Pyth, and DEX feeds. Material divergence beyond calibrated tolerance thresholds triggers an Institutional Exit Warning and score deduction.
2. Unique LP Fingerprinting (ULP)
On-chain wallet clustering detects Sybil liquidity — volume concentrated in a small number of wallets is heavily discounted. Identifies when apparent depth is controlled by a single entity.
3. Wash Trade Detection (WTD)
Compares reported APY against actual trading volume patterns. Protocols with suspicious volume spikes relative to yield are flagged as potential wash trading and penalised.
4. Staleness Penalty (SP)
Data older than 24 hours is automatically penalised. Stale data reduces confidence in all detection-based penalties, preventing double-counting between staleness and other signals.
5. Yield Wash Penalty (YWP)
Flags suspiciously high yields relative to asset class norms. Category-specific yield attribution models distinguish genuine yield from incentive-inflated returns.
Aggregate penalty cap: Total penalties are subject to a calibrated cap to prevent over-punishment from correlated signals. Cap values and interaction rules are proprietary and updated quarterly based on backtesting performance.
Liquidity Accessibility Tiers
Institutional-grade. Supports 8-figure entries/exits with minimal friction. T+1 redemption guaranteed.
Suitable for mid-sized funds. Some staging or OTC coordination may be required for large redemptions.
Limited secondary market. High reliance on primary redemption with potential gating risks.
Restricted or frozen accessibility. No viable secondary exit path.
v1.7 Improvements
Aggregate Penalty Cap
Total penalty deductions are now subject to an aggregate cap calibrated against historical false-positive rates, regardless of how many risk signals fire simultaneously. This prevents over-punishment of protocols that trigger multiple correlated penalties from a single root cause.
Penalty Interaction Rules
When data is classified as stale, detection-based penalties (NAV divergence, wash trade, Sybil) are attenuated proportionally. Stale data reduces confidence in the signals those penalties are based on, preventing double-counting between staleness penalties and detection penalties.
Issuer Infrastructure Sub-Score
The issuer component now blends 70% reputation score with 30% on-chain infrastructure score. Infrastructure scoring evaluates smart contract audits, on-chain governance, automated redemption mechanisms, and multi-chain presence. This allows DeFi-native protocols to compete fairly with traditional finance issuers.
Custom Scoring Weights
Institutional clients can now provide custom scoring weights and penalty caps via the API. This enables portfolio-specific risk calibration while maintaining the same deterministic methodology framework. Custom weight usage is flagged in the audit trail.
Score Trajectory Analysis
Each protocol now includes a trajectory classification (IMPROVING / STABLE / DECLINING) computed via 30-day linear regression on historical scores. A calibrated slope threshold determines direction, providing early warning of deteriorating liquidity conditions.
Methodology Validation
IntLiq validates the MMI methodology using a backtesting framework that compares historical scores against actual protocol outcomes. The framework measures AUM drawdown prediction, tier stability rates, and score-to-retention correlation across all tracked protocols.
L1 Stability Rate
0.0%
L1 protocols retaining >75% AUM over 30d
ILLIQUID Prediction
0.0%
ILLIQUID-scored protocols with >20% AUM decline
AUM Retention Correlation
-0.9978
Pearson correlation: score vs 30d AUM retention
142 protocols analyzed · 1000 score records · v1.7-MMI
Regulatory Compliance & Audits
IntLiq MMI v1.7 aligns with GAAP and IFRS reporting cycles. Indices are considered “Current” if the protocol has a verified annual audit (<365 days). Intermittent quarterly financials (10-Q style) provide an additional confidence bonus.
Matches Institutional Data License SLA
Legal Disclosure
Technical Information Service
The IntLiq Market Microstructure Index is a technical market indicator. It is not a credit rating and does not assess the solvency of issuers. IntLiq is a data infrastructure provider, not a financial advisor. Indices are based on available on-chain data and reported issuer filings.
SLA & Infrastructure
Institutional rollouts can combine the published methodology with onboarding support, custom scoring configuration, and commercial service terms discussed during evaluation.
For enterprise rollout planning or commercial support terms, contact sales@intliq.com.