Digital Asset Data Infrastructure
Normalized market, social, ecosystem and intelligence data for digital assets — built for applications, analysts and AI agents that need one consistent model instead of a dozen incompatible feeds.
Figures measured on the production store on 2026-08-31. See the full freshness table.
Digital asset data is fragmented by default
Every provider has its own identifiers, its own field names, its own update rhythm and its own idea of what a "24 hour volume" is. Social platforms deliver counts, code hosts deliver commits, news wires deliver prose. None of it lines up on its own.
Moonlytics is the layer that makes it line up: one asset identity, one schema, one time base, one quality contract. Everything downstream — scores, dashboards, research, portfolio analytics, agent tools — reads from that single model.
{
"asset": {
"id": 1,
"symbol": "BTC",
"name": "Bitcoin"
},
"observedAt": "2026-08-31T09: 00: 00Z",
"market": {
"price": 78135,
"marketCap": 1568738541274,
"volume24h": 13029310589,
"volatility": 0.0142,
"marketDominance": 54.9
},
"social": {
"socialVolume24h": 41208,
"interactions24h": 9137442,
"socialDominance": 18.4,
"sentiment": 81
},
"score": 72.5,
"altRank": 3
}The shape of a single observation in the canonical model. Field-level documentation lives in the data model.
From raw provider payloads to applications
Five stages. Each one is documented, each one is a place where data can be wrong, and each one is something you can read about before you depend on it.
Three providers, three identifiers, one asset. How the canonical record is assembled.
Read →NormalizationDifferent schemas, units, time zones and frequencies collapsed into one model.
Read →Data qualityMissing values, stale windows, provider failures — and what we do about each.
Read →What is in the layer
Six live categories and the historical series behind them. Every page states its own coverage, cadence, fields and limitations.
Price, market cap, volume, volatility, dominance and supply — hourly, for 1,000 assets per snapshot.
Social dataSocial volume, interactions, dominance and community counts, normalized into comparable series.
SentimentAn aggregated, asset-level sentiment measure with an explicit definition and explicit limits.
NewsAggregated crypto and macro news, deduplicated, summarized and timestamped.
EcosystemCommunity growth, repository activity and contributor counts over time.
NarrativesHourly topic snapshots with a category and a source count, from clustered news flow.
What is already built on it
Moonlytics is not a proposal. It is the layer that feeds a production market intelligence application today, and the same model backs the research and portfolio surfaces around it.
Interactive crypto market intelligence: scoreboards, charts, trending topics, flash news and ecosystem activity, all reading from this layer.
Read the case study →moonlytics.aiInstitutional portfolio intelligence — the same asset identities and market series, applied to holdings, exposure and reporting.
Read more →AI agentsStructured, machine-readable digital asset context for retrieval and agent tooling. In development, documented as it lands.
Read more →Who does what
| Domain | Role | In one line |
|---|---|---|
| moonlytics.io | Data infrastructure, APIs, developer platform | Provides the data. |
| moonboard.io | Research, scoring methodology, quantitative analysis | Explains the methodology. |
| moonboard.ai | Interactive crypto market intelligence application | Provides the interactive market intelligence. |
| moonlytics.ai | Institutional portfolio intelligence | Provides institutional portfolio intelligence. |
Four domains, four search intents, no duplicated content. If you are looking for scoring formulas, they are documented on moonboard.io; if you are looking for the live market view, it is on moonboard.ai.