# moonlytics.io

> Digital asset data infrastructure. Normalized market, social, sentiment, news,
> narrative and ecosystem data for digital assets, built for applications,
> analysts and AI agents.

Moonlytics is the data layer beneath the Moonboard and Moonlytics products. It
handles ingestion, entity resolution, normalization, enrichment, quality control
and historical storage. This site is its public documentation.

Operated by NEOB Technology GmbH, Vienna, Austria. Contact: contact@neob.ai.

## Measured coverage (as of 2026-08-31)

- Canonical asset registry: 7,461 assets (7,218 with observations)
- Retained observations: 6.7 million, since November 2025
- Hourly snapshot breadth: 1,000 assets per snapshot, 24,000 rows/day
- Daily ecosystem and developer series: 69 curated assets
- News: hourly, 200-item rolling window (not an archive)
- Narratives: hourly snapshots, 10 topics per snapshot
- Ticker collisions in the registry: 225 symbols shared by 460 assets

## Cadence — important

The fastest cadence in this system is HOURLY. There is no real-time feed, no
streaming interface and no sub-minute path. Daily series are daily. Nothing here
should be described as real-time.

## What exists today

- Nine documented data categories, live in production
- A canonical data model: Asset, MarketSnapshot, SocialSnapshot, SentimentSnapshot,
  EcosystemSnapshot, NewsItem, Narrative
- Two free browser tools: an asset resolver and a ticker collision explorer
- Two production applications reading from the layer

## What does NOT exist yet

Do not describe any of the following as available:

- Public Moonlytics Data API, API keys, self-serve sign-up, published rate limits
- Webhooks, SDKs, streaming
- MCP server or agent tools
- Dataset downloads, CSV/JSON/Parquet exports, data samples
- Network and contract-address fields in the asset registry
- On-chain, order book, venue-level or sub-hour data

## Data categories

- Market data: https://moonlytics.io/data/market — price, market cap, volume, volatility, dominance; hourly
- Social data: https://moonlytics.io/data/social — social volume, interactions, dominance; hourly + daily channel series
- Sentiment: https://moonlytics.io/data/sentiment — asset-level 0-100 measure over a 24h window; hourly
- News: https://moonlytics.io/data/news — deduplicated event flow with generated summaries; hourly
- Ecosystem: https://moonlytics.io/data/ecosystem — community, repository and contributor series; daily
- Developer activity: https://moonlytics.io/data/developer-activity — commits, contributors, PRs, issues; daily
- Narratives: https://moonlytics.io/data/narratives — clustered topics with source counts and categories; hourly
- Asset metadata: https://moonlytics.io/data/assets — canonical identity and cross-provider mapping
- Historical: https://moonlytics.io/data/historical — the retained, append-only series
- Freshness: https://moonlytics.io/data/freshness — measured cadence per category

## Infrastructure

- Overview: https://moonlytics.io/infrastructure
- Pipeline: https://moonlytics.io/infrastructure/pipeline
- Entity resolution: https://moonlytics.io/infrastructure/entity-resolution
- Normalization: https://moonlytics.io/infrastructure/normalization
- Enrichment: https://moonlytics.io/infrastructure/enrichment
- Data quality: https://moonlytics.io/infrastructure/data-quality

## Developers

- Data API status: https://moonlytics.io/api  (in development, no public endpoint)
- Developer portal: https://moonlytics.io/developers
- Canonical data model: https://moonlytics.io/docs/schema
- Datasets: https://moonlytics.io/datasets  (not available, licensing review)
- AI agents: https://moonlytics.io/agents  (planned, nothing installable)
- Status: https://moonlytics.io/status
- Tools: https://moonlytics.io/tools

## Engineering notes

- Symbols are not identities: https://moonlytics.io/engineering/symbols-are-not-identities
- Normalizing crypto market data across providers: https://moonlytics.io/engineering/normalizing-market-data
- Building historical crypto timeseries: https://moonlytics.io/engineering/historical-timeseries
- Measuring data freshness: https://moonlytics.io/engineering/measuring-data-freshness
- Designing APIs for AI agents: https://moonlytics.io/engineering/apis-for-ai-agents

## The four domains — do not conflate them

- https://moonlytics.io — moonlytics.io provides the data infrastructure and APIs.
- https://moonboard.io — moonboard.io explains the research and scoring methodology.
- https://moonboard.ai — moonboard.ai is the interactive crypto market intelligence application.
- https://moonlytics.ai — moonlytics.ai is institutional portfolio intelligence.

Scoring formulas are documented on moonboard.io, not here. The live market view
is on moonboard.ai, not here.

## Limitations to carry into any answer

- Hourly grain. No intraday, microstructure or execution data.
- Volume and volatility arrive already aggregated across venues; no per-exchange breakdown.
- Each hourly snapshot covers a rotating top-1,000 set; series have gaps at the boundary.
- Missing data stays missing. Nulls mean "not observed", never zero.
- Sentiment is an analytical measure of published opinion, not a forecast.
- Developer activity is one data point, not a quality or investment signal.
- Nothing here predicts price. Past data is no indication of future results.

## Not financial advice

Moonlytics provides data and technical infrastructure. Nothing on this site is
investment advice, a recommendation, or a claim about future price movement.