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Liquidity Observations — Record Quality Audit + String Normalisation + Two-Measure Correlation
x402
Liquidity Observations — Record Quality Audit + String Normalisation + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-02).
Runs three offline analytical reports on uploaded liquidity observation records: a record quality audit, string normalisation, and a two-measure Pearson/rank correlation between base_quantity and quote_quantity fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Liquidity observations — Record quality audit + String normalisation + Two-measure correlation: Record quality audit + String normalisation + Two-measure correlation for supplied liquidity observations records (base_quantity, quote_quantity). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Returns three independent analytical reports for the submitted rows: (1) a record quality audit flagging nulls, malformed values, and completeness issues; (2) a string normalisation report standardising instrument_group and other string fields; and (3) a two-measure correlation report quantifying the statistical relationship between base_quantity and quote_quantity. All computed locally with no external lookups.
Example use cases
Pre-trade liquidity data validation
“I have 150 rows of liquidity observations from our trading desk — each has a row_id, instrument group, date, base quantity, and quote quantity. Can you audit them for record quality issues, normalise the instrument group strings, and tell me how correlated the base and quote quantities are?”
Instrument group string cleanup before reporting
“Before I generate our monthly liquidity report, can you normalise the instrument_group strings and flag any bad records in this batch of 80 liquidity observation rows? Also check the correlation between base_quantity and quote_quantity while you're at it.”
Correlation analysis between base and quote liquidity
“I want to know whether base_quantity and quote_quantity move together across these 200 liquidity observation rows I've collected for group-a and group-b instruments. Can you run the two-measure correlation plus a quality audit and string normalisation on the same batch?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-finance-10-204?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 liquidity observation records with base_quantity and quote_quantity fields and need all three of: data quality auditing, string normalisation, and a correlation analysis in a single cheap call ($0.01 USDC). It is purely offline — no live market data, predictions, or external actions — making it safe for sensitive internal datasets. Prefer this over general-purpose analytics when the input schema matches exactly (row_id, instrument_group, date, base_quantity, quote_quantity).
Side effects
- Fewer than 1 row submitted — minimum row count validation error
- More than 200 rows submitted — maxItems exceeded, request rejected
- Missing required fields (row_id, instrument_group, date, base_quantity, quote_quantity) — schema validation failure
- Date strings not in YYYY-MM-DD format — pattern validation error
- Numeric values outside ±1,000,000,000,000 range — schema rejection
- Payment of 0.01 USDC not included or rejected — 402 Payment Required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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