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Liquidity Observations — Record Quality Audit + IQR Outlier Flags
x402
Liquidity Observations — Record Quality Audit + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).
Performs a record quality audit and IQR-based outlier detection on up to 200 rows of liquidity observation data (base_quantity and quote_quantity fields), returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Liquidity observations — Record quality audit + IQR outlier flags: Record quality audit + IQR outlier flags for supplied liquidity observations records (base_quantity, quote_quantity). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Two independent analytical reports: (1) a record quality audit identifying missing values, format issues, and data integrity problems across the supplied rows; and (2) IQR-based outlier flags identifying which rows have base_quantity or quote_quantity values that fall outside the interquartile range threshold. Results are computed locally with no external lookups.
Example use cases
Pre-analysis liquidity data quality check
“I'm about to run a pricing model and need to verify my liquidity dataset first — can you audit these 150 rows of base_quantity and quote_quantity observations across instrument groups and flag any records that look problematic or are IQR outliers in the base_quantity field?”
Detecting anomalous quote quantities in market data
“I have a batch of liquidity observations from last quarter grouped by instrument and I need to find any rows where quote_quantity is an outlier — please run the IQR outlier flags report on the quote_quantity field for all 80 rows.”
Daily liquidity data pipeline validation
“Every morning I ingest fresh liquidity records from our trading desk — can you run a record quality audit plus IQR outlier detection on today's 200 rows of base_quantity data so I can catch any bad records before they hit downstream systems?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-finance-10-014?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined record quality audit plus IQR outlier detection specifically for liquidity observation data with base_quantity and quote_quantity fields. It is best suited for financial data pipelines, pre-analysis data validation, or regular data quality checks on market liquidity records with up to 200 rows per call. Prefer this over general statistics endpoints when you specifically need both a quality audit and outlier flagging in a single call at low cost.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, instrument_group, date, base_quantity, quote_quantity) causes row rejection
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Quantities outside the ±1,000,000,000,000 range are rejected
- Empty rows array returns an error
- Payment failure (insufficient USDC) prevents report generation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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