{"uid":"cap_9ntQGZneJaQg7ubsppBrF","slug":"liquidity-observations-record-quality-audit-iqr-outlier-flags-f03160bd","name":"Liquidity Observations — Record Quality Audit + IQR Outlier Flags","description":"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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-finance-10-014?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","instrument_group","date","base_quantity","quote_quantity"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"base_quantity":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"quote_quantity":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"instrument_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","base_quantity":10,"quote_quantity":1,"instrument_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","base_quantity":20,"quote_quantity":2,"instrument_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","base_quantity":15,"quote_quantity":3,"instrument_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","base_quantity":30,"quote_quantity":4,"instrument_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["base_quantity","quote_quantity"],"type":"string","default":"base_quantity"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_9wqHNO1S9nnPiUUd7zYJ7","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"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.","exampleAgentPrompt":"Can you run a record quality audit and IQR outlier check on my liquidity observations? I have rows with base_quantity and quote_quantity across groups like 'group-a' and 'group-b' from 2026-10-01 through 2026-10-04 — flag any outliers in the base_quantity field.","exampleUseCases":[{"title":"Pre-analysis liquidity data quality check","prompt":"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?"},{"title":"Detecting anomalous quote quantities in market data","prompt":"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."},{"title":"Daily liquidity data pipeline validation","prompt":"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?"}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T12:40:46.941Z","isFirstParty":false,"canonicalSlug":"liquidity-observations-record-quality-audit-iqr-outlier-flags-f03160bd"}