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Trading Journal Observations — Record Quality Audit + String Normalisation + Two-Measure Correlation
x402
Trading Journal 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 2026-10-29).
Runs three local analytical reports on trading journal rows (entry_price, exit_price): a record quality audit, string normalisation, and two-measure correlation — no external lookups or predictions.
Price / call$0.01
Success—
StatusUnknown
How this service works
Trading journal observations — Record quality audit + String normalisation + Two-measure correlation: Record quality audit + String normalisation + Two-measure correlation for supplied trading journal observations records (entry_price, exit_price). 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
Three independent analytical reports for the submitted trading journal rows: (1) a record quality audit flagging missing, null, or malformed field values; (2) a string normalisation report for instrument_group and other string fields; (3) a two-measure correlation report between entry_price and exit_price. All computed locally with no external lookups.
Example use cases
Pre-analysis trade data quality check
“Before I run my monthly performance analysis, can you audit my trading journal for record quality issues, normalise the instrument group labels, and check the correlation between entry and exit prices? I have about 150 rows with row IDs, instrument groups, dates, entry prices, and exit prices.”
Identifying dirty data in trade logs
“I suspect my trading journal has some missing or malformed values — can you run a quality audit and string normalisation pass over these 80 trade records (row_id, instrument_group, date, entry_price, exit_price) and also tell me how correlated the entry and exit prices are?”
Correlation check for strategy review
“I'm reviewing a set of 60 trades from last quarter and want to know how strongly entry price and exit price correlate across my instrument groups — can you also flag any data quality issues and clean up the instrument group strings while you're at it?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-finance-09-204?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to validate, normalise, and analyse the statistical relationship between entry and exit prices in a trading journal dataset of up to 200 rows, and want all three reports (quality audit, string normalisation, correlation) returned in a single API call at low cost. Prefer this over generic data quality tools when your data model specifically matches the trading journal schema (entry_price, exit_price, instrument_group). Not suitable if you need live market data, predictions, or professional financial advice.
Side effects
- Fewer than 1 row submitted — minItems violation returns validation error
- More than 200 rows submitted — maxItems violation returns validation error
- Missing required fields (row_id, instrument_group, date, entry_price, exit_price) on any row — schema validation error
- Invalid date format (not YYYY-MM-DD) — pattern validation error
- entry_price or exit_price outside allowed numeric range — schema validation error
- Payment not included or insufficient — x402 payment required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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