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Trading Journal Observations — Record Quality Audit + String Normalisation + Date by Category Pivot
x402
Trading Journal Observations — Record Quality Audit + String Normalisation + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).
Runs three local analytical reports on trading journal rows (entry_price, exit_price): a record quality audit, string normalisation, and a date-by-category pivot — all without external lookups or predictions.
Price / call$0.01
Success—
StatusUnknown
How this service works
Trading journal observations — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot 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
Returns three independent analytical reports computed locally: (1) a record quality audit flagging issues in the supplied rows, (2) a string normalisation report on instrument_group and related text fields, and (3) a date-by-category pivot table for the selected numeric field (entry_price or exit_price) across instrument groups and dates. No external data is fetched and no predictions are made.
Example use cases
Auditing data quality before backtest
“I have 80 rows of trading journal data with instrument_group, date, entry_price, and exit_price — can you run a full record quality audit plus string normalisation on the instrument labels, and also pivot the entry_price values by date and category so I can spot gaps before I run my backtest?”
Cleaning messy instrument group labels
“My trading journal has inconsistent capitalisation and spacing in the instrument_group field across 120 rows — run the string normalisation report and date pivot on these records using exit_price so I can see cleaned categories over time.”
Monthly review of trade observation coverage
“Here are 200 rows from our end-of-month trading log with dates, instrument groups, and entry and exit prices — please run the quality audit, string normalisation, and date-by-category pivot reports on entry_price so we can review data completeness by category for the month.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-finance-09-206?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of trading journal data (with entry_price and exit_price) and need three bundled analytical passes — quality audit, string normalisation, and date-by-category pivot — in a single low-cost call ($0.01 USDC). It is ideal when you want purely local, deterministic analysis with no external lookups, predictions, or professional assessments. Prefer alternatives if you need live market data, more than 200 rows, or analytical reports outside these three specific types.
Side effects
- Fewer than 1 row supplied — rejected by minItems constraint
- More than 200 rows supplied — rejected by maxItems constraint
- Missing required fields (row_id, instrument_group, date, entry_price, exit_price) — validation error
- Malformed date format (not YYYY-MM-DD) — schema pattern rejection
- Numeric values outside ±1,000,000,000,000 — rejected by min/max constraints
- Invalid field enum value (not entry_price or exit_price) — schema validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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