# SQL diagnostics for purchase_trade business rules

These scripts are read-only diagnostics for a PostgreSQL test database.

They exist to support the same business rules enforced by Python guards. The
expected workflow is:

1. write the consultant/developer rule in the thematic documentation;
2. enforce the invariant in the application code when feasible;
3. provide a read-only SQL diagnostic to audit existing data.

## quantity_consistency_checks.sql

Checks the two core lot quantity invariants documented in
`lots-and-quantities.md` and `lots-and-quantities.en.md`.

Zero `lot_qt` rows are ignored completely. They are treated as legitimate
memory of an open quantity consumed by a physical lot. This is required because
`lot_qt` represents usable open forecast and stops at zero, while the virtual
lot may become negative to compensate the difference between theoretical and
executed quantity. A non-zero `lot_qt` row without both `lot_p` and `lot_s` is
reported as anomalous.

Run it on a restored test database:

```sql
\i modules/purchase_trade/docs/business/sql/quantity_consistency_checks.sql
```

The script returns rows only when it finds a potential issue.

Main columns:

- `check_name`: invariant or diagnostic that failed.
- `contract_model`: `purchase.purchase` or `sale.sale`.
- `contract_id`: database id of the contract.
- `contract_number`: purchase or sale contract number.
- `line_id`: `purchase.line` or `sale.line` id depending on the check.
- `virtual_lot_id`: virtual lot involved in the inconsistency.
- `observed_value`: value found in the database.
- `expected_value`: value required by the business rule.
- `diff`: observed minus expected.
- `detail`: human-readable explanation.

Cross-category UoM rows are reported as manual-review diagnostics because the
Python code may pass explicit conversion factors that cannot be inferred safely
from SQL alone.