dpm-xl¶

A Python parser and evaluator for DPM-XL, the expression language of EBA validation rules and similar checks. Give it a rule and your report data; it tells you which records fail and explains each failure in plain numbers.

dpm-xl is an independent project, not affiliated with or endorsed by the European Banking Authority.

A failure reads like this:

1,204 should equal 1,211 (1,180 + 31): difference 7, at least 5.5 given the reported precision

It is for teams that check supervisory reports before they are filed, build validation into a reporting platform, or analyse and edit DPM-XL rules.

• Parses every construct of the DPM-XL grammar, including every EBA validation rule and precondition of DPM releases 3.4–4.4 (14,000+ distinct expressions, about 0.1 ms each).
• Evaluates them with EBA's interval arithmetic, null logic and key joins, and points each failure at the cells involved.
• Finds problems before evaluation, for editors and CI: every syntax error, unknown functions and tables, coordinates a table lacks, keys and operand types, each with its position and, where one fits, a suggested fix.
• Has no dependencies. The optional columnar extra adds a polars evaluator for reports with millions of open-table rows.
• Reads no files and knows no DPM release: data comes through a three-method provider, so the same evaluator runs against a report upload, a database or test fixtures. dpm-xbrl reads xBRL-CSV and XBRL-XML reports; the end-to-end guide connects the two.

Installation¶

pip install dpm-xl               # Python 3.11+, no dependencies
pip install "dpm-xl[columnar]"   # adds the polars evaluator

In 30 seconds¶

from dpmxl import MemoryProvider, Number, TableLayout, evaluate_rule, explain

layout = TableLayout("C_01.00", rows=("0010", "0020", "0030"), columns=("0010", "0020"))
values = {  # (table, row, column, sheet, open keys) -> value with XBRL decimals
    ("C_01.00", "0010", "0010", None, ()): Number.of(100, 0),
    ("C_01.00", "0020", "0010", None, ()): Number.of(60, 0),
    ("C_01.00", "0030", "0010", None, ()): Number.of(40, 0),
    ("C_01.00", "0010", "0020", None, ()): Number.of(1204, 0),
    ("C_01.00", "0020", "0020", None, ()): Number.of(1180, 0),
    ("C_01.00", "0030", "0020", None, ()): Number.of(31, 0),
}
provider = MemoryProvider([layout], values)

expression = (
    "with {tC_01.00, c*, default: 0, interval: true}: {r0010} = {r0020} + {r0030}"
)
result = evaluate_rule(expression, provider, precondition="{v_C_01.00}")

print(result.status, result.counts)
for outcome in result.failures:
    print(outcome.key, explain(result.evaluation, expression, outcome.key).summary)

failed {'passed': 1, 'failed': 1, 'null': 0}
{'c': '0020'} 1,204 should equal 1,211 (1,180 + 31): difference 7, at least 5.5 given the reported precision

Column 0010 adds up; in column 0020, 1,204 is 7 short of 1,180 + 31, more than the rounding of three whole-unit amounts (±1.5) can explain.

Toolkit¶

Part of the dpm toolkit, three Python packages that work together:

Package	What it does
	Parse and evaluate DPM-XL validation rules (this package)
	Read, write and check xBRL-CSV and XBRL-XML reports
	Generate test reports that satisfy DPM-XL rules

The toolkit tour takes one report through all three: generated with dpm-synth, read with dpm-xbrl, then evaluated and explained with dpm-xl.

Where next¶