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SET_WDR_TRANSACT

Settlements WDR transactions summary

Column Schema

COUNTERPARTYPARTICIPANTID

Unique identifier for the counter participant id.

SQL Type: VARCHAR2(20) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

PARTICIPANTID

Unique identifier for a participant

For more information, see the dedicated documentation page.

SQL Type: VARCHAR2(20) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

PARTICIPANTROLEID

Participant role identifier - FRMP or DRSP

The SQL type is VARCHAR2(20). This is actually a factor/enum, with values:

  • FRMP

  • DRSP

This column is mandatory, and a primary key.

PERIODID

Trading interval identifier with Period 1 being the first TI for the calendar day, that is the interval ending 00:05

SQL Type: NUMBER(3,0) Polars Type: pl.Int16

This column is mandatory, and a primary key.

REGIONID

Unique identifier for the region to which the TNI belongs on the settlement date

For more information, see the dedicated documentation page.

The SQL type is VARCHAR2(20). This is actually a factor/enum, with values:

  • QLD1

  • VIC1

  • TAS1

  • NSW1

  • SA1

  • SNOWY1

This column is mandatory, and a primary key.

SETTLEMENTDATE

Settlement date

Warning

Despite what the name suggests, this is a datetime, not a date.

SQL Type: DATE Polars Type: pl.Datetime()

This column is mandatory, and a primary key.

Example: 2026/01/20 00:05:00

SETTLEMENTRUNNO

Settlement run number

SQL Type: NUMBER(3,0) Polars Type: pl.UInt16

This column is mandatory, and a primary key.

TRANSACTIONAMOUNT

Aggregate WDR transaction amount for the participant and counterparty in the settlement interval

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is not mandatory.

New Data Timing

Posting a billing run

Code Examples

Polars

Casting is enums, categoricals etc is not essential, but may speed up parsing. Swapping int for pl.UInt16, pl.Int32 would also speed up parsing. However some of the datatypes are inferred, so they may be wrong.

This code deduplicates for you. Deduplication is normally only required across multiple months.

This code assumes that all columns are present, which may not be true for older files.

import polars as pl
import polars.selectors as cs

# e.g. "2029/03/15 00:05:00"
dt_s_fmt = "%Y/%m/%d %H:%M:%S"

csv_paths = ["SOMEFILE.CSV", "OTHERFILE.CSV"]

lf = (
    pl.concat(
        [
            pl.scan_csv(
                csv_path,  # must be unzipped beforehand
                skip_rows=1,  # skip the header
                infer_schema=False,  # much faster
            )
            .filter(cs.by_index(0) == "D")  # drop the footer
            .select(~cs.by_index(range(4)))  # drop metadata columns
            for csv_path in csv_paths
        ],
        how="diagonal_relaxed",
    )
    .with_columns(
        pl.col("SETTLEMENTDATE").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "COUNTERPARTYPARTICIPANTID": pl.Categorical,
            "PARTICIPANTID": pl.Categorical,
            "PARTICIPANTROLEID": pl.Enum(["FRMP", "DRSP"]),
            "PERIODID": int,  # or pl.Int16
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "SETTLEMENTRUNNO": int,  # or pl.UInt16
            "TRANSACTIONAMOUNT": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['COUNTERPARTYPARTICIPANTID', 'PARTICIPANTID', 'PARTICIPANTROLEID', 'PERIODID', 'REGIONID', 'SETTLEMENTDATE', 'SETTLEMENTRUNNO']
    .unique(
        subset=[
            "COUNTERPARTYPARTICIPANTID",
            "PARTICIPANTID",
            "PARTICIPANTROLEID",
            "PERIODID",
            "REGIONID",
            "SETTLEMENTDATE",
            "SETTLEMENTRUNNO",
        ],
        keep="any",
    )
)

Nemosis

This table is not yet available in Nemosis.

Availability

Visibility: Private

This table is not available in the public monthly MMSDM archive. Is is only available to registered market participants over FTP inside a VPN.

Deduplication and PDR Loader Configuration

The primary keys are:

The transaction type is "insert", which means that you will usually not see duplicate data. If you do, you can deduplicate based on the primary keys, without sorting first. Rows from different files with the same primary keys should have the same values.

PDR Loader configuration is:

Report Name Report Subtype Version
SETTLEMENTS WDR_TRANSACT 1

This means that the CSV files start with metadata columns:

I,SETTLEMENTS,WDR_TRANSACT,1,...
D,SETTLEMENTS,WDR_TRANSACT,1,...

This page as JSON (schema documented here, but may change)

Download a JSON Schema specification for this table.

Official AEMO schema documentation

Other Information

Package: SETTLEMENT_DATA