PARTICIPANTACCOUNT

PARTICIPANTACCOUNT shows financial details on participants.

PARTICIPANTACCOUNT data is confidential to the relevant participant.

Source

PARTICIPANTACCOUNT updates as new participants register or existing participants change details.

Column Schema

PARTICIPANTID

Unique participant identifier

For more information, see the dedicated documentation page.

SQL Type: VARCHAR2(10) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

ABN

Australian Business Number

SQL Type: VARCHAR2(20) Polars Type: str

This column is not mandatory.

ACCOUNTNAME

Name of the account

SQL Type: VARCHAR2(80) Polars Type: str

This column is not mandatory.

ACCOUNTNUMBER

Account number

SQL Type: VARCHAR2(16) Polars Type: str

This column is not mandatory.

AUTHORISEDBY

User authorising record

SQL Type: VARCHAR2(15) Polars Type: str

This column is not mandatory.

AUTHORISEDDATE

Authorised date

Warning

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

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

This column is not mandatory.

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

BANKNAME

Bank name

SQL Type: VARCHAR2(16) Polars Type: str

This column is not mandatory.

BANKNUMBER

Bank number

SQL Type: NUMBER(10,0) Polars Type: pl.Int64

This column is not mandatory.

BRANCHNAME

Branch name

SQL Type: VARCHAR2(16) Polars Type: str

This column is not mandatory.

BRANCHNUMBER

Branch number

SQL Type: NUMBER(10,0) Polars Type: pl.Int64

This column is not mandatory.

BSBNUMBER

BSB number

SQL Type: VARCHAR2(20) Polars Type: str

This column is not mandatory.

EFFECTIVEDATE

Date record authorised

Warning

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

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

This column is not mandatory.

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

LASTCHANGED

Last date and time record changed

For more information, see the dedicated documentation page.

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

This column is not mandatory.

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

NEMMCOCREDITACCOUNTNUMBER

AEMO credit account number

SQL Type: NUMBER(10,0) Polars Type: pl.Int64

This column is not mandatory.

NEMMCODEBITACCOUNTNUMBER

AEMO debit account number

SQL Type: NUMBER(10,0) Polars Type: pl.Int64

This column is not mandatory.

New Data Timing

When new participants register or existing participants details are changed

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("AUTHORISEDDATE", "EFFECTIVEDATE", "LASTCHANGED").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    .cast(
        {
            "PARTICIPANTID": pl.Categorical,
            "ABN": str,
            "ACCOUNTNAME": str,
            "ACCOUNTNUMBER": str,
            "AUTHORISEDBY": str,
            "BANKNAME": str,
            "BANKNUMBER": int,  # or pl.Int64
            "BRANCHNAME": str,
            "BRANCHNUMBER": int,  # or pl.Int64
            "BSBNUMBER": str,
            "NEMMCOCREDITACCOUNTNUMBER": int,  # or pl.Int64
            "NEMMCODEBITACCOUNTNUMBER": int,  # or pl.Int64
        }
    )
    # Deduplicate
    # We want one row per ['PARTICIPANTID']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["PARTICIPANTID"], keep="last")
)

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:

• PARTICIPANTID

The transaction type is "INSERT-UPDATE", which means that you will likely see duplicate data with different values.

You should deduplicate by sorting based on LASTCHANGED, within each group of primary keys.

PDR Loader configuration is:

Report Name	Report Subtype	Version
PARTICIPANT_REGISTRATION	PARTICIPANTACCOUNT	1

This means that the CSV files start with metadata columns:

I,PARTICIPANT_REGISTRATION,PARTICIPANTACCOUNT,1,...
D,PARTICIPANT_REGISTRATION,PARTICIPANTACCOUNT,1,...

ROW_FILTER_TYPE is LASTCHANGED

Indexes

• LASTCHANGED

Links

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: PARTICIPANT_REGISTRATION

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