SRA_PRUDENTIAL_COMP_POSITION

The prudential position of each company at the date and time of a specific prudential run

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.

PRUDENTIAL_DATE

The prudential date of the run.

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

PRUDENTIAL_RUNNO

The run number for the prudential date

SQL Type: NUMBER(8) Polars Type: pl.UInt32

This column is mandatory, and a primary key.

PRUDENTIAL_EXPOSURE_AMOUNT

Current Prudential Exposure of the Auction Participant including Offers

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

This column is not mandatory.

TRADING_LIMIT

The Trading Limit of the company at the time of the prudential run

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

This column is not mandatory.

TRADING_MARGIN

The amount of Trading Margin available to the Auction Participant to trade (including Offered Units and Cancelled Units). Equal to TRADING_LIMIT minus PRUDENTIAL_EXPOSURE_AMOUNT.

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

This column is not mandatory.

New Data Timing

On successful completion of a prudential 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("PRUDENTIAL_DATE").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "PARTICIPANTID": pl.Categorical,
            "PRUDENTIAL_RUNNO": int,  # or pl.UInt32
            "PRUDENTIAL_EXPOSURE_AMOUNT": pl.Float64,
            "TRADING_LIMIT": pl.Float64,
            "TRADING_MARGIN": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['PARTICIPANTID', 'PRUDENTIAL_DATE', 'PRUDENTIAL_RUNNO']
    .unique(subset=["PARTICIPANTID", "PRUDENTIAL_DATE", "PRUDENTIAL_RUNNO"], 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:

• PARTICIPANTID

• PRUDENTIAL_DATE

• PRUDENTIAL_RUNNO

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
IRAUCTION	SRA_PRUDENTIAL_COMP_POSITION	1

This means that the CSV files start with metadata columns:

I,IRAUCTION,SRA_PRUDENTIAL_COMP_POSITION,1,...
D,IRAUCTION,SRA_PRUDENTIAL_COMP_POSITION,1,...

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: IRAUCTION

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