SRA_PRUDENTIAL_EXPOSURE

Records details of the Prudential Exposure of an SRA Auction Participant

Column Schema

FROMREGIONID

The source Region identifier for the Directional Interconnector

For more information, see the dedicated documentation page.

The SQL type is VARCHAR2(10). This is actually a factor/enum, with values:

• QLD1

• VIC1

• TAS1

• NSW1

• SA1

This column is mandatory, and a primary key.

INTERCONNECTORID

The identifier for the Directional Interconnector

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

This column is mandatory, and a primary key.

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.

SRA_QUARTER

Contract Relevant Quarter

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

This column is mandatory, and a primary key.

SRA_YEAR

AEMO Contract Year number starting the week beginning 1 January

SQL Type: NUMBER(4) Polars Type: pl.UInt16

This column is mandatory, and a primary key.

AUCTIONID

Unique identifier for the Auction having the Offer. Has a null value when no Offer is made for the Relevant Quarter

SQL Type: VARCHAR2(30) Polars Type: str

This column is not mandatory.

AVERAGE_CANCELLATION_PRICE

Calculated average cancellation price for product.

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

This column is not mandatory.

AVERAGE_PURCHASE_PRICE

Calculated Average Purchase Price for the Product

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

This column is not mandatory.

CANCELLATION_VOLUME

Calculated cancellation volume for product.

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

This column is not mandatory.

MAX_TRANCHE

The largest Tranche where the Unit was either sold or offered. Used in the calculation of the Average Purchase Price for the Trading Position of the Product. The most recent Tranche where Units were cancelled or offered (if the Offer is below the Average Purchase Price)

SQL Type: NUMBER(2) Polars Type: pl.UInt8

This column is not mandatory.

OFFER_SUBMISSIONTIME

Timestamp of the Offer File submitted by the Auction Participant. Has a null value when no Offer is made for the Relevant Quarter

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

This column is not mandatory.

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

TRADING_POSITION

Calculated trading position for product.

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

This column is not mandatory.

New Data Timing

After each settlement 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", "OFFER_SUBMISSIONTIME").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    .cast(
        {
            "FROMREGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1"]),
            "INTERCONNECTORID": pl.Categorical,
            "PARTICIPANTID": pl.Categorical,
            "PRUDENTIAL_RUNNO": int,  # or pl.UInt32
            "SRA_QUARTER": int,  # or pl.Int16
            "SRA_YEAR": int,  # or pl.UInt16
            "AUCTIONID": str,
            "AVERAGE_CANCELLATION_PRICE": pl.Float64,
            "AVERAGE_PURCHASE_PRICE": pl.Float64,
            "CANCELLATION_VOLUME": pl.Float64,
            "MAX_TRANCHE": int,  # or pl.UInt8
            "TRADING_POSITION": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['FROMREGIONID', 'INTERCONNECTORID', 'PARTICIPANTID', 'PRUDENTIAL_DATE', 'PRUDENTIAL_RUNNO', 'SRA_QUARTER', 'SRA_YEAR']
    .unique(
        subset=[
            "FROMREGIONID",
            "INTERCONNECTORID",
            "PARTICIPANTID",
            "PRUDENTIAL_DATE",
            "PRUDENTIAL_RUNNO",
            "SRA_QUARTER",
            "SRA_YEAR",
        ],
        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:

• FROMREGIONID

• INTERCONNECTORID

• PARTICIPANTID

• PRUDENTIAL_DATE

• PRUDENTIAL_RUNNO

• SRA_QUARTER

• SRA_YEAR

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_EXPOSURE	1

This means that the CSV files start with metadata columns:

I,IRAUCTION,SRA_PRUDENTIAL_EXPOSURE,1,...
D,IRAUCTION,SRA_PRUDENTIAL_EXPOSURE,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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