BILLING_CASHSEC_INTEREST_PAY
Indicates the amount of cash security interest payment the participant receives
Column Schema
BILLRUNNO
The Billing RunNo
SQL Type: NUMBER(3,0)
Polars Type: pl.UInt16
This column is mandatory, and a primary key.
CASH_SECURITY_ID
The Cash Security ID
SQL Type: VARCHAR2(20)
Polars Type: str
This column is mandatory, and a primary key.
CONTRACTYEAR
ContractYear/ WeekNo/ BillRunNo for the Interest payment
SQL Type: NUMBER(4,0)
Polars Type: pl.UInt16
This column is mandatory, and a primary key.
WEEKNO
The Billing WeekNo
Warning
There may be 53 weeks in a year, depending on how weeks align to the year start/end.
SQL Type: NUMBER(3,0)
Polars Type: pl.UInt16
This column is mandatory, and a primary key.
CASH_AMOUNT
Dollar amount of the cash security
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
INTEREST_ACCT_ID
The interest account ID for calculating the interest payment
SQL Type: VARCHAR2(20)
Polars Type: str
This column is not mandatory.
INTEREST_AMOUNT
Interest amount calculated for the billing week
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
LASTCHANGED
The last changed date time of the record
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
PARTICIPANTID
Unique identifier for the Participant lodging the cash security
For more information, see the dedicated documentation page.
SQL Type: VARCHAR2(20)
Polars Type: pl.Categorical
This column is not mandatory.
New Data Timing
On change
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("LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
)
.cast(
{
"BILLRUNNO": int, # or pl.UInt16
"CASH_SECURITY_ID": str,
"CONTRACTYEAR": int, # or pl.UInt16
"WEEKNO": int, # or pl.UInt16
"CASH_AMOUNT": pl.Float64,
"INTEREST_ACCT_ID": str,
"INTEREST_AMOUNT": pl.Float64,
"PARTICIPANTID": pl.Categorical,
}
)
# Deduplicate
# We want one row per ['BILLRUNNO', 'CASH_SECURITY_ID', 'CONTRACTYEAR', 'WEEKNO']
# Tie-breaker: greatest value of LASTCHANGED
.sort(LASTCHANGED, descending=False)
.unique(
subset=["BILLRUNNO", "CASH_SECURITY_ID", "CONTRACTYEAR", "WEEKNO"], 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:
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.
| Report Name | Report Subtype | Version |
|---|---|---|
| BILLING | CASHSEC_INTEREST_PAY | 1 |
This means that the CSV files start with metadata columns:
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: BILLING_RUN