BANK_GUARANTEE
Indicates the amount of bank guarantee the Company has with AEMO
Column Schema
GUARANTEE_ID
The Bank Guarantee ID
SQL Type: VARCHAR2(30)
Polars Type: str
This column is mandatory, and a primary key.
COMMENCEMENT_DATE
Date AEMO received the bank guarantee details
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
COMPANY_ID
Company ID owning the Bank Guarantee
SQL Type: VARCHAR2(20)
Polars Type: str
This column is not mandatory.
CREDIT_PROVIDER
Bank Guarantee Provider Id
SQL Type: VARCHAR2(20)
Polars Type: str
This column is not mandatory.
GUARANTEE_AMOUNT
Dollar Amount of the bank guarantee
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
Participant Id belonging to the company (Even though BG belongs to a Company, Participant Id is also added as all reports are published to the Participants)
For more information, see the dedicated documentation page.
SQL Type: VARCHAR2(20)
Polars Type: pl.Categorical
This column is not mandatory.
RETURN_DATE
Date of Return
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
TERMINATION_DATE
Date of Termination
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
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(
"COMMENCEMENT_DATE", "LASTCHANGED", "RETURN_DATE", "TERMINATION_DATE"
).str.strptime(pl.Datetime, dt_s_fmt),
)
.cast(
{
"GUARANTEE_ID": str,
"COMPANY_ID": str,
"CREDIT_PROVIDER": str,
"GUARANTEE_AMOUNT": pl.Float64,
"PARTICIPANTID": pl.Categorical,
}
)
# Deduplicate
# We want one row per ['GUARANTEE_ID']
# Tie-breaker: greatest value of LASTCHANGED
.sort(LASTCHANGED, descending=False)
.unique(subset=["GUARANTEE_ID"], 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-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.
| Report Name | Report Subtype | Version |
|---|---|---|
| PRUDENTIAL | BANK_GUARANTEE | 1 |
This means that the CSV files start with metadata columns:
ROW_FILTER_TYPE is 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: PRUDENTIALS