MNSP_FILETRK
MNSP_FILETRK shows all MNSPOFFERS transmitted to the MMS system.
Deprecation Warning
This table is in the HISTORICAL TABLES package, so is likely deprecated.
MNSP_FILETRK is confidential to the relevant participant.
Source
MNSP_FILETRK updates for every submitted MNSP bid.
Volume
4000 per year, being one per bid containing an MNSP bid
Column Schema
FILENAME
File name for default bids, bids, rebids, re-offers or meter files, as appropriate to table
SQL Type: VARCHAR2(40)
Polars Type: str
This column is mandatory, and a primary key.
OFFERDATE
The actual date and time the bid file was submitted by the participant
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
PARTICIPANTID
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.
SETTLEMENTDATE
Market Date from which bid is active
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
ACKFILENAME
Acknowledge file name for bids, rebids
SQL Type: VARCHAR2(40)
Polars Type: str
This column is not mandatory.
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
STATUS
Load status [SUCCESSFUL/CORRUPT]
SQL Type: VARCHAR2(10)
Polars Type: str
This column is not mandatory.
New Data Timing
A submitted MNSP bid
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("OFFERDATE", "SETTLEMENTDATE", "LASTCHANGED").str.strptime(
pl.Datetime, dt_s_fmt
),
)
.cast(
{
"FILENAME": str,
"PARTICIPANTID": pl.Categorical,
"ACKFILENAME": str,
"STATUS": str,
}
)
# Deduplicate
# We want one row per ['FILENAME', 'OFFERDATE', 'PARTICIPANTID', 'SETTLEMENTDATE']
# Tie-breaker: greatest value of ['LASTCHANGED']
.sort(["LASTCHANGED"], descending=False)
.unique(
subset=["FILENAME", "OFFERDATE", "PARTICIPANTID", "SETTLEMENTDATE"], 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 |
|---|---|---|
| BID | MNSP_FILETRK | 1 |
This means that the CSV files start with metadata columns:
Indexes
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: HISTORICAL TABLES