OFFERFILETRK

OFFERFILETRK shows an audit trail of all bid files submitted containing energy bids, including corrupt bids/rebids.

Deprecation Warning

This table is in the HISTORICAL TABLES package, so is likely deprecated.

Status

OFFERFILETRK is obsolete. Please see BIDOFFERFILETRK.

Source

OFFERFILETRK is obsolete.

Column Schema

FILENAME

Load file name

SQL Type: VARCHAR2(40) Polars Type: str

This column is mandatory, and a primary key.

OFFERDATE

Date file offered

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

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.

ACKFILENAME

Acknowledge file name

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.

ENDDATE

Not used

Warning

Despite what the name suggests, this is a datetime, not a date.

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

This column is unused.

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

New Data Timing

As bids are processed

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", "LASTCHANGED", "ENDDATE").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    .cast(
        {
            "FILENAME": str,
            "PARTICIPANTID": pl.Categorical,
            "ACKFILENAME": str,
            "STATUS": str,
        }
    )
    # Drop deprecated/unused columns
    .drop("ENDDATE")
    # Deduplicate
    # We want one row per ['FILENAME', 'OFFERDATE', 'PARTICIPANTID']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["FILENAME", "OFFERDATE", "PARTICIPANTID"], 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:

FILENAME
OFFERDATE
PARTICIPANTID

PDR Loader configuration does not mention this table, so it is unclear how to choose which row to keep when there are multiple rows for a given set of primary keys.

You should probably take the row with the maximum LASTCHANGED.