You can create and store a custom data series which consists of a list of dates and their associated values. You can then use these series in your rules as explained here . There are 3 operations to create or update, delete or upload data to a custom data series.

data_series_create_update(params)

Create or update a data series. To create a new series omit the id parameter. The id of the newly created series is returned.

Args:
    params: {
    name : 'Name of Series',
    # ID of the data series to update, omit to create new one
    id : N,
    # optional parameters
    description : 'Series description optional'
    }

data_series_delete(id)

Deletes a data series and the data associated with it.

Args:
    id: id of the series

data_series_info(id | name)

Deletes a data series and the data associated with it.

Args:
    id: Series ID.
    or
    name: Series name

Returns:
    An object containing the basic data series info.

Examples:
    >>> client.data_series_info(name='Data series name')
    DataSeriesInfoResult(dataSeriesId=123, name='Data series name')

data_series_upload( id, data, [existing_data, date_format, decimal_separator, ignore_errors, ignore_duplicates, contains_header_row)

Upload the series data. Data must be tabular with date and value in each row.

Uploads delimited content to the specified data series.

Args:
    series_id: Unique identifier of the data series.
    data: Delimited content string or file-like containing 
          delimited content. Must not exceed 100 MB.
    existing_data: Policy for dealing with collisions against 
          stored dates. Defaults to ``overwrite``.
          - ``overwrite``: Overwrite stored values.
          - ``skip``: Retaine stored values.
          - ``delete``: Clear before storing uploaded data.
    date_format: Date format. Defaults to ``yyyy-mm-dd``.
    decimal_separator: Decimal separator. Defaults to period. 
          If comma is used, the thousands separator, if used, 
          is assumed to be period.
    ignore_errors: If ``True``, lines in the data with errors 
          will be silently discarded.
    ignore_duplicates: If ``True``, additional occurrences of 
          a date in the data are skipped.
    contains_header_row: If ``True``, the first line of the 
          uploaded data will be skipped.

Data:

The data must contain dates in the first column and values in the second column. If the data includes a header row, the names are not processed.

The ``value`` column may specify ``na``, ``nan``, or ``null`` (case-insensitive) to clear a prior value on an observation date.

Example input:
    date,value
    2026-01-31,1.25
    2026-02-28,na

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