Dear Support Team,
Our goal is to retrieve a clean, consolidated time series of quarterly financial reports using multiple fields (e.g., "TR.TotalRevenue.date", "TR.TotalRevenue", "TR.CostOfRevenue", "TR.GrossProfit", "TR.TotalOperatingExpense", etc.). Ideally, we expect four records per year, each representing one quarter, with no duplicate records for the same date.
However, we have encountered an issue: when we retrieve these fields using a shared date field—such as "TR.TotalRevenue.date" combined with "TR.TotalOperatingExpense"—we observe multiple duplicate rows for the same date. Furthermore, the values sometimes differ from those obtained when using the field with its corresponding date field—for example, "TR.TotalOperatingExpense" together with "TR.TotalOperatingExpense.date".
We attempted to retrieve the data via the lseg.data Python API, querying each field individually. Nevertheless, we still receive multiple records for the same quarter, where we expect only a single consolidated value per field.
For example:
```
fields = ["TR.EarningsRetentionRatePct", "TR.EarningsRetentionRatePct.date"]
df = ld.get_data(
universe=['MWA'],
fields=fields,
parameters = {'SDate':'2000-01-01', 'EDate':'2025-01-01', 'Frq':'FQ', 'Period':'FQ0', 'Adjusted': '0', 'Curn': 'USD', 'ReportType': 'MR'}
)
```
returns this output:
Earnings Retention Rate, % Date
3 1.0 2005-09-30
4 0.882353 2005-09-30
5 0.888268 2005-09-30
9 0.880682 2007-03-31
10 0.608696 2007-03-31
11 0.542373 2007-09-30
…
As you can see, the same reporting date appears multiple times with different values. We expected only one value per field per reporting date.
Could you please advise on the correct approach to obtain a consistent and non-duplicated time series of quarterly financials across multiple fields?
