TER — MF Schemes
Total Expense Ratio data for mutual fund schemes. Fetch as JSON / polars DataFrame or download the raw Excel file.
Data dictionary: TER field reference →
amfipy.ter.TERClient
Sync client for TER data.
months(year='2025-2026')
Return available months for a financial year.
Returns list of {"MonthYear": "March-2026", "MonthNumber": "03-2026"}.
First item is the most recent month.
sub_categories(fund_type=ALL_TYPE, category=ALL_CAT)
Return sub-categories for a given fund type and category.
fetch(month=None, year='2025-2026', mf_id=ALL, category=ALL_CAT, fund_type=ALL_TYPE, as_df=False)
Fetch TER data as JSON (list of dicts) or polars DataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
month
|
str | None
|
MM-YYYY string. Defaults to latest available month. |
None
|
year
|
str
|
Financial year for month lookup (only used when month=None). |
'2025-2026'
|
mf_id
|
str | int
|
"All" or numeric MF ID. |
ALL
|
category
|
str
|
"-1"=All | "Equity Scheme" | "Debt Scheme" | "Hybrid Scheme" | "Other Scheme" | "Solution Oriented Scheme" |
ALL_CAT
|
fund_type
|
str
|
"-1"=All | "Open Ended" | "Close Ended" | "Interval Fund" |
ALL_TYPE
|
as_df
|
bool
|
Return a polars DataFrame (requires |
False
|
fetch_range(months, mf_id=ALL, category=ALL_CAT, fund_type=ALL_TYPE, as_df=False)
Fetch TER data for multiple months.
Returns {"03-2026": <data>, "02-2026": <data>, ...}.
download_excel(month=None, year='2025-2026', mf_id=ALL, category=ALL_CAT, fund_type=ALL_TYPE)
Download TER data as raw Excel bytes.
Example::
data = client.download_excel(month="03-2026")
Path("ter_march.xlsx").write_bytes(data)