amfipy 0.3.0

Python client for AMFI India — NAV, TER, Fund Performance, Tracking Error, Risk Parameters and more

amfipy

Python client for AMFI India — the Association of Mutual Funds in India.

Provides clean, typed access to NAV data, TER (Total Expense Ratio), Fund Performance, Tracking Error, Risk Parameters, New Fund Offers, AMFI publications, and more. Both sync and async interfaces are included.

📖 Full documentation → skamalj.github.io/amfipy
    Quick Start & User Guide · API Reference · Data Dictionary

Data Coverage

Data	Client method	Excel / file	as_df=True	Notes
NAV — Latest	client.nav.latest()	—	✅	as_df flattens nested type → category → scheme
NAV — Latest by category	client.nav.latest_by_category()	—	—	Category-level summary
NAV — All schemes for date	client.nav.all_navs_for_date()	—	✅	One row per scheme
NAV — History	client.nav.history()	—	✅	as_df auto-flattens nested nav_groups
NAV — High / Low	client.nav.high_low()	—	✅	period_type: Month | Annual
NAV — Compare two dates	client.nav.compare()	—	✅
NAV — Flat file	client.nav.download_file()	✅ (txt)	—	Full NAVAll.txt for any date; raw bytes
TER — MF schemes	client.ter.fetch() / download_excel()	✅ xlsx	✅	Excel = single sheet; JSON = flat list
TER — SIF schemes	client.sif_ter.fetch() / download_excel()	✅ xlsx	✅	Same interface as TER; uses sif_id
Fund Performance	client.fund_performance.fetch()	—	✅	No server-side Excel (client-side only)
Tracking Error	client.tracking.error()	—	✅	Includes Benchmark column per scheme
Tracking Difference	client.tracking.difference()	—	✅
Risk Parameters	client.risk_parameters.fetch()	—	✅	Std dev, beta, Sharpe; requires category_id
NFO	client.nfo.fetch()	—	✅	as_df flattens AMC-grouped structure
Publications — Monthly	client.publications.monthly_flat() + download_file(url)	✅ xls	—	Returns metadata + URLs; xls has multiple data sheets
Publications — Quarterly	client.publications.quarterly_flat() + download_file(url)	✅ xls	—	Returns metadata + URLs
Publications — Commission	client.publications.commission() + download_file(url)	✅ pdf	—	Annual commission disclosure PDFs
CDMDF NAV	client.cdmdf.history()	—	✅	Corporate Debt Market Dev Fund NAV
AUM — Average (fund-wise)	client.aum.average_aum_fundwise() / _excel()	✅ xlsx	✅	Quarterly; one row per AMC
AUM — Average (scheme-wise)	client.aum.average_aum_schemewise() / _excel()	✅ xlsx	✅	str_type: Categorywise | Typewise
AUM — Disclosure by category	client.aum.disclosure_by_category()	✅ (URL in data)	✅	Quarterly; each record has pdfURL/excelURL
AUM — Disclosure by geography	client.aum.disclosure_by_geography()	✅ (URL in data)	✅	Quarterly
AUM — Age-wise / Folio	client.aum.agewise_folio() / _excel()	✅ xlsx	✅	as_df flattens nested scheme-type + age-band rows
AUM — State-wise classified	client.aum.statewise() / _excel()	✅ xlsx	✅
AUM — Scheme-category-wise	client.aum.scheme_catwise() / _excel()	✅ xlsx	✅
AUM — Bifurcation (Direct Plan)	client.aum.bifurcation() / _excel()	✅ xlsx	✅	Direct vs adviser vs PMS vs DIY split
Investor Complaints — Monthly	client.other_data.investor_complaints_monthly()	—	✅	4 parts (A–D); fetch each separately
Investor Complaints — Yearly	client.other_data.investor_complaints_yearly()	—	✅	Historical data up to FY 2021
AMC Directors	client.other_data.amc_directors()	—	✅
Trustees	client.other_data.trustees()	—	✅
Group Companies	client.other_data.group_companies_all()	—	✅	~599 records; auto-paginated
Scheme Dividends	client.other_data.scheme_dividends()	—	✅	Call populate_schemes() first to get scheme_id
Scheme NAV variants	client.other_data.scheme_data()	—	✅	One row per plan/option (ISIN, price, date)
Scheme Profile	client.other_data.scheme_details()	—	—	Returns single dict: objective, load, min amt, etc.
Categorisation of Stocks	client.research.categorisation_of_stocks() + download_categorisation_file(url)	✅ xlsx + pdf	✅	Bi-annual avg market cap data from 2017; as_df = flat year/period rows

Excel / file — ✅ means raw bytes are returned (save with .write_bytes()).
as_df=True — requires pip install amfipy[polars]. Convert to Spark with .to_arrow().

Features

• Sync + Async — AMFIClient and AsyncAMFIClient with identical APIs
• Typed & documented — full docstrings, type hints throughout
• Polars support — optional as_df=True returns a polars DataFrame (pip install amfipy[polars])
• Raw bytes default — Excel/file downloads return bytes; save wherever you want
• Historical ranges — fetch_range() on every module for batch multi-period pulls
• No heavy deps — only httpx required

Installation

pip install amfipy

With polars DataFrame support:

pip install amfipy[polars]

Quick Start

from amfipy import AMFIClient

client = AMFIClient()

# Latest NAV for all funds
nav = client.nav.latest()

# Latest NAV as a flat DataFrame
nav_df = client.nav.latest(as_df=True)

# TER Excel for March 2026 (all funds)
excel_bytes = client.ter.download_excel(month="03-2026")
open("ter_march_2026.xlsx", "wb").write(excel_bytes)

# Fund performance — Open Ended Equity, all AMCs
perf = client.fund_performance.fetch(category=1, sub_category=1)

# Tracking error for all funds on 31-Mar-2026
error = client.tracking.error(date="31-mar-2026")

Async

import asyncio
from amfipy import AsyncAMFIClient

async def main():
    client = AsyncAMFIClient()
    nav   = await client.nav.latest()
    excel = await client.ter.download_excel(month="03-2026")

asyncio.run(main())

---

Module Reference

client.nav — NAV Data

# Latest NAV — all funds
nav = client.nav.latest()
# → {"data": [{type, categories: [...]}], "count": N}

# Latest NAV — single AMC (62 = 360 ONE Mutual Fund)
nav = client.nav.latest(mf_id=62, fund_type="Open Ended")

# Latest NAV as flat polars DataFrame (one row per scheme)
nav_df = client.nav.latest(as_df=True)

# Category-level summary
cat = client.nav.latest_by_category(mf_id="all")

# Scheme list for an AMC (needed for history calls)
schemes = client.nav.schemes(mf_id=85)
# → [{"nav_id": "154043", "nav_name": "Abakkus Flexi Cap Fund - Direct - Growth", "MF_ID": "85"}, ...]

# All scheme NAVs for a specific date
all_navs = client.nav.all_navs_for_date(date="2026-03-31")

# Historical NAV for a scheme over a period (use nav_id from schemes() as sd_id)
hist = client.nav.history(sd_id=154043, from_date="2026-01-01", to_date="2026-03-31")
# → {"mf_name": "...", "scheme_name": "...", "date_range": "...", "nav_groups": [...]}
hist_df = client.nav.history(sd_id=154043, from_date="2026-01-01", to_date="2026-03-31", as_df=True)
# → polars DataFrame with columns: mf_name, scheme_name, nav_name, date, nav, ...

# High/Low NAV (period_type: "Month"|"Annual", nav_type: "high"|"Low"|"Both")
hl = client.nav.high_low(sd_id=154043, from_date="2026-01-01", to_date="2026-03-31")
hl = client.nav.high_low(sd_id=154043, from_date="2026-01-01", to_date="2026-03-31", period_type="Annual", nav_type="high")

# Compare NAV on two dates
cmp = client.nav.compare(sd_id=154043, date1="2026-01-01", date2="2026-03-31")

# Download full NAV flat file (all schemes, one date) — returns bytes
nav_file = client.nav.download_file(date="31-Mar-2026")
open("navs.txt", "wb").write(nav_file)

# Batch historical fetch across multiple ranges
ranges = [("2026-01-01","2026-01-31"), ("2026-02-01","2026-02-28")]
results = client.nav.fetch_range(sd_id=154043, months=ranges)

Fund type values: "" (all), "Open Ended", "Close Ended", "Interval Fund"

---

client.ter — TER for MF Schemes

# List available months for a financial year
months = client.ter.months(year="2025-2026")
# → [{"MonthYear": "March-2026", "MonthNumber": "03-2026"}, ...]

# Fetch TER data as JSON
data = client.ter.fetch(month="03-2026")
data = client.ter.fetch(month="03-2026", mf_id=62, category="Equity Scheme", fund_type="Open Ended")
data = client.ter.fetch(month="03-2026", as_df=True)  # polars DataFrame

# Fetch latest month automatically
data = client.ter.fetch(year="2025-2026")  # picks months()[0]

# Batch fetch across months
results = client.ter.fetch_range(months=["03-2026", "02-2026", "01-2026"])
# → {"03-2026": <data>, "02-2026": <data>, "01-2026": <data>}

# Download Excel (raw bytes)
excel = client.ter.download_excel(month="03-2026")
excel = client.ter.download_excel(month="03-2026", category="Equity Scheme")
open("ter.xlsx", "wb").write(excel)

# Sub-category lookup
subcats = client.ter.sub_categories(fund_type="Open Ended", category="Equity Scheme")

Category values: "-1" (All), "Equity Scheme", "Debt Scheme", "Hybrid Scheme", "Other Scheme", "Solution Oriented Scheme"
Fund type values: "-1" (All), "Open Ended", "Close Ended", "Interval Fund"

---

client.sif_ter — TER for SIF Schemes

Same interface as ter, but uses SIF endpoints and sif_id instead of mf_id:

months = client.sif_ter.months(year="2025-2026")
data   = client.sif_ter.fetch(month="03-2026", sif_id="All")
excel  = client.sif_ter.download_excel(month="03-2026")

---

client.fund_performance — Fund Performance

# Get all filter options (maturity types, categories, fund list)
filters = client.fund_performance.filters()
# → {"maturityTypeList": [...], "investmentTypeList": [...], "mutualFundList": [...]}

# Get sub-categories for Equity (category=1)
subcats = client.fund_performance.sub_categories(category=1)

# Fetch performance data
perf = client.fund_performance.fetch(
    maturity_type=1,   # 1=Open Ended, 2=Close Ended
    category=1,        # 1=Equity, 2=Debt, 3=Hybrid, 4=Solution Oriented, 5=Other
    sub_category=1,    # from sub_categories()
    mf_id=0,           # 0=All, or fund ID from filters()["mutualFundList"]
    report_date="07-May-2026",  # "DD-Mon-YYYY", defaults to last business day
)

# As polars DataFrame
perf_df = client.fund_performance.fetch(category=1, sub_category=1, as_df=True)

Note: Excel export is client-side only in the AMFI website; no server download endpoint exists.

---

client.tracking — Tracking Error & Difference

# Tracking Error — date format: "DD-mon-YYYY" (lowercase month)
error = client.tracking.error(date="31-mar-2026")
error = client.tracking.error(date="31-mar-2026", mf_id=62)
error = client.tracking.error(date="31-mar-2026", as_df=True)

# Batch tracking error
errors = client.tracking.error_range(dates=["31-mar-2026", "28-feb-2026"])

# Tracking Difference — date format: "01-Mon-YYYY" (title-case month, always day=01)
diff = client.tracking.difference(month="01-Apr-2026")
diff = client.tracking.difference(month="01-Apr-2026", mf_id="all")

# Batch tracking difference
diffs = client.tracking.difference_range(months=["01-Apr-2026", "01-Mar-2026"])

---

client.risk_parameters — Risk Parameters

# date format: "01-Mon-YYYY" (always day=01)
# category_id: numeric (e.g. 17 = Mid Cap Fund)
risk = client.risk_parameters.fetch(date="01-Mar-2026", category_id=17)
risk = client.risk_parameters.fetch(date="01-Mar-2026", category_id=17, as_df=True)

# Batch
results = client.risk_parameters.fetch_range(
    dates=["01-Mar-2026", "01-Feb-2026"],
    category_id=17,
)

---

client.nfo — New Fund Offers

# Grouped by AMC
nfos = client.nfo.fetch()
# → {"NewFundOffer": [{"MutualFund": "...", "items": [{Scheme_Id, SchemeName, MF_Id}]}], "type": "summary"}

# Flat list
items = client.nfo.flat()
# → [{"Scheme_Id": 14475, "MutualFund": "...", "SchemeName": "...", "MF_Id": 53}, ...]

# As polars DataFrame
df = client.nfo.fetch(as_df=True)

---

client.publications — AMFI Monthly, Quarterly & Commission Disclosure

# Monthly reports — grouped by financial year
monthly = client.publications.monthly()

# Flat list of all monthly entries
flat = client.publications.monthly_flat()
# → [{"Title": "March 2026", "month": "03", "year": "2026",
#     "pdf_url": "...", "excel_url": "...", "financial_year": "..."}, ...]

# Quarterly issues — grouped by financial year
quarterly = client.publications.quarterly()
flat_q = client.publications.quarterly_flat()
# → [{"issue_no": "Issue IV", "period": "(Jan - Mar 2026)", "pdf_url": "...", "excel_url": "..."}, ...]

# Commission disclosure — annual PDF list
commission = client.publications.commission()
# → [{"Period": "FY 2024 - 2025", "URL": "https://portal.amfiindia.com/spages/ACDVol-2024-2025.pdf"}, ...]

# Download any file (PDF or XLS) — returns bytes
xls_bytes = client.publications.download_file(flat[0]["excel_url"])
pdf_bytes = client.publications.download_file(commission[0]["URL"])
open("report.xls", "wb").write(xls_bytes)

---

client.cdmdf — CDMDF NAV

# Historical CDMDF NAV — date format: "DD-mon-YYYY" (lowercase month)
cdmdf = client.cdmdf.history(date="31-mar-2026")
cdmdf = client.cdmdf.history(date="31-mar-2026", as_df=True)

# Batch
results = client.cdmdf.history_range(dates=["31-mar-2026", "28-feb-2026"])

Note: Latest CDMDF NAV is server-rendered on the AMFI page — no JSON API exists for it.

---

client.aum — AUM (Assets Under Management)

Five sub-sections covering all AMFI AUM pages.

# ── Average AUM ──────────────────────────────────────────────────────────────

# List financial years (returns id + financial_year label)
fys = client.aum.financial_years()
# → [{"id": 1, "financial_year": "April 2025 - March 2026"}, ...]

# List quarters within a financial year
periods = client.aum.periods(fy_id=1)
# → {"financial_year": "...", "periods": [{"id": 1, "period": "January - March 2026"}, ...]}

# Fund-wise Average AUM for a quarter
data = client.aum.average_aum_fundwise(fy_id=1, period_id=1)
# → [{"Sr_No": "1", "MutualFundName": "...", "averageAUM": {...}}, ...]

# Scheme-wise Average AUM (str_type: "Categorywise" | "Typewise", mf_id: 0=All)
data = client.aum.average_aum_schemewise(fy_id=1, period_id=1, str_type="Categorywise", mf_id=62)

# Excel downloads (raw bytes)
excel = client.aum.average_aum_fundwise_excel(fy_id=1, period_id=1)
excel = client.aum.average_aum_schemewise_excel(fy_id=1, period_id=1, str_type="Typewise")

# ── AUM–AAUM Disclosure ──────────────────────────────────────────────────────

# Quarterly disclosure by fund category — fyId format: "2025-26", "2024-25"
disc_cat  = client.aum.disclosure_by_category(fy_id="2024-25")
# → [{"Period": "(Oct - Dec 2024)", "pdfURL": "...", "excelURL": "..."}, ...]

disc_geo  = client.aum.disclosure_by_geography(fy_id="2024-25")

# ── Age-wise / Folio Data ────────────────────────────────────────────────────

# Month format: "March-2026" (MonthName-YYYY)
af = client.aum.agewise_folio(month="March-2026")
# → {"data": {"investorClassification": [...], "ageWiseAUM": [...]}, "Month": "March-2026"}

af_df = client.aum.agewise_folio(month="March-2026", as_df=True)  # flattens ageWiseAUM
excel  = client.aum.agewise_folio_excel(month="March-2026")

# ── Classified Average AUM ───────────────────────────────────────────────────

# Date format: "01-mon-yyyy" (lowercase month, always day=01)
# e.g. "01-apr-2026", "01-mar-2026"

# List available months
dates = client.aum.classified_dates()
# → [{"date": "April-2026"}, {"date": "March-2026"}, ...]

# State-wise (mf_id: 0=All)
sw    = client.aum.statewise(date="01-apr-2026", mf_id=0)
# → [{"State": "Andaman and Nicobar Islands", "LiquidSchemes": 0.86, ...}, ...]

# Scheme-category-wise
sc    = client.aum.scheme_catwise(date="01-apr-2026", mf_id=0)

# Excel
excel = client.aum.statewise_excel(date="01-apr-2026")
excel = client.aum.scheme_catwise_excel(date="01-apr-2026")

# ── Bifurcation of AAUM ──────────────────────────────────────────────────────

# Date format: "DD-Mon-YYYY" e.g. "31-Mar-2026"
bif   = client.aum.bifurcation(date="31-Mar-2026")
# → [{"Month_Date": "31-Mar-2026", "TotalAAUMunderDirectPlan": 3904766.42,
#     "AAUMunderRegisteredAdvisers": 537599.97, "AAUMunderPMS": 89942.62,
#     "AAUMunderDIYclients": 3277223.84}]

# Batch
results = client.aum.bifurcation_range(dates=["31-Mar-2026", "28-Feb-2026"])

# Excel
excel = client.aum.bifurcation_excel(date="31-Mar-2026")

---

Common Patterns

Polars DataFrames

Any method with as_df=True returns a polars.DataFrame. Requires pip install amfipy[polars].

import polars as pl
from amfipy import AMFIClient

client = AMFIClient()
df = client.nav.history(sd_id=154043, from_date="2026-01-01", to_date="2026-03-31", as_df=True)
print(df.head())

Async batch fetch

import asyncio
from amfipy import AsyncAMFIClient

async def fetch_all():
    client = AsyncAMFIClient()
    ter, nav, nfo = await asyncio.gather(
        client.ter.download_excel(month="03-2026"),
        client.nav.all_navs_for_date(date="2026-03-31"),
        client.nfo.flat(),
    )
    return ter, nav, nfo

asyncio.run(fetch_all())

Custom httpx settings (proxy, SSL, timeout)

All clients accept **httpx_kwargs forwarded to httpx.Client / httpx.AsyncClient:

from amfipy import AMFIClient

client = AMFIClient(
    proxies={"https://": "http://myproxy:8080"},
    verify=False,
    timeout=120,
)

Using amfipy with Apache Spark and Iceberg

Use as_df=True to get a polars DataFrame directly from amfipy, then convert to a Spark DataFrame via Arrow — no pandas, no manual flattening required.

from amfipy import AMFIClient
from pyspark.sql import SparkSession

# ── 1. Spark session with Iceberg catalog ────────────────────────────────────
spark = (
    SparkSession.builder
    .appName("amfi-iceberg-ingest")
    .config("spark.sql.extensions",
            "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions")
    .config("spark.sql.catalog.local", "org.apache.iceberg.spark.SparkCatalog")
    .config("spark.sql.catalog.local.type", "hadoop")
    .config("spark.sql.catalog.local.warehouse", "/tmp/iceberg-warehouse")
    .getOrCreate()
)

client = AMFIClient()

# ── 2. NAV history → Iceberg ─────────────────────────────────────────────────
# as_df=True returns a flat polars DataFrame (no manual nav_groups unwinding needed)
nav_pl = client.nav.history(
    sd_id=154043, from_date="2026-01-01", to_date="2026-03-31", as_df=True
)
# polars → Arrow → Spark (zero-copy, no pandas)
spark.createDataFrame(nav_pl.to_arrow()).writeTo("local.amfi.nav_history").createOrReplace()

# ── 3. TER data → Iceberg ────────────────────────────────────────────────────
ter_pl = client.ter.fetch(month="03-2026", as_df=True)
spark.createDataFrame(ter_pl.to_arrow()).writeTo("local.amfi.ter_march_2026").createOrReplace()

# ── 4. Tracking error → Iceberg ──────────────────────────────────────────────
tracking_pl = client.tracking.error(date="31-mar-2026", as_df=True)
spark.createDataFrame(tracking_pl.to_arrow()).writeTo("local.amfi.tracking_error").createOrReplace()

# ── 5. AUM bifurcation → Iceberg (append mode) ───────────────────────────────
for date in ["31-Mar-2026", "28-Feb-2026", "31-Jan-2026"]:
    bif_pl = client.aum.bifurcation(date=date, as_df=True)
    spark.createDataFrame(bif_pl.to_arrow()).writeTo("local.amfi.aum_bifurcation").append()

# ── 6. Query the landed data ─────────────────────────────────────────────────
spark.sql("SELECT scheme_name, date, nav FROM local.amfi.nav_history ORDER BY date DESC").show()
spark.sql("SELECT State, LiquidSchemes FROM local.amfi.aum_bifurcation").show()

The pattern is always the same: client.<module>.<method>(as_df=True).to_arrow() gives a PyArrow Table that Spark reads natively. Install the polars extra to enable as_df:

pip install amfipy[polars]

Tips for production Iceberg pipelines:

• Use a Hive metastore or AWS Glue catalog instead of hadoop for multi-cluster access.
• Add a partition_date column before writing and partition Iceberg tables by it for efficient time-range queries.
• Use .append() for incremental daily loads; .createOrReplace() for full refreshes.
• For large datasets (all-NAV flat file), fetch client.nav.download_file() bytes, write to S3/HDFS, then read with spark.read.text().

---

Financial Year Reference

AMFI uses financial years in YYYY-YYYY format (e.g. "2025-2026" = April 2025 – March 2026).

# Available months for the current year
months = client.ter.months(year="2025-2026")
# [{"MonthYear": "March-2026", "MonthNumber": "03-2026"}, ...]

AMC IDs (partial list)

ID	AMC
62	360 ONE Mutual Fund
85	Abakkus Mutual Fund
3	Aditya Birla Sun Life Mutual Fund
0	All (use "all" for NAV, "All" for TER)

Use client.fund_performance.filters() to get the full list with IDs.

---

client.research — Categorisation of Stocks

Data published bi-annually (Jan–Jun, Jul–Dec) from 2017 onwards. Served via the Next.js RSC page — no standalone JSON API exists.

# Dict keyed by year: {year: [{period, pdfUrl, excelUrl}]}
data = client.research.categorisation_of_stocks()
# data["2024"] → [{"period": "Jul - Dec", "pdfUrl": "...", "excelUrl": "..."}, ...]

# Flat list sorted by year
flat = client.research.categorisation_of_stocks_flat()
# → [{"year": "2017", "period": "Jul - Dec", "pdfUrl": "...", "excelUrl": "..."}, ...]

# polars DataFrame (year, period, pdfUrl, excelUrl columns)
df = client.research.categorisation_of_stocks(as_df=True)

# Download a file (PDF or Excel)
pdf = client.research.download_categorisation_file(flat[-1]["pdfUrl"])
open("categ_latest.pdf", "wb").write(pdf)

---

client.other_data — Investor Complaints, Directors, Dividends & More

# ── Investor Complaints ──────────────────────────────────────────────────────

# Monthly report (Part 1-4: A, B, C, D)
# month format: "MonthName-YYYY" (e.g. "March-2026")
complaints = client.other_data.investor_complaints_monthly(
    month="March-2026",
    mf_id=62,       # 0 = All AMCs
    part=1,         # 1=Part A, 2=Part B, 3=Part C, 4=Part D
)
complaints_df = client.other_data.investor_complaints_monthly(
    month="March-2026", mf_id=62, part=1, as_df=True
)

# Yearly report — financial year string "YYYY-YYYY", covers up to FY 2021
yearly = client.other_data.investor_complaints_yearly(year="2019-2020", mf_id=62)

# ── AMC Directors & Trustees ─────────────────────────────────────────────────

directors = client.other_data.amc_directors(mf_id=62)   # 0 = All AMCs
trustees  = client.other_data.trustees(mf_id=62)
directors_df = client.other_data.amc_directors(mf_id=0, as_df=True)

# ── Group Companies ──────────────────────────────────────────────────────────

# One page (default size=10)
page = client.other_data.group_companies(page=1, size=10)
# → {"data": [...], "totalCount": 599}

# All records (auto-paginate)
all_cos = client.other_data.group_companies_all()            # list
all_df  = client.other_data.group_companies_all(as_df=True) # polars DataFrame

# ── Scheme Dividends ─────────────────────────────────────────────────────────

# Step 1: get scheme list for an AMC (required to obtain sd_id)
schemes = client.other_data.populate_schemes(mf_id=62)
# → [{"scheme_id": "13771", "scheme_name": "..."}, ...]

# Step 2: fetch dividend history
divs = client.other_data.scheme_dividends(
    mf_id=62,
    sd_id=schemes[0]["scheme_id"],
    year=2026,
)
divs_df = client.other_data.scheme_dividends(
    mf_id=62, sd_id=schemes[0]["scheme_id"], year=2026, as_df=True
)

# ── AMC Account Disclosures ──────────────────────────────────────────────────

# Returns list of dicts with external URL references (not raw financials)
ann_links  = client.other_data.accounts_annual(mf_id=62)
half_links = client.other_data.accounts_half_yearly(mf_id=62)

# ── Scheme Data & Details ─────────────────────────────────────────────────────

# Step 1: get scheme list for an AMC
schemes = client.other_data.populate_schemes(mf_id=62)
sd_id = schemes[0]["scheme_id"]

# NAV details for all plan/option variants (Direct Growth, Regular IDCW, etc.)
nav_rows = client.other_data.scheme_data(mf_id=62, sd_id=sd_id)
# → [{"Scheme_NAV_Name": "...", "ISIN_Div_Payout_ISIN_Growth": "...",
#     "Net_Asset_Value": "13.32", "Date": "2026-05-08T...", ...}, ...]

# Full scheme profile
info = client.other_data.scheme_details(mf_id=62, sd_id=sd_id)
# → {"Scheme_Name": "...", "Scheme_Objective": "...", "SchemeType_Desc": "Open Ended",
#    "SchemeCat_Desc": "Hybrid Scheme - ...", "Scheme_min_amt": 1000,
#    "Launch_Date": "...", "Scheme_load": "...", "AMC_Website": "..."}

---

License

MIT