fundkit 0.1.4

Async-first Python library for Mutual Fund data and analytics

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Async Python library for Indian mutual fund analytics. FundKit includes native parsers which fetch NAV info directly from AMFI - no third-party APIs.

If you're building dashboards, research pipelines, or backend services that need reliable fund data, FundKit gives you pydantic validated typed DataFrames with sensible caching.

import asyncio
from fundkit import NAVClient


async def main():
    async with NAVClient() as client:
        nav = await client.get_nav(128628)
        print(nav)


asyncio.run(main())

Installation

pip install fundkit
# or
uv add fundkit

Pandas export:

pip install fundkit[pandas]

Modules

Area	Status
data	NAV, historical NAV, scheme details
schema	Pydantic models (e.g. SchemeDetails)
portfolio, analytics, tax, sip, compare, ...	planned

Client	Purpose
NAVClient	Latest NAV by scheme code, name, AMC, or fund type
HistoricalNAVClient	NAV over a date range
SchemeDetailsClient	Scheme metadata (category, launch date, minimum investment, ...)

DataFrame methods default to Polars. Pass df_format="pandas" for pandas. get_scheme_details returns a SchemeDetails model; bulk lookups return a DataFrame.

Shared helpers: scheme validation, code/AMC listing. See the data module docs.

Usage

import asyncio
from datetime import date
from fundkit import NAVClient, HistoricalNAVClient, SchemeDetailsClient


async def main():
    async with NAVClient(verbose=True) as client:
        nav = await client.get_nav(128628)
        batch = await client.get_nav([119597, 120505, 108272])

        by_name = await client.get_nav_by_name("bluechip", case_sensitive=False)
        by_amc = await client.get_nav_by_amc("SBI")
        by_type = await client.get_nav_by_type("Open Ended Schemes")

        valid = await client.is_valid_scheme_code(119597)
        schemes = await client.get_scheme_codes(query="bluechip", by="scheme_name")
        amcs = await client.get_amc_list()

    async with HistoricalNAVClient(verbose=True) as client:
        history = await client.get_history(
            124182,
            start_date=date(2023, 1, 1),
            end_date=date.today(),
            df_format="pandas",
        )

    async with SchemeDetailsClient(verbose=True) as client:
        details = await client.get_scheme_details(128628)
        bulk = await client.get_scheme_details_bulk([128628, 119597])


asyncio.run(main())

verbose=True logs cache hits and network fetches. Architecture, caching, and the full API are in src/fundkit/data/README.md.

Compared to typical MF libraries

Many libraries wrap mfapi.in (or similar), use synchronous requests, and return dicts or pandas on every call. That is fine for one-off scripts; it is awkward in async apps or when filtering large scheme lists repeatedly.

	Typical MF libraries	FundKit
Data source	Third-party proxy	AMFI
Execution	Sync (requests)	Async (httpx)
Default output	Dict / JSON / pandas	Polars DataFrame
Bulk filtering	Python loops	Polars
Caching	Often none	Memory → disk (parquet) → network
Schemas	Untyped dicts	Fixed column names and dtypes

FundKit does not aim to match every feature elsewhere (performance metrics, JSON export, MCP servers, etc.). The current focus is the data layer.

Caching

Repeated same-day calls use local cache instead of re-downloading from AMFI.

Platform	Path
Linux	~/.cache/fundkit/
macOS	~/Library/Caches/fundkit/
Windows	%LOCALAPPDATA%\fundkit\

Data	TTL
Latest NAV	Same calendar day (memory + disk)
Historical NAV	Permanent, append-only per AMC
Scheme details	7 days (memory + disk)

Historical NAV is not re-fetched once cached. Latest NAV refreshes when the calendar day changes.