fundkit 0.1.1

Async-first Python library for Mutual Fund data and analytics

Fundkit

A modern, async-first Python library for Mutual Fund data, analytics, and portfolio management.

Built on top of AMFI's public data with a typed, developer-friendly API - no third-party data vendors, no black-box calculations.

from fundkit import NAVClient
import asyncio

async def main():
    async with NAVClient() as client:
        data = await client.get_nav(123456)
        print(data)

asyncio.run(main())

Installation

pip install fundkit

OR

uv add fundkit

Current Status

Fundkit is under active development. The data/ layer is complete. schema/, portfolio/, analytics/, tax/, sip/, compare/ ... modules are in progress.

Usage

Latest NAV data

from fundkit import NAVClient
import asyncio


async def main():

    async with NAVClient(verbose=True) as client:  # verbose = TRUE for logging (defaults to False)
        # ----------- Fetch NAV data for a single scheme ----------
        nav = await client.get_nav(128628)
        print("Single Scheme NAV Data")
        print(f"Scheme Code           : {nav['scheme_code'].item()}")
        print(f"ISIN (Growth/Payout)  : {nav['isin_growth_or_payout'].item()}")
        print(f"ISIN (Div Reinvest)   : {nav['isin_div_reinvestment'].item()}")
        print(f"Scheme Name           : {nav['scheme_name'].item()}")
        print(f"NAV                   : {nav['nav'].item()}")
        print(f"Date                  : {nav['date'].item()}")
        print(f"AMC                   : {nav['amc'].item()}")
        print(f"Scheme Type           : {nav['scheme_type'].item()}")
        print()

        # ------------ Fetch NAV data for multiple schemes ---------
        df = await client.get_nav([119597, 120505, 108272])
        print(df)

        # ------------ Fetch NAV data by scheme name ---------------
        results = await client.get_nav_by_name("bluechip", case_sensitive=False)

        # ------------ Fetch NAV data by AMC -----------------------
        results = await client.get_nav_by_amc("SBI")

        # ------------ Fetch NAV data by Fund type -----------------
        results = await client.get_nav_by_type("Open Ended Schemes")
        print(results)

        # ------------ Validate scheme code -----------------------
        is_valid = await client.is_valid_scheme_code(119597)
        print(is_valid)

        # ------------ Force refresh the disk-cache ----------------
        await client.refresh_nav_cache()


asyncio.run(main())

Historical NAV data

from fundkit import HistoricalNAVClient
import asyncio
from datetime import date


async def main():
    # ------------ Fetch historical NAV data with start-date, end-date default to today ----------------
    async with HistoricalNAVClient(verbose=True) as client:
        data = await client.get_history(124182, start_date=date(2026, 1, 1))
        print(data)

    # ------------ Fetch historical NAV data with start-date and end-date ----------------
    async with HistoricalNAVClient(verbose=True) as client:
        data = await client.get_history(124182, start_date=date(2025, 1, 1), end_date=date(2026, 3, 31))
        print(data)

    # ------------ Output as pandas dataframe (defaults to polars) ----------------
    async with HistoricalNAVClient(verbose=True) as client:
        data = await client.get_history(124182, start_date=date(2025, 1, 1), df_format="pandas")
        print(data)


asyncio.run(main())

Output formats

All bulk methods support output in both polars (default) and pandas:

df = await client.get_nav([119597, 120505], fmt="pandas")

Caching

Fundkit caches data locally to avoid unnecessary network calls.

Cache is stored using platformdirs — on Linux ~/.cache/fundkit/, on macOS ~/Library/Caches/fundkit/, on Windows %LOCALAPPDATA%\fundkit\.

The table below shows the data for Linux.

Cache	Location (Platform Native)	TTL Current
NAV	~/.cache/fundkit/nav.parquet	24 hours
Historical NAV	~/.cache/fundkit/historical/{scheme_code}.parquet	Permanent (immutable past data)
Fund house IDs	~/.cache/fundkit/mf_id_map.json	7 days

Caching Hierarchy

Why Fundkit

Most existing Python tools for mutual funds either wrap third-party APIs (mfapi.in, mftool), are synchronous-only, return untyped raw dicts, or have no concept of SIPs, switches, or tax computation.

Fundkit is built differently:

• Async-first: Built on httpx and asyncio, usable in FastAPI, Django async views, or any modern async app.

• Typed everywhere: Pydantic v2 models for all domain objects, polars DataFrames with proper schemas for bulk data.

• AMFI-native: Hits AMFI directly, no middlemen that can go down.

• Polars-first: 10–100x faster than pandas for bulk NAV operations, with optional pandas export.