Introduction

mftool is a library for getting real time data from Association of Mutual Funds in India. It can be used in various types of projects which requires getting live quotes for a given scheme or build large data sets for further data analytics. The accuracy of data is only as correct as provided on amfiindia

Main Features

  • Getting last updated quotes for Mutual Fund scheme using scheme codes.
  • Return data in Dataframe, json and dictionary formats.
  • Getting quotes for all the Schemes available in AMFI, e.g Axis, DSP, SBI mutual funds
  • Helper APIs to check whether a given Scheme code is correct.
  • Getting all Historical nav’s for a scheme using scheme code.
  • Getting list of all Schemes with there Scheme codes.
  • Cent percent unittest coverage.

Installation

Installing mftool is very simple and it has no external dependencies. All its dependencies are part of standard python distribution. packages:

pip install mftool

Update

To updated to the lasted version:

pip install mftool --upgrade

A Word On Exception Handling

Since this library would form a middleware of some other project. Hence it is not handling any exception.

Warning

You need to have a working internet connection while using this library. It will raise URLErorr in case there is no internet connectivity. Hence please handle this scenario in your code.

Library Walkthrough with Examples

In this section we will focus on the basic usage and cover all the APIs which mftool offer. Though I would encourage you to take a look at the code and unittests in case you want to further customize it.

Note

All the data APIs support json return as well. Call any API with as_json=True to get the json return. For example following would provide a json return.

mf.get_scheme_quote(‘117865’, as_json=True)

Instantiation

As mentioned earlier, mftool comes pre-built with all the right url mappings and hence instantiating it requires no contructor arguments.

>>> from mftool import Mftool
>>> mf = Mftool()
>>> print mf
Driver Class for The Association of Mutual Funds in India (AMFI)

Getting a Scheme Quote

Before going though other fundamental APIs. We will first see how to get a quote. Assume that we want to fetch current nav of ANY scheme. The only thing we need is Code for this company.

>>> q = mf.get_scheme_quote('119597') # it's ok to use both string or integer as codes.
>>> print(q)
{"scheme_code": "119597",
 "scheme_name": "xxxxxxxxxxxxx",
 "last_updated": "16-Aug-2019",
 "nav": "40.0138"
}

Note

This is a scheme quote with all possible details. Since it is a dictionary you can easily chop off fields of your interest.

Warning

Always use AMFI codes of schemes.
All scheme codes are presented here - https://raw.githubusercontent.com/NayakwadiS/mftool/master/Scheme_codes.txt or use mf.get_scheme_codes()

Get Scheme Details

>>> mf.get_scheme_details("117865")
{'fund_house': 'xxxxxxxxxxxxx',
 'scheme_type': 'IL&FS Mutual Fund',
 'scheme_category': 'IDF',
 'scheme_code': 117865,
 'scheme_name': 'xxxxxxxxxxxxx - Growth Option',
 'scheme_start_date': {'date': '10-09-2012', 'nav': '10.01030'}
}

List of Mutual Funds Scheme Codes & Names

This is very trivial in general, if you are browsing manually. But there is a way to get it programatically as well.

>>> all_scheme_codes = mf.get_scheme_codes() # you can use as_json=True to get all codes in json format
>>> print(all_scheme_codes)
{
 '101306': 'DSP Short Term Fund - Monthly Dividend',
 '101305': 'DSP Short Term Fund - Regular Plan - Dividend',
 '101304': 'DSP Short Term Fund - Regular Plan - Growth',
 '140251': 'Edelweiss Short Term Fund - Direct Plan -  Growth Option',
 '140249': 'Edelweiss Short Term Fund - Direct Plan - Dividend Option',
.
.
.
.
.
}

Note

Output has been truncated for better legibility. This is a dictionary with more than thousand entries.

Get Scheme Historical NAV’s data

  1. Get data as Dataframe
>>> df = mf.get_scheme_historical_nav("119597",as_Dataframe=True)
>>> print(df)
                 nav
date
26-10-2021  81.08400
25-10-2021  79.60400
20-10-2021  82.30800
19-10-2021  83.97800
18-10-2021  85.41100
...              ...
  1. Get data as JSON
>>> data = mf.get_scheme_historical_nav("119597",as_json=True)
>>> print(data)
{'fund_house': 'xxxxxxxxxxxxx',
 'scheme_type': 'Open Ended Schemes',
 'scheme_category': 'Debt Scheme - Banking and PSU Fund',
 'scheme_code': 119597, 'scheme_name': 'xxxxxxxxxxxxx  - Direct Plan-Dividend',
 'scheme_start_date': {'date': '02-01-2013', 'nav': '103.00590'},
 'data': [{'date': '16-08-2019', 'nav': '149.33110'},
                  {'date': '14-08-2019', 'nav': '149.08090'},
                  {'date': '13-08-2019', 'nav': '149.45110'},
                  {'date': '09-08-2019', 'nav': '149.42480'},
                  .
                  .
                  .
                 ]
}
  1. Alternative, view historical data with one day change
>>> df = mf.history('0P0000XVAA',start=None,end=None,period='3mo',as_dataframe=True)
>>> print(df)
                nav     dayChange
date
03-08-2021  78.269997        NaN
04-08-2021  77.545998  -0.723999
05-08-2021  77.081001  -0.464996
06-08-2021  77.349998   0.268997
.
.

Note

To use mf.history(), we have to use new scheme codes presented here- https://github.com/NayakwadiS/Forecasting_Mutual_Funds/blob/master/codes.json

Calculate Market value of Units

This calculates the Today’s Market value of units you are having. provide with scheme code and units balance you are having

>>> value = mf.calculate_balance_units_value(119597, 445.804)
>>> print(value)
{'scheme_code': '119597',
 'scheme_name': 'xxxxxxxxxxxxx',
 'last_updated': '14-Aug-2019',
 'nav': '40.0138',
 'balance_units_value': '17838.31'
 }

Get daily performance of Equity schemes

To get daily Performance of open ended equity schemes for all AMCs

>>> value = mf.get_open_ended_equity_scheme_performance(True)
>>> print(value)
{
        "Large Cap": [{
                "scheme_name": "xxxxxxxxxxxxxxxx",
                "benchmark": "NIFTY 50 Total Return",
                "latest NAV- Regular": "xxxxx",
                "latest NAV- Direct": "xxxxx",
                "1-Year Return(%)- Regular": "8.72",
                "1-Year Return(%)- Direct": "9.48",
                "3-Year Return(%)- Regular": "10.22",
                "3-Year Return(%)- Direct": "11.22",
                "5-Year Return(%)- Regular": "7.33",
                "5-Year Return(%)- Direct": "8.33"
        },
        .
        .
        .
        .
        ],
        "Large & Mid Cap": [
        {
                "scheme_name": "xxxxxxxxxxxxxxxxxx",
                "benchmark": "NIFTY Large Midcap 250 Total Return Index",
                "latest NAV- Regular": "xxxxx",
                "latest NAV- Direct": "xxxxx",
                "1-Year Return(%)- Regular": "13.45",
                "1-Year Return(%)- Direct": "14.45",
                "3-Year Return(%)- Regular": "9.15",
                "3-Year Return(%)- Direct": "10.35",
                "5-Year Return(%)- Regular": "8.32",
                "5-Year Return(%)- Direct": "9.41"
        },
        .
        .
]}

Get daily performance of open ended Debt schemes

>>> value = mf.get_open_ended_debt_scheme_performance(True)
>>> print(value)

Get daily performance of Hybrid schemes

>>> value = mf.get_open_ended_hybrid_scheme_performance(True)
>>> print(value)

Get daily performance of Solution schemes

>>> value = mf.get_open_ended_solution_scheme_performance(True)
>>> print(value)

All AMC profiles

Methode gives us Profile data of all AMCs

>>> amc_details = mf.get_all_amc_profiles(True)
>>> print(amc_details)