The aim of this project is to calculate number of units required to sell an Equity Mutual Fund so that LTCG (Long-term capital gains) is tax Free.

Additionally it will also do portfolio analysis to show graphs for below data:

1. Portfolio summary with aggregate data split for Equity and Debt Funds
2. Display scheme wise Invested Amount, Current Amount, Gain/Loss
3. Display taxable unrealised LTCG and STCG
4. Display Fund allocation by MF scheme and MF House

For Equity Mutual funds the gains up to 100,000 INR is tax free in a financial year. To calculate number of units required to sell a MF scheme so that LTCG is tax free is a complicated process which involves multiple factors such as

1. LTCG is applicable for any Equity MF units sold after one year of its purchase data
2. LTCG up to 100,000 INR is tax free for all equity MF combined, while above this amount it is taxable at 10%
3. Grand-Fathered clause, which means if you have an investment before 31-Jan-2018, then cost of acquisition would be purchase price or NAV on 31-Jan-2018 whichever is higher
4. If you have a SIP spanning across multiple years then each cost price, sale price and LTCG has to be calculated individually for each SIP

The input to this tool is PDF File from CAMS containing all transactions for a period. It produces two output files, Aggregate file at scheme level and Transaction file.

A big thanks to team responsible for maintaining casparser package which is used to parse CAMS CAS pdf satatement.

You can read about me and thought process behind this project on VRO article where they published my story in their how-i-did-it segment.

Easy to use as no local installation or set up is required. It is based on binder sharable cloud virtual machines environments and can be run on web browsers, with required dependencies already installed

A demo of the application on youtube

1. The first step is to get the consolidated account statement from CAMS in PDF Format. Make sure that you select the option as highlighted in yellow.

2. 
   On clicking above link it will create a cloud VM and install all python dependencies and create a Jupyter Notebook to run the scripts.
   This may take 10 seconds to one minute depending on whether pre-built docker image exist or not on server.

   

3. After sucessfull creation of VM you will see web based file structure as shown. This creates new instance of VM unique to your session and only you can see and edit files.

   

4. Upload your CAS statement downloaded in step 1 and save it as cas.pdf in root directory as shown

5. Launch python notebook demo.ipynb from the web repo, it will open in a new browser window tab. Select on double arrows and then click Restart and Run All Cells

6. Input the password for cas.pdf in the input box and press ENTER

7. The script will run and show the results with Header and Transaction data and will also show you number of units required for tax free LTCG. Plese note the VM session remains active only for a small amount time and after inactivity(10 Mins), it gets deleted. Launch Binder again to restart process

8. The CSV output gets saved in data/output directory of VM as shown and can be downloaded by selecting checkbox.

   

This is recommended for advanced users who can install python libraries and use command line tools

1. The first step is to get the consolidated account statement from CAMS in PDF Format. Make sure that you select the option as highlighted in yellow.

2. Clone the current github repo. See help for more details. On windows it can be done by opening command prompt tool and runing below commands

   git clone https://github.com/ajayrawatsap/mf_data.git  
   
   

   cd mf_data
   

3. Copy the PDF file to mf_data/data/ folder of repo you downloaded in step 2

4. Install the latest python version 3.9 and required libraries.

   See requirements.txt for additional python libraries required and install them using pip command below.

   pip install -r requirements.txt
   

5. Execute python main function using command line. Make sure you are executing from path where main.py file is located.
   
   Option 1: Calculate units to sell for tax free LTCG of INR 100000 (default value).
   cams_june_27.pdf is the name of PDF file in data directory and password the password for the pdf file. Use the correct file name and password for your file.

   python main.py cams_june_27.pdf password
   

   
   Option2: Calculate units to sell for tax free LTCG of user defined value (INR 50000 in this case)

    python main.py cams_june_27.pdf password 50000
   

6. It will create two output files in directory data/output/

   1. output_mf_totals.csv: For each mutual fund scheme it will list the total LTCG, STCG, Percent Gain and Target units to sell for tax free gains. Check sample output file
   2. output_mf_transactions.csv: This will list transaction level details and claculations for LTCG, STCG and gain percent. Check sample output file.

7. Additionaly you can check the Jupyter Notebook to see how major API's are called and do further experiments.

Yes latest version works with partial redemptions. The units redeemded are exhuasted in FIFO principle and adjustments are made

Latest NAV is fetched from the CAMS statement itself, therefore if you need to have latest calculation use the latest CAMS statement .
GrandFathered NAV is fecthed from AMFI website for date 31-JAN-2018 and list has been parsed and downloaded in csv file

For some MF schemes the calculation may not be possible as the Grandfathred NAV could not be found in CSV file. To resolve such issues Manualy maintain the Grand Fathered NAV(for date 31.01.2018) in CSV File

The calculation would also be done for Debt funds and LTCG and STCG will be calculated as per rules (With threshold of 3 years of LTCG/STCG)

The tool has been tested in Windows 10 OS with python 3.9, but it should also work in linux/mac environment.

The cloud based application is OS independent and can be run from web browser on desktop or mobile phones.

The limited testing has happened on my own Mutual fund Schemes. In case you encounter bug and issue please report it in issues.

Binder runs as a public, free service, don’t require any kind of log-in that would keep track of user data. All code that is run, data analyzed, papers reproduced, classes taught - in short, everything that happens in a Binder session - is destroyed when the user logs off or becomes inactive for more than a few minutes.
Read the binder privacy and seurity information.

1. Analysis of MF portfolio with Graphs and Charts: Done
2. Forecast the future portfolio value based on historical values using Machine Learning: In Progress
3. MF Portfolio evaluation and recommendation on rebalancing portfolio based on performance of schemes.
4. Personal Finance planner based on user age, profession, risk appetite, existing net worth, goals etc.