User Guide

This guide provides a quick overview to get started with BharatFinTrack.

Verify Installation

Ensure a successful installation by running the following commands.

import BharatFinTrack

Equity Index Base Parameters

Save the MultiIndex DataFrame containing the base parameters of equity indices to an Excel file.

nse_product = BharatFinTrack.NSEProduct()

nse_product.equity_base_parameter_midf(
    excel_file=r'C:\Users\Username\Folder\base_parameter.xlsx'
)

Download Data

A brief overview of features related to data downloading. Start by instantiating the classes.

nse_pri = BharatFinTrack.NSEIndex()
nse_tri = BharatFinTrack.NSETRI()

PRI Summary

Download the daily summary report of PRI closing values, published daily on the Nifty Indices Reports, and save it as a CSV file.

nse_pri.download_equity_close(
    csv_file=r"C:\Users\Username\Folder\PRI_closing.csv"
)

Historical TRI Daily Data

Download historical daily TRI data for the NIFTY 50 index. Currently, the function supports only equity indices.

# Downloading daily closing TRI data up to a specified date
nse_tri.download_daily_data(
    index='NIFTY 50',
    start_date=None,
    end_date='31-Mar-2024',
    csv_file=r"C:\Users\Username\Folder\NIFTY 50.csv",
)

# Using the same CSV file to update daily closing TRI data to the present date
nse_tri.update_daily_data(
    index='NIFTY 50',
    csv_file=r"C:\Users\Username\Folder\NIFTY 50.csv"
)

TRI Closing Values

Download the closing TRI values for all equity indices. These values are not updated daily on the website. It is recommended to run this function at night when web traffic is lower, as it sends multiple requests to retrieve the required data.

nse_tri.download_equity_close(
    csv_file=r"C:\Users\Username\Folder\TRI_closing.csv"
)

CAGR and SIP

A brief overview of features related to Compound Annual Growth Rate (CAGR) and Systematic Investment Plan (SIP). Start by instantiating the classes.

cagr = BharatFinTrack.CAGR()
sip = BharatFinTrack.SIP()

CAGR Summary Since Inception

The following methods can be used to compute CAGR (%) of all equity indices since inception. The resulting CAGR (%) values are used to sort the equity indices in descending order, either overall or within each category.

# Sort equity indices by CAGR (%) since launch
cagr.sort_since_inception(
    csv_file=r"C:\Users\Username\Folder\TRI_closing.csv",
    excel_file=r"C:\Users\Username\Folder\sort_cagr.xlsx"
)

# Sort equity indices by CAGR (%) since launch within each category
cagr.sort_since_inception(
    csv_file=r"C:\Users\Username\Folder\TRI_closing.csv",
    within_category=True,
    excel_file=r"C:\Users\Username\Folder\sort_cagr_within_category.xlsx",
)

Year-wise SIP Growth

Compute the year-wise SIP returns for a fixed monthly contribution to a specified equity TRI index.

sip.yearly_return(
    csv_file=r"C:\Users\Username\Folder\NIFTY 50.csv",
    invest=1000,
    excel_file=r"C:\Users\Username\Folder\NIFTY 50_SIP.xlsx",
)

Year-wise SIP and CAGR Comparison

This section compares the year-wise XIRR (%) and growth multiples (X) of a fixed monthly SIP investment, along with the year-wise CAGR (%) and growth multiples of a one-time investment across selected TRI indices.

The required data are sourced from CSV files generated in the Historical TRI Daily Data section. Ensure that all input CSV files are stored in the designated folder, with each file named as {index}.csv to match the index names provided in the list. The output highlights the highest growth cells in green-yellow and the lowest growth cells in sandy brown.

index_list = [
    'NIFTY 50',
    'NIFTY ALPHA 50',
    'NIFTY MIDCAP150 MOMENTUM 50',
    'NIFTY500 MOMENTUM 50'
]

# SIP comparison
sip.compare_performances(
    indices=index_list,
    dir_path=r"C:\Users\Username\Folder",
    excel_file=r"C:\Users\Username\Folder\compare_sip.xlsx"
)

# CAGR comparison
cagr.compare_performance(
    indices=index_list,
    dir_path=r"C:\Users\Username\Folder",
    excel_file=r"C:\Users\Username\Folder\compare_cagr.xlsx"
)

Correction and Recovery Cycles

This functionality identifies key turning points in an index’s historical values based on consecutive corrections and recoveries. It applies minimum gain and multiplier filters to analyze the frequency and behavior of these movements over time.

analyzer = BharatFinTrack.Analyzer()

analyzer.correction_recovery_cycles(
    csv_file=r"C:\Users\Username\Folder\NIFTY 50.csv",
    excel_file=r"C:\Users\Username\Folder\NIFTY 50_correction_recovery.xlsx"
)

Visualization

A brief overview of features related to data visualization. Start by instantiating the class.

visual = BharatFinTrack.Visual()

CAGR Leaders

This section presents bar plots of equity index CAGR (%) in descending order, either overall or within each category.

The following code plots the top 15 equity indices by overall TRI CAGR (%).

visual.cagr_leaders(
    excel_file=r"C:\Users\Username\Folder\sort_cagr.xlsx",
    figure_file=r"C:\Users\Username\Folder\sort_cagr.png"
)
_images/sort_cagr.png

The following code plots the top five equity indices by TRI CAGR (%) within each category since launch.

visual.cagr_leaders_by_category(
    excel_file=r"C:\Users\Username\Folder\sort_cagr_within_category.xlsx",
    figure_file=r"C:\Users\Username\Folder\sort_cagr_within_category.png"
)
_images/sort_cagr_within_category.png

SIP Comparison with Government Securities

The following code displays a bar plot comparing SIP returns over time for the TRI data of an index and government securities with an assumed yield.

visual.compare_sip_to_bond_benchmark(
    excel_file=r"C:\Users\Username\Folder\NIFTY MIDCAP150 MOMENTUM 50_SIP.xlsx",
    figure_file=r"C:\Users\Username\Folder\sip_growth_vs_bond_benchmark.png"
)

The resulting plot will resemble the example shown below.

_images/sip_growth_vs_bond_benchmark.png

SIP Comparison Across Indices

This section presents a plot comparing the year-wise growth multiples (X) of a monthly SIP investment across TRI indices.

visual.compare_performance(
    excel_file=r"C:\Users\Username\Folder\compare_sip.xlsx",
    figure_file=r"C:\Users\Username\Folder\compare_sip.png"
)

The resulting plot will resemble the example shown below.

_images/compare_sip.png