https://github.com/priyanshu501/wealth_management_for_mutual_funds
This repository contains the code and documentaion for our project developed during the HackHive hackathon.
https://github.com/priyanshu501/wealth_management_for_mutual_funds
streamlit-webapp
Last synced: 3 months ago
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This repository contains the code and documentaion for our project developed during the HackHive hackathon.
- Host: GitHub
- URL: https://github.com/priyanshu501/wealth_management_for_mutual_funds
- Owner: Priyanshu501
- License: other
- Created: 2024-04-01T11:07:33.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-01T12:47:27.000Z (about 1 year ago)
- Last Synced: 2025-01-24T17:45:41.674Z (4 months ago)
- Topics: streamlit-webapp
- Language: Python
- Homepage: https://wealthmanagementformutualfunds.streamlit.app/
- Size: 45.9 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# HackHive Submission
Welcome to our HackHive submission repository! This repository contains the code and documentaion for our project developed during the HackHive hackathon.
Checkout Working Project here: [View Project](https://wealthmanagementformutualfunds.streamlit.app/)
## Description
Our problem domain concerns wealth management for the common middle-class people, focusing on Mutual Funds. There are very limited techniques for wealth management like
FDs, Stock Trading, Crypto, Estate Planning, Corporate Bonds, etc. However, all of them require time, focus, significant capital, invovle risk factors, and offer limited interest rates.
Mutual funds are investment vehicles that pool money from multiple investors to invest in a diversified portfolio of securities such as stocks, bonds, money market instruments, where they are
managed by an asset management company (AMC).
## Goal
Our Goal is to create an recommendation system for selected inputs and present descriptive analysis to understand this data and their patterns, and develop a dashboard to display past data and projections.
## Dataset Parameters
* **Scheme Name**: *Name of the mutual fund scheme*
* **Min sip**: *Min sip amount required to start*
* **Min Lumpsum**: *Min lumpsum amount required to start.*
* **Expense Ratio**: *Calculated as a percentage of the Scheme's average net Asset Value (NAV).*
* **Fund Size**: *The Total amount of money that a mutual fund manager must oversee and invest.*
* **Fund Age**: *Years since inception of scheme.*
* **Fund Manager**: *A fund manager is responsible for implementing a fund's investment strategy and managing its trading activites.*
* **Sortino**: *Sortino ration measures the risk-adjusted return of an investment asset, portfolio, or strategy.*
* **Alpha**: *Alpha is the excess returns relative to market benchmark for a given amount of risk taken by the Scheme.*
* **Standard Deviation**: *Standard Deviation is a number that can be used to show how much the returns of a mutual fund scheme are likely to deviate from its average annual returns.*
* **Beta**: *Beta in a mutual fund is often used to convey the fund's volatility (gains or losses) in relation to its respective benchmark index.*
* **Sharpe**: *Sharpe Ratio of a mutual fund reveals its potential risk-adjusted returns.*
* **Risk Level**: *1-Low Risk, 2-Low to Moderate Risk, 3-Moderate, 4-Moderately High, 5-High, 6-Very High.*
* **AMC Name**: *Mutual Fund House Managing the Assets.*
* **Rating**: *0 to 5 safety rating assigned to Scheme*
* **Category**: *The Category to which the mutual fund belongs (example: equity, debt, hybrid)*
* **Sub-Category**: *It includes category like Small Cap, Large Cap, ELSS, etc.*
* **Return_1yr**: *The return Percentage of the Mutual Fund scheme over 1 Year.*
* **Return_3yr**: *The return Percentage of the Mutual Fund scheme over 3 Year.*
* **Return_5yr**: *The return Percentage of the Mutual Fund scheme over 5 Year.*
## Introduction
In today's fast-paced financial landscape, making informed investment decisions is crucial for individuals seeking to grow their wealth. Mutual funds offer a diversified and professionally managed investment option,
but navigating the multitude of available funds can be overwhelming. To address this challenge, we present a groundbreaking solution that leverages advanced data analysis and predictive modeling to revolutionize wealth management through mutual fund insights.
Our project aims to empower investors with comprehensive insights and predictions tailored to their unique investment goals and risk preferences. By harnessing the power of data science and machine learning techniques,
we provide users with a user-friendly platform to explore, evaluate, and select mutual funds that align with their financial objectives.
## Key Features
* **Personalized Investment Recommendations**: *Users can input their age, investement amount, and risk tolerance to received personalized recommendations on mutual funds that suit their investment profile.*
* **Data-driven Fund Selection**: *We analyze a vast dataset of mutual fund performance metrics, including returns, volatility, Sharpe Ratioi, Sortino Ration, Alpha, beta, and Standard Deviation. By evaluating historical fund performance and risk characteristics, we identify top-performing funds and present them to users for consideration.*
* **Interactive User Interface**: *Our intuitive and interactive user interface allows users to explore different mutual fund options, compare fund performance metrics, and make informed investment decisions seamlessly.*
## Acknowledgements
We would like to extend our gratitude to TechHunterssss and DAVV School of DataScience and Forecasting, Indore, Madhya Pradesh, for organizing the HackHive hackathon and providing us with the opportunity to showcase our project.
Additionally, we would like to thank our mentors, teammates, and everyone who supported us throughout this journey. Your guidance, encouragement, and expertise were invaluable.
## Contact
If you have any questions, feedback, or would like to collaborate, please feel free to reach out to us:
- Priyanshu Rao, [Github](https://github.com/Priyanshu501), [Email](mailto:[email protected])
- Sushant Ghatol, [Github](https://github.com/sushantghatol), [Email](mailto:[email protected])
- Khushal Dhage, [Github](https://github.com/Khushaldhage15), [Email](mailto:[email protected])
- Sonu Tiwari, [Github](https://github.com/SonuT18), [Email](mailto:[email protected])