Stock Analyzer Python

Abstract

1. Introduction

Real time scrapping of stock data in order to get the most recent available information. Cleaning, structuring and parsing of relevant data to provide an interactive and informative dashboard. Dashboard features include: Price history (chart) Basic information (name, sector, industry, market cap...) Insider and institutional ownership information Share return over the years (absolute growth and cagr) Business summary Earnings & growth analysis (revenue, gross profit. EBITDA, net income, free cash flow...) Profitability analysis (roa, roe, roic, gross profit margin, net income margin...) Financial health analysis (debt to equity, interest coverage, quick ratio, current ratio...) Valuation analysis (p/e, p/s, p/b, p/fcf, peg) + Discounted Cash Flow Model

2. Objective

Real time scrapping of stock data in order to get the most recent available information. Cleaning, structuring and parsing of relevant data to provide an interactive and informative dashboard. Dashboard features include: Price history (chart) Basic information (name, sector, industry, market cap...) Insider and institutional ownership information Share return over the years (absolute growth and cagr) Business summary Earnings & growth analysis (revenue, gross profit. EBITDA, net income, free cash flow...) Profitability analysis (roa, roe, roic, gross profit margin, net income margin...) Financial health analysis (debt to equity, interest coverage, quick ratio, current ratio...) Valuation analysis (p/e, p/s, p/b, p/fcf, peg) + Discounted Cash Flow Model

This project demonstrates how Python, Streamlit can be applied to a real-world Data Science problem.

3. Key Features / Modules

• Real time scrapping of stock data in order to get the most recent available information.
• Cleaning, structuring and parsing of relevant data to provide an interactive and informative dashboard.
• Dashboard features include:
• Price history (chart)
• Basic information (name, sector, industry, market cap...)
• Insider and institutional ownership information
• Share return over the years (absolute growth and cagr)
• Business summary
• Earnings & growth analysis (revenue, gross profit. EBITDA, net income, free cash flow...)
• Profitability analysis (roa, roe, roic, gross profit margin, net income margin...)

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/J700070/Stock-Analyzer-Python.git
cd Stock-Analyzer-Python

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by J700070 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.