Stock Data Lifecycle Project

Overview

This project implements a complete Stock Market Data Lifecycle:

• Fetches historical stock price data using Yahoo Finance API.
• Stores raw and cleaned data into a MySQL database using SQLAlchemy.
• Applies data cleaning and feature engineering (moving averages, volatility, RSI).
• Conducts interactive Exploratory Data Analysis (EDA) with Matplotlib, Seaborn, and ipywidgets.

The project is organized using Object-Oriented Programming (OOP) principles for better modularity and scalability.

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Features

• Automated Data Fetching from Yahoo Finance.
• Database Management with SQLAlchemy (MySQL backend).
• Preprocessing and Feature Engineering (MA20, MA50, Volatility, RSI14).
• Interactive Data Visualization using widgets.
• Versioned Release: v1.0.0 (First Stable Release).

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Project Structure

├── scripts/
│   ├── fetch_data.py
│   ├── preprocess_data.py
│   ├── feature_engineering.py
│   └── eda.ipynb
├── database_manager.py
├── feature_engineer.py
├── README.md

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Tech Stack

• Language: Python
• Database: MySQL
• Libraries:
  • pandas
  • yfinance
  • SQLAlchemy
  • matplotlib, seaborn
  • ipywidgets

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How to Run

1. Clone the repository:

   git clone https://github.com/arabind-meher/Stock-Market-Data-Lifecycle-FAANG-Companies.git

2. Set up a MySQL database and update connection strings if needed.

3. Install required libraries:

   pip install -r requirements.txt

4. Run scripts sequentially:

   • Fetch stock data
   • Preprocess the data
   • Apply feature engineering
   • Explore using EDA notebook

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Version History

Version	Description	Date
v1.0.0	Initial complete pipeline	May 2025

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License

This project is licensed under the MIT License.

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Future Improvements

• Add ML models for stock trend prediction.
• Dockerize the entire project for easier deployment.
• Scheduled auto-updates for fetching latest data.