BMW Stock Analytics Dashboard 🚗

A modern, interactive dashboard built with Streamlit for analyzing BMW stock data. Features a UI with glassmorphism effects and dynamic starfield background animation.

🌟 Features

• Interactive Stock Analysis

  • Real-time candlestick charts
  • Volume analysis
  • Technical indicators (Moving Averages, Bollinger Bands)
  • RSI (Relative Strength Index) visualization

• Advanced Analytics

  • Returns distribution analysis
  • Volatility tracking
  • Key statistical metrics
  • Technical pattern detection

• Modern UI/UX

  • Glassmorphism effect
  • Dynamic starfield background
  • Responsive layout
  • Interactive controls
  • Custom styling and animations

🚀 Getting Started

Prerequisites

• Python 3.8+
• pip

Installation

1. Clone the repository:

git clone https://github.com/Ads2024/bmw-stock-analytics.git
cd bmw-stock-analytics

2. Create and activate a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required packages:

pip install -r requirements.txt

Running the Dashboard

streamlit run src/app.py

📁 Project Structure

bmw-stock-analytics/
├── .streamlit/
│   └── config.toml          # Streamlit configuration
├── assets/
│   └── giphy.webp          # Dashboard assets
├── data/
│   └── BMW_Data.csv        # Stock data
├── src/
│   ├── app.py              # Main application
│   └── styles.py           # Styling and animations
├── .gitattributes
├── .gitignore
├── LICENSE
└── README.md

🛠️ Built With

• Streamlit - The web framework
• Plotly - Interactive charts
• Pandas - Data manipulation
• NumPy - Numerical computations
• SciPy - Statistical analysis

📊 Dashboard Components

1. Main Price Chart

   • Candlestick visualization
   • Volume subplot
   • Customizable technical indicators

2. Metrics Overview

   • Current price
   • Price change
   • Average volume
   • Volatility metrics

3. Technical Analysis

   • RSI indicator
   • Moving averages
   • Bollinger Bands
   • Technical signals detection

4. Statistical Analysis

   • Returns distribution
   • Rolling volatility
   • Key performance metrics

🎨 Customization

The dashboard's appearance can be customized by modifying:

• src/styles.py - Contains styling and animation configurations
• .streamlit/config.toml - Streamlit-specific settings

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.