KasSims/Interactive_Business_Intelligence_Dashboard

📊 Interactive Business Intelligence Dashboard

A professional-grade financial analysis platform with real-time market data, AI-powered trading recommendations, and interactive visualizations.

🚀 Live Demo

View Live Demo on Hugging Face Spaces

✨ Features

Core Capabilities

• —🔴 Real-Time Market Data: Live stock prices from Yahoo Finance API
• —🤖 AI Trading Recommendations: Buy/Sell/Hold signals with confidence scores (0-100%)
• —📈 Dynamic Trending Stocks: REAL trending stocks fetched from Yahoo Finance (not hardcoded)
• —📊 TradingView Charts: Professional-grade interactive charts
• —📰 News Sentiment Analysis: Integrated into trading recommendations
• —💼 Portfolio Analysis: Multi-stock portfolio evaluation and allocation

Advanced Features

• —Technical Indicators: RSI, MACD, Bollinger Bands, Moving Averages, Volume Analysis
• —Support/Resistance Detection: Automatic identification of key price levels
• —Momentum Analysis: Price momentum and trend strength evaluation
• —Smart Data Display: Toggle between latest snapshot or full historical data
• —Correlation Analysis: Cross-stock correlation heatmaps

🛠️ Technology Stack

• —Frontend: Gradio 5.4.0
• —Data Processing: Pandas, NumPy
• —Visualizations: Plotly, Matplotlib, Seaborn
• —Statistical Analysis: SciPy
• —Design Patterns: Strategy Pattern for modular analysis approaches

📁 Project Structure

Interactive_Business_Intelligence_Dashboard/
├── app.py                          # Main application entry point
├── requirements.txt                # Python dependencies
│
├── src/                           # Source code (organized by layer)
│   ├── core/                      # Data layer
│   │   ├── live_data.py          # Real-time data fetching
│   │   ├── data_processor.py     # Historical data processing
│   │   └── symbol_search.py      # Symbol lookup & validation
│   │
│   ├── analysis/                  # Business logic layer
│   │   ├── stock_signals.py      # AI trading signals
│   │   ├── fundamental_analysis.py # Financial metrics
│   │   └── insights.py           # AI insights generation
│   │
│   ├── visualization/             # Presentation layer
│   │   ├── visualizations.py     # Chart generation
│   │   ├── visualization_strategies.py # Strategy pattern
│   │   ├── tradingview_charts.py # TradingView charts
│   │   └── tradingview_widget.py # TradingView widgets
│   │
│   └── utils.py                   # Shared utilities
│
├── docs/                          # Comprehensive documentation
│   ├── ARCHITECTURE.md            # System design & architecture
│   ├── SETUP.md                   # Installation & setup guide
│   ├── FEATURES.md                # Feature documentation
│   ├── API_REFERENCE.md           # Developer API reference
│   └── PERFORMANCE_IMPROVEMENTS.md # Optimization details
│
├── data/                          # Sample data
│   └── selected_portfolio.csv    # 20 stocks & ETFs dataset
│
├── scripts/                       # Utility scripts
│   ├── generate_sample_data.py   # Data generation
│   └── prepare_selected_data.py  # Data preparation
│
├── assets/                        # Static assets
│   ├── ARCHITECTURE_DIAGRAM.txt  # Visual architecture
│   └── screenshots/              # UI screenshots (for docs)
│
├── exports/                       # Generated outputs
└── tests/                         # Test files

📚 Documentation

Comprehensive documentation is available in the docs/ directory:

For Instructors: Start with SETUP.md for quick installation, then see ARCHITECTURE.md for technical details.

For Developers: See API_REFERENCE.md for module documentation and extension points.

🚀 Quick Start

Prerequisites

• —Python 3.8+
• —pip package manager

Installation

1. 1.Put the project folder in your desired directory The instructions assume you have cloned or downloaded the project to your local machine.

1. 1.Install dependencies

pip install -r requirements.txt

1. 1.Run the application

python app.py

1. 1.Open in browser Navigate to http://localhost:7860

⚠️ Known Behavior

Time and Market Status Display:

• —When running on localhost, the time and market status may display "Loading..." or "Checking..." due to JavaScript execution limitations in local Gradio environments
• —The Hugging Face Spaces deployment properly updates the time and market status in real-time
• —This does not affect any core functionality of the dashboard

📊 Dataset

The dashboard includes a curated portfolio of 20 major stocks and ETFs from 2013-2017:

Stocks

• —Tech Giants: AAPL, MSFT, GOOGL, AMZN, NVDA, TSLA, AMD
• —Financial: JPM, BAC, JNJ
• —Consumer: WMT
• —Energy: XOM

ETFs

• —Market Indices: SPY, QQQ, DIA, IWM
• —Sector ETFs: XLK, XLF, XLE, GLD

🎯 Usage Guide

1. Data Upload

• —Click "Load Sample Portfolio" to use the default dataset
• —Or upload your own CSV/Excel file with columns: Date, Open, High, Low, Close, Volume, Symbol

2. Data Exploration

• —Statistics Tab: View comprehensive statistics and data quality metrics
• —Filter & Explore Tab: Apply dynamic filters to focus on specific time periods or assets

3. Visualizations

Choose from multiple chart types:

• —Time Series with Moving Averages
• —Candlestick Charts with Volume
• —Distribution Histograms
• —Correlation Heatmaps
• —Scatter Plots with Trendlines
• —Performance Comparisons

4. Insights Generation

• —Click "Generate Insights" for automated analysis
• —View prioritized insights (High/Medium/Low)
• —Access key performance metrics

5. Export Results

• —Export filtered data as CSV
• —Generate comprehensive summary reports

🏗️ Architecture & Design

Strategy Pattern Implementation

The project demonstrates advanced software engineering principles through the Strategy Pattern:

# Example usage
from visualization_strategies import VisualizationContext

context = VisualizationContext()
context.set_strategy('technical')  # Switch to technical analysis
chart = context.create_visualization(df, params)

Available strategies:

• —Technical Analysis: Trading indicators and signals
• —Fundamental Analysis: Value metrics and comparisons
• —Risk Analysis: Portfolio risk assessment

📈 Key Metrics Calculated

• —Returns: Daily, monthly, yearly returns
• —Volatility: Historical and rolling volatility
• —Sharpe Ratio: Risk-adjusted returns
• —Maximum Drawdown: Peak-to-trough decline
• —Value at Risk (VaR): Potential loss estimates
• —Correlation Matrix: Asset relationships

🧪 Testing

Run the application with sample data:

python app.py

For custom data testing:

1. 1.Ensure your CSV has required columns: Date, Open, High, Low, Close, Volume
2. 2.Optional columns: Symbol (for multi-asset analysis)
3. 3.Date format: YYYY-MM-DD

🤝 AI Tools Usage

This project was developed with assistance from AI tools:

• —Code Generation: Initial boilerplate and function templates
• —Documentation: README structure and docstrings
• —Debugging: Error resolution and optimization suggestions

All AI-generated code was thoroughly reviewed, tested, and customized for the specific requirements.

📝 Future Enhancements

• —[ ] Real-time data integration via APIs
• —[ ] Portfolio optimization (Markowitz efficient frontier)
• —[ ] Monte Carlo simulation for risk assessment
• —[ ] Machine learning price predictions
• —[ ] News sentiment analysis integration
• —[ ] Custom indicator creation interface

👨‍💻 Author

Karim Semaan

• —Course: CS5130 - Applied Programming and Data Processing for AI
• —Institution: Northeastern University
• —Term: Fall 2025

📄 License

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

🙏 Acknowledgments

• —Kaggle for the US Stocks & ETFs Dataset
• —Gradio team for the excellent framework
• —Course instructors for project guidance

📞 Contact

For questions or feedback about this project, please contact:

• —Email: semaan.k@northeastern.edu
• —GitHub: karimsemaan

This project was created as part of the Business Intelligence course final project, demonstrating proficiency in data analysis, visualization, and application development.