# Quantum Index Reconstruction Web Application – Implementation Plan
## 🎯 **Project Overview**
Build a simple, clean web interface for the autonomous `quant_package` that allows users to:
– Input a date for index reconstruction
– View results in a tabular format on the web
– Download results as CSV file to local drive
## 🏗️ **Architecture Assessment**
### **Recommended Stack: Flask + Bootstrap**
– **Backend**: Flask (Python) – integrates seamlessly with existing `quant_package`
– **Frontend**: HTML + Bootstrap CSS + JavaScript
– **Database**: Uses existing FactSet database connection
– **Deployment**: Can run locally or on internal server
### **Why This Stack?**
✅ **Minimal complexity** – Flask is lightweight and simple
✅ **Direct integration** – Can import `quant_package` directly
✅ **Fast development** – Bootstrap provides ready-made UI components
✅ **Familiar technology** – Python-based, consistent with existing codebase
## 📁 **Proposed Project Structure**
“`
quantum_web_app/
├── app.py # Flask application entry point
├── templates/
│ ├── base.html # Base template with Bootstrap
│ ├── index.html # Main page with date input form
│ └── results.html # Results display page
├── static/
│ ├── css/
│ │ └── custom.css # Custom styling
│ ├── js/
│ │ └── app.js # Frontend JavaScript
│ └── downloads/ # Temporary storage for CSV downloads
├── quant_package/ # Copy of your autonomous package
│ ├── __init__.py
│ ├── factset_client.py
│ ├── quantum_index.py
│ ├── main.py
│ └── config.ini
├── requirements.txt # Python dependencies
├── config.py # Flask configuration
└── README.md # Setup and usage instructions
“`
## 🔧 **Implementation Details**
### **1. Flask Backend (`app.py`)**
“`python
# Key features:
– Route for main page (GET /)
– Route for processing date input (POST /run_index)
– Route for displaying results (GET /results/<job_id>)
– Route for CSV download (GET /download/<filename>)
– Background task processing for long-running index construction
– Error handling and logging
“`
### **2. Frontend Interface**
#### **Main Page (`index.html`)**
– **Title**: “Quantum Index Reconstruction”
– **Date Input**: HTML5 date picker with validation
– **Submit Button**: Triggers index construction
– **Progress Indicator**: Shows processing status
– **Responsive Design**: Works on desktop and mobile
#### **Results Page (`results.html`)**
– **Data Table**: Bootstrap table with sorting and pagination
– **Download Button**: CSV export functionality
– **Summary Stats**: Total constituents, processing time
– **Navigation**: Back to main page
### **3. Key Features**
#### **Date Input Validation**
– Date format validation (YYYY-MM-DD)
– Business day validation
– Historical date range limits
– Clear error messages
#### **Processing Flow**
1. User enters date → Form validation
2. Submit → Background task starts
3. Progress page → Real-time status updates
4. Results page → Table display + download option
#### **Data Display**
– **Sortable columns**: Company name, sector, score, weight
– **Pagination**: Handle large datasets efficiently
– **Search/Filter**: Quick company lookup
– **Export options**: CSV download with timestamp
## 🛠️ **Implementation Steps**
### **Phase 1: Basic Setup (Day 1)**
1. Create Flask project structure
2. Set up virtual environment and dependencies
3. Create basic HTML templates with Bootstrap
4. Implement simple date input form
5. Test integration with `quant_package`
### **Phase 2: Core Functionality (Day 2)**
1. Implement Flask routes for form processing
2. Integrate `quant_package.QuantumIndex` execution
3. Create results display page with basic table
4. Add CSV download functionality
5. Implement error handling
### **Phase 3: UI Enhancement (Day 3)**
1. Add Bootstrap styling and responsive design
2. Implement progress indicators and loading states
3. Add data table sorting and pagination
4. Create custom CSS for branding
5. Add form validation and user feedback
### **Phase 4: Production Ready (Day 4)**
1. Add logging and monitoring
2. Implement background task processing
3. Add configuration management
4. Create deployment documentation
5. Performance testing and optimization
## 📋 **Technical Requirements**
### **Dependencies (`requirements.txt`)**
“`
Flask==2.3.3
pandas==2.0.3
numpy==1.24.3
pyodbc==4.0.39
sqlalchemy==2.0.21
gunicorn==21.2.0 # For production deployment
celery==5.3.1 # For background tasks (optional)
redis==4.6.0 # For task queue (optional)
“`
### **Configuration**
– **Development**: SQLite for session storage, local file downloads
– **Production**: Redis for sessions, shared storage for downloads
– **Database**: Uses existing FactSet connection from `config.ini`
## 🚀 **Deployment Options**
### **Option 1: Local Development**
“`bash
python app.py
# Access at http://localhost:5000
“`
### **Option 2: Internal Server**
“`bash
gunicorn -w 4 -b 0.0.0.0:8000 app:app
# Access at http://server-ip:8000
“`
### **Option 3: Docker Container**
“`dockerfile
FROM python:3.10-slim
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
CMD [“gunicorn”, “-w”, “4”, “-b”, “0.0.0.0:8000”, “app:app”]
“`
## 🔒 **Security Considerations**
1. **Input Validation**: Sanitize date inputs, prevent SQL injection
2. **File Access**: Secure download directory, prevent path traversal
3. **Authentication**: Add login if needed for internal use
4. **HTTPS**: Use SSL certificates for production
5. **Rate Limiting**: Prevent abuse of compute-intensive operations
## 📊 **User Experience Flow**
“`
1. User visits homepage
↓
2. Enters date (e.g., “2024-12-15”)
↓
3. Clicks “Run Index Reconstruction”
↓
4. Progress page shows “Processing…”
↓
5. Results page displays:
– Summary: “150 constituents found”
– Table with company data
– “Download CSV” button
↓
6. User downloads file: “quantum_index_2024-12-15.csv”
“`
## 🎨 **UI Mockup Description**
### **Header**
– Clean, professional design
– Title: “Quantum Index Reconstruction”
– Subtitle: “FactSet Global Quantum Computing Index”
### **Main Form**
– Large, centered date input field
– Clear label: “Select Reconstruction Date”
– Blue “Run Reconstruction” button
– Help text: “Enter a business day for index calculation”
### **Results Table**
– Bootstrap-styled responsive table
– Columns: Company, Ticker, Sector, QCCK Score, Weight (%)
– Sortable headers, striped rows
– Download button above table
## ✅ **Success Criteria**
1. **Functionality**: Successfully runs index reconstruction for any valid date
2. **Usability**: Intuitive interface, clear error messages
3. **Performance**: Handles reconstruction within reasonable time (<5 minutes)
4. **Reliability**: Robust error handling, graceful failure recovery
5. **Maintainability**: Clean code, easy to update and extend
## 🔄 **Future Enhancements**
– **Historical Analysis**: Compare index changes over time
– **Visualization**: Charts and graphs for sector allocation
– **Batch Processing**: Multiple date ranges
– **API Endpoints**: RESTful API for programmatic access
– **Real-time Updates**: WebSocket for live progress updates
—
This plan provides a comprehensive roadmap for building a professional, user-friendly web interface for your quantum index package. The Flask-based approach ensures seamless integration with your existing Python codebase while providing a modern, responsive user experience.
Naixian Zhang 