Quantum Predictor is a production-grade financial dashboard designed for high-fidelity market analysis and predictive intelligence. Built with a sleek, modern aesthetic and robust backend integration, it empowers users to track, compare, and analyze global and Indian stock markets with institutional precision.
- Live Data Integration: Powered by
yfinanceto fetch the latest market pricing and historical data. - Dynamic Visualization: High-performance trend charts using
Chart.jswith premium gradients and tooltips. - Global & Local Support: Support for NASDAQ, NYSE, and Indian NSE/BSE stock symbols.
- Smooth Autocomplete: A refined search experience with animated, icon-driven suggestions.
- Decoupled Frontend Support: CORS-enabled backend allowing for hosting the frontend separately on platforms like Vercel or Netlify.
- Side-by-Side Analytics: Compare two assets simultaneously to evaluate relative performance.
- Winner Detection: Automatic performance evaluation highlights the asset with the strongest 30-day growth.
- Overlaid Trends: Specialized chart overlay to visualize market correlation instantly.
- Predictive UI: High-fidelity simulation of buy/sell signals based on model accuracy and confidence metrics.
- Asset Allocation: Professional dashboard for tracking portfolio value and net asset distribution.
- Sky-Themed Glassmorphism: A stunning UI featuring white-glass surfaces, serif typography, and Lucide icons.
- Full Mobile Responsiveness: A touch-friendly, adaptive layout that looks beautiful on any device.
- Backend: Python / Flask
- Frontend: HTML5, Vanilla CSS (Embedded for portability), JavaScript
- Data Source: yfinance (Yahoo Finance API)
- Icons: Lucide Icons
- Charts: Chart.js 4.4.0
- Python 3.11 or 3.12
# Clone the repository
git clone <your-repo-link>
# Navigate to the project
cd ai-stock-application
# Install dependencies
pip install -r requirements.txtpython app.pyVisit http://localhost:5000 to view the dashboard.
The project is pre-configured with a Procfile and .python-version for seamless deployment on Render.com.
The index.html is fully self-contained. You can deploy it as a static site by pointing it to your Render backend URL.
This project is licensed under the MIT License - see the LICENSE file for details.
Built with ❤️ by [Your Name/Team]