AI-powered IPO Analysis & Archive Engine
Stock TraQ is a professional-grade platform designed to provide deep insights into IPO performance. It leverages advanced machine learning models to predict listing gains and audit financial health, while maintaining a comprehensive archive of historical IPOs.
While generative AI models (like ChatGPT) are suited for text synthesis and general descriptions, StockTraQ addresses critical shortcomings in financial predictive modeling:
- Analytical Regression vs. Generative Text: AI chatbots rely on language correlations prone to hallucinating numerical estimates. StockTraQ executes rigorous local mathematical regression using local pre-trained weights for exact deterministic outputs over verified data.
- Interactive Analytic Dashboard: Standard AI tools cannot render unblocked sliders, continuous visual charts, or calculate threshold unified aggregates efficiently over layered datasets.
- Ensemble Modeling over Approximations: Common calculators use static averages; StockTraQ combines Random Forest, Gradient Boosting, and Linear Regression ensembles concurrently safeguarding outputs from statistical outliers.
- Unified IPO Rating: A hybrid 1-10 score generated by 5 specialized ML models.
- Archive Explorer: Interactive search and audit for historical listings.
- AI Performance Audit: Real-time backtesting comparing AI predictions vs. actual historical gains.
- Neural Engine: Combines Random Forest, Gradient Boosting, and Linear Regression for high-precision forecasting.
- Premium Interface: A glassmorphic, responsive UI built for modern financial intelligence terminals.
- Frontend: React 18 (Vite), Tailwind CSS, Lucide Icons, Framer Motion.
- Intelligence Backend: FastAPI (Python 3.10+), Pydantic, Scikit-learn.
- Management Backend: Node.js (Express), JSON Web Tokens (JWT) for Admin/User Auth.
- Database: MongoDB (Archival data storage).
- Listing Gain Predictor: Forecasts opening day performance based on subscription tiers (QIB, NII, Retail).
- Financial Strength Audit: Scores company fundamentals using Revenue, PAT, ROE, and ROCE metrics.
- Valuation Impact: Analyzes pricing efficiency relative to P/E ratios and sector trends.
- Demand Tier Classification: Categorizes market interest from 'Low' to 'Blockbuster'.
- Long-Term Projection: Estimates performance trends over a 6-12 month horizon.
- Node.js (v16+)
- Python (v3.10+)
- MongoDB (Local or via Docker)
- Docker & Docker-Compose (Recommended for easiest setup)
If you have Docker installed, simply run the ecosystem with:
docker-compose up --buildNote: This automatically orchestrates the Node.js API, FastAPI ML engine, and the Client bundle.
Ensure MongoDB is running and contains the setup tables for ongoing_ipos, closed_ipos, and master_db.
cd backend
python -m venv .venv
source .venv/bin/activate # or .venv\Scripts\activate on Windows
pip install -r requirements.txt
python main.pycd node-backend
npm install
node server.jscd frontend
npm install
npm run devTo launch all services concurrently, use the provided batch script located in the project root:
.\run.batStock TraQ utilizes a time-based data split to simulate real-world forecasting:
- Training Set: Historical IPO data.
- Validation Set: High-volatility listings.
- Ensemble Weights: 40% Random Forest, 40% Gradient Boosting, 20% Linear Regression for fallback execution.
Investment Disclaimer: Stock TraQ provides AI-based predictions for educational and research purposes only. IPO investments carry significant market risk. Predictions and ratings should not be considered financial advice. Always consult with a certified financial advisor before making investment decisions.
Built for Modern Investors.