Transform your financial insights with AI-powered market predictions

Tranaly is a revolutionary financial intelligence platform that democratizes advanced market analysis through artificial intelligence. Built for investors of all levels, from beginners to professionals.

• 📊 Real-time Market Analysis - Live stock data with interactive charts
• 🤖 AI-Powered Predictions - Advanced ML models (ARIMA, LSTM, Transformers)
• 📈 Portfolio Management - Risk analysis and optimization tools
• 🔍 Custom Dataset Upload - Bring your own data for analysis
• 📱 Modern Web Interface - Responsive design built with Nuxt.js
• 🔌 RESTful API - Full API access for developers
• 📚 Educational Hub - Learn financial analysis and AI concepts

┌─────────────┐    ┌──────────────┐    ┌─────────────┐
│  Frontend   │    │   API Server │    │ ML Pipeline │
│  (Nuxt.js)  │◄──►│   (FastAPI)  │◄──►│ (PyTorch)   │
└─────────────┘    └──────────────┘    └─────────────┘
                           │
                           ▼
                   ┌──────────────┐
                   │   Database   │
                   │ (PostgreSQL) │
                   │ + TimescaleDB│
                   └──────────────┘

Frontend

• Nuxt.js 3 (Vue.js framework)
• TailwindCSS for styling
• Chart.js for data visualization
• Pinia for state management

Backend

• FastAPI (Python)
• PostgreSQL + TimescaleDB
• Redis for caching
• Celery for background tasks

AI/ML

• PyTorch for deep learning
• scikit-learn for traditional ML
• MLflow for model versioning
• Feast for feature store

Infrastructure

• Docker & Docker Compose
• GitHub Actions (CI/CD)
• Prometheus & Grafana (monitoring)

• Python 3.11+
• Node.js 18+
• Docker & Docker Compose
• PostgreSQL (or use Docker)

1. Clone the repository

   git clone https://github.com/Projet12345/tranaly-open.git
   cd tranaly-open

2. Setup with Docker (Recommended)

   docker-compose up -d

3. Or manual setup

   # Backend
   cd src/backend
   pip install -r requirements.txt
   python -m uvicorn main:app --reload
   
   # Frontend
   cd ../frontend
   npm install
   npm run dev

4. Access the application

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:8000
   • API Docs: http://localhost:8000/docs

• 📋 Project Overview - Complete project documentation
• 🛠️ API Documentation - Backend API reference
• 🎨 Frontend Guide - Frontend development guide
• 🤖 ML Pipeline - Machine learning workflow
• 🚀 Deployment Guide - Production deployment

tranaly-open/
├── src/
│   ├── frontend/          # Nuxt.js application
│   ├── backend/           # FastAPI server
│   ├── ml-pipeline/       # ML training & serving
│   ├── database/          # DB schemas & migrations
│   └── shared/            # Shared utilities
├── tests/                 # Test suites
├── deployment/            # Docker & K8s configs
├── scripts/               # Utility scripts
├── config/                # Configuration files
├── docs/                  # Documentation
├── PROJECT_OVERVIEW.md    # Complete project specs
└── README.md              # This file

Create .env files in respective directories:

# src/backend/.env
DATABASE_URL=postgresql://user:password@localhost:5432/tranaly
REDIS_URL=redis://localhost:6379
SECRET_KEY=your-secret-key
ALPHA_VANTAGE_API_KEY=your-api-key

# src/frontend/.env
NUXT_PUBLIC_API_BASE_URL=http://localhost:8000

# Backend tests
cd src/backend
pytest

# Frontend tests
cd src/frontend
npm run test

# E2E tests
cd tests
pytest e2e/

# Python formatting & linting
black src/backend/
flake8 src/backend/
mypy src/backend/

# JavaScript/Vue linting
cd src/frontend
npm run lint
npm run type-check

• ARIMA - Traditional time series forecasting
• GARCH - Volatility modeling
• LSTM/GRU - Deep learning for sequences
• Transformers - State-of-the-art attention models
• Ensemble Methods - Combined model predictions

• Yahoo Finance (primary)
• Alpha Vantage (backup)
• Custom data upload support
• Real-time WebSocket feeds

• JWT authentication
• Rate limiting
• Input validation
• SQL injection protection
• CORS configuration
• API key management

We welcome contributions! See our Contributing Guide for details.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

# Install pre-commit hooks
pip install pre-commit
pre-commit install

# Run the full test suite
make test-all

# Start development environment
make dev-up

This project follows an Open Core model:

• Core Platform: MIT Licensed (free for all uses)
• Advanced Features: Commercial license available
• Hosted Service: Demo available at andersonnguetoum.com/tranaly with premium features

For commercial licensing of advanced AI models and enterprise features, contact: anderson@andersonnguetoum.com

See the LICENSE file for details.

• Project setup and architecture
• Basic stock data ingestion
• Simple prediction models
• Web interface prototype

• Advanced ML models
• Portfolio management
• User authentication
• API documentation

• Real-time predictions
• Mobile app
• Premium features
• Payment integration

• 📧 Email: anderson@andersonnguetoum.com
• 🌐 Website: andersonnguetoum.com
• 📖 Wiki: GitHub Wiki
• 🐛 Issues: GitHub Issues

• Yahoo Finance API for market data
• FastAPI for the amazing Python framework
• Nuxt.js for the excellent Vue.js framework
• TimescaleDB for time-series optimization

Made with ❤️ by Anderson Nguetoum - Building the future of financial intelligence

🎯 AI Engineer specialized in Predictive Analytics, Time Series Forecasting, Computer Vision & MLOps

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