Quantis is a comprehensive quantitative trading and investment analytics platform that combines advanced statistical models, machine learning algorithms, and real-time market data to provide powerful insights and automated trading strategies.

• Overview
• Project Structure
• Key Features
• Architecture
• Technology Stack
• Getting Started
• API Documentation
• Testing
• CI/CD Pipeline
• Documentation
• Contributing
• License

Quantis provides a robust platform for quantitative analysis, algorithmic trading, and investment portfolio optimization. The system leverages advanced statistical models and machine learning algorithms to analyze market data, identify trading opportunities, and execute automated trading strategies. The microservices architecture ensures that each component from data ingestion to strategy execution is independent, resilient, and can be scaled horizontally to handle high-volume, low-latency market operations.

The project is organized into several main components:

Quantis/
├── code/                   # Core backend logic, services, and shared utilities
├── docs/                   # Project documentation
├── infrastructure/         # DevOps, deployment, and infra-related code
├── mobile-frontend/        # Mobile application
├── web-frontend/           # Web dashboard
├── scripts/                # Automation, setup, and utility scripts
├── LICENSE                 # License information
└── README.md               # Project overview and instructions

Quantis's functionality is divided into four main domains, each powered by dedicated services.

• Real-time and historical market data
• Alternative data support
• Automated data validation

• Statistical and machine learning models
• Risk metrics including VaR
• Portfolio optimization tools

• Strategy creation and testing
• Signal generation
• Backtesting and execution

• Asset allocation
• Risk monitoring
• Performance tracking and rebalancing

Quantis follows a microservices architecture, logically grouped into three main service layers, supported by a common infrastructure.

The platform is built using a modern, performant, and well-supported technology stack.

To set up the platform, ensure you have the following installed:

• Python (v3.9+)
• Node.js (v16+)
• Docker and Docker Compose
• Kubernetes (for production deployment)

The recommended way to set up the development environment is using the provided scripts:

Access Points:

• Web Dashboard: http://localhost:3000
• API Documentation: http://localhost:8000/docs
• Monitoring Dashboard: http://localhost:9090

Quantis supports both Docker Compose for local environments and Kubernetes for production deployment.

Quantis exposes a comprehensive, versioned API for all platform interactions, accessible via the API Gateway.

Full API documentation, including request/response schemas, is available at http://localhost:8000/docs.

The project maintains an overall test coverage of 82% across all components, ensuring reliability and accuracy in financial calculations and trading logic.

Testing is categorized into four main types:

Running Tests: All tests can be executed using the pytest command from the root directory: pytest. Specific categories can be targeted (e.g., pytest tests/unit/).

Quantis uses GitHub Actions for continuous integration and deployment:

We welcome contributions to Quantis! To get involved:

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/your-feature-name).
3. Commit your changes and push to the branch.
4. Open a Pull Request for review.

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