Welcome to the comprehensive documentation for Quantis - a quantitative trading and investment analytics platform that combines advanced statistical models, machine learning algorithms, and real-time market data.
- Installation Guide - Installation options, prerequisites, and setup instructions
- Quick Start Guide - Get up and running in 3 steps
- Configuration - Environment variables and configuration options
- Usage Guide - Common usage patterns for CLI and library
- API Reference - Complete REST API documentation with examples
- CLI Reference - Command-line interface commands and flags
- Architecture Overview - System design and module structure
- Feature Matrix - Comprehensive feature table and capabilities
- Examples - Working code examples demonstrating key features
- Troubleshooting Guide - Common issues and solutions
- Contributing Guide - How to contribute to the project
- Test Output - Latest test execution results
- Deliverable Checklist - Documentation completeness verification
Quantis is a comprehensive platform for quantitative trading analysis, featuring microservices architecture, ML-powered prediction models, and real-time data processing.
# 1. Clone and navigate to the repository
git clone https://github.com/quantsingularity/Quantis.git && cd Quantis
# 2. Run the automated setup script
./scripts/setup_quantis_env.sh
# 3. Start all services
./scripts/run_quantis.sh devAfter starting the services:
- Web Dashboard: http://localhost:3000
- API Documentation: http://localhost:8000/docs
- Monitoring Dashboard: http://localhost:9090
- Grafana: http://localhost:3000/grafana
Quantis provides a robust platform for:
- Quantitative Analysis: Statistical models, ML algorithms, and time series forecasting
- Algorithmic Trading: Automated strategy execution with backtesting capabilities
- Portfolio Management: Optimization, risk management, and performance analytics
- Real-time Data Processing: Market data ingestion, validation, and analysis
- Comprehensive Monitoring: Prometheus metrics, Grafana dashboards, and audit logging
| Capability | Description |
|---|---|
| Data Processing | Real-time market data, historical analysis, alternative data integration |
| ML Models | Time series forecasting (LSTM, Temporal Fusion Transformer), classification, regression |
| Trading Strategies | Strategy development, backtesting, signal generation, automated execution |
| Risk Management | VaR calculation, stress testing, portfolio optimization (MPT) |
| API-First Design | RESTful API with FastAPI, comprehensive authentication and rate limiting |
| Scalable Infrastructure | Docker, Kubernetes, Terraform support for cloud deployment |
- Backend: Python 3.9+, FastAPI, SQLAlchemy, Celery
- ML/Analytics: scikit-learn, PyTorch, pandas, numpy
- Databases: PostgreSQL (relational), InfluxDB (time series), Redis (caching)
- Frontend: React, TypeScript, Redux Toolkit, D3.js, TradingView
- Infrastructure: Docker, Kubernetes, Terraform, Ansible
- Monitoring: Prometheus, Grafana, MLflow
Quantis/
├── code/
│ ├── api/ # FastAPI backend application
│ ├── data/ # Data processing modules
│ ├── models/ # ML model training and serving
│ └── scripts/ # Utility scripts
├── docs/ # This documentation
├── infrastructure/ # Docker, Kubernetes, Terraform configs
├── monitoring/ # Prometheus, Grafana dashboards
├── scripts/ # Build, test, and deployment scripts
├── tests/ # Test suites
└── web-frontend/ # React web application