Quantex - Quantitative Trading Library

Overview

Quantex is a lightweight, open-source Python library designed for building, backtesting, and deploying quantitative trading strategies. The library provides a clean, event-driven architecture that maintains code consistency between backtesting and live trading environments, making it an ideal foundation for systematic trading research and deployment.

Current Features

Core Architecture

• Event-driven design with shared codebase between backtesting and live trading
• Modular data source abstraction supporting CSV, Parquet, and custom data feeds
• Immutable data models using Python dataclasses for market data (Bar, Tick)
• Stateful portfolio management with automatic P&L tracking and position accounting
• Strategy base class with intuitive trading API (buy(), sell(), close_position())

Data Management

• Multiple data source formats: CSV and Parquet file support out-of-the-box
• Time-aligned processing: Automatic synchronization across multiple data sources
• Historical data access: Built-in lookback functionality for technical analysis
• Missing data handling: Robust handling of gaps in time series data

Backtesting Engine

• High-fidelity simulation with realistic execution modeling
• Performance metrics: Sharpe ratio, total returns, and extensible metrics framework
• Order execution simulation with configurable commission and slippage
• Portfolio tracking: Real-time NAV calculation and position monitoring

Trading Infrastructure

• Position management: Automatic tracking of long/short positions with average cost basis
• Order management: Support for market and limit orders with proper validation
• Risk controls: Cash availability checks and position sizing constraints
• Trade history: Complete audit trail of all orders and fills

Development & Testing

• Comprehensive test suite with >90% code coverage using pytest
• Type safety with full type annotations throughout the codebase
• Code quality tools: Black formatting, Ruff linting, and pre-commit hooks
• Documentation: Auto-generated API docs with MkDocs and comprehensive guides

Key Design Principles

• Python-first approach with modern language features (Python 3.13+)
• Immutable data structures for market data to prevent accidental modifications
• Dependency injection for data sources and execution simulators
• Plugin architecture for extending functionality without core modifications

Technologies Used

Core Dependencies

• Python 3.13+: Latest language features and performance improvements
• Pandas: Time series data manipulation and analysis
• FastParquet: High-performance Parquet file I/O
• NumPy: Numerical computing for performance-critical calculations

Development Stack

• Poetry: Modern dependency management and packaging
• pytest: Testing framework with parallel execution support
• Black + Ruff: Code formatting and linting
• pre-commit: Automated code quality checks
• MkDocs: Documentation generation with Material theme

CI/CD & Documentation

• GitHub Actions: Automated testing and documentation deployment
• GitHub Pages: Hosted documentation with auto-generated API reference
• pytest-xdist: Parallel test execution for faster development cycles