QUANTAXIS is a Python-based quantitative trading framework that supports backtesting, live trading, and portfolio management across stocks, futures, and options with task scheduling and distributed deployment capabilities.
The framework addresses the need for a complete local quantitative trading solution by integrating data acquisition, backtesting engines, paper trading, live trading execution, and visualization tools in a single system. Its core approach combines a Python interface with a Rust backend (QARS2) to achieve high performance. The tool handles account management through a QIFI-compatible protocol, supports multiple asset classes with specialized order types for each, and provides zero-copy data exchange between Python and Rust components with automatic fallback to pure Python implementations when the Rust extensions are unavailable.
Developers should choose this framework if they need an all-in-one local quantitative trading platform rather than assembling separate components. It suits projects requiring backtesting across multiple asset classes, multi-account management, and the ability to transition from historical analysis to live trading without changing the core codebase. The framework is particularly relevant for teams wanting to avoid cloud dependencies and maintain complete control over their trading infrastructure locally.
The project maintains active development with recent modernization of its Python dependency stack to support versions 3.9 through 3.12, including updates to core libraries like pandas, pymongo, and pyarrow. Performance optimization work is ongoing, evidenced by the integration of Rust components that claim significant speedups in account operations and backtesting. The codebase includes multiple related implementations in different languages, suggesting sustained effort to provide both high-performance and accessible versions of the core trading engine.
