Description
#Quantitative Trading #Open Source Project #GitHub #vnpy #VeighNa #AI Quantitative #Stocks #Futures #Options
The open-source framework that cannot be ignored in the Chinese quantitative trading circle—vnpy (VeighNa).
It is a quantitative trading system built by traders for traders, which has been continuously developed for many years, from the early event-driven core to a complete trading ecosystem, and now with the addition of the AI module vnpy.alpha in v4.0, it has always emphasized real trading usability and engineering capabilities.
Full Market Support: Covers mainstream interfaces such as domestic CTP, Femas, and also supports international interfaces like Interactive Brokers.
Multi-Asset Trading: Supports asset classes such as futures, options, and stocks.
Rich App Ecosystem: Includes modules for CTA strategy engine, backtesting optimization, options pricing, algorithmic trading, data management, risk control, Web Trader, and more.
AI Module vnpy.alpha: Supports factor engineering, machine learning models, and integrated backtesting research processes, and can be combined with tools like LightGBM for quantitative research.
Production-Grade Usability: Has been used by many private equity firms, brokerages, and futures companies for real trading system construction.
It is a quantitative trading system built by traders for traders, which has been continuously developed for many years, from the early event-driven core to a complete trading ecosystem, and now with the addition of the AI module vnpy.alpha in v4.0, it has always emphasized real trading usability and engineering capabilities.
Software Features
Event-Driven Architecture: Asynchronous and high-performance, suitable for building real trading systems.Full Market Support: Covers mainstream interfaces such as domestic CTP, Femas, and also supports international interfaces like Interactive Brokers.
Multi-Asset Trading: Supports asset classes such as futures, options, and stocks.
Rich App Ecosystem: Includes modules for CTA strategy engine, backtesting optimization, options pricing, algorithmic trading, data management, risk control, Web Trader, and more.
AI Module vnpy.alpha: Supports factor engineering, machine learning models, and integrated backtesting research processes, and can be combined with tools like LightGBM for quantitative research.
Production-Grade Usability: Has been used by many private equity firms, brokerages, and futures companies for real trading system construction.
