vnpy/vnpy

Health v2 · maintenance only

Adoption not part of the score

44786 stars · 12404 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

VeighNa (vn.py) is an open-source Python framework for developing quantitative trading systems, covering strategy development, backtesting, live trading, and market data management. Its 4.x release adds an alpha module for multi-factor machine learning strategy research, including feature engineering, model training (Lasso, LightGBM, MLP), and backtesting workflows.

Use cases

• develop and backtest CTA trading strategies in Python
• connect to futures, options, and stock exchange gateways for live trading
• run algorithmic order execution like TWAP or iceberg orders
• train multi-factor machine learning models on stock market data
• record and manage historical tick and bar market data
• trade volatility arbitrage and options strategies with delta hedging
• optimize strategy parameters with genetic algorithms

When to choose

• you need a full-stack quant trading platform in Python with exchange connectivity
• you want both GUI and CLI modes for strategy management
• you are doing factor-based ML research and live trading in one framework
• you trade Chinese futures, options, or A-share markets

When to avoid

• you only need simple backtesting without live trading infrastructure
• you require crypto exchange trading as the primary focus
• you need a lightweight library rather than a full platform
• your stack is not Python

Facets

framework · maturity stable

trading machine-learning data-science gui cli webhook fintech machine-learning data-science python windows cross-platform quantitative-trading algotrading backtesting cta-strategies event-driven-engine market-data gateway-interfaces factor-modeling ml-strategies trading quantitative-finance linux macos

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