Development framework for open-source quantitative trading platform based on Python
VeighNa is a Python-based open-source quantitative trading system development framework, officially released in January 2015, that has grown step by step to become the market's leading open-source backtesting framework.
More than a backtesting framework, VeighNa is a full-featured quantitative trading platform with 6 years of continuous contributions from the open-source community. Currently, it has many users, including hedge funds, investment banks, futures brokers, university research institutions, proprietary trading companies, etc.
✔️ Rich interface
Support a large number of high-performance trading Gateway interfaces, including futures, options, stocks, futures options, gold T + d, interbank fixed income, foreign markets, etc.
✔️ Easy to Extend
Combining the core architecture of the event-driven engine and the glue language features of Python, users can quickly interface with new trading interfaces or develop upper-level strategy applications according to their needs.
✔️ Plug and play
Built-in many mature quantitative trading strategy app modules, users can freely choose to manage through the GUI graphical interface mode or use the CLI script command line mode operation.
✔️ Open source
Follow the open and flexible MIT open source protocol, you can get all the project source code on Github, free to use in their own open source projects or commercial projects, and free forever!
VeighNa’s Quantitative Strategies
CTA strategy (CtaStrategy)
Rapid development of trend-based quantitative strategies based on strategy templates, supporting K-line and Tick backtesting, providing multi-process and genetic algorithm parameter optimization
Algorithmic Trading (AlgoTrading)
Provides a variety of intelligent trading algorithms: TWAP, Sniper, Iceberg, BestLimit, Arbitrage, Grid, DMA, etc.
Spread arbitrage (SpreadTrading)
Supports one active leg and multiple passive legs, automatically calculates spread positions and combinations of positions based on any spread ratio, realizing spread algorithmic trading
Options Strategy (OptionMaster)
Built-in Volatility Surface Calculation, Portfolio Greeks Risk Control, Automated Delta Hedging and Volatility Algorithmic Trading for Volatility Arbitrage Strategies
Ticker Tape (DataRecorder)
Record Tick or K line lines to the database in real time according to demand, graphically manage the model, and meet the needs of strategic retrospection and real-world initialization
CTA strategy graphical backtesting (CtaBacktester), CSV data loading tool (CsvLoader), trading risk management (RiskManager), etc.