A Python wrapper for the Shanghai Futures Exchange CTP API, enabling programmatic trading and market data access.
CTPWrapper is a Python wrapper for the Shanghai Futures Exchange's Comprehensive Transaction Platform (CTP) API. It enables Python developers to programmatically access real-time market data, execute trades, and manage accounts for futures trading on Chinese exchanges. The project bridges the gap between CTP's native C++ API and the Python ecosystem.
Quantitative developers, algorithmic traders, and financial technology teams working with Chinese futures markets who need programmatic access to Shanghai Futures Exchange data and trading capabilities.
CTPWrapper provides the only open-source Python interface to the official CTP API, offering better performance through Cython integration and specific optimizations for PyPy3 compared to alternative implementations.
上海期货交易所CTP接口 Shanghai Future CTP Interface CTP Python API Wrapper
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Uses Cython for direct bindings to the C++ API, ensuring low-latency data access critical for algorithmic trading, as highlighted in the Python Native Interface feature.
Specifically optimized for PyPy3-3.6 on Linux 64-bit, offering potential speed improvements for computational tasks, as noted in the README's special support section.
Provides a clean Pythonic interface to the official CTP API, enabling precise control over real-time market data, order placement, and account management for futures trading.
Regular updates on PyPI, CI/CD badges, and GitHub actions show ongoing maintenance, reducing the risk of abandonment for critical trading systems.
README admits occasional lack of trading data streams, requiring workarounds like time.sleep(2) after Init(), which can disrupt real-time applications and indicate underlying stability problems.
UnicodeDecodeError issues with GBK encoding, as shown in the provided temporary fix, reveal poor string handling and lack of robust error management in the wrapper.
No comprehensive Python API docs; users must rely on CTP's original documentation and limited samples, increasing the learning curve and debugging effort.