TuShare

TuShare operates as a free, open-source Python financial data interface package, primarily serving the Chinese market. The platform was developed to streamline the process of data acquisition for quantitative investment analysts and developers, enabling them to focus on strategy and model development rather than data collection and cleaning. It aggregates financial data from various sources, including stock exchanges and major financial news portals.

The service provides extensive data categories such as historical and real-time stock quotes, fundamental data like financial reports and corporate information, macroeconomic data, and news events. Its initial version was released around January 2015. The platform is known for its seamless integration with the pandas library, a popular tool for data analysis in Python, returning most data in the form of a DataFrame. This structure facilitates easy analysis and visualization. Users can also store the data in various formats like CSV, Excel, or relational databases for offline analysis.

The project was started by an individual known as '米哥' (Mi Ge), who developed the platform in their spare time. The motivation was to address the challenges financial analysts face in obtaining clean and diverse datasets. Over time, TuShare evolved with community feedback and contributions, leading to the launch of TuShare Pro. This enhanced version offers more stable and higher-quality data that is processed and stored in a database before being provided to users, as opposed to the original version which scraped data directly from web sources. The Pro version operates on a freemium model, where users register to get a token for API access. While basic access is free, higher data access permissions are granted based on a points system, which can be earned by contributing to the community or through a paid membership that funds server and bandwidth upgrades.

The target audience includes quantitative analysts, individual and institutional researchers conducting financial market analysis, and companies developing finance-related products. The platform offers SDKs and a RESTful HTTP interface to accommodate users with different technical backgrounds. The data scope has expanded to cover not only stocks but also funds, futures, bonds, foreign exchange, and even digital currencies.

Keywords: financial data API, Python financial data, quantitative analysis tools, Chinese stock market data, market data provider, open-source finance, quantitative trading, financial data analysis, stock market API, pandas DataFrame, data acquisition, algorithmic trading, investment research, macroeconomic data, financial modeling