AData is an open-source Python library designed for quantitative A-share trading, providing a unified interface to access diverse financial data. It aims to simplify data acquisition for individual quantitative traders by consolidating information from multiple sources, ensuring high availability through dynamic data source switching and proxy support.

How It Works

The library leverages a multi-source data fusion approach, integrating data from providers like Eastmoney, Tonghuashun, Sina Finance, and Tencent Finance. It offers a Python SDK with functions to retrieve various data types, including stock basic information, market data (daily, weekly, monthly K-lines, intraday, tick data), concept and index data, financial reports, ETF and bond information, and sentiment data like stock unlocks and margin trading balances. Proxy support is included to manage potential API restrictions.

Quick Start & Requirements

• Install via pip: pip install adata
• Official mirror sources are provided for installation.
• Example usage for fetching stock codes and market data is available in the README.
• Data Dictionary and Data List are linked for detailed API usage.

Highlighted Details

• Comprehensive data coverage for A-shares, including stocks, ETFs, bonds, and sentiment indicators.
• Multi-source data fusion with dynamic switching and proxy support for high availability.
• Functions are designed for potential database storage, aligning with data governance principles.
• Active development with a roadmap including funds and bonds.

Maintenance & Community

The project is actively maintained, with a roadmap indicating planned additions of fund and bond data. A WeChat group is available for community exchange and discussion.

Licensing & Compatibility

The project is licensed under a free and open-source model, specifically for A-share quantitative trading. No specific license (e.g., MIT, Apache) is explicitly stated in the README, but the description implies permissive use for personal quantitative trading.

Limitations & Caveats

The README mentions that financial data might be delayed and could be subject to manipulation, advising users to focus on transaction-related data for quantitative and AI training. Some data sources might have specific requirements or re-encoding (e.g., index codes).