mpquant/ashare

股票行情实时数据接口-A股,完全免费的沪深证券股票数据-中国股市,python最简封装的API接口,包含日线,历史K线,分时线,分钟线,全部实时采集,系统包括新浪腾讯双数据核心采集获取,自动故障切换,STOCK数据格式成DataFrame格式,可用来查询研究量化分析,股票程序自动化交易系统.为量化研究者在数据获取...

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Summary Information

Updated 40 minutes ago
Added to GitGenius on September 18th, 2026
Created on June 11th, 2021
Open Issues & Pull Requests: 17 (+0)
GitHub issues: Enabled
Number of forks: 668
Total Stargazers: 3,866 (+0)
Total Subscribers: 27 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 3.9 days
Mean response time: 358.2 days
90th percentile: 1070.6 days
Tracked items: 3

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Open issues: 4
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 911 days
Stale 30+ days: 4
Stale 90+ days: 2

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Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

ashare is a Python library that provides real-time stock market data access for Chinese A-shares through a minimal API wrapper.

The tool solves the problem of obtaining free, real-time Chinese stock data for quantitative research and automated trading systems. It aggregates data from multiple sources including Sina and Tencent, with automatic failover between data providers to ensure reliability. The library returns stock data in DataFrame format, supporting daily candlesticks, historical K-line data, intraday tick data, and minute-level bars. All data collection happens in real time, allowing researchers to focus on strategy development rather than data infrastructure.

Developers building quantitative trading systems or conducting stock market research in China should consider this tool if they need free access to A-share data without managing multiple data source integrations. The dual-source architecture with automatic switching provides resilience against individual provider outages, which is valuable for production trading systems that cannot tolerate data gaps.

The project shows active maintenance with regular updates addressing data source changes and API improvements. Development activity demonstrates responsiveness to issues affecting data collection reliability and user-reported problems with specific stock queries or data format handling.