Summary

free-stockdb provides a self-contained, locally runnable "quantitative data engine" designed to simplify data acquisition and processing for quantitative finance. It targets engineers, researchers, and power users by delivering a pre-built workflow that includes data sourcing, storage, calculation, and multiple access interfaces, eliminating the need for users to build and maintain complex data pipelines themselves. The primary benefit is immediate access to cleaned, processed historical and near real-time stock market data with zero setup overhead beyond downloading a single executable.

How It Works

This project is presented as a 7-module integrated solution: data source, database, cache, query SDK, calculation engine, and update program. Its core innovation lies in its extreme simplicity and self-sufficiency: a single 2.2MB executable with no external dependencies like Python packages, DLLs, or separate database servers. It automates data cleaning, incremental updates, storage, and provides batch query and calculation interfaces, abstracting away the typical complexities of data ingestion, governance, and processing in quantitative workflows.

Quick Start & Requirements

• Primary install/run command: Download the executable and double-click "数据更新.exe" to update, then double-click the main program to run.
• Prerequisites: None explicitly stated beyond the executable itself. The "exe" file suggests Windows compatibility.
• Setup time: Near-instantaneous via double-click execution.
• Relevant pages: Download links are mentioned but not provided as URLs. GitHub issues link: https://github.com/hello245m/free-stockdb/issues/19

Highlighted Details

• "One-click startup" local quantitative data engine with no external dependencies.
• Core program size is 2.2MB.
• Offers five distinct interfaces: Python SDK (sync/async), HTTP API, Excel/WPS Macro, HTML view, and AI MCP protocol.
• Provides daily and minute-level data for over 7000 stocks and ETFs from the year 2000 to the present, including various forms of data复权 (price adjustments).
• Demonstrates high performance: reading 16.7 million data points in 5 seconds (3.35 million/second).
• Data verification confirms 100% alignment with mainstream financial data services for historical adjusted prices.

Maintenance & Community

The project links to GitHub issues for discussions regarding its open-source aspects. No specific community channels (like Discord or Slack) or details on active maintainers or sponsorships are provided in the README.

Licensing & Compatibility

The core algorithms are explicitly stated as closed-source ("闭源保护核心算法"). While the project is described as a "public welfare project" and can be "sandboxed," a formal open-source license (like MIT, Apache, GPL) is not specified. This implies potential restrictions on commercial use or integration into closed-source products, and users concerned about proprietary code should consider sandboxing. Compatibility is implied for Windows due to the .exe format.

Limitations & Caveats

The core proprietary algorithms are not open-source, necessitating sandboxing for users with strict security or intellectual property requirements. The README implies Windows-centric deployment via executable files, with no explicit mention of cross-platform support (Linux, macOS).