# hello245m/free-stockdb

面向 A 股日K、分钟K与ETF分钟数据的本地量化引擎，集成增量同步、本地缓存、复权、批量查询、回测与指标计算。

Repository: https://github.com/hello245m/free-stockdb
Canonical: https://ross.abutalabs.com/products/free-stockdb
Language: HTML
License: MIT
License Family: permissive
Topics: a-share, algorithmic-trading, backtesting, china-stock-market, kline, local-first, market-data, mcp, quant-research, quantitative-finance, stock-data, stock-market, technical-analysis, time-series-database
Last push: 2026-08-23T00:14:35+00:00

## Health v2 (maintenance only)
Score: 68/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 61, longevity 8
- inputs: {"age_days": 117, "days_push": 11, "days_rel": 45, "gap_med": null, "n_releases_24m": 1}
- flags: young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2276, forks 342 (observed 2026-08-28T04:06:33.297848+00:00)

## What it is
A local-first quantitative data engine for China A-share daily/minute K-lines and ETF minute/tick data, combining incremental sync, cleaning, price adjustment, a custom C++ time-series store, and a Rust-based indicator/backtest computation core. It exposes one local protocol through five access modes: Python SDK, HTTP API, Excel/WPS, HTML web UI, and AI MCP.

## Use cases
- run full-market backtests over 7000+ A-share stocks without remote API rate limits
- query minute-level K-line data for thousands of tickers locally
- compute technical indicators like MACD and KDJ in batch across the whole market
- get forward/backward adjusted prices with built-in adjustment factors
- feed stock market data to an AI assistant via MCP
- analyze stock data in Excel or WPS without writing code
- serve local market data to a team over LAN for distributed backtesting
- keep researching offline with all market data stored on disk

## When to choose
- you need whole-market minute-level A-share or ETF data for backtesting and remote APIs are too slow or rate-limited
- you want to avoid weeks of data engineering (sync, cleaning, adjustment factors, storage) before running your first strategy
- you need offline, local-first access to market data with no dependency on remote request chains
- you want one data layer serving Python, HTTP, Excel, web, and AI/MCP clients
- you need fast batch indicator computation faster than pandas can deliver

## When to avoid
- you need non-Chinese markets or global equities data
- you only need low-frequency fundamentals like financial reports or macro data (supported only as custom extensions)
- you require a managed cloud service rather than a self-hosted local engine
- you need real-time streaming tick feeds rather than synced historical data
- you want a fully open data pipeline - the binary is distributed via releases and third-party download links rather than buildable source

## Facets
- artifact type: application
- maturity: active
- function: database, search-engine, mcp, api-framework, http-server, data-science, benchmarking
- domain: fintech, time-series, developer-tools
- platform: windows, python, cpp, rust, self-hosted, cli
- tags: a-share, china-stock-market, kline, market-data, backtesting, technical-analysis, local-first, quant-research, stock-data, etf, incremental-sync, zstd-compression, excel-integration, mcp-server, time-series-engine, adjustment-factors, sector-mapping, offline-data, time-series-database, quantitative-finance, data-engineering, algorithmic-trading, macos, linux, desktop

## Member repositories
- hello245m/free-stockdb (main) score 68

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:33.297848+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:41:27.603809+00:00, confidence not recorded.
  - readme: https://github.com/hello245m/free-stockdb (fetched 2026-08-28T04:06:33.297848+00:00, sha 6f6dba4e74a0)
- Data as of 2026-08-30T08:39:29.467469+00:00.