hello245m/free-stockdb

Health v2 · maintenance only

Flags: young

Adoption not part of the score

2276 stars · 342 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

application · maturity active

database search-engine mcp api-framework http-server data-science benchmarking fintech time-series developer-tools windows python cpp rust self-hosted cli 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

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