Topics / #a-stock

A-stock projects on GitHub

Quantitative data pipelines and daily analysis for Chinese A-share stock trading.

As of , the most-starred A-stock projects on GitHub are daily_stock_analysis (67.2K stars), UZI-Skill (7.4K stars) and tick-stock-panel (6.4K stars). GitHub.Locker tracks 5 A-stock repositories with 85.3K stars combined. The fastest-growing on October 27 was Vibe-Research, with +210 stars in a day.

5Projects
85.3KStars in total
+301Stars in a day

Hot right now

Most stars gained in a day · Oct 27

The all-time top

5 projects · by stars

  1. #1 · ZhuLinsen/daily_stock_analysis

    daily_stock_analysis

    An AI-driven stock analysis system that pulls market data and news for A-share, Hong Kong, US, Japanese, Korean and Taiwanese stocks, then generates a daily AI decision report and pushes it to WeChat Work, Feishu, Telegram, Discord, Slack or email.

    67.2K
  2. #2 · wbh604/UZI-Skill

    UZI-Skill

    A free skill that turns your AI coding agent into a personal stock analyst: give it a ticker and it pulls 22 dimensions of data, runs 22 institutional methods (DCF, comps, LBO), lets 66 investor personas vote, and outputs a ~600KB Bloomberg-style HTML report in 5-8 minutes. Covers A-shares, Hong Kong and US stocks.

    7.4K
  3. #3 · shy3130/tick-stock-panel

    tick-stock-panel

    TSP is a self-hosted, zero-ops quantitative trading workbench for China's A-share stock market that combines stock screening, real-time anomaly monitoring, and strategy backtesting, with optional LLM-assisted analysis.

    6.4K
  1. 4

    Vibe-Research

    3.4Ksimonlin1212/Vibe-Research · TypeScript

    Vibe-Research:A 股、美股、港股的投研工作台 · 自选与持仓、每日复盘、产业资讯、产业研究、个股深挖、回测、研报库,168 个数据端点。支持 Claude Code、Codex、WorkBuddy 订阅或任意模型 API。 | A research workbench for China A-shares, US and HK stocks.

  2. 5

    CNEquity

    890rootSunc/CNEquity · Python

    CNEquity is local data infrastructure for the Chinese A-share market: it organizes stock quotes, futures, fundamentals, corporate events and capital-flow data from multiple sources into a local Parquet data lake with incremental collection, resumable failures, quality audits and unified querying.

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