Xiaoyuan-Liu

Xiaoyuan-Liu / national-team-position

Public

Claude Code skill：估计 A 股「国家队」(中央汇金) 宽基 ETF 持仓变动趋势 / Estimate China's national team (Central Huijin) broad-base ETF positioning

a-shares agent-skill akshare china-stock-market claude-code
16
0
80% credibility
Found Jun 01, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A tool that estimates China's state investment entity holdings by tracking share changes in major broad-based ETFs listed on the Shanghai Stock Exchange, generating charts and data files showing how these positions evolved over time.

How It Works

1
🔍 You discover a tool for tracking state investments

You become curious about China's state investment activities in the stock market and want to understand how they move.

2
📅 You pick a date range to analyze

You choose a time period to study, like the past year of market activity.

3
🌐 The tool fetches real ETF share data

The tool automatically pulls share data for major market ETFs directly from the Shanghai Stock Exchange.

4
📊 Data gets organized by market index

It organizes all the data by six major market indices, showing how state holdings are distributed.

5
You choose your preferred output format
📈
See colorful charts

The tool creates visual charts showing how holdings changed over time for each index.

📄
Get data files

You get raw data files to study or share with others.

🎉 You see the complete picture of state holdings

You now have a clear picture of estimated state investment holdings across six major indices, with visual charts showing how positions shifted over time.

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Star Growth

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AI-Generated Review

What is national-team-position?

This Python tool estimates how China's "National Team" (Central Huijin) is positioning in the stock market by tracking share changes across six major broad-base ETFs on the Shanghai Stock Exchange. It pulls real-time data from the SSE ETF interface and uses AKShare to fetch index prices, then generates both charts and JSON data showing the historical flow of capital into indices like CSI 300, SSE 50, CSI 500, CSI 1000, CSI A500, and STAR 50.

Run it from the command line with a date range and frequency (weekly or monthly sampling), and it spits out a combined six-panel overview chart plus individual charts per index, along with structured JSON for further analysis.

Why is it gaining traction?

The tool addresses a real gap: there's no official disclosure of national team positions, but ETFs provide a useful proxy through shareholding data. By monitoring ETF scale changes, developers and investors can infer where central government money is flowing. The charts overlay index prices alongside share counts, making it easy to spot correlations between national team activity and market movements. The approach is transparent -- data comes from public exchange feeds -- and the output is immediately actionable whether you're building a dashboard or doing manual research.

Who should use this?

Quantitative researchers and retail investors interested in Chinese market dynamics will find this most useful. It is particularly relevant for those tracking sovereign wealth fund behavior. Academic researchers studying state intervention in equity markets can also use the JSON output for time-series analysis.

Verdict

This is a clever niche tool that solves a specific data problem cleanly. However, the project has only 16 stars and limited documentation, so treat it as a starting point rather than a polished product. The 0.80% credibility score reflects its early-stage nature -- useful for prototyping your own analysis pipeline, but expect to read the source code and handle edge cases yourself.

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