Guosen Securities Smart Stock Picking
Paste the following prompt into your AI chat to install this skill:
To install @guosen-securities/guosen-stock-market-query in your AI assistant, follow the guide at https://skillhub.cn/install/skillhub.md.
About this skill
Problem to Solve
When screening stocks, engineers and analysts often have to switch between financial metrics, technical signals, sector filters, and fund-flow data, then manually assemble API conditions. The Guosen Securities smart stock-picking skill compresses this into a single natural-language query, such as tech stocks with PE under 20 and net-profit growth above 20%, and returns a structured list of matching instruments.
How It Works
The skill calls the MCP endpoint smart_stock_picking with these key inputs:
- searchstring: a Chinese condition, for example today's limit-up stocks or stocks with net inflow of main capital
- searchtype: asset scope, supporting stock, fund, HK_stock, US_stock, NEEQ, and index
- apiKey: Guosen API credential, typically stored as gs_api_key in SECRET.md
The script entry point is gs_stock_picking.py. It prints the query context, execution status, and result table; status 0 means success and -1 means failure. This makes it useful as a structured screening step inside an agent workflow.
Boundaries and Caveats
- Results show at most 100 matching stocks, and ordering may change due to system state.
- Screening results may differ from real-time quotes and are not investment advice.
searchstringshould express the condition clearly; vague or inconsistent wording can affect the returned fields and result completeness.
Use Cases
- Before a morning research meeting, filter candidate tech stocks by P/E, net-profit growth, and sector.
- Monitor intraday fund flow and screen stocks with net inflow of main capital for review.
- Compare HK and A-share conditions, then find low-valuation names by P/B and market-cap range.
- Screen indices or funds using natural-language conditions to build a sector-aware asset pool.
Best For
- Securities research analysts: need to build candidate stock pools using financial and technical indicators.
- Quant strategy engineers: need to convert natural-language screening conditions into structured API query results.
- Financial product managers: need to validate how the picking API returns sector and fund-flow conditions.
- Investment assistants: need to query limit-up, limit-down, and main-capital-inflow names for intraday monitoring.
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