china-stock-analysis_skill

This skill helps you analyze Chinese A-share stocks with screening, deep financials, industry comparison, and valuation to identify value plays.
  • Python

61

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill sugarforever/01coder-agent-skills --skill china-stock-analysis

  • SKILL.md9.5 KB

Overview

This skill is a China A-share value investing analysis tool for low-frequency retail investors. It provides stock screening, single-stock deep analysis, industry comparison, valuation calculation, and financial anomaly detection using public data fetched via akshare. The tool emphasizes value-investing metrics and produces JSON and Markdown outputs suitable for reporting and further processing.

How this skill works

The skill fetches public financial and market data via akshare, computes key ratios (PE, PB, ROE, growth rates, cash flow metrics), runs anomaly detection rules, and applies valuation methods (DCF, DDM, relative). Users provide targets or screening criteria; the skill returns structured results including scores, risk flags, and valuation conclusions. Outputs are JSON for programmatic use and an optional Markdown report for human review.

When to use it

  • Screen a universe of A-share stocks by valuation, profitability, growth, dividend, or balance-sheet safety
  • Request a summary, standard, or deep analysis of a specific A-share stock
  • Compare several peers or an industry cohort on standardized financial metrics
  • Estimate intrinsic value and safety-margin using DCF, DDM, or relative valuation
  • Detect financial red flags such as receivables, cash-flow divergence, inventory or related-party concerns

Best practices

  • Ensure akshare and required Python libs (pandas, numpy) are installed and up to date before running analyses
  • Use the latest quarterly or annual reports for financial inputs and use closing price for valuation comparisons
  • Combine quantitative outputs with qualitative checks (policy sensitivity, corporate governance, shareholder actions)
  • Treat results as reference only; clearly state model assumptions (discount rate, growth) when sharing valuations
  • Include anomaly detection and risk grade in every report to highlight potential accounting or operational issues

Example use cases

  • Run a screener to find hs300 stocks with PE < 15, ROE > 15% and debt ratio < 60%
  • Generate a standard analysis for code 600519 with 5 years of history and DCF + relative valuation
  • Compare top 5 companies in the liquor industry on PE, ROE, gross margin and composite score
  • Calculate intrinsic price and safety-margin for a dividend stock using DDM and historical payout trends
  • Automatically flag companies where operating cash flow diverges from net profit or receivables spike

FAQ

It uses public financial and market data fetched via akshare; ensure akshare is installed and network access is available.

Are the results investment advice?

No. Analyses are for informational purposes only and should not be treated as personalized investment advice.

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