China Stock Analysis Skill

When to Use

• Analyze a specific A-share stock
• Screen stocks that meet the criteria
• Compare multiple stocks or stocks within the same industry
• Calculate stock valuation or intrinsic value
• Check the financial health of a stock
• Detect financial anomaly risks

Prerequisites

Python Environment Requirements

pip install akshare pandas numpy

Dependency Check

python -c "import akshare; print(akshare.__version__)"

pip install akshare

Core Modules

1. Stock Screener (股票筛选器)

2. Financial Analyzer (财务分析器)

3. Industry Comparator (行业对比)

4. Valuation Calculator (估值计算器)

Workflow 1: Stock Screening

Step 1: Collect Screening Criteria

• PE (Price-to-Earnings Ratio): e.g., PE < 15
• PB (Price-to-Book Ratio): e.g., PB < 2
• PS (Price-to-Sales Ratio): e.g., PS < 3

• ROE (Return on Equity): e.g., ROE > 15%
• ROA (Return on Assets): e.g., ROA > 8%
• Gross Profit Margin: e.g., > 30%
• Net Profit Margin: e.g., > 10%

• Revenue Growth Rate: e.g., > 10%
• Net Profit Growth Rate: e.g., > 15%
• Number of Consecutive Growth Years: e.g., >= 3 years

• Dividend Yield: e.g., > 3%
• Number of Consecutive Dividend Years: e.g., >= 5 years

• Asset-Liability Ratio: e.g., < 60%
• Current Ratio: e.g., > 1.5
• Quick Ratio: e.g., > 1

• All A-shares
• CSI 300 Constituent Stocks
• CSI 500 Constituent Stocks
• ChiNext Board/STAR Market
• User-defined list

Step 2: Execute Screening

python scripts/stock_screener.py \
    --scope "hs300" \
    --pe-max 15 \
    --roe-min 15 \
    --debt-ratio-max 60 \
    --dividend-min 2 \
    --output screening_result.json

• --scope

  : Screening scope (all/hs300/zz500/cyb/kcb/custom:600519,000858,...)

• --pe-max/--pe-min

  : PE range

• --pb-max/--pb-min

  : PB range

• --roe-min

  : Minimum ROE

• --growth-min

  : Minimum growth rate

• --debt-ratio-max

  : Maximum asset-liability ratio

• --dividend-min

  : Minimum dividend yield

• --output

  : Output file path

Step 3: Present Results

screening_result.json

Workflow 2: Stock Analysis

Step 1: Collect Stock Information

1. Stock code or name
2. Analysis depth level:
   • Summary Level: Key indicators + investment conclusion (1 page)
   • Standard Level: Financial analysis + valuation + industry comparison + risk warning
   • In-depth Level: Complete research report including historical data tracking

Step 2: Fetch Stock Data

python scripts/data_fetcher.py \
    --code "600519" \
    --data-type all \
    --years 5 \
    --output stock_data.json

• --code

  : Stock code

• --data-type

  : Data type (basic/financial/valuation/holder/all)

• --years

  : Number of years of historical data to retrieve

• --output

  : Output file

Step 3: Run Financial Analysis

python scripts/financial_analyzer.py \
    --input stock_data.json \
    --level standard \
    --output analysis_result.json

• --input

  : Input stock data file

• --level

  : Analysis depth (summary/standard/deep)

• --output

  : Output file

Step 4: Calculate Valuation

python scripts/valuation_calculator.py \
    --input stock_data.json \
    --methods dcf,ddm,relative \
    --discount-rate 10 \
    --growth-rate 8 \
    --output valuation_result.json

• --input

  : Stock data file

• --methods

  : Valuation methods (dcf/ddm/relative/all)

• --discount-rate

  : Discount rate (%)

• --growth-rate

  : Perpetual growth rate (%)

• --margin-of-safety

  : Margin of safety (%)

• --output

  : Output file

Step 5: Generate Report

templates/analysis_report.md

1. Company Overview: Basic information, main business
2. Financial Health: Balance sheet analysis
3. Profitability: DuPont analysis, profit margin trends
4. Growth Analysis: Revenue/profit growth trends
5. Valuation Analysis: DCF/DDM/Relative Valuation
6. Risk Warning: Financial anomaly detection, shareholder reduction
7. Investment Conclusion: Comprehensive score, operation recommendations

Workflow 3: Industry Comparison

Step 1: Collect Comparison Targets

1. Target stock codes (multiple allowed)
2. Or: Industry classification + number of comparisons

Step 2: Fetch Industry Data

python scripts/data_fetcher.py \
    --codes "600519,000858,002304" \
    --data-type comparison \
    --output industry_data.json

python scripts/data_fetcher.py \
    --industry "Liquor" \
    --top 10 \
    --output industry_data.json

Step 3: Generate Comparison

python scripts/financial_analyzer.py \
    --input industry_data.json \
    --mode comparison \
    --output comparison_result.json

Step 4: Present Comparison Table

Workflow 4: Valuation Calculator

Step 1: Collect Valuation Parameters

• Discount Rate (WACC): Default 10%
• Forecast Period: Default 5 years
• Perpetual Growth Rate: Default 3%

• Required Rate of Return: Default 10%
• Dividend Growth Rate: Calculated using historical data

• Comparison Benchmark: Industry average / historical average

Step 2: Run Valuation

python scripts/valuation_calculator.py \
    --code "600519" \
    --methods all \
    --discount-rate 10 \
    --terminal-growth 3 \
    --forecast-years 5 \
    --margin-of-safety 30 \
    --output valuation.json

Step 3: Present Valuation Results

Financial Anomaly Detection

Detection Items

1. Abnormal Accounts Receivable
   • Accounts receivable growth rate > Revenue growth rate × 1.5
   • Significant increase in accounts receivable turnover days
2. Cash Flow Divergence
   • Net profit continues to grow but operating cash flow declines
   • Cash-to-revenue ratio < 80%
3. Abnormal Inventory
   • Inventory growth rate > Revenue growth rate × 2
   • Significant increase in inventory turnover days
4. Abnormal Gross Profit Margin
   • Gross profit margin volatility > Industry average volatility × 2
   • Gross profit margin significantly deviates from peers
5. Related Party Transactions
   • High proportion of related party transactions (> 30%)
6. Shareholder Reduction
   • Recent reduction announcements by major shareholders
   • Concentrated reduction by executives

Risk Levels

• 🟢 Low Risk: No obvious anomalies
• 🟡 Medium Risk: 1-2 minor anomalies
• 🔴 High Risk: Multiple anomalies or severe anomalies

A-Share Specific Analysis

Policy Sensitivity

• Real Estate: "House is for living, not for speculation" policy
• New Energy: Changes in subsidy policies
• Pharmaceuticals: Impact of centralized procurement policies
• Internet: Anti-monopoly, data security

Shareholder Structure Analysis

1. Controlling shareholder type (State-owned enterprise/Private enterprise/Foreign capital)
2. Share concentration
3. Recent increase/reduction situations
4. Pledge ratio

Output Format

JSON Output Format

{
  "code": "600519",
  "name": "Kweichow Moutai",
  "analysis_date": "2025-01-25",
  "level": "standard",
  "summary": {
    "score": 85,
    "conclusion": "Undervalued",
    "recommendation": "Recommended to pay attention"
  },
  "financials": { ... },
  "valuation": { ... },
  "risks": [ ... ]
}

Markdown Report

templates/analysis_report.md

Error Handling

Network Errors

1. Check network connection
2. Retry later (may be due to interface rate limiting)
3. Try changing data sources

Invalid Stock Code

Incomplete Data

Best Practices

1. Data Timeliness: Financial data is based on the latest quarterly/annual report, and price data is the closing price of the day
2. Investment Recommendations: All analysis is for reference only and does not constitute investment advice
3. Risk Warning: Always include risk warnings, especially the results of financial anomaly detection
4. Comparative Analysis: When analyzing individual stocks, automatically include industry average comparisons

Important Notes

• All analysis is based on public financial data and does not involve any insider information
• Parameter assumptions in valuation models have a significant impact on results, which should be explained to users
• The A-share market is greatly affected by policies, so quantitative analysis should be combined with qualitative judgment