china-stock-analysis

China Stock Analysis Skill

A China A-share analysis tool based on value investing theory, intended for ordinary investors who trade infrequently.

When to Use

Invoke this skill when the user requests the following operations:

Analyze a certain A-share stock
Screen stocks that meet the criteria
Compare multiple stocks or stocks within an industry
Calculate stock valuation or intrinsic value
View a stock's financial health
Detect financial anomaly risks

Prerequisites

Python Environment Requirements

pip install akshare pandas numpy

Dependency Check

Before performing any analysis, first check whether akshare is installed:

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

If not installed, prompt the user to install it:

pip install akshare

Core Modules

1. Stock Screener

Screen stocks that meet the criteria

2. Financial Analyzer

In-depth financial analysis of individual stocks

3. Industry Comparator

Horizontal comparison analysis within the same industry

4. Valuation Calculator

Intrinsic value estimation and margin of safety calculation

Workflow 1: Stock Screening

Use when the user requests stock screening.

Step 1: Collect Screening Criteria

Ask the user for screening conditions. Provide the following options for the user to select or customize:

Valuation metrics:

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

Profitability:

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

Growth:

Revenue growth rate: e.g. > 10%
Net profit growth rate: e.g. > 15%
Number of consecutive years of growth: e.g. >= 3 years

Dividends:

Dividend yield: e.g. > 3%
Number of consecutive years of dividend payments: e.g. >= 5 years

Financial safety:

Debt-to-asset ratio: e.g. < 60%
Current ratio: e.g. > 1.5
Quick ratio: e.g. > 1

Screening scope:

All A-shares
CSI 300 constituent stocks
CSI 500 constituent stocks
ChiNext/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

Parameter description:

--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 debt-to-asset ratio
--dividend-min: Minimum dividend yield
--output: Output file path

Step 3: Present Results

Read screening_result.json and present it to the user in table form:

Code	Name	PE	PB	ROE	Dividend Yield	Score
600519	Kweichow Moutai	25.3	8.5	30.2%	2.1%	85

Workflow 2: Stock Analysis

Used when the user requests analysis of a certain stock.

Step 1: Collect Stock Information

Ask the user:

Stock code or name
Analysis depth level:
- Summary level: key metrics + investment conclusion (1 page)
- Standard level: financial analysis + valuation + industry comparison + risk disclosure
- Deep 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

Parameter description:

--code: stock code
--data-type: data type (basic/financial/valuation/holder/all)
--years: how many years of historical data to fetch
--output: output file

Step 3: Run Financial Analysis

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

Parameter description:

--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

Parameter description:

--input: stock data file
--methods: valuation method (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

Read the analysis results and refer to the templates/analysis_report.md template to generate a Chinese analysis report.

Report structure (standard level):

Company Overview: basic information, main business
Financial Health: balance sheet analysis
Profitability: DuPont analysis, profit margin trends
Growth Analysis: revenue/profit growth trends
Valuation Analysis: DCF/DDM/relative valuation
Risk Disclosure: financial anomaly detection, shareholder reductions
Investment Conclusion: overall score, operational recommendations

Workflow 3: Industry Comparison

Step 1: Collect Comparison Targets

Ask the user:

Target stock codes (multiple allowed)
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

Or fetch by industry:

python scripts/data_fetcher.py \
    --industry "白酒" \
    --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

Metric	Kweichow Moutai	Wuliangye	Yanghe Stock	Industry Average
PE	25.3	18.2	15.6	22.4
ROE	30.2%	22.5%	20.1%	18.5%
Gross Margin	91.5%	75.2%	72.3%	65.4%
Score	85	78	75	-

Workflow 4: Valuation Calculator

Step 1: Collect Valuation Parameters

Ask the user for valuation parameters (or use default values):

DCF model parameters:

Discount rate (WACC): default 10%
Forecast period: default 5 years
Perpetual growth rate: default 3%

DDM model parameters:

Required rate of return: Default 10%
Dividend growth rate: Estimated using historical data

Relative valuation parameters:

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

Valuation Method	Intrinsic Value	Current Price	Margin of Safety Price	Conclusion
DCF	¥2,150	¥1,680	¥1,505	Undervalued
DDM	¥1,980	¥1,680	¥1,386	Undervalued
Relative Valuation	¥1,850	¥1,680	¥1,295	Fair

Financial Anomaly Detection

The following anomaly signals are automatically detected during analysis:

Detection Item

Accounts Receivable Anomaly
- Accounts receivable growth > Revenue growth × 1.5
- Accounts receivable turnover days increase significantly
Cash Flow Divergence
- Net profit continues to grow but operating cash flow declines
- Cash-to-revenue ratio < 80%
Inventory Anomaly
- Inventory growth > Revenue growth × 2
- Inventory turnover days increase significantly
Gross Margin Anomaly
- Gross margin volatility > Industry average volatility × 2
- Gross margin deviates severely from peers
Related-Party Transactions
- Related-party transaction ratio too high (> 30%)
Shareholder Reduction
- Recent reduction announcement by major shareholder
- Concentrated reduction by senior management

Risk Level

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

A-Share Specific Analysis

Policy Sensitivity

Provide policy-related notes based on industry classification:

Real Estate: Housing is for living, not for speculation policy
New Energy: Subsidy policy changes
Pharmaceuticals: Impact of centralized procurement policy
Internet: Antitrust, data security

Shareholder Structure Analysis

Type of controlling shareholder (state-owned/private/foreign)
Ownership concentration
Recent increases/decreases in holdings
Pledge ratio

Output Format

JSON Output Format

All scripts output in JSON format for downstream processing:

{
  "code": "600519",
  "name": "贵州茅台",
  "analysis_date": "2025-01-25",
  "level": "standard",
  "summary": {
    "score": 85,
    "conclusion": "低估",
    "recommendation": "建议关注"
  },
  "financials": { ... },
  "valuation": { ... },
  "risks": [ ... ]
}

Markdown Report

Generate a structured Chinese Markdown report, refer to templates/analysis_report.md。

Error Handling

Network Error

If akshare data retrieval fails, prompt the user to:

Check network connection
Retry later (may be API rate limiting)
Try switching to another data source

Invalid Stock Code

Prompt the user to check whether the stock code is correct, and provide possible matching suggestions.

Incomplete Data

For newly listed stocks or cases with incomplete financial data, explain the data limitations and conduct the analysis based on available data.

Best Practices

Data timeliness: Financial data shall be based on the latest quarterly/annual reports, and price data shall be the closing price of the day
Investment advice: All analyses are for reference only and do not constitute investment advice
Risk disclosure: Always include risk disclosures, especially for financial anomaly detection results
Comparative analysis: When analyzing a single stock, automatically include a comparison with the industry average

Important Notes

All analyses are based on public financial data and do not involve any inside information
The parameter assumptions of valuation models have a significant impact on the results and must be explained to the user
The A-share market is significantly affected by policy, so quantitative analysis needs to be combined with qualitative judgment

About this skill

When to Use

Prerequisites

Python Environment Requirements

Dependency Check

Core Modules

1. Stock Screener

2. Financial Analyzer

3. Industry Comparator

4. Valuation Calculator

Workflow 1: Stock Screening

Step 1: Collect Screening Criteria

Step 2: Execute Screening

Step 3: Present Results

Workflow 2: Stock Analysis

Step 1: Collect Stock Information

Step 2: Fetch Stock Data

Step 3: Run Financial Analysis

Step 4: Calculate Valuation

Step 5: Generate Report

Workflow 3: Industry Comparison

Step 1: Collect Comparison Targets

Step 2: Fetch Industry Data

Step 3: Generate Comparison

Step 4: Present Comparison Table

Workflow 4: Valuation Calculator

Step 1: Collect Valuation Parameters

Step 2: Run Valuation

Step 3: Present Valuation Results

Financial Anomaly Detection

Detection Item

Risk Level

A-Share Specific Analysis

Policy Sensitivity

Shareholder Structure Analysis

Output Format

JSON Output Format

Markdown Report

Error Handling

Network Error

Invalid Stock Code

Incomplete Data

Best Practices

Important Notes