Financial Risk Scanner

Scan listed company financial statements for potential fraud signals and accounting anomalies using Tushare data APIs.

Quick Start

python3 scripts/analyze_company.py <ts_code>

Example:

python3 scripts/analyze_company.py 000001.SZ

Workflow

1. Fetch Financial Data: Retrieve 10+ years of annual financial statements (balance sheet, income statement, cash flow statement) via Tushare
2. Calculate Risk Metrics: Compute 21 risk indicators with historical trend analysis
3. Cross-validate: Check company announcements for supporting evidence when anomalies detected
4. Generate Report: Produce structured risk assessment with severity ratings and recommendations

Risk Indicators

Key Metrics Formulas

Cash-Debt Paradox Ratio = (Cash / Total Assets) × (Interest Debt / Total Assets)
Cash-Profit Ratio = Operating Cash Flow / Net Profit (threshold: < 0.5 for 3+ years)
Receivables Growth Ratio = Receivables Growth Rate / Revenue Growth Rate
Inventory Turnover Ratio = COGS / Average Inventory
Gross Margin = (Revenue - COGS) / Revenue
Debt Ratio = Total Liabilities / Total Assets
Liquidity Pressure = Short-term Borrowing / Cash Balance

References

For detailed detection logic and thresholds, see references/risk_indicators.md.

Cross-Validation Sources

When anomalies are detected, cross-validate with:

• Company announcements (via Tushare announcement API)
• Audit reports in annual reports
• Regulatory filings and enforcement notices
• News and media coverage

Output Format

Reports are saved to ~/.openclaw/workspace/memory/financial-risk/_.md

Report sections:

1. Company Overview: Basic info, industry, listing date
2. Risk Summary: Total risks, severity distribution, top concerns
3. Detailed Analysis: Each indicator with historical trend charts
4. Cross-validation: Supporting evidence from announcements
5. Recommendations: Priority actions and monitoring points

Severity Levels

Environment Variables

How to obtain TUSHARE_TOKEN:

1. Register at https://tushare.pro/register
2. Get your token from the user center
3. Set environment variable:

```bash

export TUSHARE_TOKEN="your_token_here"

```

Python Dependencies

Install dependencies:

pip install tushare pandas

Notes

• Annual data preferred for stability; quarterly available for recent trends
• Industry comparison essential for margin and expense analysis
• Historical context matters: 10+ years for trend significance

Limitations

Data Sources

Provenance