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Financial Analyzer

by @jason-aka-chen

AI-powered financial analysis assistant for financial statement analysis, ratio analysis, cash flow analysis, investment evaluation, and financial health ass...

Versionv1.0.0
Downloads658
#finance#legal#health
TERMINAL
clawhub install financial-analyzer

📖 About This Skill

name: financial-analyzer description: AI-powered financial analysis assistant for financial statement analysis, ratio analysis, cash flow analysis, investment evaluation, and financial health assessment. Supports Chinese and international accounting standards. Essential tool for investors and analysts. tags: - finance - analysis - investment - financial-statements - valuation - accounting version: 1.0.0 author: chenq

Financial Analyzer

AI-powered financial analysis and investment evaluation tool.

Features

1. Financial Statement Analysis

  • Balance Sheet: Assets, liabilities, equity analysis
  • Income Statement: Revenue, expenses, profit analysis
  • Cash Flow Statement: Operating, investing, financing
  • Statement of Changes: Equity changes tracking

    • 2. Ratio Analysis

  • Liquidity Ratios: Current, quick, cash ratio
  • Solvency Ratios: Debt, interest coverage, D/E
  • Profitability Ratios: ROE, ROA, margins
  • Efficiency Ratios: Turnover, asset utilization
  • Market Ratios: P/E, P/B, PEG, dividend yield

    • 3. Cash Flow Analysis

  • Operating Cash Flow: Quality of earnings
  • Free Cash Flow: Valuation and health
  • Cash Conversion: Efficiency metrics
  • Burn Rate: Startup sustainability

    • 4. Investment Evaluation

  • DCF Valuation: Discounted cash flow
  • Relative Valuation: Peer comparison
  • Graham Number: Value investing
  • Intrinsic Value: Multiple methods

    • 5. Risk Assessment

  • Altman Z-Score: Bankruptcy prediction
  • Piotroski F-Score: Financial health
  • Credit Risk: Default probability
  • Operational Risk: Business stability

    • Installation

    pip install numpy pandas

    Usage

    Basic Analysis

    from financial_analyzer import FinancialAnalyzer
    analyzer = FinancialAnalyzer()
    Analyze a company
    
result = analyzer.analyze(
    company="茅台",
    statements={
        'balance_sheet': balance_data,
        'income_statement': income_data,
        'cash_flow': cash_flow_data
    }
)
    print(result['summary'])

    Ratio Analysis

    # Calculate all ratios
ratios = analyzer.calculate_ratios(statements)
    print(ratios['liquidity'])

    {
    

        'current_ratio': 2.5,
    

        'quick_ratio': 1.8,
    

        'cash_ratio': 0.5
    

    }
    print(ratios['profitability'])

    {
    

        'roe': 0.28,
    

        'roa': 0.18,
    

        'gross_margin': 0.75,
    

        'net_margin': 0.52
    

    }

    Valuation

    # DCF Valuation
dcf = analyzer.dcf_valuation(
    free_cash_flow=50e9,
    growth_rate=0.05,
    discount_rate=0.10,
    terminal_growth=0.03
)
print(f"Intrinsic Value: {dcf['enterprise_value']:,.0f}")
    Relative Valuation
    
relative = analyzer.relative_valuation(
    company="茅台",
    peers=["五粮液", "泸州老窖"],
    metrics={'pe': 35, 'pb': 8}
)

    Risk Assessment

    # Altman Z-Score (bankruptcy risk)
z_score = analyzer.altman_z_score(statements)
print(f"Z-Score: {z_score['score']:.2f}")
print(f"Risk Level: {z_score['risk_level']}")

    Z-Score: 5.2
    

    Risk Level: Safe (Z > 2.99)
    Piotroski F-Score (financial health)
    
f_score = analyzer.piotroski_f_score(statements)
print(f"F-Score: {f_score['score']}/9")

    Financial Health Check

    # Comprehensive health check
health = analyzer.health_check(statements)
    print(health['overall_score'])  # 85/100
print(health['strengths'])
print(health['weaknesses'])
print(health['recommendations'])

    API Reference

    Statement Analysis

    | Method | Description | |--------|-------------| | analyze(company, statements) | Full analysis | | analyze_balance_sheet(data) | Balance sheet analysis | | analyze_income(data) | Income statement analysis | | analyze_cash_flow(data) | Cash flow analysis |

    Ratios

    | Method | Description | |--------|-------------| | calculate_ratios(statements) | All ratios | | liquidity_ratios(data) | Liquidity metrics | | solvency_ratios(data) | Solvency metrics | | profitability_ratios(data) | Profitability metrics | | efficiency_ratios(data) | Efficiency metrics |

    Valuation

    | Method | Description | |--------|-------------| | dcf_valuation(...) | DCF model | | relative_valuation(...) | Peer comparison | | graham_number(...) | Graham's formula | | earnings_power_value(...) | EPV valuation |

    Risk

    | Method | Description | |--------|-------------| | altman_z_score(statements) | Bankruptcy risk | | piotroski_f_score(statements) | Financial health | | credit_risk_score(statements) | Credit assessment | | operational_risk(statements) | Business risk |

    Reports

    | Method | Description | |--------|-------------| | generate_report(analysis) | Full report | | summary_report(analysis) | Summary | | peer_comparison(company, peers) | Compare with peers |

    Key Ratios

    Liquidity

    | Ratio | Formula | Good Range | |-------|---------|------------| | Current Ratio | Current Assets / Current Liabilities | 1.5 - 3.0 | | Quick Ratio | (CA - Inventory) / CL | 1.0 - 2.0 | | Cash Ratio | Cash / CL | 0.2 - 0.5 |

    Profitability

    | Ratio | Formula | Interpretation | |-------|---------|----------------| | ROE | Net Income / Equity | Higher is better | | ROA | Net Income / Assets | Higher is better | | Gross Margin | Gross Profit / Revenue | Industry dependent | | Net Margin | Net Income / Revenue | Higher is better |

    Leverage

    | Ratio | Formula | Good Range | |-------|---------|------------| | Debt/Equity | Total Debt / Equity | < 2.0 | | Interest Coverage | EBIT / Interest | > 3.0 | | Debt/Assets | Total Debt / Assets | < 0.6 |

    Efficiency

    | Ratio | Formula | Interpretation | |-------|---------|----------------| | Asset Turnover | Revenue / Assets | Higher is better | | Inventory Turnover | COGS / Inventory | Industry dependent | | Receivables Turnover | Revenue / Receivables | Higher is better |

    Valuation Models

    DCF Model

    {
    'method': 'dcf',
    'steps': [
        'Project free cash flows',
        'Calculate terminal value',
        'Discount to present value',
        'Subtract debt, add cash'
    ],
    'inputs': {
        'fcf': 'Free cash flow',
        'growth_rate': 'Expected growth',
        'wacc': 'Weighted average cost of capital',
        'terminal_growth': 'Long-term growth'
    }
}

    Graham Number

    graham_number = sqrt(22.5 * EPS * Book_Value_Per_Share)

    Risk Models

    Altman Z-Score

    Z = 1.2*X1 + 1.4*X2 + 3.3*X3 + 0.6*X4 + 1.0*X5
    X1 = Working Capital / Total Assets
X2 = Retained Earnings / Total Assets
X3 = EBIT / Total Assets
X4 = Market Value Equity / Total Liabilities
X5 = Sales / Total Assets
    Interpretation:
Z > 2.99: Safe Zone
1.81 < Z < 2.99: Grey Zone
Z < 1.81: Distress Zone

    Piotroski F-Score

    9 criteria, 1 point each:
1. Positive ROA
2. Positive Operating Cash Flow
3. ROA improving
4. OCF > Net Income
5. Lower debt ratio
6. Higher current ratio
7. No share dilution
8. Higher gross margin
9. Higher asset turnover
    Score interpretation:
8-9: Strong
6-7: Good
4-5: Average
0-3: Weak

    Example: Full Analysis

    from financial_analyzer import FinancialAnalyzer
    analyzer = FinancialAnalyzer()
    Company financial data
    
statements = {
    'balance_sheet': {
        'total_assets': 200e9,
        'total_liabilities': 50e9,
        'current_assets': 80e9,
        'current_liabilities': 30e9,
        'cash': 40e9,
        'inventory': 10e9,
        'equity': 150e9
    },
    'income_statement': {
        'revenue': 100e9,
        'cost_of_goods': 25e9,
        'operating_expenses': 10e9,
        'net_income': 50e9,
        'ebit': 60e9
    },
    'cash_flow': {
        'operating_cf': 55e9,
        'investing_cf': -15e9,
        'financing_cf': -10e9,
        'free_cash_flow': 40e9
    }
}
    Run full analysis
    
result = analyzer.analyze("Example Corp", statements)
    print(f"ROE: {result['ratios']['profitability']['roe']:.1%}")
print(f"Z-Score: {result['risk']['z_score']:.2f}")
print(f"Health Score: {result['health_score']}/100")

    Chinese Accounting Standards

    Supports both:

  • CAS (Chinese Accounting Standards)
  • IFRS (International Financial Reporting Standards)
  • GAAP (US Generally Accepted Accounting Principles)

    • Use Cases

  • Investment Analysis: Evaluate investment opportunities
  • Credit Analysis: Assess creditworthiness
  • Due Diligence: M&A analysis
  • Performance Tracking: Monitor company health
  • Screening: Filter investment candidates

    • Best Practices

    1. Use multiple ratios together 2. Compare with industry peers 3. Analyze trends over time 4. Consider qualitative factors 5. Understand accounting policies

    Future Capabilities

  • Real-time data integration
  • AI-powered insights
  • Automated report generation
  • Multi-company comparison
  • Industry benchmarking

    • ⚡ When to Use

    TriggerAction
    - **Credit Analysis**: Assess creditworthiness
    - **Due Diligence**: M&A analysis
    - **Performance Tracking**: Monitor company health
    - **Screening**: Filter investment candidates

    💡 Examples

    Basic Analysis

    from financial_analyzer import FinancialAnalyzer
    analyzer = FinancialAnalyzer()
    Analyze a company
    
result = analyzer.analyze(
    company="茅台",
    statements={
        'balance_sheet': balance_data,
        'income_statement': income_data,
        'cash_flow': cash_flow_data
    }
)
    print(result['summary'])

    Ratio Analysis

    # Calculate all ratios
ratios = analyzer.calculate_ratios(statements)
    print(ratios['liquidity'])

    {
    

        'current_ratio': 2.5,
    

        'quick_ratio': 1.8,
    

        'cash_ratio': 0.5
    

    }
    print(ratios['profitability'])

    {
    

        'roe': 0.28,
    

        'roa': 0.18,
    

        'gross_margin': 0.75,
    

        'net_margin': 0.52
    

    }

    Valuation

    # DCF Valuation
dcf = analyzer.dcf_valuation(
    free_cash_flow=50e9,
    growth_rate=0.05,
    discount_rate=0.10,
    terminal_growth=0.03
)
print(f"Intrinsic Value: {dcf['enterprise_value']:,.0f}")
    Relative Valuation
    
relative = analyzer.relative_valuation(
    company="茅台",
    peers=["五粮液", "泸州老窖"],
    metrics={'pe': 35, 'pb': 8}
)

    Risk Assessment

    # Altman Z-Score (bankruptcy risk)
z_score = analyzer.altman_z_score(statements)
print(f"Z-Score: {z_score['score']:.2f}")
print(f"Risk Level: {z_score['risk_level']}")

    Z-Score: 5.2
    

    Risk Level: Safe (Z > 2.99)
    Piotroski F-Score (financial health)
    
f_score = analyzer.piotroski_f_score(statements)
print(f"F-Score: {f_score['score']}/9")

    Financial Health Check

    # Comprehensive health check
health = analyzer.health_check(statements)
    print(health['overall_score'])  # 85/100
print(health['strengths'])
print(health['weaknesses'])
print(health['recommendations'])

    📋 Tips & Best Practices

    1. Use multiple ratios together 2. Compare with industry peers 3. Analyze trends over time 4. Consider qualitative factors 5. Understand accounting policies