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

Ratios

Valuation

Risk

Reports

Key Ratios

Liquidity

Profitability

Leverage

Efficiency

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