financial-analyzer
# 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
```bash
pip install numpy pandas
```
## Usage
### Basic Analysis
```python
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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
{
'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
```python
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
```python
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
标签
skill
ai
