Financial Analyzer

v1.0.0

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

0· 180· 1 versions· 1 current· 1 all-time· Updated 7h ago· MIT-0

by@jason-aka-chen

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Install

openclaw skills install 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

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

Use multiple ratios together
Compare with industry peers
Analyze trends over time
Consider qualitative factors
Understand accounting policies

Future Capabilities

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

Version tags

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