serejaris Financial Ratio Toolkit (Stock Fundamental Analyzer)
Performs comprehensive fundamental analysis on public company financial statements, covering 20+ core financial metrics and DuPont Analysis. Simply provide key data from the balance sheet, income statement, and cash flow statement to get a structured analysis report.
Quick Start
Save the financial data as a JSON file, then run the analysis script:
python3 scripts/analyze.py --input financial_data.json
Output in JSON format (for programmatic consumption):
python3 scripts/analyze.py --input financial_data.json --json
Read data from standard input:
cat financial_data.json | python3 scripts/analyze.py --stdin
Input Data Format
Input is in JSON format with the following sections:
| Field | Type | Required | Description |
|---|---|---|---|
company_name |
string | No | Company name |
report_period |
string | No | Reporting period (e.g., "2025Q4") |
currency |
string | No | Currency unit (default: "CNY") |
balance_sheet |
object | Yes | Balance sheet data |
income_statement |
object | Yes | Income statement data |
cash_flow_statement |
object | No | Cash flow statement data |
market_data |
object | No | Market data (stock price, shares, etc.) |
prior_year |
object | No | Prior year comparative data (for growth rate calculations) |
Balance Sheet (balance_sheet)
| Field | Description |
|---|---|
total_assets |
Total assets |
total_liabilities |
Total liabilities |
shareholders_equity |
Shareholders' equity (net assets) |
current_assets |
Current assets |
current_liabilities |
Current liabilities |
inventory |
Inventory |
accounts_receivable |
Accounts receivable |
cash_and_equivalents |
Cash and cash equivalents |
Income Statement (income_statement)
| Field | Description |
|---|---|
revenue |
Revenue |
cost_of_goods_sold |
Cost of goods sold |
operating_income |
Operating income |
net_income |
Net income |
interest_expense |
Interest expense |
depreciation_amortization |
Depreciation and amortization |
income_tax |
Income tax expense |
Cash Flow Statement (cash_flow_statement)
| Field | Description |
|---|---|
operating_cash_flow |
Net cash from operating activities |
capital_expenditure |
Capital expenditure |
Market Data (market_data)
| Field | Description |
|---|---|
shares_outstanding |
Total shares outstanding |
stock_price |
Current stock price |
Prior Year Data (prior_year)
| Field | Description |
|---|---|
revenue |
Prior year revenue |
net_income |
Prior year net income |
Analysis Metrics (20+ Items)
Profitability (6 items)
- ROE (Return on Equity)
- ROA (Return on Assets)
- Gross Profit Margin
- Net Profit Margin
- Operating Profit Margin
- EBITDA Margin
Solvency (4 items)
- Debt-to-Asset Ratio
- Equity Multiplier
- Interest Coverage Ratio (EBIT / Interest Expense)
- Debt-to-Equity Ratio (Liabilities / Equity)
Liquidity (3 items)
- Current Ratio
- Quick Ratio
- Cash Ratio
Operational Efficiency (6 items)
- Asset Turnover
- Inventory Turnover
- Days Inventory Outstanding
- Receivables Turnover
- Days Sales Outstanding
- Working Capital Turnover
Per-Share Metrics (3 items)
- Earnings Per Share (EPS)
- Book Value Per Share (BVPS)
- Price-to-Earnings Ratio (P/E)
Cash Flow Metrics (3 items)
- Operating Cash Flow Ratio
- Free Cash Flow
- Cash Flow to Net Income Ratio
Growth (2 items)
- Revenue Growth Rate (YoY)
- Net Income Growth Rate (YoY)
DuPont Analysis
Decomposes ROE into three factors:
ROE = Net Profit Margin x Asset Turnover x Equity Multiplier
Output Example
The script produces a structured analysis report containing values, reference ranges, and brief assessments for each metric. See scripts/analyze.py --help for detailed usage.
Notes
- All metrics are calculated from user-provided static financial data; no live market data is fetched
- Reference ranges vary significantly across industries; the assessments in the report are for reference only
- It is recommended to consider industry characteristics and company-specific context for a comprehensive evaluation
- The script uses only the Python standard library with no additional dependencies required