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name: financial-ratio-toolkit description: "Analyzes company fundamentals by computing 20+ financial ratios (profitability, solvency, liquidity, efficiency, and growth) and DuPont Analysis from user-provided financial statements. Triggered by requests for financial ratio calculation, DuPont/ROE analysis, or phrases like "analyze these financials" or "calculate solvency ratios." license: MIT

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