financial-ratio-toolkit

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