FinRatioAnalysis 0.2.1

Quantitative Finance Ratio Analysis with VCG (Value, Quality, Growth) Metrics

FinRatioAnalysis

A comprehensive Python package for financial ratio analysis and fundamental analysis of publicly traded companies using real-time data from Yahoo Finance.

Features

Profitability & Return Metrics:

• Return on Equity (ROE)
• Return on Assets (ROA)
• Return on Capital Employed (ROCE)
• NEW: Return on Invested Capital (ROIC) - Quality metric for capital efficiency
• Gross Margin, Operating Margin, Net Profit Margin

Valuation & Growth Metrics:

• NEW: Historical P/E Ratio - Time series of Price-to-Earnings
• NEW: Historical EV/EBITDA - Enterprise Value to EBITDA over time
• NEW: Historical FCF Yield - Free Cash Flow Yield trends
• Current P/E, P/S, Forward P/E
• Dividend Yield & Payout Ratio
• Revenue & Net Income CAGR (3-year growth)
• Beta (market volatility)

Leverage & Solvency:

• Debt-to-Equity Ratio
• Equity Ratio, Debt Ratio
• Interest Coverage Ratios

Efficiency Ratios:

• Asset Turnover
• Receivables Turnover
• Inventory Turnover
• Fixed Asset Turnover
• NEW: FCF Margin - Free Cash Flow efficiency
• NEW: Reinvestment Rate - Growth capital allocation

Liquidity Analysis:

• Current Ratio, Quick Ratio, Cash Ratio
• Days Inventory Ratio (DIR)
• Operating Cash Flow Ratios

Advanced Metrics:

• Cash Conversion Cycle (CCC)
• Altman Z-Score (bankruptcy prediction)
• Capital Asset Pricing Model (CAPM)
• Sharpe Ratio
• Weighted Average Cost of Capital (WACC)

Special Features:

• ✅ VCG (Value, Quality, Growth) Analysis Framework
• ✅ Historical valuation metrics with time series data
• ✅ Supports both tech companies and financial institutions (banks)
• ✅ Automatic fallback logic for missing financial data
• ✅ Quarterly and yearly data frequency
• ✅ Interactive Z-Score visualization with Plotly
• ✅ Comprehensive unit and integration tests

Installation

pip install FinRatioAnalysis

Dependencies (automatically installed):

• yfinance - Real-time financial data
• pandas - Data manipulation
• numpy - Numerical operations
• plotly - Interactive visualizations
• requests - API calls

Quick Start

from FinRatioAnalysis import FinRatioAnalysis

# Create analyzer instance
apple = FinRatioAnalysis('AAPL', freq='yearly')  # freq: 'yearly' or 'quarterly'

# Calculate profitability ratios (now includes ROIC)
returns = apple.return_ratios()
print(returns)  # ROE, ROA, ROCE, ROIC, Gross Margin, Operating Margin, Net Profit

# Calculate efficiency ratios (now includes FCF Margin & Reinvestment Rate)
efficiency = apple.efficiency_ratios()
print(efficiency)  # Asset Turnover, FCF_Margin, ReinvestmentRate, etc.

# Get valuation & growth metrics (current market values)
valuation = apple.valuation_growth_metrics()
print(valuation)  # P/E, EV/EBITDA, FCF Yield, Revenue CAGR, etc.

# NEW: Historical valuation metrics (time series with dates)
historical_val = apple.historical_valuation_metrics()
print(historical_val)  # P/E, EV/EBITDA, FCF Yield for each period

# Calculate leverage ratios
leverage = apple.leverage_ratios()
print(leverage)  # Debt-to-Equity, Equity Ratio, Debt Ratio

# Calculate liquidity ratios
liquidity = apple.liquidity_ratios()
print(liquidity)  # Current Ratio, Quick Ratio, Cash Ratio, etc.

# Cash Conversion Cycle
ccc = apple.ccc()
print(ccc)  # DIO, DSO, DPO, CCC

# Altman Z-Score (bankruptcy risk)
z_score = apple.z_score()
print(z_score)  # Z-Score value and classification

# Visualize Z-Score
apple.z_score_plot()  # Interactive Plotly chart

# CAPM and Sharpe Ratio
capm = apple.capm()
print(capm)  # Expected Return, Sharpe Ratio

# Weighted Average Cost of Capital
wacc = apple.wacc()
print(wacc)  # WACC percentage

VCG (Value, Quality, Growth) Analysis

The package now includes comprehensive VCG metrics for fundamental analysis:

from FinRatioAnalysis import FinRatioAnalysis
import pandas as pd

analyzer = FinRatioAnalysis('AAPL', freq='yearly')

# VALUE METRICS - Historical trends
historical = analyzer.historical_valuation_metrics()
print("Historical Valuation:")
print(historical[['PE_Ratio', 'EV_EBITDA', 'FCF_Yield']])

# QUALITY METRICS - Profitability & efficiency
quality = pd.concat([
    analyzer.return_ratios()[['ROIC', 'ROE']],
    analyzer.efficiency_ratios()[['FCF_Margin']]
], axis=1)
print("\nQuality Metrics:")
print(quality)

# GROWTH METRICS - Revenue & investment trends
growth = analyzer.valuation_growth_metrics()[['revenue_cagr_3y', 'net_income_cagr_3y']]
reinvestment = analyzer.efficiency_ratios()[['ReinvestmentRate']]
print("\nGrowth Metrics:")
print(pd.concat([growth.T, reinvestment], axis=1))

Quarterly vs Yearly Analysis

# Yearly analysis (default) - 4 annual periods
yearly = FinRatioAnalysis('AAPL', freq='yearly')
print(yearly.historical_valuation_metrics())  # 4 years of data

# Quarterly analysis - 4 quarterly periods
quarterly = FinRatioAnalysis('AAPL', freq='quarterly')
print(quarterly.historical_valuation_metrics())  # Last 4 quarters
print(quarterly.return_ratios())  # Quarterly ROIC, ROE, etc.

Example: Analyzing Multiple Companies

from FinRatioAnalysis import FinRatioAnalysis
import pandas as pd

tickers = ['AAPL', 'MSFT', 'GOOGL', 'JPM']  # Tech + Bank
results = {}

for ticker in tickers:
    analyzer = FinRatioAnalysis(ticker, freq='yearly')
    
    # Get latest values
    returns = analyzer.return_ratios().iloc[0]
    leverage = analyzer.leverage_ratios().iloc[0]
    valuation = analyzer.valuation_growth_metrics().iloc[0]
    z = analyzer.z_score().iloc[0]
    wacc = analyzer.wacc()
    
    results[ticker] = {
        'ROE': returns['ROE'],
        'ROIC': returns['ROIC'],
        'Debt/Equity': leverage['DebtEquityRatio'],
        'P/E': valuation['pe_ratio'],
        'EV/EBITDA': valuation['ev_ebitda'],
        'FCF Yield': valuation['fcf_yield'],
        'Revenue CAGR': valuation['revenue_cagr_3y'],
        'Z-Score': z['Z Score'],
        'WACC': wacc['WACC'].iloc[0]
    }

df = pd.DataFrame(results).T
print(df.round(2))

API Reference

Core Methods

return_ratios() -> pd.DataFrame

Returns profitability metrics with dates as index:

• ROE - Return on Equity
• ROA - Return on Assets
• ROCE - Return on Capital Employed
• ROIC - Return on Invested Capital (NEW)
• GrossMargin, OperatingMargin, NetProfit

efficiency_ratios() -> pd.DataFrame

Returns efficiency metrics with dates as index:

• AssetTurnover, ReceivableTurnover, InventoryTurnover, FixedAssetTurnover
• FCF_Margin - Free Cash Flow Margin (NEW)
• ReinvestmentRate - Capital reinvestment rate (NEW)

valuation_growth_metrics() -> pd.DataFrame

Returns current market valuation metrics (single row):

• pe_ratio, forward_pe, ps_ratio
• ev_ebitda - Enterprise Value / EBITDA (NEW)
• fcf_yield - Free Cash Flow Yield (NEW)
• dividend_yield, payout_ratio, beta
• revenue_cagr_3y, net_income_cagr_3y

historical_valuation_metrics() -> pd.DataFrame (NEW)

Returns historical valuation metrics with dates as index:

• Market_Cap - Historical market capitalization
• PE_Ratio - Price-to-Earnings for each period
• EV_EBITDA - Enterprise Value / EBITDA for each period
• FCF_Yield - Free Cash Flow Yield for each period

Works with both yearly and quarterly data!

leverage_ratios() -> pd.DataFrame

Returns leverage metrics with dates as index:

• DebtEquityRatio, EquityRatio, DebtRatio

liquidity_ratios() -> pd.DataFrame

Returns liquidity metrics with dates as index:

• CurrentRatio, QuickRatio, CashRatio
• DIR, TIE, TIE_CB, CAPEX_OpCash, OpCashFlow

ccc() -> pd.DataFrame

Returns Cash Conversion Cycle metrics with dates as index:

• DIO, DSO, DPO, CCC

z_score() -> pd.DataFrame

Returns Altman Z-Score for bankruptcy prediction (single row):

• Symbol, Z Score, Zone

z_score_plot() -> None

Displays interactive Plotly gauge chart of Z-Score

capm() -> pd.DataFrame

Returns CAPM metrics (single row):

• Symbol, CAPM, Sharpe

wacc() -> pd.DataFrame

Returns Weighted Average Cost of Capital (single row):

• Symbol, WACC

Supported Company Types

The package intelligently handles different types of companies:

Tech/Manufacturing Companies: Full metrics including ROCE, ROIC, Gross Margin, Operating Margin, FCF Margin

Financial Institutions (Banks): Automatically adapts metrics - uses alternative calculations when standard fields (like GrossProfit, CurrentLiabilities) are unavailable

What's New in Latest Version

VCG (Value, Quality, Growth) Metrics

• ROIC - Added to return_ratios() for quality assessment
• Historical Valuation Metrics - New historical_valuation_metrics() method provides time series of P/E, EV/EBITDA, and FCF Yield
• FCF Margin - Added to efficiency_ratios() to measure cash flow efficiency
• Reinvestment Rate - Added to efficiency_ratios() to track growth capital allocation
• Enhanced Valuation - valuation_growth_metrics() now includes EV/EBITDA and FCF Yield

Key Improvements

• All VCG metrics support both yearly and quarterly analysis
• Historical valuation metrics match the format of other ratio methods (dates in index)
• Automatic timezone handling for stock price data
• Comprehensive error handling and fallback logic

Testing

The package includes comprehensive tests:

# Run all tests
pytest

# Run specific test categories
pytest tests/test_return_ratios.py
pytest tests/test_efficiency_ratios.py
pytest tests/test_valuation_growth_metrics.py
pytest tests/test_integration_real_data.py
pytest -m integration  # Real data tests only

Documentation

For detailed financial ratio formulas and interpretations:

• Example Notebook - Complete walkthrough of all features including VCG analysis
• CFI Financial Ratios Cheat Sheet

Version History

Latest Version:

• Added VCG (Value, Quality, Growth) metrics framework
• New historical_valuation_metrics() method
• Enhanced return_ratios() with ROIC
• Enhanced efficiency_ratios() with FCF Margin and Reinvestment Rate
• Enhanced valuation_growth_metrics() with EV/EBITDA and FCF Yield

License

MIT License - See LICENCE file

Author

Lorenzo Cárdenas Cárdenas

• GitHub: @lorenzo1285
• LinkedIn: lorenzocardenas

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Changelog

Version 1.x (Latest)

• ✨ Added VCG (Value, Quality, Growth) analysis framework
• ✨ New method: historical_valuation_metrics() - Time series P/E, EV/EBITDA, FCF Yield
• ✨ Enhanced return_ratios() - Added ROIC (Return on Invested Capital)
• ✨ Enhanced efficiency_ratios() - Added FCF Margin and Reinvestment Rate
• ✨ Enhanced valuation_growth_metrics() - Added EV/EBITDA and FCF Yield
• 🐛 Fixed timezone compatibility issues in historical price lookups
• 📚 Updated documentation with VCG examples and API reference
• 📚 Enhanced example notebook with quarterly/yearly comparisons