Batch Compute

Computes all applicable ratios from a single data object in one call.

Usage

from fin_ratios.utils.compute_all import compute_all

data = {
    'revenue': 300e9,
    'gross_profit': 120e9,
    'ebit': 75e9,
    'net_income': 65e9,
    'operating_cash_flow': 80e9,
    'capex': 8e9,
    'total_assets': 300e9,
    'total_equity': 62e9,
    'total_debt': 90e9,
    'cash': 30e9,
    'current_assets': 110e9,
    'current_liabilities': 75e9,
    'depreciation': 12e9,
    'income_tax_expense': 16e9,
    'ebt': 77e9,
    'market_cap': 3e12,
}

# Optional: prior year data unlocks Piotroski, growth rates, etc.
prior = {'revenue': 275e9, 'gross_profit': 106e9, ...}

ratios = compute_all(data, prior=prior)

print(ratios['pe'])             # 46.15
print(ratios['gross_margin'])   # 0.40
print(ratios['roic'])           # 0.14
print(ratios['fcf_margin'])     # 0.24
print(ratios['current_ratio'])  # 1.47

What's Computed

compute_all returns a flat dict with 40+ ratio keys:

Valuation: pe, pb, ps, p_fcf, ev_ebitda, ev_revenue Profitability: gross_margin, operating_margin, net_profit_margin, ebitda_margin, roe, roa, roic Liquidity: current_ratio, quick_ratio, cash_ratio Solvency: debt_to_equity, net_debt_to_ebitda, interest_coverage_ratio Cash Flow: free_cash_flow, fcf_margin, fcf_yield, fcf_conversion Growth (requires prior): revenue_growth, eps_growth Composite (requires prior): piotroski_score, altman_z_score Health Score: health_score (0–100)

Notes

• Unknown or missing fields are gracefully skipped — no exception raised
• Returns None for any ratio where required inputs are absent
• Accepts dicts, dataclasses, or any object with matching attributes