financial-ratios 0.7.1

The most comprehensive financial ratios library. 136+ ratios, batch compute, caching, trends, scenario DCF, peer comparison, Quantitative Moat Score, FastAPI, MCP server, and more.

fin-ratios

The most comprehensive open-source financial ratios library.

136+ ratios across 13 categories, plus six institutional-grade scoring models. TypeScript + Python with identical APIs. Caching, batch compute, REST API, MCP server for AI agents, React hooks, and Pandas/Polars integration. Zero runtime dependencies in the core. Formula transparency on every function.

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Why fin-ratios?

Existing libraries either fetch data or compute 10–20 basic ratios. fin-ratios does both and goes much further:

Feature	fin-ratios	Competitors
Ratio count	136+	10–30
Composite scores (Piotroski, Beneish, Altman, Montier, Shiller)	✅	Rarely
Risk/portfolio ratios (Sharpe, CVaR, Omega)	✅	No
SaaS metrics (Rule of 40, NRR, Burn Multiple)	✅	No
REIT / Banking / Insurance ratios	✅	No
Intrinsic value models (DCF, Reverse DCF, Graham)	✅	No
Economic Moat Score (formula-based, open-source first)	✅	No
Capital Allocation Quality Score	✅	No
Earnings Quality Score (accruals, cash backing, stability)	✅	No
Fair Value Range (5-method bear/base/bull)	✅	No
Quality Factor Score (QMJ composite)	✅	No
Portfolio Quality Aggregation (weighted scoring)	✅	No
Batch compute all ratios from one object	✅	No
Historical ratio trends + linear regression	✅	No
Scenario DCF (bull/base/bear)	✅	No
Peer comparison with percentile ranking	✅	No
Sector benchmarks (11 GICS sectors + score distributions)	✅	No
Caching layer (memory/disk/Redis)	✅	No
Pandas + Polars DataFrame integration	✅	No
Jupyter notebook rich display	✅	No
FastAPI REST API (6 endpoints)	✅	No
MCP server for AI agents (Claude Desktop)	✅	No
React hooks (useRatios, useHealthScore)	✅	No
Formula on every function	✅	No
Academic citations for every ratio	✅	No
TypeScript + Python	✅	Usually one
Zero core dependencies	✅	Usually no

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What's New in v0.7

v0.7 — Quality Factor Score + Portfolio Quality

• qualityScore() / quality_score() — composite 0–100 score combining Earnings Quality (35%), Moat Score (35%), Capital Allocation (30%). Maps to the QMJ factor (Asness et al. 2019)
• portfolioQuality() / portfolio_quality() — weighted quality analysis across a portfolio of holdings
• 89-test TypeScript test suite covering all scoring utilities and core ratios
• Sector benchmarks expanded with score distributions for all 11 GICS sectors

v0.6 — Fair Value Range

• fairValueRange() / fair_value_range() — up to five valuation methods (DCF, Graham Number, FCF Yield, EV/EBITDA, EPV) returning bear (p25), base (trimmed mean), bull (p75) per-share estimates

v0.5 — Earnings Quality Score

• earningsQualityScore() / earnings_quality_score() — 5-signal framework: accruals ratio, cash earnings quality, revenue recognition, gross margin stability, asset efficiency trend

v0.4 — Capital Allocation Quality Score

• capitalAllocationScore() / capital_allocation_score() — 4-signal framework: value creation (ROIC vs WACC), FCF quality, reinvestment yield, payout discipline

v0.3 — Quantitative Economic Moat Score

• moatScore() / moat_score() — first open-source formula-based economic moat score derived entirely from financial statements. Five signals with academic grounding.

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Installation

# TypeScript / JavaScript
npm install fin-ratios

# Python (core only — zero dependencies)
pip install financial-ratios

# Python with data fetchers (yfinance, httpx, requests)
pip install "financial-ratios[fetchers]"

# Python with REST API server (FastAPI + uvicorn)
pip install "financial-ratios[api]"

# Python with MCP server for AI agents
pip install "financial-ratios[mcp]"

# Python with Pandas/Polars DataFrame integration
pip install "financial-ratios[pandas]"

# Python with everything
pip install "financial-ratios[all]"

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Quick Start

TypeScript — Core Ratios

import {
  pe, evEbitda, peg, pb,
  roic, nopat, investedCapital,
  sharpeRatio, conditionalVaR,
  piotroskiFScore, altmanZScore, beneishMScore,
  ruleOf40, netRevenueRetention, burnMultiple,
  dcf2Stage, reverseDcf, grahamNumber,
} from 'fin-ratios'

// Valuation
const peRatio = pe({ marketCap: 3_000_000_000_000, netIncome: 100_000_000_000 })
// => 30

const ev = evEbitda({ enterpriseValue: 3_060_000_000_000, ebitda: 130_000_000_000 })
// => 23.5

// Access formula on any function
console.log(pe.formula)        // "Market Capitalization / Net Income"
console.log(evEbitda.formula)  // "Enterprise Value / EBITDA"

// ROIC (value creation test)
const ic = investedCapital({ totalEquity: 74e9, totalDebt: 110e9, cash: 62e9 })
const nopatVal = nopat({ ebit: 120e9, taxRate: 0.15 })
const roicVal = roic({ nopat: nopatVal, investedCapital: ic })
// => 0.99 (99% ROIC)

// Piotroski F-Score (0-9)
const score = piotroskiFScore({
  current: { netIncome: 8e6, totalAssets: 100e6, operatingCashFlow: 12e6,
             longTermDebt: 20e6, currentAssets: 35e6, currentLiabilities: 15e6,
             sharesOutstanding: 10e6, grossProfit: 45e6, revenue: 90e6 },
  prior:   { netIncome: 5e6, totalAssets: 95e6, longTermDebt: 25e6,
             currentAssets: 28e6, currentLiabilities: 14e6, sharesOutstanding: 10.5e6,
             grossProfit: 38e6, revenue: 80e6 },
})
console.log(`F-Score: ${score.score}/9 — ${score.interpretation}`)

TypeScript — Scoring Utilities (v0.3–v0.7)

import { moatScore, capitalAllocationScore, earningsQualityScore,
         fairValueRange, qualityScore, portfolioQuality } from 'fin-ratios'

// Economic Moat Score
const moat = moatScore(annualData)
// => { score: 68, width: 'wide', components: { roicPersistence: 72, ... } }

// Capital Allocation Score
const ca = capitalAllocationScore(annualData)
// => { score: 74, rating: 'good', components: { valueCreation: 81, ... } }

// Earnings Quality Score
const eq = earningsQualityScore(annualData)
// => { score: 66, rating: 'medium', components: { accrualsRatio: 58, ... } }

// Fair Value Range (bear / base / bull per share)
const fv = fairValueRange({
  fcf: 110e9, shares: 15.4e9, growthRate: 0.10, terminalGrowth: 0.03,
  wacc: 0.09, dcfYears: 10, eps: 6.5, bvps: 4.8, targetYield: 0.04,
  ebitda: 130e9, totalDebt: 120e9, cash: 62e9, evEbitdaMultiple: 20,
  ebit: 112e9, taxRate: 0.15, currentPrice: 185,
})
console.log(`Bear: $${fv.bear.toFixed(0)}  Base: $${fv.base.toFixed(0)}  Bull: $${fv.bull.toFixed(0)}`)
console.log(`Margin of safety: ${(fv.marginOfSafety! * 100).toFixed(1)}%`)

// Quality Factor Score (combines EQ + Moat + Capital Allocation)
const qs = qualityScore(annualData)
// => { score: 69, grade: 'strong', components: { earningsQuality: 0.66, ... } }

// Portfolio Quality
const portfolio = portfolioQuality([
  { ticker: 'AAPL', weight: 0.40, annualData: appleData },
  { ticker: 'MSFT', weight: 0.35, annualData: msftData },
  { ticker: 'GOOGL', weight: 0.25, annualData: googlData },
])
console.log(`Portfolio quality: ${portfolio.weightedScore.toFixed(0)}/100`)
console.log(`Top holding: ${portfolio.topHolding}`)

TypeScript — Batch Compute

import { computeAll } from 'fin-ratios'
import { fetchYahoo } from 'fin-ratios/fetchers/yahoo'

const data = await fetchYahoo('AAPL')
const ratios = computeAll(data)

console.log(ratios.pe)           // 28.3
console.log(ratios.roic)         // 0.55
console.log(ratios.grossMargin)  // 0.433
console.log(ratios.altmanZ?.zone) // 'safe'
console.log(ratios.piotroski?.score) // 7

TypeScript — Scenario DCF

import { scenarioDcf } from 'fin-ratios'

const result = scenarioDcf({
  baseFcf: 100e9,
  sharesOutstanding: 15.7e9,
  currentPrice: 185,
  scenarios: {
    bear: { growthRate: 0.05, wacc: 0.12, terminalGrowth: 0.02, years: 10 },
    base: { growthRate: 0.10, wacc: 0.10, terminalGrowth: 0.03, years: 10 },
    bull: { growthRate: 0.18, wacc: 0.09, terminalGrowth: 0.04, years: 10 },
  },
})
console.log(result.base.intrinsicValuePerShare)  // 198.4
console.log(result.base.upsidePct)              // 0.074 (7.4% upside)

TypeScript — React Hooks

import { useRatios, useHealthScore, useCompareRatios } from 'fin-ratios/hooks'
import { fetchYahoo } from 'fin-ratios/fetchers/yahoo'

function StockCard({ ticker }: { ticker: string }) {
  const { data, loading, error } = useRatios(ticker, fetchYahoo)
  if (loading) return <div>Loading...</div>
  if (error) return <div>Error: {error}</div>
  return (
    <div>
      <div>P/E: {data?.pe?.toFixed(1)}</div>
      <div>ROIC: {(data?.roic ?? 0 * 100).toFixed(1)}%</div>
      <div>Gross Margin: {(data?.grossMargin ?? 0 * 100).toFixed(1)}%</div>
    </div>
  )
}

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Python — Core Ratios

from fin_ratios import (
    pe, ev_ebitda, graham_number,
    roic, nopat as compute_nopat, invested_capital,
    sharpe_ratio, conditional_var,
    piotroski_f_score, altman_z_score, beneish_m_score,
    rule_of_40, net_revenue_retention, burn_multiple,
    dcf_2_stage, reverse_dcf,
)

# Valuation
ratio = pe(market_cap=3e12, net_income=100e9)
# => 30.0

# Access formula and description
print(pe.formula)     # "Market Capitalization / Net Income"
print(pe.description) # "How much investors pay per $1 of earnings."

# Altman Z-Score
z = altman_z_score(
    working_capital=50e9, retained_earnings=200e9, ebit=90e9,
    market_cap=3000e9, total_liabilities=210e9,
    total_assets=411e9, revenue=212e9,
)
print(z["interpretation"])  # "Z-Score 4.82: Safe zone — Low bankruptcy risk"

Python — Scoring Utilities (v0.3–v0.7)

from fin_ratios import (
    moat_score_from_series, capital_allocation_score_from_series,
    earnings_quality_score_from_series,
    fair_value_range, quality_score_from_series,
    portfolio_quality_from_series,
)

# All *_from_series functions accept a list of annual dicts (oldest → newest)
annual_data = [
    {'revenue': 200e9, 'ebit': 40e9, 'total_assets': 300e9, 'total_equity': 80e9,
     'operating_cash_flow': 50e9, 'capex': 10e9, 'net_income': 30e9,
     'total_debt': 60e9, 'cash': 25e9, 'gross_profit': 90e9},
    # ... more years (oldest first) ...
]

# Economic Moat Score
moat = moat_score_from_series(annual_data)
print(f"Moat: {moat.score}/100 ({moat.width})")   # "Moat: 68/100 (wide)"
print(moat.table())

# Capital Allocation Score
ca = capital_allocation_score_from_series(annual_data)
print(f"Capital Allocation: {ca.score}/100 ({ca.rating})")  # "Capital Allocation: 74/100 (good)"

# Earnings Quality Score
eq = earnings_quality_score_from_series(annual_data)
print(f"Earnings Quality: {eq.score}/100 ({eq.rating})")   # "Earnings Quality: 66/100 (medium)"

# Fair Value Range
fv = fair_value_range(
    fcf=110e9, shares=15.4e9, growth_rate=0.10, terminal_growth=0.03,
    wacc=0.09, dcf_years=10, eps=6.50, bvps=4.80, target_yield=0.04,
    ebitda=130e9, total_debt=120e9, cash=62e9, ev_ebitda_multiple=20,
    ebit=112e9, tax_rate=0.15, current_price=185.0,
)
print(f"Bear: ${fv.bear:.0f}  Base: ${fv.base:.0f}  Bull: ${fv.bull:.0f}")
print(f"Margin of safety: {fv.margin_of_safety:.1%}")
print(fv.table())

# Quality Factor Score (EQ 35% + Moat 35% + Capital Allocation 30%)
qs = quality_score_from_series(annual_data)
print(f"Quality: {qs.score}/100 ({qs.grade})")  # "Quality: 69/100 (strong)"

# Portfolio Quality
holdings_data = {
    'AAPL':  (0.40, apple_annual_data),   # (weight, annual_data_list)
    'MSFT':  (0.35, msft_annual_data),
    'GOOGL': (0.25, googl_annual_data),
}
portfolio = portfolio_quality_from_series(holdings_data)
print(f"Portfolio quality score: {portfolio.weighted_score:.0f}/100")
for h in portfolio.holdings:
    print(f"  {h.ticker}: {h.quality_score:.0f}/100  (weight: {h.weight:.0%})")

Python — Convenience Wrappers (auto-fetch)

All scoring utilities have a single-ticker wrapper that fetches data automatically:

from fin_ratios import moat_score, capital_allocation_score, earnings_quality_score
from fin_ratios.utils import quality_score, portfolio_quality

# Fetches 10 years of data from Yahoo Finance automatically
moat = moat_score('AAPL')
ca   = capital_allocation_score('AAPL')
eq   = earnings_quality_score('AAPL')
qs   = quality_score('AAPL')

# Jupyter — renders as color-coded HTML table
moat   # _repr_html_() called automatically

# Portfolio (fetches each holding)
portfolio = portfolio_quality({'AAPL': 0.40, 'MSFT': 0.35, 'GOOGL': 0.25})

Python — Batch Compute

from fin_ratios.utils import compute_all
from fin_ratios.fetchers.yahoo import fetch_yahoo

data = fetch_yahoo('AAPL')
ratios = compute_all(data)

print(f"P/E:          {ratios['pe']:.1f}")
print(f"ROIC:         {ratios['roic']:.1%}")
print(f"Gross Margin: {ratios['gross_margin']:.1%}")
print(f"Altman Zone:  {ratios['altman_z']['zone']}")

Python — Ratio History & Trends

from fin_ratios.utils import ratio_history

history = ratio_history('AAPL', metrics=['roic', 'gross_margin', 'pe'], years=5)

print(history.trend('roic'))        # 'improving'
print(history.trend('gross_margin')) # 'stable'
print(f"ROIC CAGR: {history.cagr('roic'):.1%}")
print(history.table())

Python — Scenario DCF

from fin_ratios.utils import scenario_dcf

result = scenario_dcf(
    base_fcf=100e9,
    shares_outstanding=15.7e9,
    current_price=185.0,
    scenarios={
        'bear': {'growth_rate': 0.05, 'wacc': 0.12, 'terminal_growth': 0.02, 'years': 10},
        'base': {'growth_rate': 0.10, 'wacc': 0.10, 'terminal_growth': 0.03, 'years': 10},
        'bull': {'growth_rate': 0.18, 'wacc': 0.09, 'terminal_growth': 0.04, 'years': 10},
    },
)
print(result.table())
# Scenario  IV/Share  Upside
# bear      $142.30   -23.1%
# base      $198.40   +7.2%
# bull      $287.60   +55.5%

Python — Sector Benchmarks & Percentile Ranking

from fin_ratios.utils.benchmarks import sector_benchmarks, percentile_rank

b = sector_benchmarks("Technology")
print(b.roic_median)            # 0.20 (20%)
print(b.moat_score_median)      # 52
print(b.quality_score_median)   # 55

# Percentile rank a value against peers
rank = percentile_rank("Technology", "roic", 0.55)
print(f"ROIC of 55% is in the {rank:.0f}th percentile for Tech")  # => 97th

# Works with all new scoring metrics too
moat_rank = percentile_rank("Technology", "moat_score", 68)
print(f"Moat score of 68 is in the {moat_rank:.0f}th percentile")  # => 83rd

Python — Caching

from fin_ratios.cache import set_cache, invalidate, clear_cache

set_cache('memory')                    # in-process (default)
set_cache('disk', ttl_hours=24)        # JSON files in ~/.fin_ratios_cache/
set_cache('redis', ttl_hours=1, url='redis://localhost:6379')

# Fetchers use the cache automatically
from fin_ratios.fetchers.yahoo import fetch_yahoo
data = fetch_yahoo('AAPL')  # live fetch
data = fetch_yahoo('AAPL')  # served from cache

invalidate('AAPL')    # clear one ticker
clear_cache()         # clear everything

Python — REST API

pip install "financial-ratios[api]"
fin-ratios api --port 8000
# OpenAPI docs at http://localhost:8000/docs

GET /ratios/{ticker}           — all 40+ ratios
GET /ratios/{ticker}/{ratio}   — single ratio
GET /health/{ticker}           — health score (0-100)
GET /history/{ticker}          — 5-year ratio trends
GET /peers/{ticker}            — peer comparison
GET /screen?tickers=AAPL,MSFT&roic_gt=0.15&pe_lt=30  — screening

Python — MCP Server for AI Agents

pip install "financial-ratios[mcp]"
fin-ratios serve

Add to your Claude Desktop claude_desktop_config.json:

{
  "mcpServers": {
    "fin-ratios": {
      "command": "fin-ratios",
      "args": ["serve"]
    }
  }
}

Available tools for Claude:

• analyze_ticker — all ratios for a ticker
• health_score — financial health score (0–100)
• ratio_history — multi-year trends
• compare_peers — peer comparison
• screen_stocks — filter by ratio thresholds
• compute_ratio — compute a specific ratio from raw inputs

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Data Fetchers (Optional)

Fetchers are separate optional modules — zero network code in the core.

Yahoo Finance (free, no API key)

from fin_ratios.fetchers.yahoo import fetch_yahoo
data = fetch_yahoo('AAPL')

import { fetchYahoo } from 'fin-ratios/fetchers/yahoo'
const data = await fetchYahoo('AAPL')

SEC EDGAR (free, no API key, US companies only)

from fin_ratios.fetchers.edgar import fetch_edgar
filings = fetch_edgar('AAPL', num_years=3)  # newest-first list

import { fetchEdgar, fetchEdgarFlat } from 'fin-ratios/fetchers/edgar'
// fetchEdgarFlat returns oldest-first flat records, ready for scoring utilities
const annualData = await fetchEdgarFlat('AAPL')

Financial Modeling Prep (free tier: 250 req/day)

from fin_ratios.fetchers.fmp import fetch_fmp
data = fetch_fmp('AAPL', api_key='your_key', periods=4)

SimFin (free tier: 500 req/day)

from fin_ratios.fetchers.simfin import fetch_simfin, set_api_key
set_api_key('your_simfin_key')  # or SIMFIN_API_KEY env var
data = fetch_simfin('AAPL')

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S&P 500 Bulk Analysis

pip install "financial-ratios[fetchers]" pandas
cd python
python scripts/sp500_analysis.py              # All 503 companies (~30-45 min)
python scripts/sp500_analysis.py --sample 50  # Quick test with 50 companies

Output file	Contents
sp500_ratios.csv	All 30+ ratios for every company
sp500_top_piotroski.csv	Top 20 companies by F-Score
sp500_distressed.csv	Companies in Altman distress zone
sp500_manipulation_flags.csv	Companies flagged by Beneish M-Score

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Full Ratio Catalog

Valuation (18)

Ratio	Formula	Key Reference
P/E (Trailing)	Market Cap / Net Income	Graham & Dodd (1934)
P/E (Forward)	Price / Forward EPS	—
PEG Ratio	P/E / EPS Growth %	Lynch (1989)
P/B	Market Cap / Total Equity	Graham & Dodd (1934)
P/S	Market Cap / Revenue	—
P/FCF	Market Cap / FCF	—
EV/EBITDA	EV / EBITDA	Koller et al. (2020)
EV/EBIT	EV / EBIT	—
EV/Revenue	EV / Revenue	—
EV/FCF	EV / FCF	—
EV/Invested Capital	EV / IC	—
Tobin's Q	(MC + Debt) / Assets	Tobin (1969)
Graham Number	√(22.5 × EPS × BVPS)	Graham (1973)
Graham Intrinsic Value	EPS × (8.5 + 2g) × 4.4 / Y	Graham (1974)
2-Stage DCF	Σ FCF/(1+r)^t + TV	Williams (1938)
Gordon Growth DDM	D1 / (r - g)	Gordon (1959)
Earnings Power Value	NOPAT / WACC	Greenwald et al. (2001)
Reverse DCF	Solve for g	Mauboussin (2006)

Profitability (15)

Gross Margin, Operating Margin, EBITDA Margin, Net Margin, NOPAT Margin, ROE, ROA, ROIC, ROCE, ROTE, DuPont 3-Factor, Invested Capital, Revenue/Employee, Profit/Employee, NOPAT

Liquidity (9)

Current Ratio, Quick Ratio, Cash Ratio, OCF Ratio, DSO, DIO, DPO, Cash Conversion Cycle, Defensive Interval Ratio

Solvency (9)

D/E, Net D/E, Net Debt/EBITDA, Debt/Assets, Debt/Capital, Interest Coverage, EBITDA Coverage, DSCR, Equity Multiplier

Efficiency (8)

Asset Turnover, Fixed Asset Turnover, Inventory Turnover, Receivables Turnover, Payables Turnover, Working Capital Turnover, Capital Turnover, Operating Leverage

Cash Flow (11)

FCF, Levered FCF, Unlevered FCF (FCFF), Owner Earnings, FCF Yield, FCF Margin, FCF Conversion, OCF/Sales, Capex/Revenue, Capex/Depreciation, Cash Return on Assets

Growth (8)

Revenue YoY, Revenue CAGR, EPS Growth, EBITDA Growth, FCF Growth, Book Value Growth, Dividend Growth, EPV

Risk / Portfolio (18)

Beta, Jensen's Alpha, Sharpe Ratio, Sortino Ratio, Treynor Ratio, Calmar Ratio, Information Ratio, Omega Ratio, Maximum Drawdown, Tracking Error, Historical VaR, Parametric VaR, CVaR/Expected Shortfall, Ulcer Index, Upside Capture, Downside Capture, R-Squared

Composite Scores (7)

Score	What it detects	Reference
Piotroski F-Score (0–9)	Financial strength	Piotroski (2000)
Altman Z-Score	Bankruptcy probability	Altman (1968)
Beneish M-Score	Earnings manipulation	Beneish (1999)
Ohlson O-Score	Bankruptcy probability (logistic)	Ohlson (1980)
Greenblatt Magic Formula	Value + Quality ranking	Greenblatt (2005)
Montier C-Score (0–6)	Earnings quality signals	Montier (2008)
Shiller CAPE	Cyclically adjusted P/E	Shiller (2000)

Scoring Models (v0.3–v0.7)

Model	Signals	Output	Reference
Moat Score (0–100)	ROIC persistence, pricing power, reinvestment quality, op. leverage, CAP	wide / narrow / none	Mauboussin & Johnson (1997)
Capital Allocation Score (0–100)	Value creation, FCF quality, reinvestment yield, payout discipline	excellent / good / fair / poor	Koller et al. (2020), Mauboussin (2012)
Earnings Quality Score (0–100)	Accruals ratio, cash earnings, revenue recognition, GM stability, asset efficiency	high / medium / low / poor	Sloan (1996), Richardson et al. (2005)
Fair Value Range	DCF, Graham Number, FCF Yield, EV/EBITDA, EPV	bear / base / bull per share	Graham & Dodd (1934), Koller et al. (2020)
Quality Factor Score (0–100)	EQ (35%) + Moat (35%) + Capital Allocation (30%)	exceptional → poor	Asness, Frazzini & Pedersen (2019)
Portfolio Quality	Weighted average across holdings	portfolio score + per-holding breakdown	—

SaaS / Tech (11)

Metric	Formula	Benchmark
Rule of 40	Growth % + FCF Margin %	> 40 healthy
Net Revenue Retention	Net ARR / Beginning ARR	> 110% elite
Gross Revenue Retention	(ARR - Churn) / ARR	> 90% good
Magic Number	Net New ARR × 4 / S&M	> 0.75 efficient
LTV/CAC	Customer LTV / CAC	> 3x healthy
CAC Payback	CAC / Monthly Margin	< 12 mo excellent
Burn Multiple	Net Burn / New ARR	< 1 excellent
SaaS Quick Ratio	(New + Exp) / (Churn + Contraction)	> 4 excellent
ARR/FTE	ARR / Employees	> $200K elite
Customer LTV	(ARPU × GM) / Churn	—
CAC	S&M Spend / New Customers	—

REIT (7)

FFO, AFFO, P/FFO, P/AFFO, NOI, Cap Rate, Occupancy Rate

Banking (8)

NIM, Efficiency Ratio, Loan/Deposit, NPL Ratio, Provision Coverage, Tier 1 Capital, CET1 Ratio, Tangible BVPS

Insurance (5)

Loss Ratio, Expense Ratio, Combined Ratio, Underwriting Margin, Premiums/Surplus

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Design Principles

1. Pure functions — (inputs) → number | null
2. Formula on every function — pe.formula, sharpeRatio.formula
3. Null-safe — returns null on division by zero, never NaN
4. Tree-shakeable — import only what you need
5. Zero core deps — only fetchers/api/mcp have external dependencies
6. Strict TypeScript — exactOptionalPropertyTypes, noUncheckedIndexedAccess
7. Transparent caching — opt-in cache layer; fetchers unchanged
8. Batch-first — compute_all() / computeAll() for single-call analysis

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Examples

Run the included examples without any API keys:

cd python
python examples/01_valuation_examples.py    # DCF, Graham Number, EV multiples
python examples/03_composite_scores.py      # Piotroski, Altman, Beneish, Magic Formula
python examples/04_risk_portfolio.py        # Sharpe, Sortino, VaR, CVaR, Capture Ratios
python examples/05_saas_metrics.py          # Rule of 40, NRR, Burn Multiple, LTV/CAC

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Documentation

• Quantitative Moat Score — methodology, signals, and usage
• Academic Citations
• Changelog

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Academic Citations

Every ratio is backed by either academic research or established industry practice. See docs/CITATIONS.md for the full bibliography.

Key papers:

• Altman (1968) — Z-Score bankruptcy prediction, Journal of Finance
• Piotroski (2000) — F-Score value investing, Journal of Accounting Research
• Beneish (1999) — M-Score earnings manipulation, Financial Analysts Journal
• Sharpe (1966) — Risk-adjusted return, Journal of Business
• Gordon (1959) — Dividend discount model, Review of Economics and Statistics
• Rockafellar & Uryasev (2000) — CVaR optimization, Journal of Risk
• Tobin (1969) — Q ratio, Journal of Money, Credit and Banking
• Ohlson (1980) — O-Score bankruptcy probability, Journal of Accounting Research
• Keating & Shadwick (2002) — Omega ratio, Journal of Performance Measurement
• Montier (2008) — C-Score earnings quality, Société Générale Cross Asset Research
• Shiller (2000) — CAPE cyclically adjusted P/E, Irrational Exuberance
• Mauboussin & Johnson (1997) — Competitive Advantage Period (CAP)
• Sloan (1996) — Accruals anomaly, Accounting Review
• Richardson et al. (2005) — Accruals and future earnings, Journal of Accounting & Economics
• Novy-Marx (2013) — Gross profitability, Journal of Financial Economics
• Mauboussin (2012) — True measures of capital allocation success, Harvard Business Review
• Asness, Frazzini & Pedersen (2019) — Quality Minus Junk, Review of Accounting Studies

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Project Structure

fin-ratios/
├── typescript/
│   ├── src/
│   │   ├── ratios/           # 136+ ratio functions
│   │   ├── fetchers/         # Yahoo, FMP, EDGAR, Alpha Vantage
│   │   ├── hooks/            # React hooks (useRatios, useHealthScore, ...)
│   │   ├── types/            # IncomeStatement, BalanceSheet, MarketData, etc.
│   │   ├── utils/            # computeAll, scenarioDcf, cache, moatScore,
│   │   │                     # capitalAllocationScore, earningsQualityScore,
│   │   │                     # fairValueRange, qualityScore, portfolioQuality
│   │   └── __tests__/        # 89-test vitest suite
│   └── examples/
├── python/
│   ├── fin_ratios/
│   │   ├── ratios/           # Python port of all ratios
│   │   ├── fetchers/         # Yahoo, FMP, EDGAR, Alpha Vantage, SimFin
│   │   ├── utils/            # compute_all, trends, scenario_dcf, peers,
│   │   │                     # moat_score, capital_allocation, earnings_quality,
│   │   │                     # fair_value, quality_score, portfolio, benchmarks
│   │   ├── cache.py          # Memory / disk / Redis caching layer
│   │   ├── pandas_ext.py     # Pandas + Polars DataFrame integration
│   │   ├── notebook.py       # Jupyter rich display (RatioCard, ComparatorCard)
│   │   ├── api.py            # FastAPI REST server
│   │   ├── mcp_server.py     # MCP server for AI agents
│   │   └── cli.py            # fin-ratios CLI
│   ├── tests/                # 306-test pytest suite
│   ├── examples/
│   └── scripts/
│       └── sp500_analysis.py
├── docs/
│   ├── MOAT_SCORE.md         # Moat score methodology deep-dive
│   └── CITATIONS.md          # Full academic bibliography
└── CHANGELOG.md

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Contributing

See CONTRIBUTING.md. All contributions welcome.

When adding a new ratio, include:

1. The formula as a string on the function (.formula = "...")
2. A description (.description = "...")
3. An academic or industry citation in docs/CITATIONS.md
4. At least one test in the test suite

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License

MIT — use freely in commercial projects.