financial-ratios 0.4.0

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. 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
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
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 v0.2

• compute_all(data) — compute all 40+ ratios from a single data object in one call
• ratio_history(ticker, metrics, years=5) — multi-year trends with linear regression (improving/stable/deteriorating)
• scenario_dcf(base_fcf, shares, scenarios) — bull/base/bear intrinsic value analysis
• compare_peers(ticker, metrics) — peer comparison with built-in sector maps and percentile ranks
• Caching layer — memory, disk (JSON), and Redis backends with TTL; @cached(namespace) decorator
• Pandas/Polars integration — ratios_from_df(df) with 24+ column alias variants
• Jupyter notebook display — color-coded RatioCard and ComparatorCard rich HTML
• FastAPI REST API — fin-ratios api --port 8000, OpenAPI docs at /docs
• MCP Server — fin-ratios serve for Claude Desktop (6 AI agent tools)
• SimFin fetcher — free structured financial data (500 req/day free tier)
• Montier C-Score — 6-signal earnings quality score (Montier 2008)
• Shiller CAPE — cyclically adjusted P/E with CPI adjustment (Shiller 2000)
• TypeScript — computeAll(), scenarioDcf(), montierCScore(), in-memory cache, React hooks

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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)

// DCF Valuation
const dcf = dcf2Stage({
  baseFcf: 5_000_000_000,
  growthRate: 0.20,
  years: 10,
  terminalGrowthRate: 0.03,
  wacc: 0.10,
  netDebt: -10_000_000_000,
  sharesOutstanding: 1_000_000_000,
})
console.log(`Intrinsic value: $${dcf?.intrinsicValuePerShare?.toFixed(2)}`)

// 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}`)
// => "F-Score: 9/9 — Strong: High financial strength"

TypeScript — Batch Compute (v0.2)

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 (v0.2)

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 (v0.2)

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

// Fetch and compute all ratios
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>
  )
}

// Compare multiple tickers
function PeerTable({ tickers }: { tickers: string[] }) {
  const { data } = useCompareRatios(tickers, fetchYahoo)
  return (
    <table>
      {tickers.map(t => (
        <tr key={t}>
          <td>{t}</td>
          <td>{data?.[t]?.pe?.toFixed(1)}</td>
          <td>{data?.[t]?.roic?.toFixed(2)}</td>
        </tr>
      ))}
    </table>
  )
}

---

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"

# Piotroski F-Score
result = piotroski_f_score(
    current_net_income=8e6,    current_total_assets=100e6,
    current_operating_cf=12e6, current_long_term_debt=20e6,
    current_current_assets=35e6, current_current_liabilities=15e6,
    current_shares_outstanding=10e6, current_gross_profit=45e6,
    current_revenue=90e6,
    prior_net_income=5e6,      prior_total_assets=95e6,
    prior_long_term_debt=25e6, prior_current_assets=28e6,
    prior_current_liabilities=14e6, prior_shares_outstanding=10.5e6,
    prior_gross_profit=38e6,   prior_revenue=80e6,
)
print(result["score"])          # 9
print(result["interpretation"]) # "Strong (9/9): High financial strength"

Python — Batch Compute (v0.2)

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"Current:      {ratios['current_ratio']:.2f}")
print(f"Altman Zone:  {ratios['altman_z']['zone']}")
# Also works with plain dicts or any object with attributes
ratios2 = compute_all({
    'revenue': 400e9, 'net_income': 100e9,
    'total_assets': 350e9, 'total_equity': 74e9,
    'market_cap': 3e12, ...
})

Python — Ratio History & Trends (v0.2)

from fin_ratios.utils import ratio_history

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

# Trend direction via linear regression
print(history.trend('roic'))        # 'improving'
print(history.trend('gross_margin')) # 'stable'

# CAGR
print(f"ROIC CAGR: {history.cagr('roic'):.1%}")

# ASCII table
print(history.table())
# Year    roic    gross_margin   pe
# 2020    0.56    0.38           28.4
# 2021    0.61    0.42           31.2
# ...

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

Python — Scenario DCF (v0.2)

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 — Peer Comparison (v0.2)

from fin_ratios.utils import compare_peers

comp = compare_peers('AAPL', metrics=['roic', 'gross_margin', 'pe'])

print(comp.rank('roic'))        # 1  (best among peers)
print(comp.percentile('roic'))  # 0.95

# In Jupyter — color-coded HTML table with rank superscripts
comp  # _repr_html_() called automatically

Python — Pandas Integration (v0.2)

from fin_ratios.pandas_ext import ratios_from_df, check_columns
import pandas as pd

df = pd.DataFrame({
    'ticker': ['AAPL', 'MSFT', 'GOOGL'],
    'market_cap': [3e12, 2.5e12, 1.8e12],
    'net_income': [100e9, 88e9, 73e9],
    'revenue':    [400e9, 220e9, 305e9],
    'gross_profit': [172e9, 152e9, 174e9],
    'ebit':       [120e9, 108e9, 84e9],
    'total_assets': [350e9, 510e9, 400e9],
    'total_equity': [74e9, 206e9, 283e9],
})

# Compute selected ratios
result = ratios_from_df(df, ratios=['pe', 'gross_margin', 'roe', 'roa'])

# Debug column resolution
print(check_columns(df))
# {'resolved': {'market_cap': 'market_cap', ...}, 'missing': ['operating_cash_flow'], ...}

# Works with Polars DataFrames too
import polars as pl
result = ratios_from_df(pl.from_pandas(df), ratios=['pe', 'roic'])

Python — Caching (v0.2)

from fin_ratios.cache import set_cache, invalidate, clear_cache, cache_stats

# Configure cache backend
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 automatically use the cache — no code change needed
from fin_ratios.fetchers.yahoo import fetch_yahoo
data = fetch_yahoo('AAPL')  # live fetch
data = fetch_yahoo('AAPL')  # served from cache

# Cache management
invalidate('AAPL')    # clear one ticker
clear_cache()         # clear everything
print(cache_stats())  # {'hits': 12, 'misses': 3, 'entries': 8}

Python — Jupyter Notebook (v0.2)

from fin_ratios.notebook import RatioCard, ComparatorCard, display_ratios

# Single-ticker color-coded card
RatioCard('AAPL', {
    'pe': 28.3, 'roic': 0.55, 'gross_margin': 0.433,
    'current_ratio': 1.07, 'debt_to_equity': 1.49,
})

# Side-by-side comparison
ComparatorCard(
    ticker_ratios={'AAPL': {...}, 'MSFT': {...}, 'GOOGL': {...}},
    metrics=['pe', 'roic', 'gross_margin', 'current_ratio'],
)

Python — REST API (v0.2)

# Install with API extras
pip install "fin-ratios[api]"

# Start server
fin-ratios api --port 8000

# OpenAPI docs at http://localhost:8000/docs

# Available endpoints:
# 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 (v0.2)

# Install with MCP extras
pip install "fin-ratios[mcp]"

# Start MCP server (stdio transport — use with Claude Desktop)
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

---

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')
print(f"P/E: {pe(data.market_cap, data.net_income):.1f}")

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)
for f in filings:
    print(f"{f.fiscal_year}: Revenue ${f.revenue/1e9:.1f}B")

SimFin (free tier: 500 req/day) — New in v0.2

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')

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)

import { fetchFmp } from 'fin-ratios/fetchers/fmp'
const data = await fetchFmp('AAPL', { apiKey: 'your_key', periods: 4 })

Alpha Vantage (free tier: 25 req/day)

from fin_ratios.fetchers.alphavantage import fetch_alphavantage
data = fetch_alphavantage('IBM', api_key='your_key')

---

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 files:

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

---

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)

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

---

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

---

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

---

Documentation

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

---

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

---

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, safeDivide
│   └── examples/             # Runnable TypeScript examples
├── python/
│   ├── fin_ratios/
│   │   ├── ratios/           # Python port of all ratios
│   │   ├── fetchers/         # Yahoo, FMP, EDGAR, Alpha Vantage, SimFin
│   │   ├── utils/            # compute_all, trends, scenario_dcf, peers, health_score
│   │   ├── 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
│   ├── examples/             # Runnable Python examples
│   └── scripts/
│       └── sp500_analysis.py # S&P 500 bulk computation
├── docs/
│   └── CITATIONS.md          # Full academic bibliography
└── CHANGELOG.md              # Version history

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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.