Scoring Models¶

fin-ratios provides 8 institutional-grade scoring models that measure distinct dimensions of business quality and investment attractiveness.

Overview¶

Model	Input	Output	Min Years
Economic Moat	Annual time-series	0–100, wide/narrow/none	3
Capital Allocation	Annual time-series	0–100, excellent–poor	2
Earnings Quality	Annual time-series	0–100, high–poor	2
Quality Factor	Annual time-series	0–100, A–F	3
Valuation Attractiveness	Point-in-time snapshot	0–100, attractive–overvalued	—
Management Quality	Annual time-series	0–100, excellent–poor	3
Dividend Safety	Annual time-series	0–100, safe–danger/non-payer	2
Investment Score	All of the above	0–100, A+–F, conviction	3

Common Pattern¶

All time-series models follow the same API:

PythonTypeScript

# Pattern 1: bring your own data
from fin_ratios.utils.moat_score import moat_score_from_series
score = moat_score_from_series(annual_data)

# Pattern 2: fetch from ticker
from fin_ratios.utils.moat_score import moat_score
score = moat_score(ticker='AAPL', years=7, source='edgar')

# All results have the same interface
print(score.score)         # int 0–100
print(score.rating)        # 'wide' | 'narrow' | 'none'
print(score.table())       # formatted text table
print(score.to_dict())     # dict for JSON/API
score._repr_html_()        # Jupyter rich display

import { moatScore } from 'fin-ratios'

const score = moatScore(annualData)
console.log(score.score)    // number 0–100
console.log(score.rating)   // 'wide' | 'narrow' | 'none'
console.log(score.evidence) // string[]

Weighting in Investment Score¶

When combined into the Investment Score, models are weighted as:

Model	Weight
Economic Moat	25%
Capital Allocation	20%
Earnings Quality	20%
Management Quality	15%
Valuation Attractiveness	20%

If Valuation is not provided, its 20% is redistributed proportionally across the other four.

Score Interpretation Guide¶

Range	Interpretation
75–100	Excellent / Wide Moat / Safe
50–74	Good / Narrow Moat / Adequate
25–49	Fair / No Moat / Risky
0–24	Poor / Danger