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Quality Factor Score

A composite score that blends Earnings Quality, Economic Moat, and Capital Allocation into a single quality grade — the quantitative definition of a "quality" stock.

Score: 0–100 Grade: exceptional (≥85) | high (70–84) | above_average (55–69) | average (40–54) | below_average (25–39) | poor (<25)

Weights

Component Weight
Earnings Quality 35%
Economic Moat 35%
Capital Allocation 30%

Usage

from fin_ratios.utils.quality_score import quality_score_from_series, quality_score
from fin_ratios.fetchers.edgar import fetch_edgar

annual_data = fetch_edgar('AAPL', num_years=7)
score = quality_score_from_series(annual_data, wacc=0.09)

print(score.score)     # 80
print(score.grade)     # 'high'
print(score.table())   # full breakdown

# One-liner
score = quality_score('AAPL', years=7, source='yahoo', wacc=0.09)

import { qualityScoreFromSeries } from 'fin-ratios'
import { fetchEdgarFlat } from 'fin-ratios/fetchers/edgar'

const annualData = await fetchEdgarFlat('AAPL')
const score = qualityScoreFromSeries(annualData, 0.09)

console.log(score.score)   // 80
console.log(score.grade)   // 'high'

Output Structure

@dataclass
class QualityFactorScore:
    score: int
    grade: str
    components: QualityComponents
    evidence: list[str]
    interpretation: str

@dataclass
class QualityComponents:
    earnings_quality: Optional[int]
    moat: Optional[int]
    capital_allocation: Optional[int]

Use with Portfolio

The quality score is designed to be used as a screening and ranking tool:

from fin_ratios.utils.portfolio import PortfolioQuality
from fin_ratios.utils.quality_score import quality_score

holdings = [
    {'ticker': 'AAPL', 'weight': 0.30},
    {'ticker': 'MSFT', 'weight': 0.25},
    {'ticker': 'GOOGL', 'weight': 0.20},
    {'ticker': 'NVDA', 'weight': 0.25},
]

# Compute individual quality scores
scored = [
    quality_score(h['ticker'], years=5, source='yahoo')
    for h in holdings
]

# Weighted portfolio quality
portfolio = PortfolioQuality(
    holdings=[
        {'score': s.score, 'weight': h['weight']}
        for s, h in zip(scored, holdings)
    ]
)
print(portfolio.weighted_score)

References

  • Asness, C., Frazzini, A., Pedersen, L.H. (2019). Quality Minus Junk. Review of Accounting Studies.
  • Novy-Marx, R. (2013). The Other Side of Value: The Gross Profitability Premium. Journal of Financial Economics.