Investment Score¶

The Investment Score is a grand synthesis of all scoring models into a single, actionable rating.

Score: 0–100 Grade: A+ (≥90) | A (80–89) | B+ (70–79) | B (60–69) | C (45–59) | D (25–44) | F (<25) Conviction: strong_buy | buy | hold | sell | strong_sell

Weights¶

Component	Weight	Notes
Economic Moat	25%	Business durability
Capital Allocation	20%	Management discipline
Earnings Quality	20%	Accruals and cash backing
Management Quality	15%	ROIC trend, margins, dilution
Valuation	20%	Attractiveness vs intrinsic value

If Valuation is omitted, its 20% is redistributed proportionally.

Usage¶

PythonTypeScript

from fin_ratios.utils.investment_score import (
    investment_score_from_series,
    investment_score_from_scores,
)
from fin_ratios.fetchers.edgar import fetch_edgar

# Option A: from raw annual data
annual_data = fetch_edgar('AAPL', num_years=7)
score = investment_score_from_series(
    annual_data,
    pe_ratio=22.0,     # optional valuation inputs
    ev_ebitda=14.0,
)
print(score.score, score.grade, score.conviction)
# → 74 B+ buy

# Option B: from pre-computed sub-scores
score = investment_score_from_scores(
    moat_score=78,
    capital_allocation_score=71,
    earnings_quality_score=82,
    management_score=68,
    valuation_score=45,
)

import { investmentScoreFromSeries, investmentScoreFromScores } from 'fin-ratios'
import { fetchEdgarNormalized } from 'fin-ratios/fetchers/edgar'

// Option A: from raw annual data
const annualData = await fetchEdgarNormalized('AAPL', { numYears: 7 })
const score = investmentScoreFromSeries(annualData, {
  peRatio: 22.0,
  evEbitda: 14.0,
})
console.log(score.score, score.grade, score.conviction)

// Option B: from pre-computed scores
const score2 = investmentScoreFromScores({
  moatScore: 78,
  capitalAllocationScore: 71,
  earningsQualityScore: 82,
  managementScore: 68,
  valuationScore: 45,
})

Output Structure¶

@dataclass
class InvestmentScore:
    score: int              # 0–100
    grade: str              # A+ / A / B+ / B / C / D / F
    conviction: str         # strong_buy / buy / hold / sell / strong_sell
    components: InvestmentComponents
    evidence: list[str]
    interpretation: str

@dataclass
class InvestmentComponents:
    moat: Optional[int]
    capital_allocation: Optional[int]
    earnings_quality: Optional[int]
    management: Optional[int]
    valuation: Optional[int]

Conviction Mapping¶

Score Range	Conviction
≥ 80	Strong Buy
65–79	Buy
45–64	Hold
25–44	Sell
< 25	Strong Sell