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8ed954c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | """Candidate scoring and ranking (Proposal D).
Batch-scores screened candidates on quantitative criteria so the expensive
LLM analyst step only runs on the best picks.
"""
from src.core.logger import get_logger
from src.finance_tools import calculate_graham_number
logger = get_logger(__name__)
def score_candidate(candidate: dict) -> dict:
"""Score a single candidate dict (as returned by ``screen_microcaps``).
Returns the same dict with ``score`` (0-100) and ``score_breakdown``
keys added.
"""
score = 0
breakdown: list[str] = []
price = candidate.get("price", 0)
eps = candidate.get("eps", 0)
bv = candidate.get("book_value", 0)
fcf = candidate.get("free_cashflow", 0)
ebitda = candidate.get("ebitda", 0)
total_debt = candidate.get("total_debt", 0)
total_cash = candidate.get("total_cash", 0)
current_ratio = candidate.get("current_ratio", 0)
pb = candidate.get("price_to_book", 0)
# 1. Profitability (20 pts)
if eps and eps > 0:
score += 20
breakdown.append("+20 profitable (EPS > 0)")
# 2. Graham undervaluation (25 pts)
info_proxy = {"trailingEps": eps, "bookValue": bv}
graham = calculate_graham_number(info_proxy)
if graham > 0 and price > 0:
margin = (graham - price) / graham
if margin > 0.3:
score += 25
breakdown.append(f"+25 Graham margin {margin:.0%}")
elif margin > 0:
pts = int(margin * 50)
score += pts
breakdown.append(f"+{pts} Graham margin {margin:.0%}")
# 3. Price-to-Book deep value (15 pts)
if pb and 0 < pb < 1.0:
score += 15
breakdown.append(f"+15 P/B={pb:.2f} < 1.0")
elif pb and 0 < pb < 1.5:
score += 8
breakdown.append(f"+8 P/B={pb:.2f} < 1.5")
# 4. Free cash flow positive (15 pts)
if fcf and fcf > 0:
score += 15
breakdown.append("+15 FCF positive")
# 5. Low debt burden (10 pts)
if ebitda and ebitda > 0 and total_debt is not None:
net_debt_ebitda = (total_debt - (total_cash or 0)) / ebitda
if net_debt_ebitda < 1.0:
score += 10
breakdown.append(f"+10 low debt ({net_debt_ebitda:.1f}x)")
elif net_debt_ebitda < 2.5:
score += 5
breakdown.append(f"+5 moderate debt ({net_debt_ebitda:.1f}x)")
# 6. Liquidity (10 pts)
if current_ratio and current_ratio > 1.5:
score += 10
breakdown.append(f"+10 liquid (CR={current_ratio:.1f})")
elif current_ratio and current_ratio > 1.0:
score += 5
breakdown.append(f"+5 adequate liquidity (CR={current_ratio:.1f})")
# 7. Cash runway for unprofitable companies (5 pts)
if eps <= 0 and fcf and fcf < 0 and total_cash:
runway_years = total_cash / abs(fcf)
if runway_years >= 2:
score += 5
breakdown.append(f"+5 runway {runway_years:.1f}y")
candidate["score"] = score
candidate["score_breakdown"] = breakdown
return candidate
def rank_candidates(candidates: list[dict], top_n: int = 5) -> list[dict]:
"""Score and sort candidates, returning the top N.
Each candidate dict gets ``score`` and ``score_breakdown`` added.
"""
scored = [score_candidate(c) for c in candidates]
scored.sort(key=lambda c: c["score"], reverse=True)
for i, c in enumerate(scored[:top_n]):
logger.info(
"Rank #%d: %s score=%d %s",
i + 1,
c["ticker"],
c["score"],
" | ".join(c["score_breakdown"]),
)
return scored[:top_n]
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