feat(fundamentals): A4 — pure peer comparison + deterministic reads

Completes the pure read-time core.

fundamentals_peers.py: median + polarity-aware favorable percentile + peer_count
for a subject within its SIC group (CIK-deduped by the caller); returns None
below MIN_PEERS=5 so the caller omits the industry object. Absolute net_debt is
intentionally NOT peer-eligible (size-dependent) — leverage compares via
net_debt_to_ebitda. HIGHER_IS_BETTER polarity map + two_digit_sic() grouping key.

fundamentals_reads.py: one shared deterministic rule set (no LLM): growth_read
(+-2pp), margin_read (latest vs mean-of-prior, +-1pp), share_count_read (+-1%),
peer_read (60/40 bands, polarity-aware phrasing per metric), header_sentence
(growth · margins · valuation, omitting empty). Tunable named constants; >=3
periods required for a series read.

Tests: 8 peer + 5 reads, anchored on the boundary cases (exactly +2.0pp, exactly
60th percentile, exactly +1.0pp margin). 13 passed.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

app/services/fundamentals_peers.py

@@ -0,0 +1,85 @@
+"""Pure peer comparison for fundamentals (read-time).
+
+Peers are tracked-universe issuers sharing the **first two SIC digits**,
+deduplicated by CIK (GOOG/GOOGL are one issuer, one observation). This module is
+the pure statistics core: given a subject value and the peer group's values for a
+metric, it returns median + polarity-aware favorable percentile + peer_count, or
+None when there are fewer than the minimum valid peers (the caller then omits the
+industry object entirely rather than show a misleading comparison).
+
+Grouping (which issuers share a 2-digit SIC, CIK-dedup) is the API's job; this
+module only does the math. **Absolute net_debt is size-dependent and must not get
+a peer percentile** — leverage is compared via net_debt_to_ebitda.
+"""
+
+from __future__ import annotations
+
+import statistics
+from dataclasses import dataclass
+
+MIN_PEERS = 5
+
+# Metric -> is a higher value more favorable? (Peer-eligible metrics only;
+# absolute net_debt is intentionally absent — size-dependent.)
+HIGHER_IS_BETTER: dict[str, bool] = {
+    "revenue_growth_yoy": True,
+    "eps_growth_yoy": True,
+    "operating_margin": True,
+    "fcf_margin": True,
+    "fcf_yield": True,
+    "net_debt_to_ebitda": False,   # lower leverage is better
+    "pe": False,                   # cheaper is better
+    "share_count_change_yoy": False,  # dilution is bad
+}
+
+
+@dataclass
+class PeerStat:
+    median: float
+    favorable_percentile: int  # 0-100, polarity-aware (higher = more favorable)
+    peer_count: int            # valid issuers in the group
+
+
+def peer_stat(
+    subject: float | None,
+    group_values: list[float | None],
+    *,
+    higher_is_better: bool,
+    min_peers: int = MIN_PEERS,
+) -> PeerStat | None:
+    """Median + favorable percentile for ``subject`` within its group.
+
+    ``group_values`` is every issuer's value for the metric (including the
+    subject), CIK-deduplicated by the caller. Nulls are excluded. Returns None
+    when fewer than ``min_peers`` valid values exist, or the subject is null.
+    """
+    valid = [v for v in group_values if v is not None]
+    if subject is None or len(valid) < min_peers:
+        return None
+    median = statistics.median(valid)
+    if higher_is_better:
+        favorable = sum(1 for v in valid if v <= subject)
+    else:
+        favorable = sum(1 for v in valid if v >= subject)
+    percentile = round(favorable / len(valid) * 100)
+    return PeerStat(median=median, favorable_percentile=percentile, peer_count=len(valid))
+
+
+def peer_stat_for(
+    metric_key: str, subject: float | None, group_values: list[float | None], **kwargs
+) -> PeerStat | None:
+    """Convenience wrapper that looks up polarity by metric key. Returns None for
+    metrics not eligible for peer comparison (e.g. absolute net_debt)."""
+    if metric_key not in HIGHER_IS_BETTER:
+        return None
+    return peer_stat(
+        subject, group_values, higher_is_better=HIGHER_IS_BETTER[metric_key], **kwargs
+    )
+
+
+def two_digit_sic(sic: str | None) -> str | None:
+    """The 2-digit SIC prefix used for grouping, or None if unusable."""
+    if not sic:
+        return None
+    digits = str(sic).strip()
+    return digits[:2] if len(digits) >= 2 and digits[:2].isdigit() else None

app/services/fundamentals_reads.py

@@ -0,0 +1,104 @@
+"""Deterministic text 'reads' for the fundamentals panel (pure, one rule set).
+
+The tape reads and the header sentence use identical outputs — no LLM, no new
+composite score. Thresholds are tunable named constants, not scattered literals
+(plan: ±2pp growth, ±1pp margins, ±1% dilution, 60/40 peer bands, ≥3 periods).
+
+Consumers pass metric series (value + dated history, from
+``fundamentals_derivation``) and peer percentiles; these functions return short
+strings or None (render "—", no read).
+"""
+
+from __future__ import annotations
+
+from statistics import mean
+from typing import Any
+
+MIN_PERIODS = 3
+GROWTH_ACCEL_PP = 2.0
+MARGIN_MOVE_PP = 1.0
+SHARE_DILUTION_PCT = 1.0
+PEER_FAVORABLE = 60
+PEER_ADVERSE = 40
+
+
+def _history_values(history: list[Any]) -> list[float]:
+    return [p.value for p in history if p.value is not None]
+
+
+def growth_read(history: list[Any]) -> str | None:
+    """Change in a YoY-growth series: latest − prior. Needs >= 3 periods."""
+    vals = _history_values(history)
+    if len(vals) < MIN_PERIODS:
+        return None
+    delta = vals[-1] - vals[-2]
+    if delta >= GROWTH_ACCEL_PP:
+        return "accelerating"
+    if delta <= -GROWTH_ACCEL_PP:
+        return "decelerating"
+    return "steady"
+
+
+def margin_read(history: list[Any]) -> str | None:
+    """Latest margin vs the mean of prior periods (pp). Needs >= 3 periods."""
+    vals = _history_values(history)
+    if len(vals) < MIN_PERIODS:
+        return None
+    delta = vals[-1] - mean(vals[:-1])
+    if delta >= MARGIN_MOVE_PP:
+        return "improving"
+    if delta <= -MARGIN_MOVE_PP:
+        return "deteriorating"
+    return "stable"
+
+
+def share_count_read(value: float | None) -> str | None:
+    """Share-count YoY %: >+1% dilution, <-1% buying back, else flat."""
+    if value is None:
+        return None
+    if value > SHARE_DILUTION_PCT:
+        return f"{value:.1f}% dilution"
+    if value < -SHARE_DILUTION_PCT:
+        return "buying back"
+    return "flat"
+
+
+def peer_read(metric_key: str, favorable_percentile: int | None) -> str | None:
+    """Peer-relative read for a metric, polarity already baked into the
+    percentile (higher = more favorable)."""
+    if favorable_percentile is None:
+        return None
+    if favorable_percentile >= PEER_FAVORABLE:
+        return _FAVORABLE.get(metric_key, "above peers")
+    if favorable_percentile <= PEER_ADVERSE:
+        return _ADVERSE.get(metric_key, "below peers")
+    return "in line"
+
+
+_FAVORABLE = {
+    "pe": "attractively valued",
+    "fcf_yield": "above peers",
+    "net_debt_to_ebitda": "conservative leverage",
+}
+_ADVERSE = {
+    "pe": "priced above peers",
+    "fcf_yield": "below peers",
+    "net_debt_to_ebitda": "elevated leverage",
+}
+
+
+def header_sentence(
+    growth: str | None, margin: str | None, valuation: str | None
+) -> str:
+    """Join the growth / margin / peer-valuation reads with ' · ', omitting
+    segments with no read. Segment sources are fixed by the caller (growth =
+    revenue-growth read, margin = operating-margin read, valuation = P/E peer
+    read falling back to FCF yield)."""
+    parts = []
+    if growth:
+        parts.append(f"growth {growth}")
+    if margin:
+        parts.append(f"margins {margin}")
+    if valuation:
+        parts.append(f"valuation {valuation}")
+    return " · ".join(parts)