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fix(fundamentals): pure-core review — tie-aware percentile, null-safe reads

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1. Peer percentile is now a tie-aware rank against the OTHER issuers
   ((worse + 0.5*tied)/(peers-1)): an all-equal group maps to 50 (not 100), the
   median maps to 50, a unique best to 100, a unique worst to 0.
2. Deterministic reads use the consecutive non-null suffix ending at the latest
   point (>=3 values): a null latest or an internal gap yields no read, so a read
   never reflects a period displayed as n/a.
3. Peer filtering excludes non-finite (NaN/±inf) as well as null, including an
   invalid subject.

Tests updated + added (all-equal, median rank, non-finite, latest-null history,
internal gap). 15 passed.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
dennisthiessen
2026-07-22 20:41:18 +02:00
co-authored by Claude Opus 4.8
parent 2038b84b72
commit 979b4047dc
4 changed files with 73 additions and 22 deletions
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app/services/fundamentals_reads.py
+17 -6
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@@ -22,13 +22,23 @@ 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 _latest_run(history: list[Any]) -> list[float]:
"""The consecutive non-null values ending at the latest point (oldest->newest).
A null latest, or an internal gap, truncates the run — so a read never reflects
a period whose displayed value is n/a."""
run: list[float] = []
for p in reversed(history):
if p.value is None:
break
run.append(p.value)
run.reverse()
return run
def growth_read(history: list[Any]) -> str | None:
"""Change in a YoY-growth series: latest − prior. Needs >= 3 periods."""
vals = _history_values(history)
"""Change in a YoY-growth series: latest − prior. Needs >= 3 consecutive
non-null values ending at the latest point."""
vals = _latest_run(history)
if len(vals) < MIN_PERIODS:
return None
delta = vals[-1] - vals[-2]
@@ -40,8 +50,9 @@ def growth_read(history: list[Any]) -> str | None:
def margin_read(history: list[Any]) -> str | None:
"""Latest margin vs the mean of prior periods (pp). Needs >= 3 periods."""
vals = _history_values(history)
"""Latest margin vs the mean of prior periods (pp). Needs >= 3 consecutive
non-null values ending at the latest point."""
vals = _latest_run(history)
if len(vals) < MIN_PERIODS:
return None
delta = vals[-1] - mean(vals[:-1])