feat(risk-monitor): measure the rule that fires, and give fundamentals their own channel

The Warning study measured a fitted percentile crossing that nothing consumes.
What reaches Telegram is a quadrant change: fixed 50/40 dividers, hysteresis,
two-session confirmation, 3-day cooldown. Those thresholds are constants, not
fits, so there is no training set to protect and all 11 detected corrections are
evaluable instead of the 4 that fell in a holdout.

Replaying it: 1/10 corrections, 0.9 false alarms/year. Random alarms at the same
firing rate match or beat that in 65% of draws. The panel now carries ablations
(does the quadrant machinery earn its place?), external baselines (does the score
earn its complexity?), and that null, because a bare "2 of 4" was unreadable in
either direction. Nothing in the alert path was retuned on the strength of it.

Fundamentals become a third channel rather than a term in either score. v3 cut
them arguing 12+8 of 100 points "could not change any published conclusion" --
true only when every technical sensor reads zero; weighted they moved the bar for
the 40 divider from 40 to 25. But no fusion weight is measurable either: with ~10
events and no fundamental history, any weight is a policy preference presented as
a measurement. So the read is a categorical state (supportive/neutral/adverse/
unknown) with an evidence grade, derived by fixed rules from stored facts, read
by confluence. The LLM extracts and explains; it does not score.

Absence stays absence throughout. `unknown` is unreachable by averaging, a stale
or empty observation may display but never confirm, extraction failures map to
`unknown` rather than `mixed`, and the study rows are coverage-matched and marked
not-measurable until enough corrections are covered -- otherwise a fortnight of
observations renders as 0/10 and reads as a failed test.

Observations become a real time series (migration 033); they lived in a single
overwritten settings slot, so no history existed to replay. Pre-rename snapshots
are adapted rather than discarded. METHODOLOGY stays v4 -- no score changed --
so no reseed; STUDY_SCHEMA moves to 3 and discards the cached report.

Post-deploy: re-run Event Study from Admin -> Jobs. The panel reads "not run yet"
until then.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

app/models/regime_fundamental_observation.py

@@ -0,0 +1,44 @@
+from datetime import date as date_type
+from datetime import datetime
+
+from sqlalchemy import Date, DateTime, Float, String, Text
+from sqlalchemy.orm import Mapped, mapped_column
+
+from app.database import Base
+
+
+class RegimeFundamentalObservation(Base):
+    """Point-in-time record of the sourced hyperscaler capex / earnings read.
+
+    One row per ``effective_date`` (unique, upserted). Before this table the
+    observation lived in a single ``SystemSetting`` slot, so every refresh
+    overwrote the previous one and no history existed at all — which made the
+    read impossible to replay, impossible to backtest, and meant a snapshot
+    rebuild could only ever score historical sessions as if nothing had been
+    observed.
+
+    The read is a categorical channel reported beside State and Warning, never a
+    term in either, so this series is not a scoring input. It is the record that
+    makes the channel replayable at all -- and the only route to eventually
+    testing whether it improves prediction conditional on Warning, which is the
+    one thing that could justify combining the channels later.
+
+    ``effective_date`` rather than ``fetched_at`` is the key: it is the session
+    the observation becomes usable on (normally the next weekday), and the gate
+    that stops a rebuild stamping today's reading onto historical rows.
+    """
+
+    __tablename__ = "regime_fundamental_observations"
+
+    id: Mapped[int] = mapped_column(primary_key=True)
+    effective_date: Mapped[date_type] = mapped_column(
+        Date, nullable=False, unique=True, index=True
+    )
+    f1_score: Mapped[float | None] = mapped_column(Float, nullable=True)
+    f3_score: Mapped[float | None] = mapped_column(Float, nullable=True)
+    capex_json: Mapped[str] = mapped_column(Text, nullable=False)
+    good_news_stock_down: Mapped[str] = mapped_column(String(10), nullable=False)
+    reasoning: Mapped[str | None] = mapped_column(Text, nullable=True)
+    source: Mapped[str] = mapped_column(String(30), nullable=False)
+    fetched_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False)
+    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False)