ruff F401 failed the deploy: `import pytest` in test_event_study.py outlived the
pytest.approx assertion it was added for.
Compiling the migration for Postgres while checking that turned up a second
defect worth fixing while the table is still empty. It created a unique
constraint *and* a plain index on effective_date, while the model declares
`unique=True, index=True` -- one unique index. Both enforce uniqueness, but the
pairing left a redundant second index on the column and a permanent diff for
autogenerate to keep trying to reconcile. Now renders byte-for-byte what the
model declares, matching RegimeSnapshot.date.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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>
dennisthiessenandClaude Opus 5