signal-platform/docs/research/regime-monitor-v4.md

AI/Tech Risk Monitor v4 methodology

Named "Regime Monitor" until 2026-08-07; the filename's regime stem, the regime_monitor job id, the /regime route and the METHODOLOGY/snapshot fields keep the old word, because those are persisted or externally linked.

The AI/Tech Risk Monitor is an observational risk thermometer. It does not gate entries, exits, position size, ranking, or alerts about individual setups.

v4 supersedes v3 (2026-08-08). Unlike v3, whose calibration was ad-hoc and never landed, every number below is reproducible:

.venv/Scripts/python.exe scripts/run_regime_monitor_calibration.py --methodology v2_reconstruction,v2_reconstruction_oas400,v3,v4,v4-vix-only,v4-p1-only --cache-dir .calib-cache

v3 and v4 are mandatory — the row-wise state_v4 <= state_v3 invariant is a hard gate and needs both — and the replayed start date is asserted against the published window. The session count alone proves nothing, since the harness slices the tail of the price series to whatever was asked for.

The harness replays the 408 sessions ending 2026-07-24 from the live inputs (Alpaca for all 33 symbols, FRED for VIX and HY OAS) with no database, and reproduces the published v2 and v3 figures before it will emit anything:

figure	published	replayed
v2 State avg	22.6	22.68
v2 State p80	35.1	35.1
v2 State max	91.2	91.2
v2 P3 pegged	39	39
v2 W1 live	108	108
v3 State max	87.4	87.4
v3 band shares	73.3 / 15.0 / 8.3 / 3.4	73.0 / 15.4 / 8.1 / 3.4

It refuses to emit a band recommendation, and exits non-zero, unless every hard gate passes — 33 symbols fetched with full warm-up, the whole basket on every session, the calendar anchors, 100% coverage on every row, and a row-wise state_v4 <= state_v3 invariant. Reading a calibration result out of a run whose pipeline did not validate is meant to be structurally impossible.

The fundamental channel (2026-08-12)

The monitor has three channels, not two scores with a decoration:

• State — current observable technical stress (price, breadth, credit, volatility).
• Warning — observable deterioration that may precede stress (breadth divergence, relative strength, credit impulse).
• Fundamental context — a categorical state (supportive / neutral / adverse / unknown) with an evidence_quality grade.

The third is never a term in the other two. They are read together by confluence:

Warning	Fundamentals	Reading
Calm	Supportive/neutral	Normal
Elevated	Supportive/neutral	Technical warning, not fundamentally confirmed
Calm	Adverse	Fundamental concern; tape has not confirmed
Elevated	Adverse	Confluence — highest attention

METHODOLOGY stays v4: no score changed, so partitioning the history API and discarding the event study cache would be churn. STUDY_SCHEMA moved to 3 instead, and is now the only thing that discards a stale report.

Why the read is a channel and not a weight

Two things are true at once, and only this shape honours both.

v3's reason for removing fundamentals from the score was wrong. Not stale — wrong. v3 argued that F1 (capex) and F3 (good-news-stock-down), carrying 12 + 8 of 100 Warning points, "could not change any published conclusion" because pegged they produced a Warning of exactly 20.0, below the alarm threshold. That arithmetic holds only when every technical sensor reads exactly zero, which is the one case that never matters. Warning is a weighted average, so the sensors add:

technical Warning	without fundamentals	with them pegged	delta
0	0.0	20.0	+20.0
20	20.0	36.0	+16.0
25	25.0	40.0	+15.0
35	35.0	48.0	+13.0
50	50.0	60.0	+10.0
80	80.0	84.0	+4.0

Pegged fundamentals lowered the technical Warning needed to reach the 40 quadrant divider from 40 to 25. That is a 15-point shift in where the alert fires, which is emphatically a changed conclusion. The v3 section below is kept as written, with this correction attached, because its reasoning is cited elsewhere in this file and a silent overwrite would hide that the error was ever made.

But no weight is measurable either. A weighted modifier was built and reverted: 0–25 points added onto the technical Warning, sized so a maxed-out read carried a calm tape over the 40 divider on its own. Nothing could justify the 25. With ~10 correction events and essentially no fundamental history, any fusion weight is a policy preference presented as a measurement — and the debate it invites ("does the read deserve 10%, 20%, 30%?") has no evidence that can settle it. Adding a slow categorical judgement to a fast continuous score also manufactures precision by summing unlike things, and it forces a missing observation to silently redistribute its weight onto the technical sensors, which is the opposite of leaving it unknown.

So: the read gets a channel, not a coefficient. Both facts survive — the v3 removal was badly argued and no weight is defensible — because "report it separately" is the only design that neither buries the observation nor invents a number for it.

Derivation

Deterministic, from the stored categorical facts. The LLM is an extraction and explanation layer: it finds the capex guidance, classifies it, and cites it. Fixed rules turn those facts into a state, so the same observation always yields the same category.

capex_signal: any cutting → adverse; else any holding → neutral; else all known raising → supportive; nothing known → unknown. reaction_signal: yes → adverse, mixed → neutral, no → supportive, unknown → unknown.

mixed and unknown are different reaction states and were merged until 2026-08-13. A failed LLM parse fell back to mixed, so an extraction error became neutral evidence — an observation of normality manufactured out of a bug. mixed now means an observed mixed reaction; anything unreadable, missing or unattempted is unknown and contributes nothing.

Combined by precedence, never by averaging: any adverse read carries; both unknown → unknown; every observed signal supportive → supportive; otherwise neutral.

unknown is deliberately unreachable by combination. Averaging would let two cutting reads and two unknown ones land on "neutral", presenting missing evidence as evidence of normality — the same conflation current_observation already refuses between "no observation" and "an observation of zero". Two cuts and two unknowns read adverse with evidence_quality: partial.

evidence_quality is ordered by what an operator needs first: unavailable (nothing collected) → stale (past fundamental_staleness_days) → manual (hand override) → complete / partial.

Presentation and alerts

The Path view colours each dot by the fundamental state recorded that day; the axes are untouched, because context is confluence information rather than a position on either axis. The card leads with the state and evidence grade.

Alerts stay separate, off one toggle:

• quadrant change — the market axes moved (existing);
• regime_fundamental — the context changed, e.g. neutral → adverse;
• regime_confluence — Warning elevated and fundamentals adverse.

unknown never alerts: an absence of evidence is not a change in the evidence, and alerting on it would train the reader to ignore the channel. Both new triggers seed silently on first run, as the quadrant alert does.

The observation is now a real time series

regime_fundamental_observations (migration 033), one row per effective_date, upserted. Before this it lived in a single SystemSetting slot that every refresh overwrote, so no history existed at all — which made the read impossible to replay, impossible to backtest, and meant a rebuild recorded every historical session as if nothing had been observed. update_regime_monitor carries the pre-existing single-slot observation into the series on its next run.

What this does not establish

The table starts empty and fills one observation at a time, so the fundamental rows are untested, not failed. Two things enforce that rather than one:

• they are coverage-matched — scored only on sessions where the channel had usable context and on corrections whose warning horizon fell inside it, with a market-only comparator over the identical window so any difference between them is the channel and not the window;
• measurable stays false until MIN_EVENTS_FOR_CONFIDENCE corrections are covered, and the panel prints "insufficient exposure" rather than a ratio.

Without the first, one day of coverage would render as 0/10 — recreating, one observation later, exactly the tested-versus-unavailable confusion the flag was added to prevent. The market rows are unchanged, and the 1/10 shipped-rule figure remains a verdict on the technical sensors and the alert machinery alone.

The rationale for expecting the read to matter is the operator's: hyperscaler capex is the demand side of the entire AI trade, and good earnings being sold is a classic late-cycle tell. Both are plausible. Neither is measured here, and this file's convention is that published numbers are reproducible.

The path forward is accumulation, then a test — in that order. Once enough point-in-time observations exist, test whether the state improves prediction conditional on Warning. If it does, a fitted and calibrated model has something to fit; until then there is nothing to calibrate against. Backfilling would get there faster: capex direction is derivable from the 10-Q/10-K capex line, which the SEC fundamentals import already carries, and "good news, stock down" from earnings dates plus next-day returns, which the Dolt earnings import already carries. That last one is worth computing deterministically rather than asking the LLM to judge, for the same reason the state derivation is rule-based.

What changed in v4

V1 stopped saturating at VIX 30. (vix - 15) / 15 reached 100 at VIX 30 — the same defect v3 had just removed from P3, left in place one sensor over. VIX 30 is a bad week, 50 is a crisis and 82 was March 2020, and all three scored identically. In the calibration window this flattened five distinct April-2025 prints (52.33, 46.98, 45.31, 40.72, 38.57) into a single 100. It pegged on 14 of 408 sessions; under the anchors below, none.

The trend break is graded by depth, not a yes/no. _under_200 returned a bare 0/100, so P1 printed 100 the moment SMH and QQQ were both under their average — and because the price pillar takes max(P1, P2, P3), that pinned the pillar and stopped P3's anchored ladder resolving anything for the whole of a selloff. It pegged on 46 of 408 sessions; now none. A 2% break reads ~30 where it used to read 100.

max() was kept. The defect was the step function feeding it, not the vote itself, and v3's "one capped vote for correlated reads" rationale still holds. The P1_SCORE_CAP fallback drafted during design was to fire if P1 became the sole price argmax on more than 80% of sessions with State ≥ 40 — i.e. if it had quietly become a second drawdown sensor. Measured on that population: 47 qualifying sessions, P1 sole argmax on 17 of them (36.2%), against P2's 16 and P3's 14. Well under the threshold, so the cap is not shipped.

The top State band moved 80 → 65. See Calibration; this is the one change that is about the band rather than a sensor.

Scope. All three are State-side. WARNING_BANDS, WARNING_WEIGHTS, QUADRANT_WARNING_DIVIDER and the event study's frozen threshold are untouched. QUADRANT_STATE_DIVIDER stays 50 because only breaking moved.

What changed in v3

Fundamentals left the score. F1 (capex) and F3 (good-news-stock-down) carried 12 + 8 of 100 Warning points. Pegged at maximum stress they produced a Warning of exactly 20.0 — below the event study's 25.3 alarm threshold, and still inside the "stable" band. The sourced observation could not change any published conclusion, so refreshing it looked like it did nothing. They are now a qualitative overlay reported beside the scores. Capex also stopped scoring raising and holding identically at 0: holding is the deceleration case and now scores 50, so a boom no longer reads the same as a stall.

Corrected 2026-08-12. The claim in this paragraph is false. "Pegged they produced a Warning of exactly 20.0" describes only the case where every technical sensor reads zero; Warning is a weighted average, so in the general case those 20 points added +10 to +20 and moved the technical score needed to reach the 40 quadrant divider from 40 to 25. The observation was removed for being underweighted, on reasoning that mistook a corner case for the whole range. See "The fundamental channel" above for what replaced it — a separate categorical channel, not a restored weight. The capex holding rescale in the second half of this paragraph stands and is still live.

The drawdown sensor stopped saturating. v2 used dd_pct * 5, reaching 100 at a 20% drawdown — the 90th percentile of the observed distribution. 39 of 408 sessions sat at exactly 100 with no resolution left, and the price pillar showed the top band on 13.5% of sessions. v3 uses named anchors with headroom past the observed 36% maximum, and blends leader/confirm 2:1 as P1 and P2 already did instead of taking max(). P3's realized share of State falls from 65% to 40%, matching its nominal weight.

Warning gained a sensor with range. The HY OAS level is pinned at zero below the 3.5 mild anchor (2.77 at the cutover), so credit contributed nothing in a calm tape. Its 20-session rate of change still does, and spread widening is a classic lead.

The credit percentile leg was removed. Its reference window silently shrank from 10 years to 3 when ICE restricted the upstream series in April 2026, after which it scored 20 points of stress at a spread the same sensor's anchors call "mild". See Calibration below.

Breadth loss counts during declines. v2's divergence gate was price_ret >= 0, so the sensor zeroed during every selloff. On 2026-07-24 the basket shed 10 points of participation in 20 sessions while SMH fell 11.9% and Warning printed exactly 0. v3 tapers to a floor instead: deterioration counts fully when price masks it (true divergence, the dangerous pre-top case) and at 35% when price confirms it. Breadth level lives in State, but breadth velocity appears nowhere else, so this is not double counting.

Bands are per axis. v2 Warning never exceeded 64.9 in 408 sessions while State reached 91.2, yet both used 30/60/80 with quadrant dividers at 60. The upper half of the Warning axis was unreachable.

Outputs

State — current structural stress:

• Price structure, 40%: max(P1, P2, P3), one capped vote for correlated reads.
• Fixed-basket breadth level, 25%.
• HY option-adjusted credit spread level, 20%.
• VIX level, 15%.

Warning — deterioration and divergence:

• Fixed-basket breadth divergence, 45%.
• 60-session SMH/SPY relative-strength deterioration, 30%.
• HY OAS 20-session widening, 25%.

Fundamental context — a categorical third channel, not a term in either score. See "The fundamental channel" above.

Combined, RSP/SPY (former F4), and the NVDA canary (former P6) do not enter v3 or v4.

Calibration

Interpolated sensor tables

All three are (x, stress score) pairs read by _interpolate, flat outside the first and last anchor.

sensor	anchors
P3 drawdown (% below the 52w high)	0→0, 4→10, 8→25, 16→50, 28→78, 40→100
P1 trend break (% below the 200-DMA)	0→20, 3→35, 8→55, 15→75, 25→100
V1 volatility (VIX level)	15→0, 20→20, 25→38, 30→55, 40→80, 55→100

P1's floor of 20 at the crossing is deliberate: the break itself is a genuine binary event and deserves a floor; only the depth past it is graded. P1 is calibrated to sit alongside P3 rather than swamp it — the 200-DMA lags, so a 20% drawdown typically coincides with ~10% below the average, where P1 reads ~61 against P3's ~59.

V1 reaches full scale at 55 rather than at 2020's ~82: anchoring the top at a once-in-a-generation print would make VIX 50 — a genuine crisis — read only ~70. The anchors encode the long-run distribution as constants, the same argument the credit level uses. Unlike P1 and V1, whose slopes ease off monotonically, P3's do not (2.5, 3.75, 3.125, 2.33, 1.83) — its gentle onset is intentional and the monotone-slope test excludes it.

Credit impulse is relative (+35% over 20 sessions = 100) rather than absolute, because +0.5pp means something very different at an OAS of 2.7 than at 8.0.

Bands

Round, meaning-anchored numbers, not percentile fits — those would drift on every rebuild and silently rewrite what past snapshots meant.

Why breaking moved 80 → 65. With credit calm, f2_credit_spreads returns 0.0 (not None), so it keeps its full 20 points pinned at zero. Price, breadth and volatility at literal maximum therefore sum to:

(100×40 + 100×25 + 0×20 + 100×15) / 100 = 80.0 exactly

band_for uses >=, so v3's top band was reachable only by touching its floor to the decimal, with nothing above it. The band was fit on v2, when credit's since-removed percentile leg still contributed regularly; the sensor is not wrong — a calm-credit selloff genuinely is less stressed than one with credit contagion — the threshold was stale.

Chosen by scenario arithmetic on unchanged weights (_scenarios in the harness computes these, so they are machine-checked, not prose):

scenario	price	breadth	C1	V1	State
Ordinary tape (3% dd, breadth 65%, VIX 16, OAS 2.8)	7.5	0	0	4.0	3.6
10% correction, calm credit (2% below, breadth 35%, VIX 24)	31.2	62.5	0	34.4	33.3
2022-style drawdown, calm credit, no death cross	90.8	100	0	60.0	70.3
same, with death cross (P2 pegged)	100	100	0	60.0	74.0
Credit event on top (OAS 6.0, VIX 45)	100	100	75.0	86.7	93.0
March 2020 (everything pegged)	100	100	100	100	100

Rows 3 and 4 are the case this monitor exists to measure, and they must print breaking. At 80 they do not. 65 clears them under either P2 assumption, which matters because P2 is set by the 50/200-DMA gap and no drawdown figure implies it; 70 would have left 0.33 points of headroom in row 3, reproducing the defect being fixed.

Realized shares, reported not fitted, over the 408 sessions to 2026-07-24:

Axis	stable	watch	elevated	breaking	thresholds
State (v4)	78.9%	13.0%	4.7%	3.4%	20 / 50 / 65
Warning	69.4%	19.6%	7.6%	3.4%	20 / 40 / 60

The v4 breaking share lands on 3.4% — the same as v3's — having been chosen by scenario reasoning rather than aimed at that number. Sensitivity: 60 gives 5.1%, 70 gives 1.2%.

Quadrant dividers sit at each axis's watch/elevated boundary: State 50, Warning 40. Only breaking moved in v4, so the dividers and every alert threshold are unchanged. test_quadrant_dividers_match_the_band_boundaries now enforces that relationship, which nothing did before.

Scores renormalize over available fixed weights, but a band is published only at 75% or greater coverage. Trend deltas are suppressed when the participating pillar set changes. Zero means ordinary/healthy; only stress contributes.

Credit level is the named HY OAS anchors alone: 3.5 mild, 5.0 elevated, 7.0 stressed, linear between, and nothing else. v2 blended those anchors at 70% with a 30% upper-tail percentile over a nominally 10-year window.

That leg was removed rather than repaired. ICE restricted FRED to a rolling 3-year window for BAMLH0A0HYM2 in April 2026 — the series metadata states it outright ("Starting in April 2026, this series will only include 3 years of observations"), and an unbounded request returns the same 795 observations as a 30-year one. The v2 percentile therefore ranked the current spread against three uniformly tight years (range 2.59–4.61 over the calibration window), which made it fire early and saturate absurdly: at an OAS of 3.50 — the level the anchors call mild, scoring zero stress — the blended sensor read 20.1, and the percentile leg pegged at 100 by an OAS of 4.5. Across the 408 sessions it roughly tripled the credit sensor's average (2.70 vs 1.00) and more than doubled its nonzero days (60 vs 27).

The anchors already encode the long-run distribution as constants, so the percentile was a second, noisier estimate of the same thing. What it was genuinely reaching for — "unusual versus recent history" — is now W3 on the Warning axis, computed as a rate of change, which is where deterioration belongs. Removing it moved State's average by −0.4 and its maximum by −3.8, left Warning bit-identical, and did not shift any band threshold.

A long-history alternative (BAA10Y, Fed-published, 7,712 observations back to 1997) was considered and rejected: ranking an HY spread against investment-grade history is not a coherent statistic, and it would rescue a leg that is redundant anyway.

Every snapshot now records data_quality.credit_history_days and vix_history_days. This defect was invisible for roughly three months because nothing asserted the window the code claimed; the spans make a future upstream truncation show up in the record instead of quietly reshaping a sensor.

Survivorship caveat. The basket was frozen 2026-07-15 but the calibration window reaches back to 2024, so names were partly selected for having done well. Every distribution above inherits that bias. It is the same bias v2 carried, so the v2/v3 comparison is like-for-like, but the absolute band shares are optimistic.

Which OAS window the published v2 figures used. v2 requested 13 years of HY OAS and sliced HY_OAS_REFERENCE_YEARS = 10.0 per session; ICE serves only ~3 years (778 observations from 2023-08-08), so the effective window was that. But production v2 also fetched only 400 calendar days at one point — the bug fixed 2026-08-07 — and whether the published numbers predate that was not recoverable from the text. Settled by replay rather than assumed: the v2_reconstruction_oas400 variant truncates the OAS source series to 400 days (patching the per-session window cannot simulate data that was simply absent) and yields avg 26.54, p80 42.52, max 100.00, against published 22.6 / 35.1 / 91.2. Full coverage reproduces all three. So the published figures correspond to the untruncated fetch.

The top VIX anchors are exercised, not just asserted. The window contains a 52.33 close (2025-04-08), so the 40 → 80 → 55 → 100 segment is fed by real data rather than justified from long-run history alone.

Point-in-time record

The first run under a new METHODOLOGY rebuilds every session inside REBUILD_LOOKBACK_DAYS — 672 calendar days, roughly 464 trading sessions; routine runs thereafter insert/update only the latest trading date. The bound is in calendar days rather than a session count because the binding constraint is the OAS fetch: each replayed row needs W3's lookback inside HY_OAS_WINDOW_DAYS, so replaying further back would recreate the credit gap a reseed exists to close. The history API and main chart show only snapshots matching the current methodology, so a bump reseeds the series rather than splicing two formulas into one line.

The fundamental channel keeps its effective date (normally the next session after collection) and is never replayed backward, so a rebuild cannot stamp today's observation onto historical snapshots. Since the observations became a real series (regime_fundamental_observations, migration 033), the effective-date lookup is the gate: a replayed session gets whichever observation was live on it, and sessions before the first one read unknown.

Two functions, deliberately: fundamental_context is the record and keeps the gate — it runs for every replayed date during a rebuild, so it must never grow a bypass flag. current_observation is the live reading behind fundamental_live, and reports the effective date instead of blanking the content.

Until 2026-08-07 the live reading called the gated function, so a just-collected observation stayed hidden until the next weekday — three days over a weekend — and refreshing appeared to do nothing. That was the opposite of what this section already claimed. Showing it early cannot leak into a published score, because nothing in the channel is scored.

current_observation gates on observed (a non-null fetched_at, the one field every path writing real content stamps). Without it, the default override — unknown for every hyperscaler and, since 2026-08-13, unknown for the reaction — was reported as a live observation with available: true, so the card presented placeholders as a collected reading. Those are the absence of an observation, not an observation of absence. fundamental_context never had this problem: no observation means no effective date, which means pending, which already blanks the content.

usable is what may confirm; available is only what to display. Three distinct things, and collapsing any two of them is a bug:

• state — the last thing observed. Survives going stale, so the card can show it.
• available — timing: there is an effective, non-stale record to display.
• usable — content: available and the observation actually determined something (state != "unknown").

The confluence alert and all three coverage-matched study rules gate on usable. Gating on available instead has two failure modes, and both were live at some point in this design:

1. a reading past fundamental_staleness_days would corroborate every Warning crossing indefinitely — the strongest claim this channel makes, from the data with the least right to make it;
2. an LLM run that failed to extract anything produces a perfectly fresh observation that knows nothing. Counting it as exposure means repeated extraction failures slowly accumulate coverage until the fundamental rows flip to a measurable 0/8 — a failed result published for a channel that never saw a thing, which is precisely what coverage-matching exists to prevent.

Pre-rename snapshots are adapted, not discarded. The channel was stored as fundamental_overlay until 2026-08-12. The rename shipped without a methodology bump — no score changed — so those rows are still served and were never reseeded. Reading only the new key would have turned every one of them into unknown, silently dropping real recorded evidence: historical Path colours, and exposure the event study can legitimately count. _parse_snapshot derives the channel from a legacy overlay's own stored facts (its capex map supplies the basket, so the derivation uses the names observed at the time rather than today's config). Normalising there rather than at each call site means no reader can receive an un-adapted row. Delete only after a reseed has rewritten the whole window.

The blob and the series row are one transaction. They are the same observation seen by the live card and by the point-in-time replay; committing them separately leaves a window where a failure publishes one and not the other, and the two then disagree permanently with nothing to detect it. Both writers use settings_store.upsert_setting (which does not commit) plus a single commit; record_fundamental_observation deliberately takes no commit of its own so update_regime_monitor keeps its own transaction boundary.

Each snapshot stores the fixed basket symbols, hash, and freeze date. Reconstructed history before that freeze date is retrospective/exploratory.

Presentation

The page is deliberately thin: two gauges, one chart card, one pillar table, the overlay, and a provenance strip. Time and Path are two projections of the same snapshot series and share one card and one query key — they were previously two panels, which read as two datasets. Methodology rationale lives in this document, not on the page; page text is limited to what changes how the reader interprets today's number. The quadrant dividers rendered in Path view come from quadrant_config and are the same constants the alert path consumes (alert_service), so the chart cannot drift from what actually fires.

Warning study

The study calls the outcome a 10% correction, not a regime break. It measures two rules against that outcome, plus enough context to tell whether either number is any good.

A cached report is discarded when its methodology no longer matches or when STUDY_SCHEMA moves, so the panel reverts to "not run yet" rather than showing stale numbers or a report missing half its blocks. Re-run the Event Study job after a methodology cutover or a schema bump.

The headline is the rule that actually fires

Until 2026-08-12 the study measured a bare rising-edge crossing of an 80th-percentile threshold fitted on the first 70% of sessions. Nothing consumes that rule. What reaches Telegram is _collect_regime_quadrant: a quadrant change with State ≥ 50 and Warning ≥ 40 as fixed dividers, a ±5 hysteresis deadband, a two-session confirmation, a 3-day cooldown, and a 75% coverage gate on both axes. The two differ on every one of those axes, including the threshold itself (a fitted ~32 against a shipped 40).

replay_quadrant_changes replays the shipped state machine over the whole sample. Three details are reproduced rather than cleaned up, because a state machine written from first principles gets each of them wrong:

• the prior session is classified against the current baseline, not against its own predecessor, so confirmation asks "did yesterday already look like this change" rather than "did yesterday change too";
• the baseline advances only when an alert actually fires, so a change blocked by confirmation or cooldown is re-evaluated against the old quadrant next session;
• one cooldown is shared by every quadrant change, so a 3→4 alert can swallow a 4→2 alert three days later.

Two consequences worth stating. The alarm is dated at the confirmation, not at the first crossing, which costs one session of lead by construction. And the rule alerts on changes in both directions, so the replay's exits are recorded but filtered out by entry_alarms — only entering a Warning-high quadrant is a warning about anything.

The replay reuses _compute_index rather than re-deriving the axes. That is the same anti-drift argument that produced warning_sensor_scores: the v2 study re-derived Warning by hand and would have kept measuring the old construct through a scoring change. State has no equivalent shared helper, so the snapshot builder itself is the shared definition.

Nothing is fitted, so nothing needs protecting from a training set. There is no split, and every detected correction is evaluable instead of the four that happen to land in the last 30%. The underpowered and "threshold frozen on a different construct" caveats do not apply to this variant.

Reading the result

A bare "2 of 4" is unreadable in either direction, so the report scores four more rules through the same evaluate_alarms harness over the same events and sessions, and adds a null. All use fixed thresholds — a threshold fitted on the full sample would have lookahead the shipped rule does not, and one fitted on a split could only be scored on the holdout events.

kind	rules	the question
ablation	Warning ≥ 40 bare, State ≥ 50 bare	does the quadrant machinery earn its place?
baseline	leader below its 50-DMA, VIX ≥ 20	does the score earn its complexity?
null	K random alarms at the observed firing rate	is any of this better than chance?

The two kinds must not be read as one list. If a baseline matches the score, the composite is not earning its complexity and that is the finding — it does not mean the monitor is worthless, since State and Warning exist to be read, but it caps how much further calibration is justified. If the bare Warning crossing beats the shipped rule, the machinery (not the sensor) is what is costing recall.

The null draws only from sessions a rule could actually have fired on. Over the whole sample it would be diluted by warm-up sessions and would understate what chance achieves — which matters, because with ~11 events and a 20-session horizon roughly a sixth of the sample already sits inside a hit window. It is seeded, so a re-run cannot move the report. Corrections cluster and uniform placement does not, so it is the floor, not the bar: an alarm process that clustered would beat it for reasons unrelated to foresight.

First result (2026-08-12): the shipped rule is not distinguishable from chance

Replayed over 2021-07-14 → 2026-08-12. The 200-DMA warm-up means the baseline only seeds on 2022-05-26, so 1056 of 1276 sessions are evaluable and 10 of the 11 detected corrections fall inside them.

rule	kind	warned	FA/yr	median lead
Quadrant alert (shipped)		1/10	0.9	19d
Quadrant alert, both axes high	ablation	0/10	0.9	—
Warning ≥ 40, bare crossing	ablation	3/10	4.8	20d
State ≥ 50, bare crossing	ablation	0/10	0.7	—
SMH below its 50-DMA	baseline	7/10	6.7	8d
VIX ≥ 20	baseline	4/10	7.2	9.5d
Random alarms, same firing rate	null	0.9 ± 0.8	—	—

P(chance ≥ 1/10) = 0.65. Alarms scattered at random over the same sessions at the rule's own firing rate match or beat it two times in three. Whatever the score knows, this rule is not transmitting it.

Three readings, in order of how much they should change:

The machinery costs more than it protects. The bare Warning crossing catches 3 with a 20-session lead; wrapping it in the quadrant rule drops that to 1. The State condition is the largest single cost — requiring both axes high catches nothing at all, which is what a coincident axis gating a leading one predicts. Hysteresis, the two-session confirmation and the shared cooldown between them take the rest, and the cooldown is shared across every quadrant change, so exits consume the budget that entries need. Only 5 of the 15 replayed changes are Warning-high entries.

The crude baselines beat everything on recall, at a price. SMH below its 50-DMA catches 7 of 10 — but at 6.7 false alarms a year against the shipped rule's 0.9. That is a 7× recall improvement for 7× the noise, so it is not a clean dominance and this table cannot settle it; the missing axis is what a false alarm actually costs, which nothing here measures. What it does settle is that the composite is not buying recall the 50-DMA does not already have.

The 0.9 false alarms/year is not the achievement it looks like. A rule that almost never fires has few false alarms by construction. Read the two columns together or not at all.

Recorded from an offline replay (live Alpaca + FRED, no database, breadth computed from the same Alpaca closes rather than the stored universe). The job in Admin → Jobs is the canonical path and reads breadth from the DB, so re-run it to confirm these figures before treating them as the record.

This is a verdict on the market channels only. The fundamental and confluence rows in the same table are marked measurable: false and print "not measurable" rather than a ratio: with an empty observation series they never fire, and a 0/10 sitting in a comparison column would read as tested-and-failed. false here means the input does not exist yet, not that the rule lost.

(The figures above were also produced under a briefly-built weighted modifier and came back bit-identical, which is what confirmed the modifier was inert over the whole window — the numbers depend on the technical sensors alone either way.)

Not acted on. Nothing in the alert path was changed on the strength of this. The obvious candidates — dropping the State condition from the entry test, separating the entry and exit cooldowns, or lowering the Warning divider — are threshold changes to a live alerting rule and want their own decision.

The coverage gap relocates, it does not close

Dropping the fitted threshold makes the whole sample evaluable, but most of the extra events predate 2023-08. W3 does not exist there, so Warning renormalises to (W1×45 + W2×30)/75 and the fixed 40 divider is applied to a different construct than it was reasoned about. The report therefore splits shipped-rule metrics at the credit sensor's first session and the panel states both, because replacing one misleading headline with a differently misleading one would be no gain.

Convenient side effect: the pre-credit era is the "Warning without W3" ablation, measured on real sessions rather than simulated ones, so that ablation is not run separately.

Alarms and events are assigned to eras by index, so an alarm days before the boundary matching an event days after it lands in the earlier era. With the eras years long and the events sparse, that costs nothing.

The fitted variant, kept for continuity

The 70/30 percentile study is still computed and still reported, collapsed, with its reliability block intact — it is a genuinely different question, and it is what earlier revisions of this document report. Its caveats stand:

The holdout is thin. The study detects 11 corrections across 5 years but the 70/30 split leaves only 4 in the test period. Recall is one event away from a materially different headline, and in practice the event that flips is decided by where the frozen threshold happens to land rather than by whether the score saw anything. The v3 cutover run illustrates it: v3 scored 2/4 against v2's 3/4, but "v3 without the credit sensor" scores 3/4 at a higher threshold (35.5) than shipped v3 misses it at (32.3) — because the alarm rule needs a rising edge, and a lower threshold can mean the alarm already fired outside the 20-session horizon and never reset below. Below MIN_EVENTS_FOR_CONFIDENCE holdout events the report says so explicitly.

Some events carry no information at all for comparison: in that run every variant caught 2026-03-06, every variant missed 2026-06-05, and every variant "caught" 2025-11-20 with a 1-session lead, which is coincident rather than a warning. The headline recall does not currently discount those; a minimum-lead rule is the obvious next change and has not been made.

Sensor coverage straddles the split. The score renormalises over available sensors, so a training window predating a sensor's history freezes the threshold on a different construct than the holdout is measured against. At the v3 cutover only 39% of training sessions had all three Warning sensors versus 100% of the test period, because credit history begins 2023-07-25.

Restricting the threshold to sensor-matched training sessions was tried and is not the fix: those sessions are a calm recent stretch, so the threshold drops from 32.3 to 22.5 and false alarms rise from 3.3 to 8.6 per year. It trades a coverage bias for a regime-selection bias. The honest position is that a fitted threshold is hypersensitive to window choice at this sample size — which is the strongest argument for making the unfitted shipped rule the headline.

Considered and not done

An ETF credit proxy (HYG/IEF) to extend W3 back over the whole sample. It would trade "two sensors versus three" for "proxy sensor versus real sensor" — still a construct straddle, but no longer flagged by the coverage split. This is the same objection that rejected BAA10Y as a percentile reference. If ever revisited, check the impulse correlation on the three years of real-OAS overlap first and report it as a sensitivity, never as the headline.

A depth sweep (5%/7%/15% corrections) for more events. EVENT_COOLDOWN_DAYS is 40, so at shallower thresholds re-triggers inside a single decline merge or drop and the denominator moves for cooldown reasons rather than market ones.

Resolved in v4 (raised 2026-08-07, shipped 2026-08-08)

The three questions this section used to hold are now answered. Kept here because the reasoning that resolved them is not obvious from the code.

1. breaking had zero headroom — resolved by moving the band, not the sensor. f2_credit_spreads returns 0.0, not None, below the 3.5 mild anchor, so credit stays available at weight 20 and is pinned at zero on roughly 93% of sessions rather than being renormalized out. Price + breadth + volatility at literal maximum therefore summed to exactly 80.0 — v3's threshold, to the decimal.

The sensor is deliberately unchanged. A calm-credit selloff genuinely is less stressed than one with credit contagion, so scoring it lower is correct; what was stale was STATE_BANDS, fit on v2 while credit's since-removed percentile leg still contributed. Making credit None when calm was considered and rejected: it would leave State on 80% coverage, which still publishes, but consumes the whole buffer — any second missing pillar would then suppress the band, and the 7d/30d trend deltas would null out every time OAS crossed 3.5, because _delta suppresses on a change of participating pillars. See Calibration for the scenario arithmetic behind 65.

2. V1 saturated at VIX 30 — resolved with an anchor table. See "What changed in v4".

3. max(P1, P2, P3) defeated P3's anchoring — resolved by grading _under_200, keeping max(). The max was deliberate ("one capped vote for correlated reads") and survives; the binary step feeding it was the defect.

Its limit, stated precisely. _death_cross is clamp(-gap_pct * 20), so P2 pegs at a −5% 50/200-DMA gap — routine in a real downtrend. In a deep selloff the price pillar therefore still reaches 100 via P2 even with P1 graded. What v4 repairs is the shallow-to-moderate break, which is where resolution was most obviously missing: a 10% correction 2% below the average now scores 31 where v3 scored 100. It would be wrong to claim "the price pillar no longer pegs".

P2 did not peg once in the 408-session calibration window, so this is a property of the sensor rather than an observed problem. Grading P2 the same way is the natural next item if it starts binding; the replay reports a P2-pegged census alongside P3 and V1 so the evidence accumulates.

Fixed 2026-08-07: the OAS fetch window did not cover a rebuild

HY_OAS_WINDOW_DAYS was 400 calendar days, but a rebuild replays leader_series[-REBUILD_SESSIONS:] — 400 trading sessions, about 579 calendar days. The oldest ~180 calendar days of any rebuild therefore got no OAS data at all, so f2_credit_spreads and w3_credit_impulse both returned None. Verified: State then lands at 80% coverage and Warning at exactly 75.0% — MIN_COVERAGE — so both still publish bands. The rebuilt series would look homogeneous while its oldest rows had been scored without credit, the tell being a null data_quality.credit_history_days on exactly those rows.

The window is now 700 days: it must cover the oldest replayed date (~579) plus W3's lookback and slack, while staying under ICE's ~3-year cap so FRED still honours the request. This required no methodology bump — C1 reads oas_values[-1] and W3 reads oas_values[-21], both indexed from the end, so widening only prepends older observations and every live score is bit-identical. Confirmed by evaluating both windows against a varying synthetic series: today's C1/W3 match exactly, while the oldest rebuild row goes from None/None to real values.

Expect credit_history_days on new snapshots to rise from ~400 to ~700. That is the widened request, not new upstream history — and it makes the chip a better truncation canary, since a 700-day request returning ~1095 days' worth is now the visible ceiling.

Widening the window alone does not repair stored history. Routine runs recompute only the latest trading date, and rebuilding was keyed on "no v3 snapshot exists at all" — which is false once the cutover has run — so every row already written would have kept its credit gap indefinitely. SENSOR_REVISION fixes that: it is stamped into each snapshot, snapshots predating it read as 1, and a stored revision below the current one triggers exactly one reseed.

It is deliberately not METHODOLOGY. That constant partitions the history API and discards the cached event study; neither is warranted here, because the study recomputes its Warning series from source (_warning_series calls warning_sensor_scores against freshly fetched prices and OAS) rather than reading snapshots, so a reseed cannot stale it.

The reseed is bounded by REBUILD_LOOKBACK_DAYS in calendar days rather than a session count, because the binding constraint is the OAS fetch: each replayed row needs W3's 20-business-day lookback inside HY_OAS_WINDOW_DAYS. At 672 days the replay reaches ~464 sessions, W3's oldest requirement lands exactly on the first fetched OAS day, and the ~400-session series the v3 cutover wrote is fully covered. A test asserts that relationship so the two constants cannot drift into recreating the gap.

The fix was sequenced deliberately: acting on items 1–3 above bumped METHODOLOGY, which fires rebuilding, which would have baked the credit-less rows into the fresh series. Fixing the window first meant the v4 reseed replayed a clean window; doing it the other way round would have meant reseeding twice.

Operator rule

Quadrant alerts default off for new/reset configurations. When enabled they require fresh inputs, at least 75% coverage on both axes, two consecutive daily confirmations, hysteresis, and cooldown. Every alert states: Risk thermometer — not a trade signal.