dennisthiessenandClaude Opus 5
c3ae5ad949
v3 was calibrated by replaying the series offline, but that harness was never
committed -- so its published numbers could not be re-derived, and a v4 cut would
have had to choose anchors by argument rather than measurement. This is that
harness, and it reproduces the published figures.
scripts/run_regime_monitor_calibration.py replays State/Warning session by
session from the same inputs the live job uses (Alpaca for all 33 symbols, FRED
for VIX and HY OAS), with no database: breadth and divergence come from
breadth_service's pure helpers. It never reimplements an unchanged live sensor --
_compute_index, _score_pillars, P2, P4 and the Warning sensors are imported and
called. Only candidate formulas (proposed v4) and retired ones (v2, gone from the
codebase) are defined here and patched onto the module for a variant's duration.
Reproduction of the 408 sessions ending 2026-07-24, against the figures in
docs/research/regime-monitor-v3.md:
v2 State avg 22.6 -> 22.68
v2 State p80 35.1 -> 35.1 exact
v2 State max 91.2 -> 91.2 exact
v2 P3 pegged 39 -> 39 exact
v2 W1 live 108 -> 108 exact
v3 State max 87.4 -> 87.4 exact
v3 band shares 73.3/15.0/8.3/3.4 -> 73.0/15.4/8.1/3.4
Three things the harness had to get right to reach that, each of which was
initially wrong and caught by a gate rather than by inspection:
- "W1 live 108" counts NONZERO sessions, not non-null ones. v2's divergence
gate returned 0.0 during any decline (v3 tapers instead), so the retired
divergence formula had to be reconstructed too.
- v2 sliced HY_OAS_REFERENCE_YEARS = 10.0 per session, not v3's 700 days. The
percentile leg ranks against that window, so replaying it short shifted the
middle of the distribution while leaving the max exact.
- The published v2 numbers correspond to FULL OAS coverage. Replaying v2 with
the 400-calendar-day fetch it shipped with yields max 100.0 and 133
credit-less sessions -- so that truncation was not in force when the figures
were taken. Recorded rather than assumed.
The script refuses to emit a band recommendation unless every hard gate passes
(33 symbols fetched, per-symbol warm-up and final bar, full basket on every
session, calendar anchors, 100% coverage, and a row-wise state_v4 <= state_v3
invariant), and exits non-zero. It is meant to be structurally impossible to read
a calibration result out of a run whose pipeline did not validate. No v4 code
ships in this commit.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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60 lines
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# Python
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__pycache__/
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*.py[cod]
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*.egg-info/
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*.egg
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dist/
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build/
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.venv/
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.env
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# Hypothesis (PBT)
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.hypothesis/
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# Pytest
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.pytest_cache/
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# IDE
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.vscode/
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.idea/
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.claude/settings.local.json
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*.swp
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*.swo
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# Local AI tool metadata
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mcps/
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# OS
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.DS_Store
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Thumbs.db
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# Frontend
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frontend/node_modules/
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frontend/dist/
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frontend/tsconfig.tsbuildinfo
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# Alembic
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alembic/versions/__pycache__/
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# Generated SSL bundle
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combined-ca-bundle.pem
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# Dolt local dev clones. Production keeps clones in DOLT_DATA_DIR OUTSIDE the
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# repo tree (deploy is rsync --delete of the tree); this dir is dev-only.
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dolt-data/
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# Local research artifacts
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# Backtest reports in reports/ are tracked: they are the evidence behind the
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# production baseline in the README. The snapshot DBs they run against are not.
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backtest_snapshots/
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# Rebuildable pickle caches are local accelerators, not decision evidence.
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reports/*.pkl
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reports/*.pk1
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reports/.cache/
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# Runtime A5 parity bundles are generated on the production server. Research
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# conclusions belong in docs/research, not as an ever-growing artifact archive.
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reports/fundamentals-parity/
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# Calibration harness raw-pull cache (Alpaca/FRED); regenerable, not a record.
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.calib-cache/
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