dennisthiessenandClaude Opus 5
333989eeab
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>
45 lines
2.1 KiB
Python
45 lines
2.1 KiB
Python
from datetime import date as date_type
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from datetime import datetime
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from sqlalchemy import Date, DateTime, Float, String, Text
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from sqlalchemy.orm import Mapped, mapped_column
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from app.database import Base
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class RegimeFundamentalObservation(Base):
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"""Point-in-time record of the sourced hyperscaler capex / earnings read.
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One row per ``effective_date`` (unique, upserted). Before this table the
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observation lived in a single ``SystemSetting`` slot, so every refresh
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overwrote the previous one and no history existed at all — which made the
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read impossible to replay, impossible to backtest, and meant a snapshot
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rebuild could only ever score historical sessions as if nothing had been
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observed.
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The read is a categorical channel reported beside State and Warning, never a
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term in either, so this series is not a scoring input. It is the record that
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makes the channel replayable at all -- and the only route to eventually
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testing whether it improves prediction conditional on Warning, which is the
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one thing that could justify combining the channels later.
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``effective_date`` rather than ``fetched_at`` is the key: it is the session
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the observation becomes usable on (normally the next weekday), and the gate
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that stops a rebuild stamping today's reading onto historical rows.
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"""
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__tablename__ = "regime_fundamental_observations"
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id: Mapped[int] = mapped_column(primary_key=True)
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effective_date: Mapped[date_type] = mapped_column(
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Date, nullable=False, unique=True, index=True
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)
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f1_score: Mapped[float | None] = mapped_column(Float, nullable=True)
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f3_score: Mapped[float | None] = mapped_column(Float, nullable=True)
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capex_json: Mapped[str] = mapped_column(Text, nullable=False)
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good_news_stock_down: Mapped[str] = mapped_column(String(10), nullable=False)
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reasoning: Mapped[str | None] = mapped_column(Text, nullable=True)
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source: Mapped[str] = mapped_column(String(30), nullable=False)
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fetched_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False)
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created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False)
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