news corpus for a local scorer + agent panel v2 + dashboard read-only (2026-09-17); books byte-identical
Agent panel v2: personas return p_win anchored to the book base rates with a code-computed decision context; verdict = equal-weight mean (verdict_source "mean"); only the macro persona runs live (agent_panel.personas ["macro"]); prompt version and exact payloads logged; offline replay harness scripts/panel_replay.py; base-rate table agent_panel_base_rates.json (scripts/build_panel_base_rates.py). Backtester `strategy` column now carries the generator's own stamp (label-only; five reference books re-pinned, all 62 other columns identical). News corpus (after evaluating the owner's ML-NEWS scikit model against DeepSeek: golden set 39/78 vs 78/78, out-of-sample agreement 64% against a 90% self-agreement ceiling; verdict: fallback only, not a replacement): - NewsSentiment.xau_score + archive column xau_score keep DeepSeek's gold score for every headline (930 of the first 1,546 DeepSeek rows had none); an existing newsdata.db gains the column in place on first open - headlines naming no tracked currency are scored and archived (currencies_json []) but never returned to the platform - _apply_llm_sentiment sends every new headline in its cycle, 25 per call; a failed call ends the cycle's sending Dashboard publisher never writes a database: main() calls app_config.set_db_writes_for_process(False), honoured by signal_log_enabled() and news_archive_enabled() (owner decision: the bot is the single writer). Test suite: the hermetic app config now points both writers at a suite-private temp dir — the RSS fixture tests were writing fixture headlines into the real logs/newsdata.db — with a Module 32 test pinning it. 529 tests. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
This commit is contained in:
Harwinder Sekhon
+42
-9
@@ -43,10 +43,19 @@ Contract
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headline seen 50 times is one row with ``last_seen`` bumped.
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* **stdlib only** (``sqlite3``), like ``signal_log``.
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Known corpus limitation: a headline mentioning no tracked currency is
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never constructed by the news engine at all (``news_engine`` drops it
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before scoring), so DeepSeek never labels it either and it cannot appear
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here.
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Two columns exist for training only (2026-09-17):
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* ``xau_score`` — DeepSeek's gold-specific score for the headline,
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whatever currencies it names. ``score_by_currency_json["XAU"]``
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carries it only for gold-keyword headlines; 930 of the first 1,546
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DeepSeek rows had no gold label without this column. NULL for a
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keyword-scored row and for rows written before the column existed
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(an existing database is migrated in place, values are not
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recoverable).
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* ``currencies_json`` ``[]`` marks a headline naming no tracked currency.
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The news engine used to drop those before scoring; now it scores and
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archives them but never returns them to the platform. About 23% of a
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day's feed; DeepSeek gave 10 of 24 a gold direction.
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"""
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from __future__ import annotations
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@@ -85,7 +94,8 @@ CREATE TABLE IF NOT EXISTS headlines (
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score_source TEXT NOT NULL DEFAULT 'keyword',
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teacher_model TEXT,
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teacher_prompt_sha1 TEXT,
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origin TEXT NOT NULL DEFAULT 'live'
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origin TEXT NOT NULL DEFAULT 'live',
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xau_score REAL
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);
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CREATE INDEX IF NOT EXISTS idx_headlines_source ON headlines (score_source);
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CREATE INDEX IF NOT EXISTS idx_headlines_first_seen ON headlines (first_seen);
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@@ -155,8 +165,22 @@ class NewsArchive:
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self._with_lock_retry(
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lambda: self._conn.execute("PRAGMA journal_mode=WAL"))
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self._with_lock_retry(lambda: self._conn.executescript(_SCHEMA))
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self._with_lock_retry(self._migrate)
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self._conn.commit()
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#: Columns added after the first databases were created, in order.
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#: CREATE TABLE IF NOT EXISTS leaves an existing table untouched, so
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#: each one is added in place on open; existing rows read NULL.
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_ADDED_COLUMNS = (("xau_score", "REAL"),)
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def _migrate(self) -> None:
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present = {row[1] for row in
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self._conn.execute("PRAGMA table_info(headlines)")}
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for name, decl in self._ADDED_COLUMNS:
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if name not in present:
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self._conn.execute(
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f"ALTER TABLE headlines ADD COLUMN {name} {decl}")
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@staticmethod
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def _with_lock_retry(fn, attempts: int = 8, delay: float = 0.05):
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"""Run ``fn`` again on 'database is locked' (concurrent openers
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@@ -202,8 +226,8 @@ class NewsArchive:
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" published_at, first_seen, last_seen, seen_count,"
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" score, score_by_currency_json, currencies_json,"
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" keywords_json, score_source, teacher_model,"
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" teacher_prompt_sha1, origin"
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") VALUES (?,?,?,?,?,?,?,?,?,1,?,?,?,?,?,?,?,?)"
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" teacher_prompt_sha1, origin, xau_score"
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") VALUES (?,?,?,?,?,?,?,?,?,1,?,?,?,?,?,?,?,?,?)"
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" ON CONFLICT(hash) DO UPDATE SET"
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# The backfill stamps historical timestamps, so clamp rather
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# than assign: first_seen only ever moves earlier, last_seen
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@@ -236,6 +260,13 @@ class NewsArchive:
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" ELSE headlines.teacher_prompt_sha1 END,"
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f" score_source = CASE WHEN {wins} THEN excluded.score_source"
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" ELSE headlines.score_source END,"
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# Same upgrade-only rule as score, and COALESCE on top: a
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# keyword re-sighting carries no gold score and loses on
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# rank, and even a same-rank write without one (a future
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# backfill shape) must never blank a stored value.
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f" xau_score = CASE WHEN {wins}"
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" THEN COALESCE(excluded.xau_score, headlines.xau_score)"
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" ELSE headlines.xau_score END,"
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# Enrichment fills gaps but never blanks what is already there
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# (the legacy backfill shape carries no description or link).
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" description = COALESCE(excluded.description, headlines.description),"
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@@ -246,9 +277,9 @@ class NewsArchive:
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# The rank is bound once per CASE arm. Positional binding means a
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# miscount silently shifts every parameter, so assert it instead of
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# trusting the arithmetic: 17 INSERT values + one rank per arm.
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# trusting the arithmetic: 18 INSERT values + one rank per arm.
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n_rank = sql.count("? >= ")
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n_values = 17
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n_values = 18
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assert sql.count("?") == n_values + n_rank, (
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f"placeholder count drifted: {sql.count('?')} "
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f"!= {n_values} + {n_rank}")
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@@ -264,6 +295,7 @@ class NewsArchive:
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row["currencies_json"], row["keywords_json"],
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row["score_source"], row["teacher_model"],
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row["teacher_prompt_sha1"], row.get("origin", "live"),
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row.get("xau_score"),
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) + (rank,) * n_rank)
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def _write() -> None:
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@@ -321,6 +353,7 @@ def build_row(headline: Any, *, teacher_model: Optional[str] = None,
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"teacher_prompt_sha1": (
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teacher_prompt_sha1 if source == "llm" else None),
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"origin": origin,
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"xau_score": _finite_score(getattr(headline, "xau_score", None)),
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}
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