4f33480ce1ec3eba9db45eb117774d36bfb1ff03
3 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Harwinder Sekhon
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1f4741b557 |
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> |
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Harwinder Sekhon
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28b591d786 |
news archive: fix three bugs found reviewing 301bf90
1. A backfill pass silently wiped teacher identity from live rows.
The provenance compare is `>=`, and backfill rows are llm-scored but
carry no teacher_model/teacher_prompt_sha1 (neither source DB recorded
one), so a bare assignment blanked what the live hook had stored.
Re-running the backfill — e.g. after importing the live server's
signals.db, which is the next planned step — would have erased it from
every live row. COALESCE now keeps a stored identity when the incoming
one is NULL, while a genuinely newer teacher still replaces it.
Timestamps clamp for the same reason: backfill stamps historical
values, so first_seen only moves earlier and last_seen only later.
2. A retried batch could be applied twice. executemany does not roll
itself back, so a batch that half-landed and then hit a lock was
re-applied whole by _with_lock_retry, double-counting seen_count on
the rows that already landed. Writes now run in an explicit BEGIN
IMMEDIATE with a rollback, which also takes the write lock up front so
contention fails before any row is applied.
3. The archive hook could break a news fetch. archive_headlines swallows
its own failures, but its ARGUMENTS are evaluated first, so a raising
get_llm_client() escaped that guard. Guarded at the call site too:
archiving is a side effect and must never cost us the headlines we
just fetched.
Both 1 and 2 were verified to reproduce against
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Harwinder Sekhon
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301bf90f86 |
news archive: keep every headline + its DeepSeek score in its own DB
Headlines only ever survived as a side effect of signal logging, and logs/signals.db was rotated around 2026-09-04 — destroying the 3-week, 4,054-headline corpus news_sentiment_value_2026-09-04.md was written on. logs/ is gitignored, so git could not recover it and nobody noticed for four days. There is no way to backfill a fetch window once it passes. news_archive.py writes to its OWN database (logs/newsdata.db) so a signal-log rotation cannot take the news with it. One row per distinct headline (sha256 key) with the text, description, link, publisher, publish time, the score, and — new — which scorer produced that score. Provenance is the load-bearing part. DeepSeek scores 78/78 on the 2026-08-06 golden set; the keyword scorer it overrides scores 24/78 (31%), and that is what silently stands on no key, disabled, daily cap, timeout, HTTP/parse failure, or the >25-new-headlines-per-cycle batch overflow. Until now both landed in the same column, indistinguishable, so a model trained on the mix would inherit the keyword scorer's errors. NewsSentiment.score_source now records it, mirrored into signals.db via signal_log._headline_to_dict, and teacher_model/teacher_prompt_sha1 keep labels made under a changed prompt from being pooled with older ones. Writes are one atomic UPSERT per row, upgrade-only: an llm score always replaces a keyword one (the batch-overflow tail gets labelled on the next window in a long-running process — verified 13/13), a keyword score never overwrites an llm one, and re-seeing a headline always counts. A read-then-write pair lost increments when the bot, the dashboard publisher and the tests first saw a headline together, and the WAL switch itself needed _with_lock_retry; both are regression-tested with three concurrent writers. The hook sits at the end of _fetch_news_sentiment_raw, BEFORE _is_neutral_headline runs in get_fundamental_context. Archiving downstream of that ±0.1 filter is why signals.db holds almost no neutral headlines and would have taught a trained scorer that "neutral" barely exists. scripts/backfill_news_archive.py seeds it from both DB shapes on disk, read-only and idempotent, recomputing provenance by comparing each stored score against the keyword scorer (neither source recorded it): 1,016 distinct headlines, 846 DeepSeek-labelled, growing ~1,400/week. Nothing in the signal core changed. Both news gating flags stay False, no gate reads any of this, and the backtester never builds a NewsEngine so replays never archive (test-locked). 468 -> 483 tests, including a new guard that AST-walks live code for sklearn/scipy/lightgbm imports — that check did not exist before. Do not train yet: ~800 labels gives a holdout too small to grade (SE +/-0.032). Target is ~4,000 deduplicated headlines over >=3 weeks. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Bzv4ETVapFNauFDQht7QtA |