Extends the local-LLM layer from news to TRADES, reusing the proven enrichment
pattern (structured JSON, Postgres cache, off-hot-path scheduler, graceful
degradation). Advisory only — never gates a trade.
strategy/evaluation/trade_analysis.py: four analyses keyed by analysis_type —
* armed — does a loaded setup still make sense to pursue? (pursue/avoid/
monitor + confidence + concerns), weighing numbers + catalyst
* weak — why a too-soft setup failed and whether a thesis remains
* postmortem — closed-trade lesson (uses r_multiple / exit_reason)
* eod — once-a-day session narrative
Schema-constrained decoding (ANALYSIS_SCHEMA), tolerant _parse, and a
context_hash so an unchanged board isn't re-sent to the GPU. run_* helpers
upsert into ai_trade_analysis_cache; trade_analysis_map / query_trade_analysis
read it back. NEVER raise.
Wiring:
- ai_trade_analysis_cache table (storage/pg_schema.sql)
- OllamaConfig.trade_analysis_{enabled,interval_seconds,batch_limit} + env
- run_live_paper step_analyze on the Scheduler, gated by ollama.enabled AND
TRADE_ANALYSIS_ENABLED (ships OFF); reads the in-memory trigger book + day P&L
- query_trade_analysis projection; api snapshots() attaches the verdict to armed
triggers + approval rows and exposes trade_analysis
- dashboard: per-name AI verdict chip (pursue=green/avoid=red/monitor=amber) +
an "AI session note" panel
- .env.example documents the toggles
Tests: parse/enum/clamp, prompt builders, persistence + context_hash dedupe,
closed-trade + EOD paths, config from_env, and the dashboard projection. 230 pass.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M5HTMkaadGDnAPtXe2hWwg
Accuracy: news_enrichment now sends a JSON schema to Ollama's `format`
(enum-locked catalyst_type, typed fields) so decoding is constrained at
generation time, and build_prompt carries 3 few-shot examples (offering,
FDA, earnings) — the biggest accuracy lever for a small local model.
classify_headline gains use_schema (default True; False = plain json).
Validation:
- research/ingestion/catalyst_eval.py — hand-labeled fixture + pure
score_predictions (type accuracy, is_dilutive precision/recall, sentiment
sign). catalyst_eval.py CLI A/Bs models (--model) against the same set.
- validate_catalysts.py — joins enriched labels to each stored gapper's
open->close / open->high outcome; pure outcome_correlation splits by
dilution flag and bullish-score bucket. Reports coverage honestly (news is
live-only) and writes a CSV. Run before flipping the Phase 2 flags.
Tests: schema-in-request assertions, pure eval/aggregator coverage. 212 pass.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M5HTMkaadGDnAPtXe2hWwg
Completes the Phase 2 loop: the catalyst score now actually reaches the runtime
decision, not just the pure functions. Gated behind its own flag so enabling the
Phase 1 advisory/dashboard never silently changes trade scoring.
- OllamaConfig.catalyst_score_enabled (NEWS_CATALYST_SCORE_ENABLED, default off).
- Watcher accepts an optional catalyst_score_provider (symbol -> 0..1 | None) and
passes it into evaluate_setup; a None provider reproduces today's behavior. The
provider is injected so runtime/ stays free of research/ imports.
- run_live_paper: one TTL-cached catalyst provider shared by the watcher and the
execution service; the watcher gets the score provider only when the blend is
enabled. step_arm gains a gated graded ranking multiplier (bullish catalyst
boosts by 1+gain*score; a confirmed dilutive offering is sunk below the armed
cut), falling back to the existing binary news boost when off.
- Add the enrich task to the run_pass pipeline order so a single --once pass also
runs enrichment (previously only the continuous loop did).
- .env.example documents the new flags; watcher tests cover the blend + the
no-op None-provider case.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M5HTMkaadGDnAPtXe2hWwg
Leverage a local Ollama model to interpret news catalysts for the small-cap
momentum strategy — distinguishing bullish catalysts (FDA approval, M&A) from
dilutive offerings the technicals alone can't see. Advisory-first, then automate.
Phase 1 (advisory; no change to trade behavior):
- research/ingestion/news_enrichment.py: stdlib urllib call to Ollama with
structured JSON output, classifying each headline into a catalyst type,
sentiment, conviction and dilution flag. Graceful degradation (None on any
failure) so trading is never blocked.
- news_catalyst_cache table + populate the previously-dead news_events.sentiment.
- Runs on the existing interval Scheduler (off the hot path), gated by
OllamaConfig; deduped by the raw_news_items payload hash.
- Surface the advisory on the dashboard approval queue (projection + api + UI).
Phase 2 (automation; ships gated OFF):
- Optional catalyst_score blended into calculate_setup_quality (None reproduces
the exact legacy weights); pure pass-through via evaluate_setup.
- Dilution veto in TradingExecutionService (slow + fast paths) emitting a
catalyst_dilution_veto risk event, behind NEWS_DILUTION_VETO_ENABLED.
Config (OllamaConfig.from_env), .env.example docs, and tests for parsing,
persistence/dedupe, the projection, the score blend, the veto, and an
end-to-end enrich->advisory flow (Ollama mocked). Default model moves to
qwen2.5:7b-instruct (best small-model JSON, fits a 12GB GPU).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M5HTMkaadGDnAPtXe2hWwg
Claude