GO_LIVE flags `spread_pct` as a not-yet-real field. The reason it was null is
structural: OHLCV bars carry no NBBO, so `minute_bars.spread_pct` can never be
populated from the bars endpoint. The execution-relevant figure is the spread at
the moment a setup is evaluated — a wide book quietly eats R on the thin sub-$20
names this system trades.
This makes that metric real, shadow-first (measure before gating), mirroring how
the catalyst score was introduced:
- `strategy/evaluation/liquidity.py`: pure `compute_spread_pct(bid, ask)` →
(ask-bid)/mid, or None for missing/crossed/non-positive quotes (None never
collapses to a real 0.0). Unit-tested.
- `AlpacaPaperClient.get_latest_quotes`: batched latest-NBBO fetch, mirrors
`get_latest_trades`.
- `Watcher`: optional injected `spread_provider` (same shape as
`catalyst_score_provider`); default None == prior behavior byte-for-byte.
Computed spread is logged onto the criteria_evaluated and signal_ready events.
Observe-only — it does NOT gate ready/blocked.
- `run_live_paper`: TTL-cached spread provider off the client; degrades to None
on any fault, SPREAD_PCT_ENABLED=0 to silence.
The dead `scanner.spread_pct_max` knob is intentionally left for a later
promotion, once the logged data shows spread separates outcomes.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019psgRHdtTXz6TCnnqPiqGw
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