Claude
e3cb2b8e5f
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
63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
"""Phase 2: optional catalyst_score blend stays pure & backward-compatible."""
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import pytest
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from strategy.evaluation.quality import calculate_setup_quality
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_KW = dict(
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gap_pct=0.08,
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relative_volume=3.0,
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structure_quality=0.7,
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above_vwap=True,
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opening_strength="strong",
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data_quality=1.0,
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)
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def test_none_catalyst_reproduces_legacy_score():
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# The exact pre-catalyst weighting (gap .20 / rvol .25 / structure .30 /
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# vwap .10 / opening .05 / data .10) must be unchanged when score is None.
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q = calculate_setup_quality(**_KW, catalyst_score=None)
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expected = (
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0.20 * 0.8 + 0.25 * 0.6 + 0.30 * 0.7 + 0.10 * 1.0
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+ 0.05 * 1.0 + 0.10 * 1.0
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)
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assert q.score == pytest.approx(round(expected, 4))
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assert "catalyst" not in q.components
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def test_default_arg_matches_explicit_none():
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assert calculate_setup_quality(**_KW).score == calculate_setup_quality(
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**_KW, catalyst_score=None
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).score
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def test_strong_catalyst_raises_score_and_adds_component():
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base = calculate_setup_quality(**_KW, catalyst_score=None).score
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high = calculate_setup_quality(**_KW, catalyst_score=1.0)
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assert high.score > base
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assert high.components["catalyst"] == 1.0
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def test_zero_catalyst_lowers_score_vs_legacy():
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# re-balancing toward a 0 catalyst pulls a strong setup down a touch
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base = calculate_setup_quality(**_KW, catalyst_score=None).score
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weak = calculate_setup_quality(**_KW, catalyst_score=0.0).score
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assert weak < base
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def test_catalyst_weights_sum_to_one():
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# an all-1.0 setup with a 1.0 catalyst must score exactly 1.0 (weights sum=1)
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q = calculate_setup_quality(
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gap_pct=1.0, relative_volume=10.0, structure_quality=1.0,
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above_vwap=True, opening_strength="strong", data_quality=1.0,
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catalyst_score=1.0,
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)
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assert q.score == pytest.approx(1.0)
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def test_catalyst_score_clamped():
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q = calculate_setup_quality(**_KW, catalyst_score=5.0)
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assert q.components["catalyst"] == 1.0
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