dtarkent2-sys 7ad9e1d1ce feat: rebuild as structured Pydantic equity ranking engine ... Replace generic LLM debate system with a tiered, macro-aware equity ranking pipeline where every agent returns Pydantic structured output and scoring is deterministic Python — no prose drives downstream decisions. Architecture: Validation → Tier 1 (Macro+Liquidity parallel) → Tier 2 (8 agents parallel) → Scoring (Archetype+MasterScore) → Tier 3 (Bull/Bear debate + Risk + FinalDecision) → END Master Score: 25% business_quality + 20% macro + 15% institutional_flow + 10% valuation + 10% entry_timing + 10% earnings_revisions + 5% backlog + 5% crowding. Hard veto gates, confidence penalties, position role assignment all computed deterministically. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>		2026-03-09 21:30:46 +00:00
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agents	feat: rebuild as structured Pydantic equity ranking engine	2026-03-09 21:30:46 +00:00
dataflows	feat: rebuild as structured Pydantic equity ranking engine	2026-03-09 21:30:46 +00:00
graph	feat: rebuild as structured Pydantic equity ranking engine	2026-03-09 21:30:46 +00:00
llm_clients	chore: clean up dependencies and fix Ollama auth	2026-02-03 23:08:12 +00:00
default_config.py	Switch LLM models to glm-5:cloud via Ollama	2026-03-01 05:05:33 +00:00
models.py	feat: rebuild as structured Pydantic equity ranking engine	2026-03-09 21:30:46 +00:00