TradingAgents/tradingagents/graph
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
..
__init__.py feat: rebuild as structured Pydantic equity ranking engine 2026-03-09 21:30:46 +00:00
conditional_logic.py refactor: rename risky/safe agents to aggressive/conservative 2026-02-03 22:27:20 +00:00
parallel_analysts.py Fix parallel research/risk: use async+asyncio.gather instead of ThreadPoolExecutor 2026-02-20 18:01:54 +00:00
propagation.py feat: add footer statistics tracking with LangChain callbacks 2026-02-03 22:27:20 +00:00
reflection.py chore(release): v0.1.0 – initial public release of TradingAgents 2025-06-05 04:27:57 -07:00
setup.py feat: rebuild as structured Pydantic equity ranking engine 2026-03-09 21:30:46 +00:00
signal_processing.py chore(release): v0.1.0 – initial public release of TradingAgents 2025-06-05 04:27:57 -07:00
trading_graph.py feat: rebuild as structured Pydantic equity ranking engine 2026-03-09 21:30:46 +00:00