fix: correct risk manager data bug, incomplete state init, and LLM type hints

- Fix copy-paste bug in risk_manager.py where fundamentals_report was
  reading from news_report instead of fundamentals_report, causing the
  risk manager to ignore fundamentals data in its final decision
- Initialize all TypedDict fields for InvestDebateState and
  RiskDebateState in propagation.py to prevent KeyError in _log_state
- Update setup.py type hints from ChatOpenAI to ChatModel union type
  to correctly reflect multi-provider LLM support

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

tradingagents/agents/managers/risk_manager.py

@@ -11,7 +11,7 @@ def create_risk_manager(llm, memory):
         risk_debate_state = state["risk_debate_state"]
         market_research_report = state["market_report"]
         news_report = state["news_report"]
-        fundamentals_report = state["news_report"]
+        fundamentals_report = state["fundamentals_report"]
         sentiment_report = state["sentiment_report"]
         trader_plan = state["investment_plan"]
 

tradingagents/graph/propagation.py

@@ -24,14 +24,26 @@ class Propagator:
             "company_of_interest": company_name,
             "trade_date": str(trade_date),
             "investment_debate_state": InvestDebateState(
-                {"history": "", "current_response": "", "count": 0}
+                {
+                    "bull_history": "",
+                    "bear_history": "",
+                    "history": "",
+                    "current_response": "",
+                    "judge_decision": "",
+                    "count": 0,
+                }
             ),
             "risk_debate_state": RiskDebateState(
                 {
+                    "aggressive_history": "",
+                    "conservative_history": "",
+                    "neutral_history": "",
                     "history": "",
+                    "latest_speaker": "",
                     "current_aggressive_response": "",
                     "current_conservative_response": "",
                     "current_neutral_response": "",
+                    "judge_decision": "",
                     "count": 0,
                 }
             ),

tradingagents/graph/setup.py

@@ -1,7 +1,10 @@
 # TradingAgents/graph/setup.py
 
-from typing import Dict, Any
+from typing import Any, Dict, Union
+
 from langchain_openai import ChatOpenAI
+from langchain_anthropic import ChatAnthropic
+from langchain_google_genai import ChatGoogleGenerativeAI
 from langgraph.graph import END, StateGraph, START
 from langgraph.prebuilt import ToolNode
 
@@ -10,14 +13,17 @@ from tradingagents.agents.utils.agent_states import AgentState
 
 from .conditional_logic import ConditionalLogic
 
+# Union of supported LLM chat model types
+ChatModel = Union[ChatOpenAI, ChatAnthropic, ChatGoogleGenerativeAI]
+
 
 class GraphSetup:
     """Handles the setup and configuration of the agent graph."""
 
     def __init__(
         self,
-        quick_thinking_llm: ChatOpenAI,
+        quick_thinking_llm: ChatModel,
-        deep_thinking_llm: ChatOpenAI,
+        deep_thinking_llm: ChatModel,
         tool_nodes: Dict[str, ToolNode],
         bull_memory,
         bear_memory,