refactor: standardize portfolio manager, five-tier rating scale, fix analyst status tracking

cli/main.py

@@ -800,9 +800,11 @@ ANALYST_REPORT_MAP = {
 
 
 def update_analyst_statuses(message_buffer, chunk):
-    """Update all analyst statuses based on current report state.
+    """Update analyst statuses based on accumulated report state.
 
     Logic:
+    - Store new report content from the current chunk if present
+    - Check accumulated report_sections (not just current chunk) for status
     - Analysts with reports = completed
     - First analyst without report = in_progress
     - Remaining analysts without reports = pending
@@ -817,11 +819,16 @@ def update_analyst_statuses(message_buffer, chunk):
 
         agent_name = ANALYST_AGENT_NAMES[analyst_key]
         report_key = ANALYST_REPORT_MAP[analyst_key]
-        has_report = bool(chunk.get(report_key))
+
+        # Capture new report content from current chunk
+        if chunk.get(report_key):
+            message_buffer.update_report_section(report_key, chunk[report_key])
+
+        # Determine status from accumulated sections, not just current chunk
+        has_report = bool(message_buffer.report_sections.get(report_key))
 
         if has_report:
             message_buffer.update_agent_status(agent_name, "completed")
-            message_buffer.update_report_section(report_key, chunk[report_key])
         elif not found_active:
             message_buffer.update_agent_status(agent_name, "in_progress")
             found_active = True

tradingagents/agents/__init__.py

@@ -15,7 +15,7 @@ from .risk_mgmt.conservative_debator import create_conservative_debator
 from .risk_mgmt.neutral_debator import create_neutral_debator
 
 from .managers.research_manager import create_research_manager
-from .managers.risk_manager import create_risk_manager
+from .managers.portfolio_manager import create_portfolio_manager
 
 from .trader.trader import create_trader
 
@@ -33,7 +33,7 @@ __all__ = [
     "create_neutral_debator",
     "create_news_analyst",
     "create_aggressive_debator",
-    "create_risk_manager",
+    "create_portfolio_manager",
     "create_conservative_debator",
     "create_social_media_analyst",
     "create_trader",

tradingagents/agents/managers/portfolio_manager.py

@@ -1,11 +1,8 @@
-import time
-import json
-
 from tradingagents.agents.utils.agent_utils import build_instrument_context
 
 
-def create_risk_manager(llm, memory):
+def create_portfolio_manager(llm, memory):
-    def risk_manager_node(state) -> dict:
+    def portfolio_manager_node(state) -> dict:
 
         instrument_context = build_instrument_context(state["company_of_interest"])
 
@@ -24,7 +21,7 @@ def create_risk_manager(llm, memory):
         for i, rec in enumerate(past_memories, 1):
             past_memory_str += rec["recommendation"] + "\n\n"
 
-        prompt = f"""As the Risk Management Judge, evaluate the debate between the Aggressive, Neutral, and Conservative analysts and deliver a final trading decision.
+        prompt = f"""As the Portfolio Manager, synthesize the risk analysts' debate and deliver the final trading decision.
 
 {instrument_context}
 
@@ -37,19 +34,18 @@ def create_risk_manager(llm, memory):
 - **Underweight**: Reduce exposure, take partial profits
 - **Sell**: Exit position or avoid entry
 
-**Guidelines:**
+**Context:**
-1. Extract the strongest points from each analyst, focusing on relevance to the current context.
+- Trader's proposed plan: **{trader_plan}**
-2. Start with the trader's original plan: **{trader_plan}**, and refine it based on the analysts' insights.
+- Lessons from past decisions: **{past_memory_str}**
-3. Apply lessons from past decisions to strengthen this analysis: **{past_memory_str}**
 
 **Required Output Structure:**
 1. **Rating**: State one of Buy / Overweight / Hold / Underweight / Sell.
-2. **Executive Summary**: A concise action plan covering entry strategy, position sizing, key risk levels, and time horizon. Keep this brief and actionable.
+2. **Executive Summary**: A concise action plan covering entry strategy, position sizing, key risk levels, and time horizon.
-3. **Investment Thesis**: Detailed reasoning anchored in the debate and past reflections.
+3. **Investment Thesis**: Detailed reasoning anchored in the analysts' debate and past reflections.
 
 ---
 
-**Analysts Debate History:**
+**Risk Analysts Debate History:**
 {history}
 
 ---
@@ -76,4 +72,4 @@ Be decisive and ground every conclusion in specific evidence from the analysts."
             "final_trade_decision": response.content,
         }
 
-    return risk_manager_node
+    return portfolio_manager_node

tradingagents/graph/conditional_logic.py

@@ -59,7 +59,7 @@ class ConditionalLogic:
         if (
             state["risk_debate_state"]["count"] >= 3 * self.max_risk_discuss_rounds
         ):  # 3 rounds of back-and-forth between 3 agents
-            return "Risk Judge"
+            return "Portfolio Manager"
         if state["risk_debate_state"]["latest_speaker"].startswith("Aggressive"):
             return "Conservative Analyst"
         if state["risk_debate_state"]["latest_speaker"].startswith("Conservative"):

tradingagents/graph/reflection.py

@@ -110,12 +110,12 @@ Adhere strictly to these instructions, and ensure your output is detailed, accur
         )
         invest_judge_memory.add_situations([(situation, result)])
 
-    def reflect_risk_manager(self, current_state, returns_losses, risk_manager_memory):
+    def reflect_portfolio_manager(self, current_state, returns_losses, portfolio_manager_memory):
-        """Reflect on risk manager's decision and update memory."""
+        """Reflect on portfolio manager's decision and update memory."""
         situation = self._extract_current_situation(current_state)
         judge_decision = current_state["risk_debate_state"]["judge_decision"]
 
         result = self._reflect_on_component(
-            "RISK JUDGE", judge_decision, situation, returns_losses
+            "PORTFOLIO MANAGER", judge_decision, situation, returns_losses
         )
-        risk_manager_memory.add_situations([(situation, result)])
+        portfolio_manager_memory.add_situations([(situation, result)])

tradingagents/graph/setup.py

@@ -23,7 +23,7 @@ class GraphSetup:
         bear_memory,
         trader_memory,
         invest_judge_memory,
-        risk_manager_memory,
+        portfolio_manager_memory,
         conditional_logic: ConditionalLogic,
     ):
         """Initialize with required components."""
@@ -34,7 +34,7 @@ class GraphSetup:
         self.bear_memory = bear_memory
         self.trader_memory = trader_memory
         self.invest_judge_memory = invest_judge_memory
-        self.risk_manager_memory = risk_manager_memory
+        self.portfolio_manager_memory = portfolio_manager_memory
         self.conditional_logic = conditional_logic
 
     def setup_graph(
@@ -101,8 +101,8 @@ class GraphSetup:
         aggressive_analyst = create_aggressive_debator(self.quick_thinking_llm)
         neutral_analyst = create_neutral_debator(self.quick_thinking_llm)
         conservative_analyst = create_conservative_debator(self.quick_thinking_llm)
-        risk_manager_node = create_risk_manager(
+        portfolio_manager_node = create_portfolio_manager(
-            self.deep_thinking_llm, self.risk_manager_memory
+            self.deep_thinking_llm, self.portfolio_manager_memory
         )
 
         # Create workflow
@@ -124,7 +124,7 @@ class GraphSetup:
         workflow.add_node("Aggressive Analyst", aggressive_analyst)
         workflow.add_node("Neutral Analyst", neutral_analyst)
         workflow.add_node("Conservative Analyst", conservative_analyst)
-        workflow.add_node("Risk Judge", risk_manager_node)
+        workflow.add_node("Portfolio Manager", portfolio_manager_node)
 
         # Define edges
         # Start with the first analyst
@@ -176,7 +176,7 @@ class GraphSetup:
             self.conditional_logic.should_continue_risk_analysis,
             {
                 "Conservative Analyst": "Conservative Analyst",
-                "Risk Judge": "Risk Judge",
+                "Portfolio Manager": "Portfolio Manager",
             },
         )
         workflow.add_conditional_edges(
@@ -184,7 +184,7 @@ class GraphSetup:
             self.conditional_logic.should_continue_risk_analysis,
             {
                 "Neutral Analyst": "Neutral Analyst",
-                "Risk Judge": "Risk Judge",
+                "Portfolio Manager": "Portfolio Manager",
             },
         )
         workflow.add_conditional_edges(
@@ -192,11 +192,11 @@ class GraphSetup:
             self.conditional_logic.should_continue_risk_analysis,
             {
                 "Aggressive Analyst": "Aggressive Analyst",
-                "Risk Judge": "Risk Judge",
+                "Portfolio Manager": "Portfolio Manager",
             },
         )
 
-        workflow.add_edge("Risk Judge", END)
+        workflow.add_edge("Portfolio Manager", END)
 
         # Compile and return
         return workflow.compile()

tradingagents/graph/trading_graph.py

@@ -99,7 +99,7 @@ class TradingAgentsGraph:
         self.bear_memory = FinancialSituationMemory("bear_memory", self.config)
         self.trader_memory = FinancialSituationMemory("trader_memory", self.config)
         self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory", self.config)
-        self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory", self.config)
+        self.portfolio_manager_memory = FinancialSituationMemory("portfolio_manager_memory", self.config)
 
         # Create tool nodes
         self.tool_nodes = self._create_tool_nodes()
@@ -117,7 +117,7 @@ class TradingAgentsGraph:
             self.bear_memory,
             self.trader_memory,
             self.invest_judge_memory,
-            self.risk_manager_memory,
+            self.portfolio_manager_memory,
             self.conditional_logic,
         )
 
@@ -283,8 +283,8 @@ class TradingAgentsGraph:
         self.reflector.reflect_invest_judge(
             self.curr_state, returns_losses, self.invest_judge_memory
         )
-        self.reflector.reflect_risk_manager(
+        self.reflector.reflect_portfolio_manager(
-            self.curr_state, returns_losses, self.risk_manager_memory
+            self.curr_state, returns_losses, self.portfolio_manager_memory
         )
 
     def process_signal(self, full_signal):