Merge remote-tracking branch 'upstream/main' into crypto-analysis-mvp

# Conflicts:
#	tradingagents/agents/researchers/bear_researcher.py
#	tradingagents/agents/researchers/bull_researcher.py
#	tradingagents/graph/propagation.py

tradingagents/agents/trader/trader.py

@@ -1,47 +1,61 @@
+"""Trader: turns the Research Manager's investment plan into a concrete transaction proposal."""
+
+from __future__ import annotations
+
 import functools
 
+from langchain_core.messages import AIMessage
+
+from tradingagents.agents.schemas import TraderProposal, render_trader_proposal
 from tradingagents.agents.utils.agent_utils import build_instrument_context
+from tradingagents.agents.utils.structured import (
+    bind_structured,
+    invoke_structured_or_freetext,
+)
 
 
-def create_trader(llm, memory):
+def create_trader(llm):
+    structured_llm = bind_structured(llm, TraderProposal, "Trader")
+
     def trader_node(state, name):
         company_name = state["company_of_interest"]
         asset_type = state.get("asset_type", "stock")
         instrument_context = build_instrument_context(company_name, asset_type)
         investment_plan = state["investment_plan"]
-        market_research_report = state["market_report"]
-        sentiment_report = state["sentiment_report"]
-        news_report = state["news_report"]
-        fundamentals_report = state["fundamentals_report"]
-
-        curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
-        past_memories = memory.get_memories(curr_situation, n_matches=2)
-
-        past_memory_str = ""
-        if past_memories:
-            for i, rec in enumerate(past_memories, 1):
-                past_memory_str += rec["recommendation"] + "\n\n"
-        else:
-            past_memory_str = "No past memories found."
-
-        context = {
-            "role": "user",
-            "content": f"Based on a comprehensive analysis by a team of analysts, here is an investment plan tailored for {company_name}. {instrument_context} This plan incorporates insights from current technical market trends, macroeconomic indicators, and social media sentiment. Use this plan as a foundation for evaluating your next trading decision.\n\nProposed Investment Plan: {investment_plan}\n\nLeverage these insights to make an informed and strategic decision.",
-        }
 
         messages = [
             {
                 "role": "system",
-                "content": f"""You are a trading agent analyzing market data to make investment decisions. Based on your analysis, provide a specific recommendation to buy, sell, or hold. End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**' to confirm your recommendation. Apply lessons from past decisions to strengthen your analysis. Here are reflections from similar situations you traded in and the lessons learned: {past_memory_str}""",
+                "content": (
+                    "You are a trading agent analyzing market data to make investment decisions. "
+                    "Based on your analysis, provide a specific recommendation to buy, sell, or hold. "
+                    "Anchor your reasoning in the analysts' reports and the research plan."
+                ),
+            },
+            {
+                "role": "user",
+                "content": (
+                    f"Based on a comprehensive analysis by a team of analysts, here is an investment "
+                    f"plan tailored for {company_name}. {instrument_context} This plan incorporates "
+                    f"insights from current technical market trends, macroeconomic indicators, and "
+                    f"social media sentiment. Use this plan as a foundation for evaluating your next "
+                    f"trading decision.\n\nProposed Investment Plan: {investment_plan}\n\n"
+                    f"Leverage these insights to make an informed and strategic decision."
+                ),
             },
-            context,
         ]
 
-        result = llm.invoke(messages)
+        trader_plan = invoke_structured_or_freetext(
+            structured_llm,
+            llm,
+            messages,
+            render_trader_proposal,
+            "Trader",
+        )
 
         return {
-            "messages": [result],
-            "trader_investment_plan": result.content,
+            "messages": [AIMessage(content=trader_plan)],
+            "trader_investment_plan": trader_plan,
             "sender": name,
         }