feat: structured-output Trader and Research Manager (#434, finishes the trio)

Extends the canonical structured-output pattern from the Portfolio Manager
to the other two decision-making agents.  Each of the three agents now
returns a typed Pydantic instance via llm.with_structured_output() in a
single primary call, and a render helper turns the result into the same
markdown shape downstream agents and saved reports already consume.

- ResearchPlan: 5-tier recommendation, conversational rationale, concrete
  strategic actions for the trader.
- TraderProposal: 3-tier action (transaction direction is naturally Buy /
  Hold / Sell — position sizing happens later at the Portfolio Manager),
  reasoning, and optional entry_price / stop_loss / position_sizing.
  Rendered output preserves the trailing "FINAL TRANSACTION PROPOSAL:
  **BUY/HOLD/SELL**" line for backward compatibility with the analyst
  stop-signal text.
- PortfolioDecision: 5-tier rating, executive summary, investment thesis,
  optional price_target / time_horizon (unchanged).

The shared try-structured-then-fallback pattern is extracted into
tradingagents/agents/utils/structured.py (bind_structured +
invoke_structured_or_freetext) so all three agents go through the same
code path and log the same warning when a provider lacks structured
output and the agent falls back to free-text generation.

Net effect for users: every saved markdown report (research/manager.md,
trading/trader.md, portfolio/decision.md) now has consistent section
headers across runs and providers, easier to scan.

Net effect for the runtime: the rating extraction round-trip is gone —
the rating comes from the structured response itself, not a second
LLM call. SignalProcessor was already simplified to a heuristic adapter
in the previous commit.

11 new tests in tests/test_structured_agents.py cover the Trader and
Research Manager render functions, structured-output happy paths, and
free-text fallback. Full suite: 88 tests pass in ~2s without API keys.

tradingagents/agents/managers/research_manager.py

@@ -1,8 +1,18 @@
+"""Research Manager: turns the bull/bear debate into a structured investment plan for the trader."""
 
+from __future__ import annotations
+
+from tradingagents.agents.schemas import ResearchPlan, render_research_plan
 from tradingagents.agents.utils.agent_utils import build_instrument_context
+from tradingagents.agents.utils.structured import (
+    bind_structured,
+    invoke_structured_or_freetext,
+)
 
 
 def create_research_manager(llm):
+    structured_llm = bind_structured(llm, ResearchPlan, "Research Manager")
+
     def research_manager_node(state) -> dict:
         instrument_context = build_instrument_context(state["company_of_interest"])
         history = state["investment_debate_state"].get("history", "")
@@ -24,31 +34,31 @@ def create_research_manager(llm):
 
 Commit to a clear stance whenever the debate's strongest arguments warrant one; reserve Hold for situations where the evidence on both sides is genuinely balanced.
 
-**Required Output Structure:**
-1. **Recommendation**: State one of Buy / Overweight / Hold / Underweight / Sell.
-2. **Rationale**: Summarise the key points from both sides and explain which arguments led to this recommendation.
-3. **Strategic Actions**: Concrete steps for the trader to implement the recommendation, including position sizing guidance consistent with the rating.
-
-Present your analysis conversationally, as if speaking naturally to a teammate.
-
 ---
 
 **Debate History:**
 {history}"""
-        response = llm.invoke(prompt)
+
+        investment_plan = invoke_structured_or_freetext(
+            structured_llm,
+            llm,
+            prompt,
+            render_research_plan,
+            "Research Manager",
+        )
 
         new_investment_debate_state = {
-            "judge_decision": response.content,
+            "judge_decision": investment_plan,
             "history": investment_debate_state.get("history", ""),
             "bear_history": investment_debate_state.get("bear_history", ""),
             "bull_history": investment_debate_state.get("bull_history", ""),
-            "current_response": response.content,
+            "current_response": investment_plan,
             "count": investment_debate_state["count"],
         }
 
         return {
             "investment_debate_state": new_investment_debate_state,
-            "investment_plan": response.content,
+            "investment_plan": investment_plan,
         }
 
     return research_manager_node