Switch parallel research/risk to sync ThreadPoolExecutor with timing logs

Use sync functions with pool.submit() instead of async+run_in_executor
to avoid potential asyncio event-loop interaction issues with LangGraph.
Added timing logs to diagnose parallelism.

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

tradingagents/graph/parallel_analysts.py

@@ -83,31 +83,62 @@ def create_parallel_analyst_node(analyst_fns, tool_nodes, selected_analysts):
     return parallel_analysts_node
 
 
+def _snapshot_research_state(state):
+    """Extract research-relevant fields into a plain dict."""
+    return {
+        "investment_debate_state": dict(state.get("investment_debate_state", {})),
+        "market_report": state.get("market_report", ""),
+        "sentiment_report": state.get("sentiment_report", ""),
+        "news_report": state.get("news_report", ""),
+        "fundamentals_report": state.get("fundamentals_report", ""),
+    }
+
+
+def _snapshot_risk_state(state):
+    """Extract risk-relevant fields into a plain dict."""
+    return {
+        "risk_debate_state": dict(state.get("risk_debate_state", {})),
+        "market_report": state.get("market_report", ""),
+        "sentiment_report": state.get("sentiment_report", ""),
+        "news_report": state.get("news_report", ""),
+        "fundamentals_report": state.get("fundamentals_report", ""),
+        "trader_investment_plan": state.get("trader_investment_plan", ""),
+    }
+
+
 def create_parallel_research_node(bull_fn, bear_fn):
     """Create a node that runs Bull and Bear researchers in parallel.
 
-    Both agents receive the same state (reports + empty debate state) and
-    produce independent arguments. Results are merged into a single
-    investment_debate_state with both histories and count=2.
+    Uses a sync function with ThreadPoolExecutor.submit() to avoid any
+    asyncio event-loop interaction issues. LangGraph handles running sync
+    nodes in its own thread, and from there we spawn our own pool.
     """
 
-    async def parallel_research_node(state):
-        # Snapshot into plain dicts — LangGraph state proxies serialize
-        # concurrent dict access, which would force sequential execution.
-        state_snap = {
-            "investment_debate_state": dict(state.get("investment_debate_state", {})),
-            "market_report": state.get("market_report", ""),
-            "sentiment_report": state.get("sentiment_report", ""),
-            "news_report": state.get("news_report", ""),
-            "fundamentals_report": state.get("fundamentals_report", ""),
-        }
+    def parallel_research_node(state):
+        import time
+
+        state_snap = _snapshot_research_state(state)
+        t0 = time.time()
+
+        def run_bull():
+            logger.info("Bull researcher starting")
+            result = bull_fn(state_snap)
+            logger.info("Bull researcher done in %.1fs", time.time() - t0)
+            return result
+
+        def run_bear():
+            logger.info("Bear researcher starting")
+            result = bear_fn(state_snap)
+            logger.info("Bear researcher done in %.1fs", time.time() - t0)
+            return result
 
-        loop = asyncio.get_running_loop()
         with ThreadPoolExecutor(max_workers=2) as pool:
-            bull_result, bear_result = await asyncio.gather(
-                loop.run_in_executor(pool, bull_fn, state_snap),
-                loop.run_in_executor(pool, bear_fn, state_snap),
-            )
+            bull_future = pool.submit(run_bull)
+            bear_future = pool.submit(run_bear)
+            bull_result = bull_future.result()
+            bear_result = bear_future.result()
+
+        logger.info("Parallel research total: %.1fs", time.time() - t0)
 
         bull_debate = bull_result["investment_debate_state"]
         bear_debate = bear_result["investment_debate_state"]
@@ -130,30 +161,44 @@ def create_parallel_research_node(bull_fn, bear_fn):
 def create_parallel_risk_node(aggressive_fn, conservative_fn, neutral_fn):
     """Create a node that runs all 3 risk analysts in parallel.
 
-    All agents receive the same state (trader plan + empty risk debate state)
-    and produce independent arguments. Results are merged into a single
-    risk_debate_state with all histories and count=3.
+    Uses a sync function with ThreadPoolExecutor.submit() to avoid any
+    asyncio event-loop interaction issues. LangGraph handles running sync
+    nodes in its own thread, and from there we spawn our own pool.
     """
 
-    async def parallel_risk_node(state):
-        # Snapshot into plain dicts — LangGraph state proxies serialize
-        # concurrent dict access, which would force sequential execution.
-        state_snap = {
-            "risk_debate_state": dict(state.get("risk_debate_state", {})),
-            "market_report": state.get("market_report", ""),
-            "sentiment_report": state.get("sentiment_report", ""),
-            "news_report": state.get("news_report", ""),
-            "fundamentals_report": state.get("fundamentals_report", ""),
-            "trader_investment_plan": state.get("trader_investment_plan", ""),
-        }
+    def parallel_risk_node(state):
+        import time
+
+        state_snap = _snapshot_risk_state(state)
+        t0 = time.time()
+
+        def run_agg():
+            logger.info("Aggressive analyst starting")
+            result = aggressive_fn(state_snap)
+            logger.info("Aggressive analyst done in %.1fs", time.time() - t0)
+            return result
+
+        def run_con():
+            logger.info("Conservative analyst starting")
+            result = conservative_fn(state_snap)
+            logger.info("Conservative analyst done in %.1fs", time.time() - t0)
+            return result
+
+        def run_neu():
+            logger.info("Neutral analyst starting")
+            result = neutral_fn(state_snap)
+            logger.info("Neutral analyst done in %.1fs", time.time() - t0)
+            return result
 
-        loop = asyncio.get_running_loop()
         with ThreadPoolExecutor(max_workers=3) as pool:
-            agg_result, con_result, neu_result = await asyncio.gather(
-                loop.run_in_executor(pool, aggressive_fn, state_snap),
-                loop.run_in_executor(pool, conservative_fn, state_snap),
-                loop.run_in_executor(pool, neutral_fn, state_snap),
-            )
+            agg_future = pool.submit(run_agg)
+            con_future = pool.submit(run_con)
+            neu_future = pool.submit(run_neu)
+            agg_result = agg_future.result()
+            con_result = con_future.result()
+            neu_result = neu_future.result()
+
+        logger.info("Parallel risk total: %.1fs", time.time() - t0)
 
         agg_debate = agg_result["risk_debate_state"]
         con_debate = con_result["risk_debate_state"]