"""Parallel execution nodes for TradingAgents. Provides parallel wrappers for: - Analyst phase (Market, Social, News, Fundamentals) - Research debate phase (Bull + Bear) - Risk debate phase (Aggressive + Conservative + Neutral) """ import asyncio import logging from concurrent.futures import ThreadPoolExecutor from langchain_core.messages import HumanMessage, RemoveMessage logger = logging.getLogger(__name__) def create_parallel_analyst_node(analyst_fns, tool_nodes, selected_analysts): """Create a single LangGraph node that runs all analysts in parallel. Each analyst gets its own isolated message state and runs its complete tool-calling loop independently. Results are merged at the end. Args: analyst_fns: dict mapping analyst type (e.g. "market") to node function tool_nodes: dict mapping analyst type to ToolNode instance selected_analysts: list of analyst types to run """ async def parallel_analysts_node(state): """Run all analysts concurrently and merge their reports.""" async def run_single(analyst_type): """Run one analyst through its complete tool-calling loop.""" fn = analyst_fns[analyst_type] tn = tool_nodes[analyst_type] # Each analyst gets its own isolated message state local_state = { "messages": list(state["messages"]), "trade_date": state["trade_date"], "company_of_interest": state["company_of_interest"], } result = {} for _ in range(10): # safety limit on tool rounds result = await asyncio.to_thread(fn, local_state) ai_msg = result["messages"][0] local_state["messages"] = local_state["messages"] + [ai_msg] if not ai_msg.tool_calls: break # Process tool calls tool_result = await asyncio.to_thread(tn.invoke, local_state) local_state["messages"] = ( local_state["messages"] + tool_result["messages"] ) # Return only report fields (not messages) return {k: v for k, v in result.items() if k != "messages"} # Run all analysts concurrently tasks = [run_single(at) for at in selected_analysts if at in analyst_fns] results = await asyncio.gather(*tasks) # Merge all report fields merged = {} for r in results: merged.update(r) # Clear messages and add placeholder (same as Msg Clear nodes) messages = state.get("messages", []) removal_ops = [ RemoveMessage(id=m.id) for m in messages if hasattr(m, "id") and m.id ] merged["messages"] = removal_ops + [HumanMessage(content="Continue")] return merged 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. 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. """ 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 with ThreadPoolExecutor(max_workers=2) as pool: 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"] merged_debate = { "bull_history": bull_debate.get("bull_history", ""), "bear_history": bear_debate.get("bear_history", ""), "history": bull_debate.get("bull_history", "") + "\n" + bear_debate.get("bear_history", ""), "current_response": bear_debate.get("current_response", ""), "judge_decision": "", "count": 2, } return {"investment_debate_state": merged_debate} return parallel_research_node def create_parallel_risk_node(aggressive_fn, conservative_fn, neutral_fn): """Create a node that runs all 3 risk analysts in parallel. 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. """ 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 with ThreadPoolExecutor(max_workers=3) as pool: 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"] neu_debate = neu_result["risk_debate_state"] merged_debate = { "aggressive_history": agg_debate.get("aggressive_history", ""), "conservative_history": con_debate.get("conservative_history", ""), "neutral_history": neu_debate.get("neutral_history", ""), "history": agg_debate.get("aggressive_history", "") + "\n" + con_debate.get("conservative_history", "") + "\n" + neu_debate.get("neutral_history", ""), "latest_speaker": "Neutral", "current_aggressive_response": agg_debate.get( "current_aggressive_response", "" ), "current_conservative_response": con_debate.get( "current_conservative_response", "" ), "current_neutral_response": neu_debate.get( "current_neutral_response", "" ), "judge_decision": "", "count": 3, } return {"risk_debate_state": merged_debate} return parallel_risk_node