92 lines
3.8 KiB
Python
92 lines
3.8 KiB
Python
import time
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import json
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from langchain_core.messages import SystemMessage, HumanMessage
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from tradingagents.log_utils import add_log, step_timer, symbol_progress
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def create_research_manager(llm, memory):
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def research_manager_node(state) -> dict:
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history = state["investment_debate_state"].get("history", "")
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market_research_report = state["market_report"]
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sentiment_report = state["sentiment_report"]
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news_report = state["news_report"]
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fundamentals_report = state["fundamentals_report"]
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investment_debate_state = state["investment_debate_state"]
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curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
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past_memory_str = ""
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if memory is not None:
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past_memories = memory.get_memories(curr_situation, n_matches=2)
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for i, rec in enumerate(past_memories, 1):
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past_memory_str += rec["recommendation"] + "\n\n"
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system_prompt = """You are a Research Manager at a financial research firm. You MUST stay in character as a financial professional at all times.
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CRITICAL RULES:
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- NEVER mention that you are an AI, Claude, a language model, or an assistant
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- NEVER offer to help with code, software, or implementation tasks
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- NEVER say "I don't have access to" or "I can't see the data" — analyze whatever data is provided below
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- If data sections are empty, state that data is unavailable and make a decision based on available information
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Your task: Review the Bull vs Bear arguments and provide a clear investment recommendation.
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Your response must include:
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1. RECOMMENDATION: BUY, SELL, or HOLD
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2. RATIONALE: Why this recommendation based on the strongest arguments
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3. KEY FACTORS: The most compelling evidence from the debate
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RESPONSE FORMAT:
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- Maximum 1500 characters. Lead with your recommendation, then key rationale.
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- Complete your ENTIRE response in a SINGLE message.
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Respond only with your analysis and recommendation. No disclaimers or meta-commentary."""
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user_prompt = f"""Review this investment debate and provide your recommendation:
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DEBATE HISTORY:
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{history}
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PAST LEARNINGS:
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{past_memory_str if past_memory_str else "None"}
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Based on the bull and bear arguments above, what is your investment recommendation?"""
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messages = [
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SystemMessage(content=system_prompt),
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HumanMessage(content=user_prompt)
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]
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step_timer.start_step("research_manager")
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add_log("agent", "research_mgr", f"⚖️ Research Manager evaluating debate...")
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t0 = time.time()
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response = llm.invoke(messages)
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elapsed = time.time() - t0
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add_log("llm", "research_mgr", f"LLM responded in {elapsed:.1f}s ({len(response.content)} chars)")
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add_log("agent", "research_mgr", f"✅ Investment decision: {response.content[:300]}...")
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step_timer.end_step("research_manager", "completed", response.content[:200])
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symbol_progress.step_done(state.get("company_of_interest", ""), "research_manager")
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step_timer.set_details("research_manager", {
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"system_prompt": system_prompt,
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"user_prompt": user_prompt[:3000],
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"response": response.content[:3000],
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"tool_calls": [],
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})
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new_investment_debate_state = {
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"judge_decision": response.content,
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"history": investment_debate_state.get("history", ""),
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"bear_history": investment_debate_state.get("bear_history", ""),
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"bull_history": investment_debate_state.get("bull_history", ""),
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"current_response": response.content,
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"count": investment_debate_state["count"],
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}
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return {
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"investment_debate_state": new_investment_debate_state,
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"investment_plan": response.content,
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}
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return research_manager_node
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