50 lines
2.0 KiB
Python
50 lines
2.0 KiB
Python
import time
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import json
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from tradingagents.i18n import get_prompts
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def create_risk_manager(llm, memory):
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def risk_manager_node(state) -> dict:
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company_name = state["company_of_interest"]
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history = state["risk_debate_state"]["history"]
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risk_debate_state = state["risk_debate_state"]
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market_research_report = state["market_report"]
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news_report = state["news_report"]
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fundamentals_report = state["news_report"]
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sentiment_report = state["sentiment_report"]
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trader_plan = state["investment_plan"]
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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_memories = memory.get_memories(curr_situation, n_matches=2)
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past_memory_str = ""
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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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prompt = get_prompts("managers", "risk_manager") \
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.replace("{trader_plan}", trader_plan) \
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.replace("{past_memory_str}", past_memory_str) \
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.replace("{history}", history)
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response = llm.invoke(prompt)
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new_risk_debate_state = {
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"judge_decision": response.content,
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"history": risk_debate_state["history"],
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"risky_history": risk_debate_state["risky_history"],
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"safe_history": risk_debate_state["safe_history"],
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"neutral_history": risk_debate_state["neutral_history"],
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"latest_speaker": "Judge",
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"current_risky_response": risk_debate_state["current_risky_response"],
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"current_safe_response": risk_debate_state["current_safe_response"],
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"current_neutral_response": risk_debate_state["current_neutral_response"],
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"count": risk_debate_state["count"],
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}
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return {
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"risk_debate_state": new_risk_debate_state,
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"final_trade_decision": response.content,
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}
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return risk_manager_node
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