47 lines
2.1 KiB
Python
47 lines
2.1 KiB
Python
import functools
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import time
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import json
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def create_trader(llm, memory):
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def trader_node(state, name):
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company_name = state["company_of_interest"]
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investment_plan = state["investment_plan"]
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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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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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if past_memories:
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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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else:
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past_memory_str = "没有找到过去的记忆。"
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context = {
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"role": "user",
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"content": f"根据分析师团队的综合分析，这里有一份为{company_name}量身定制的投资计划。该计划结合了当前技术市场趋势、宏观经济指标和社交媒体情绪的见解。请使用此计划作为评估您下一个交易决策的基础。\n\n建议的投资计划：{investment_plan}\n\n利用这些见解做出明智的战略决策。",
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}
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messages = [
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{
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"role": "system",
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"content": f"""你是一名交易代理，负责分析市场数据以做出投资决策。根据你的分析，提供具体的买入、卖出或持有建议。以坚定的决策结束，并始终以“最终交易建议：**买入/持有/卖出**”来结束您的回应，以确认您的建议。不要忘记利用过去的决策中的教训来从错误中学习。以下是您在类似情况下进行交易的一些反思和经验教训：{past_memory_str}""",
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},
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context,
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]
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result = llm.invoke(messages)
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return {
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"messages": [result],
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"trader_investment_plan": result.content,
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"sender": name,
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}
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return functools.partial(trader_node, name="Trader")
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