77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
import functools
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def create_trader(llm, memory, config):
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"""Create the trader node with language support."""
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language = config["output_language"]
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language_prompts = {
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"en": "",
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"zh-tw": "Use Traditional Chinese as the output.",
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"zh-cn": "Use Simplified Chinese as the output.",
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}
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language_prompt = language_prompts.get(language, "")
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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 = "No past memories found."
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context = {
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"role": "user",
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"content": f"""
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Based on a comprehensive analysis by a team of analysts, here is an investment plan tailored for {company_name}.
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This plan incorporates insights from current technical market trends, macroeconomic indicators, and social media sentiment.
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Use this plan as a foundation for evaluating your next trading decision.
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Proposed Investment Plan:
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{investment_plan}
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--------------------------------------
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Leverage these insights to make an informed and strategic decision.
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""",
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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"""
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You are a trading agent analyzing market data to make investment decisions.
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Based on your analysis, provide a specific recommendation to buy, sell, or hold.
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End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**' to confirm your recommendation.
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Do not forget to utilize lessons from past decisions to learn from your mistakes.
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Here is some reflections from similar situations you traded in and the lessons learned:
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{past_memory_str}
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--------------------------------------
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Output language: ***{language_prompt}***
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""",
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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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