61 lines
2.7 KiB
Python
61 lines
2.7 KiB
Python
import functools
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from tradingagents.agents.utils.agent_utils import build_instrument_context, get_memory_matches
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from tradingagents.schemas import build_decision_output_instructions, ensure_structured_decision_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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instrument_context = build_instrument_context(company_name, state.get("instrument_profile"))
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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 = get_memory_matches(memory, curr_situation)
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past_memory_str = ""
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if past_memories:
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for rec in past_memories:
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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": (
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f"Based on a comprehensive analysis by a team of analysts, here is an investment plan tailored for {company_name}. "
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f"{instrument_context} This plan incorporates insights from market trends, macro context, sentiment, news, and fundamentals. "
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f"Use this plan as a foundation for your execution decision.\n\nProposed Investment Plan JSON: {investment_plan}\n\n"
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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": (
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"You are a trading agent analyzing market data to make execution-ready investment decisions. "
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"Translate the research manager's view into a concrete trade recommendation with entry logic, exit logic, position sizing, risk limits, catalysts, and invalidators. "
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"Use NO_TRADE when the setup is not actionable or lacks a favorable risk/reward. "
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f"Apply lessons from similar situations: {past_memory_str} "
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f"{build_decision_output_instructions('trader execution plan')}"
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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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decision_json = ensure_structured_decision_json(result.content)
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return {
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"messages": [result],
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"trader_investment_plan": decision_json,
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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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