87 lines
3.8 KiB
Python
87 lines
3.8 KiB
Python
import functools
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from tradingagents.agents.utils.agent_utils import (
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build_instrument_context,
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build_optional_decision_context,
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summarize_structured_signal,
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)
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from tradingagents.agents.utils.decision_utils import build_structured_decision
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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)
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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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portfolio_context = state.get("portfolio_context", "")
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peer_context = state.get("peer_context", "")
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research_plan_structured = state.get("investment_plan_structured") or {}
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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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decision_context = build_optional_decision_context(
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portfolio_context,
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peer_context,
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peer_context_mode=state.get("peer_context_mode", "UNSPECIFIED"),
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max_chars=500,
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)
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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 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.\n\n"
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f"Research signal summary: {summarize_structured_signal(research_plan_structured)}\n"
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f"{decision_context}\n\n"
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f"Proposed Investment Plan: {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 investment decisions. "
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"Based on your analysis, provide a specific recommendation to buy, sell, or hold. "
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"Include a machine-readable line formatted exactly as `TRADER_RATING: BUY|HOLD|SELL` and "
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"always conclude your response with `FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**`. "
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"Do not emit tool calls or ask for more data. "
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f"Apply lessons from past decisions to strengthen your analysis. Here are reflections from similar situations you traded in and the lessons learned: {past_memory_str}"
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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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structured_plan = build_structured_decision(
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result.content,
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default_rating="HOLD",
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peer_context_mode=state.get("peer_context_mode", "UNSPECIFIED"),
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context_usage={
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"portfolio_context": bool(str(portfolio_context).strip()),
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"peer_context": bool(str(peer_context).strip()),
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},
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)
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return {
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"messages": [result],
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"trader_investment_plan": result.content,
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"trader_investment_plan_structured": structured_plan,
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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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