mirror of
https://github.com/TauricResearch/TradingAgents.git
synced 2026-06-17 05:16:14 +03:00
d7b40a2a5c
Agents had no ground-truth ticker→company mapping, so the market analyst could pattern-match a price chart to the wrong company (e.g. TOTDY read as "TotalEnergies"), and every downstream agent inherited the bad framing. Resolve identity once at run start via a cached, fail-open yfinance lookup and inject company/sector/exchange into the shared instrument context that all twelve agents consume, with an explicit do-not-substitute instruction. Resolution runs on both the propagate() and CLI entry points. Also replaces the bare "Continue" message-clear placeholder, which some OpenAI-compatible providers interpreted as the user task, with a context-anchored placeholder carrying the resolved identity and date. #814 #888
66 lines
2.4 KiB
Python
66 lines
2.4 KiB
Python
"""Trader: turns the Research Manager's investment plan into a concrete transaction proposal."""
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from __future__ import annotations
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import functools
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from langchain_core.messages import AIMessage
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from tradingagents.agents.schemas import TraderProposal, render_trader_proposal
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from tradingagents.agents.utils.agent_utils import (
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get_instrument_context_from_state,
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get_language_instruction,
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)
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from tradingagents.agents.utils.structured import (
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bind_structured,
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invoke_structured_or_freetext,
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)
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def create_trader(llm):
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structured_llm = bind_structured(llm, TraderProposal, "Trader")
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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 = get_instrument_context_from_state(state)
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investment_plan = state["investment_plan"]
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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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"Anchor your reasoning in the analysts' reports and the research plan."
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+ get_language_instruction()
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),
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},
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{
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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 "
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f"plan tailored for {company_name}. {instrument_context} This plan incorporates "
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f"insights from current technical market trends, macroeconomic indicators, and "
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f"social media sentiment. Use this plan as a foundation for evaluating your next "
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f"trading decision.\n\nProposed Investment Plan: {investment_plan}\n\n"
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f"Leverage these insights to make an informed and strategic decision."
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),
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},
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]
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trader_plan = invoke_structured_or_freetext(
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structured_llm,
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llm,
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messages,
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render_trader_proposal,
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"Trader",
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)
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return {
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"messages": [AIMessage(content=trader_plan)],
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"trader_investment_plan": trader_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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