69 lines
2.9 KiB
Python
69 lines
2.9 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from tradingagents.agents.utils.agent_utils import (
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build_instrument_context,
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get_economic_indicators,
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get_fed_calendar,
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get_yield_curve,
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)
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def create_macro_analyst(llm):
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def macro_analyst_node(state):
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current_date = state["trade_date"]
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instrument_context = build_instrument_context(state["company_of_interest"])
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tools = [
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get_economic_indicators,
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get_yield_curve,
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get_fed_calendar,
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]
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system_message = (
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"You are a macroeconomic analyst responsible for turning Federal Reserve "
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"data, inflation data, labor data, and the Treasury curve into a trading "
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"usable macro view. Use `get_economic_indicators` to establish the growth, "
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"inflation, and labor backdrop, `get_yield_curve` to explain the rates "
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"curve and recession signal, and `get_fed_calendar` to summarize the policy "
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"path. Focus on regime identification, likely policy direction, cross-asset "
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"implications, and concrete risks that other analysts should incorporate."
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" Append a Markdown table that summarizes the major macro signals and their "
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"market implications."
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)
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"You are a helpful AI assistant, collaborating with other assistants."
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" Use the provided tools to progress towards answering the question."
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" If you are unable to fully answer, that's OK; another assistant with different tools"
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" will help where you left off. Execute what you can to make progress."
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" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
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" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
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" You have access to the following tools: {tool_names}.\n{system_message}"
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"For your reference, the current date is {current_date}. {instrument_context}",
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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)
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prompt = prompt.partial(system_message=system_message)
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prompt = prompt.partial(tool_names=", ".join(tool.name for tool in tools))
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prompt = prompt.partial(current_date=current_date)
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prompt = prompt.partial(instrument_context=instrument_context)
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chain = prompt | llm.bind_tools(tools)
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result = chain.invoke(state["messages"])
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report = ""
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if len(result.tool_calls) == 0:
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report = result.content
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return {
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"messages": [result],
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"macro_report": report,
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}
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return macro_analyst_node
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