70 lines
3.2 KiB
Python
70 lines
3.2 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from tradingagents.dataflows.fred_api import get_dxy_data, get_real_yields, get_inflation_data, get_fred_series
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def create_xau_macro_analyst(llm):
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"""
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Creates a node for the XAU Macro Analyst agent.
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This agent specializes in analyzing macroeconomic factors that influence the price of gold (XAU/USD).
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It replaces the traditional fundamentals analyst for equity trading.
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"""
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system_message = (
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"You are a specialized Macroeconomic Analyst for Gold (XAU/USD). Your mission is to provide a detailed analysis of the key macro drivers affecting gold's price. "
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"DO NOT analyze company fundamentals. Instead, focus exclusively on the following:"
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"\n\n1. **US Dollar Index (DXY)**: Analyze its recent trend (e.g., past 90 days). Is it strengthening or weakening? Explain how this trend typically impacts gold."
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"\n2. **Real Yields**: Analyze the trend in 10-year real yields. Are they rising or falling? Explain the inverse relationship between real yields and gold (i.e., opportunity cost)."
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"\n3. **Inflation Data**: Review the latest inflation metrics (CPI, PCE). Is inflation running hot or cooling down? Explain how inflation expectations affect gold's appeal as a hedge."
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"\n4. **Fed Policy & VIX (Optional)**: Briefly mention the current Federal Reserve stance (if known) and the VIX level as a measure of market fear."
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"\n\nUse the available tools to fetch the necessary data. Synthesize your findings into a comprehensive report. "
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"Conclude your report with a Markdown table summarizing the key macro factors and their likely impact on gold (Bullish, Bearish, or Neutral)."
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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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" The asset of interest is Gold (XAU/USD)."
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" For your reference, the current date is {current_date}."
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"\n\nTool Names: {tool_names}"
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"\n\n{system_message}",
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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)
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tools = [
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get_dxy_data,
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get_real_yields,
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get_inflation_data,
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get_fred_series, # For VIX or other specific series
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]
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prompt = prompt.partial(
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system_message=system_message,
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tool_names=", ".join([tool.name for tool in tools]),
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)
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chain = prompt | llm.bind_tools(tools)
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def xau_macro_analyst_node(state):
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"""
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The node function for the XAU Macro Analyst.
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"""
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current_date = state["trade_date"]
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# The ticker is XAU, but the tools are specific to macro data.
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chain_with_date = chain.partial(current_date=current_date)
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result = chain_with_date.invoke(state["messages"])
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report = ""
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if not result.tool_calls:
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report = result.content
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return {
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"messages": [result],
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"xau_macro_report": report,
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}
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return xau_macro_analyst_node |