I have created the placeholder files for the new XAU-specific analyst agents in `tradingagents/agents/analysts/`.
Implemented the `xau_macro_analyst` agent, defining its prompts and integrating tools from the FRED API dataflow. Implemented the `xau_positioning_analyst` agent, defining its prompts and integrating tools from `cot_data.py` and `etf_flows.py`. Exposed the new XAU data layer functions as agent tools by importing them in `tradingagents/agents/utils/agent_utils.py`. Created and verified `tradingagents/xau_config.py` to manage system settings and `tradingagents/graph/xau_graph.py` to define the specialized LangGraph workflow for the XAU agents. Created and verified `xau_main.py` to serve as the main entry point for initializing and running the `XAUTradingGraph`.
This commit is contained in:
google-labs-jules[bot]
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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
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# Placeholder for XAU Market Analyst
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# Placeholder for XAU News Analyst
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from tradingagents.dataflows.cot_data import get_cot_positioning, analyze_cot_extremes
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from tradingagents.dataflows.etf_flows import get_gold_etf_summary, get_gold_etf_flows
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def create_xau_positioning_analyst(llm):
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"""
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Creates a node for the XAU Positioning Analyst agent.
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This agent analyzes market positioning and sentiment for gold (XAU/USD)
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using COT reports and ETF flow data. It replaces the standard social media analyst.
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"""
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system_message = (
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"You are a specialized Market Positioning Analyst for Gold (XAU/USD). Your task is to analyze sentiment and capital flows from institutional and speculative traders. "
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"Ignore social media. Your analysis must be based on hard data."
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"\n\n1. **Commitment of Traders (COT) Report**: Use the `get_cot_positioning` and `analyze_cot_extremes` tools. What is the net positioning of Large Speculators vs. Commercials? Is the positioning at a historical extreme? Extreme positioning is often a strong contrarian indicator."
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"\n2. **Gold ETF Flows**: Use the `get_gold_etf_summary` tool. Are major gold ETFs (like GLD and IAU) seeing inflows or outflows? Explain what this indicates about institutional investor sentiment."
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"\n3. **Synthesis**: Combine the insights from both COT data and ETF flows. For example, are speculators heavily long while ETFs are seeing outflows? This could be a major divergence."
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"\n\nSynthesize your findings into a comprehensive report. Conclude with a Markdown table summarizing the positioning data and its 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_cot_positioning,
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analyze_cot_extremes,
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get_gold_etf_summary,
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get_gold_etf_flows,
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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_positioning_analyst_node(state):
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"""
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The node function for the XAU Positioning Analyst.
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"""
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current_date = state["trade_date"]
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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_positioning_report": report,
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}
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return xau_positioning_analyst_node
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@ -34,6 +34,30 @@ from tradingagents.agents.utils.crypto_tools import (
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get_market_overview
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)
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# XAU-specific tools (Gold)
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from tradingagents.dataflows.fred_api import (
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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,
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)
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from tradingagents.dataflows.cot_data import (
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get_cot_positioning,
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analyze_cot_extremes,
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)
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from tradingagents.dataflows.etf_flows import (
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get_gold_etf_summary,
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get_gold_etf_flows,
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analyze_etf_divergence,
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)
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from tradingagents.dataflows.correlation_tools import (
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calculate_asset_correlation,
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analyze_gold_macro_correlations,
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check_correlation_regime,
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get_rolling_correlations,
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)
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def create_msg_delete():
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def delete_messages(state):
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"""Clear messages and add placeholder for Anthropic compatibility"""
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from langgraph.prebuilt import ToolNode
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from tradingagents.graph.trading_graph import TradingAgentsGraph
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from tradingagents.xau_config import XAU_CONFIG
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# Import XAU-specific tools
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from tradingagents.agents.utils.agent_utils import (
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get_stock_data,
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get_indicators,
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get_correlation,
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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,
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get_news,
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get_global_news,
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get_cot_positioning,
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analyze_cot_extremes,
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get_gold_etf_summary,
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get_gold_etf_flows,
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)
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class XAUTradingGraph(TradingAgentsGraph):
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"""
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A specialized trading graph for XAU (Gold) trading.
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This graph uses a custom set of agents and tools tailored for macroeconomic
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and positioning analysis relevant to gold.
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"""
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def __init__(self, debug=False, config=None):
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# Use XAU-specific config and analyst team
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xau_config = config or XAU_CONFIG
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xau_analysts = xau_config.get("analyst_team", [])
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super().__init__(
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selected_analysts=xau_analysts,
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debug=debug,
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config=xau_config
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)
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def _create_tool_nodes(self):
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"""
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Override the tool node creation to use XAU-specific tools.
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"""
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return {
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"xau_market": ToolNode([
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get_stock_data, # For XAU/USD price data from yfinance (e.g., "GC=F")
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get_indicators, # Standard technical indicators
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get_correlation, # For correlation with DXY, etc.
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]),
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"xau_macro": ToolNode([
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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, etc.
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]),
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"xau_news": ToolNode([
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get_news, # For general and gold-specific news
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get_global_news,
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]),
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"xau_positioning": ToolNode([
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get_cot_positioning,
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analyze_cot_extremes,
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get_gold_etf_summary,
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get_gold_etf_flows,
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]),
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}
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from tradingagents.default_config import DEFAULT_CONFIG
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# Create a deep copy to avoid modifying the original default config
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XAU_CONFIG = {
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**DEFAULT_CONFIG,
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"asset_class": "commodity",
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"trading_hours": "24/5",
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"tick_size": 0.01,
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"contract_size": 100, # oz for futures
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"max_leverage": 50,
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"max_position_size_pct": 2.0,
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"atr_multiplier_stop": 2.5,
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"correlation_threshold": -0.6,
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}
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# Override data vendors for XAU-specific sources
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XAU_CONFIG["data_vendors"] = {
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**DEFAULT_CONFIG["data_vendors"],
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"core_stock_apis": "yfinance", # For XAU/USD price data (GC=F)
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"technical_indicators": "yfinance",
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"macro_data": "fred", # NEW: FRED for macro
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"positioning_data": "cot_api", # NEW: COT data
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"etf_data": "yfinance", # NEW: ETF flows via yfinance
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# Override equity-specific vendors
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"fundamental_data": "fred", # Gold uses macro data, not company fundamentals
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"news_data": "alpha_vantage", # Can still use for general market news
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}
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# Define the specialized analyst team for XAU
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XAU_CONFIG["analyst_team"] = [
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"xau_market",
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"xau_macro",
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"xau_news",
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"xau_positioning",
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]
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from tradingagents.graph.xau_graph import XAUTradingGraph
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from tradingagents.xau_config import XAU_CONFIG
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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def run_xau_analysis():
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"""
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Initializes and runs the XAU Trading System for a specific date.
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"""
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print("Initializing XAU Trading System...")
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# Initialize the specialized XAU graph with its config
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xau_system = XAUTradingGraph(debug=True, config=XAU_CONFIG)
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# Define the asset and date for analysis
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# "GC=F" is the Yahoo Finance ticker for Gold futures.
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# The system will use this for price data but analyze "XAU" conceptually.
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asset_ticker = "GC=F"
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trade_date = "2024-05-10"
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print(f"Running analysis for asset: {asset_ticker} (Gold) on date: {trade_date}")
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# Propagate the state through the graph to get a decision
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try:
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final_state, decision = xau_system.propagate(asset_ticker, trade_date)
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print("\n" + "="*80)
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print("XAU Trading Analysis Complete")
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print("="*80)
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# Print the reports generated by the new agents
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if final_state.get("xau_macro_report"):
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print("\n--- Macro Analyst Report ---")
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print(final_state["xau_macro_report"])
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if final_state.get("xau_positioning_report"):
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print("\n--- Positioning Analyst Report ---")
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print(final_state["xau_positioning_report"])
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print("\n--- Final Trade Decision ---")
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print(decision)
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print("="*80)
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except Exception as e:
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print(f"\nAn error occurred during XAU analysis: {e}")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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run_xau_analysis()
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