54 lines
2.2 KiB
Python
54 lines
2.2 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from tradingagents.agents.utils.scanner_tools import get_sector_performance
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from tradingagents.agents.utils.tool_runner import run_tool_loop
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def create_sector_scanner(llm):
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def sector_scanner_node(state):
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scan_date = state["scan_date"]
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tools = [get_sector_performance]
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system_message = (
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"You are a sector rotation analyst. "
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"Use get_sector_performance to analyze all 11 GICS sectors. "
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"Write a report covering: "
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"(1) Sector momentum rankings (1-day, 1-week, 1-month, YTD), "
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"(2) Sector rotation signals (money flowing from/to which sectors), "
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"(3) Defensive vs cyclical positioning, "
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"(4) Sectors showing acceleration or deceleration. "
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"Include a ranked performance table."
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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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" 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}.",
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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=scan_date)
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chain = prompt | llm.bind_tools(tools)
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result = run_tool_loop(chain, state["messages"], tools)
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report = result.content or ""
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return {
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"messages": [result],
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"sector_performance_report": report,
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"sender": "sector_scanner",
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}
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return sector_scanner_node
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