55 lines
2.3 KiB
Python
55 lines
2.3 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from tradingagents.agents.utils.scanner_tools import get_market_movers, get_market_indices
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from tradingagents.agents.utils.tool_runner import run_tool_loop
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def create_market_movers_scanner(llm):
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def market_movers_scanner_node(state):
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scan_date = state["scan_date"]
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tools = [get_market_movers, get_market_indices]
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system_message = (
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"You are a market analyst scanning for unusual activity and momentum signals. "
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"Use get_market_movers to fetch today's top gainers, losers, and most active stocks. "
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"Use get_market_indices to check major index performance. "
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"Analyze the results and write a report covering: "
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"(1) Unusual movers and potential catalysts, "
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"(2) Volume anomalies, "
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"(3) Index trends and breadth, "
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"(4) Sector concentration in movers. "
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"Include a summary table of the most significant moves."
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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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"market_movers_report": report,
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"sender": "market_movers_scanner",
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}
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return market_movers_scanner_node
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