68 lines
3.1 KiB
Python
68 lines
3.1 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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import time
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import json
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from tradingagents.agents.utils.agent_utils import get_news, get_global_news
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from tradingagents.dataflows.config import get_config
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def create_news_analyst(llm):
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def news_analyst_node(state):
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current_date = state["trade_date"]
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ticker = state["company_of_interest"]
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tools = [
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get_news,
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# get_global_news,
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]
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system_message = (
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"You are a News Analyst focused on IMMEDIATE market impact (1-2 weeks). "
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"Interpret breaking news and upcoming macro events (CPI, Fed, Geopolitics) to determine short-term direction. "
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"Focus on the 'Narrative' — what story is the market telling itself right now? "
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"Use the available tools: get_news(query, start_date, end_date) for company-specific news. "
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"Filter out noise and old news. Prioritize news that changes the short-term outlook."
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+ "\n\nDECISION LOGIC:"
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+ "\n- LONG: Distinct positive news cycle, analyst upgrades, or strong macro tailwinds."
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+ "\n- SHORT: Distinct negative news cycle, lawsuits, regulatory hits, or macro headwinds."
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+ "\n- HOLD: Mixed news, stale news, or high uncertainty waiting for a major event."
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+ """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
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+ "\n\nYOU MUST CONCLUDE YOUR REPORT WITH: 'SIGNAL: [LONG/SHORT/HOLD]'"
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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: **LONG/HOLD/SHORT** or deliverable,"
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" prefix your response with FINAL TRANSACTION PROPOSAL: **LONG/HOLD/SHORT** 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}. We are looking at the company {ticker}",
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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(ticker=ticker)
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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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"news_report": report,
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}
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return news_analyst_node
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