64 lines
3.0 KiB
Python
64 lines
3.0 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.agents.utils.data_prefetch import prefetch_news_data, format_news_context
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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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# Pre-fetch data so it's available even if the backend doesn't support tool calling
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prefetched = prefetch_news_data(ticker, current_date)
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data_context = format_news_context(prefetched)
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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 researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the provided news data below for your analysis. If you have access to callable tools (get_news, get_global_news), you may use them for additional data. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
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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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)
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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 data below to write your analysis report."
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" If you have access to callable tools, you may use them for additional data."
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" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
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" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
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"\n{system_message}"
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"\nFor your reference, the current date is {current_date}. We are looking at the company {ticker}"
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"\n\n--- PRE-FETCHED NEWS DATA ---\n{data_context}\n--- END PRE-FETCHED DATA ---",
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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(current_date=current_date)
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prompt = prompt.partial(ticker=ticker)
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prompt = prompt.partial(data_context=data_context)
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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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