60 lines
3.3 KiB
Python
Executable File
60 lines
3.3 KiB
Python
Executable File
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
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from tradingagents.dataflows.config import get_config
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def create_social_media_analyst(llm):
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def social_media_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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company_name = state["company_of_interest"]
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tools = [
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get_news,
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]
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system_message = (
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"You are a social media and company specific news researcher/analyst tasked with analyzing social media posts, recent company news, and public sentiment for SHORT-TERM trading (1-2 week horizon). Focus on current sentiment momentum, viral discussions, trending topics, and any social media catalysts that could drive short-term price movement. You will be given a company's name and your objective is to write a comprehensive report detailing your analysis of what people are currently saying about that company, sentiment trends over the past week, and any viral or trending discussions that could impact the stock in the next 1-2 weeks. Use the get_news(query, start_date, end_date) tool to search for company-specific news and social media discussions. Try to look at all sources possible from social media to sentiment to news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make SHORT-TERM decisions within the next 1-2 weeks."
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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 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 POSITION RECOMMENDATION: **LONG/HOLD/SHORT** or deliverable,"
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" prefix your response with FINAL POSITION RECOMMENDATION: **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}. The current company we want to analyze is {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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"sentiment_report": report,
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}
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return social_media_analyst_node
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