62 lines
3.2 KiB
Python
62 lines
3.2 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
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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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asset_type = state.get("asset_type", "stock")
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subject_label = "company" if asset_type == "stock" else "asset"
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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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f"You are a social media and targeted news researcher/analyst tasked with analyzing social media posts, recent {subject_label} news, and public sentiment for a specific {subject_label} over the past week. You will be given an asset identifier and your objective is to write a comprehensive long report detailing your analysis, insights, and implications for traders and investors after looking at social media, sentiment, and recent news related to that {subject_label}. Use the get_news(query, start_date, end_date) tool to search for {subject_label}-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 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 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: **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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" 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 {subject_label} 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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prompt = prompt.partial(subject_label=subject_label)
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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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