99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from typing import Callable
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def create_screening_agent(llm: Callable) -> Callable:
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"""
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Creates a screening agent node for the trading graph.
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Identifies potential stocks to analyze using market-wide screening techniques.
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Args:
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llm: Language model to use for analysis
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Returns:
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Callable agent node function that processes AgentState
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"""
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def screening_agent_node(state: dict) -> dict:
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"""
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Screening agent node.
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Scans the market for interesting stock candidates.
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Args:
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state: Current agent state containing:
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- messages: Conversation history
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- trade_date: Current trading date
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Returns:
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Updated state with screening recommendations
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"""
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from tradingagents.agents.utils.agent_utils import (
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get_market_movers,
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get_earnings_calendar,
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get_insider_transactions,
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get_indicators,
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get_trending_social,
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)
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# Tools available to the screening agent
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tools = [
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get_market_movers,
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get_earnings_calendar,
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get_insider_transactions,
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get_indicators,
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get_trending_social,
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]
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trade_date = state.get("trade_date", "")
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system_message = (
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f"You are a Market Screening Agent analyzing markets on {trade_date}. "
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"Your goal is to identify 'Hidden Gem' stocks efficiently."
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"\n\n"
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"**Instructions:**"
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"\n"
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"1. **Check Market Movers**: Use `get_market_movers` first."
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"\n"
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"2. **Quick Check**: If you see a promising ticker, verify it with ONE other tool (e.g., `get_trending_social` or `get_indicators`)."
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"\n"
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"3. **Decide Quickly**: Do not over-analyze. You have a STRICT limit of 3 tool calls. Once you have a candidate, STOP calling tools and output the recommendation."
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"\n"
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"4. **Emergency**: If you are unsure, just pick the top gainer from market movers. You MUST output a ticker."
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"\n\n"
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"**Deliverable:**"
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"\n"
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"Return candidates as a comma-separated list in the final line (e.g., 'NVDA, TSLA, AAPL')."
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"If you have enough information, output the list immediately."
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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 specialized in market analysis. "
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"You have access to the following tools: {tool_names}.\n{system_message}",
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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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# Bind tools to the LLM
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chain = prompt | llm.bind_tools(tools)
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# Initialize messages if needed
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if not state.get("messages"):
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state["messages"] = [("user", "Please screen the market and find interesting stock candidates to analyze.")]
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# Invoke the chain
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result = chain.invoke(state)
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# Add screening analysis to state
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return {
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"messages": state["messages"] + [result],
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"screening_report": result.content if hasattr(result, 'content') else str(result),
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}
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return screening_agent_node |