263 lines
10 KiB
Python
263 lines
10 KiB
Python
# TradingAgents/graph/setup.py
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from typing import Dict, Any
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from langchain_openai import ChatOpenAI
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from langgraph.graph import END, StateGraph, START
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from langgraph.prebuilt import ToolNode
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from tradingagents.agents import *
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from tradingagents.agents.utils.agent_states import AgentState
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from .conditional_logic import ConditionalLogic
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class GraphSetup:
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"""Handles the setup and configuration of the agent graph."""
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def __init__(
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self,
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quick_thinking_llm: ChatOpenAI,
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deep_thinking_llm: ChatOpenAI,
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tool_nodes: Dict[str, ToolNode],
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bull_memory,
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bear_memory,
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trader_memory,
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invest_judge_memory,
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portfolio_manager_memory,
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conditional_logic: ConditionalLogic,
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role_llms: Dict[str, Any] | None = None,
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):
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"""Initialize with required components."""
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self.quick_thinking_llm = quick_thinking_llm
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self.deep_thinking_llm = deep_thinking_llm
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self.role_llms = role_llms or {}
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self.tool_nodes = tool_nodes
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self.bull_memory = bull_memory
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self.bear_memory = bear_memory
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self.trader_memory = trader_memory
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self.invest_judge_memory = invest_judge_memory
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self.portfolio_manager_memory = portfolio_manager_memory
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self.conditional_logic = conditional_logic
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self.market_analyst_llm = self._get_role_llm("market", self.quick_thinking_llm)
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self.social_analyst_llm = self._get_role_llm("social", self.quick_thinking_llm)
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self.news_analyst_llm = self._get_role_llm("news", self.quick_thinking_llm)
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self.fundamentals_analyst_llm = self._get_role_llm(
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"fundamentals", self.quick_thinking_llm
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)
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self.macro_analyst_llm = self._get_role_llm("macro", self.quick_thinking_llm)
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self.bull_researcher_llm = self._get_role_llm(
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"bull_researcher", self.quick_thinking_llm
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)
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self.bear_researcher_llm = self._get_role_llm(
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"bear_researcher", self.quick_thinking_llm
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)
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self.research_manager_llm = self._get_role_llm(
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"research_manager", self.deep_thinking_llm
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)
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self.trader_llm = self._get_role_llm("trader", self.quick_thinking_llm)
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self.aggressive_analyst_llm = self._get_role_llm(
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"aggressive_analyst", self.quick_thinking_llm
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)
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self.neutral_analyst_llm = self._get_role_llm(
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"neutral_analyst", self.quick_thinking_llm
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)
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self.conservative_analyst_llm = self._get_role_llm(
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"conservative_analyst", self.quick_thinking_llm
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)
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self.portfolio_manager_llm = self._get_role_llm(
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"portfolio_manager", self.deep_thinking_llm
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)
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def _get_role_llm(self, role: str, fallback_llm: ChatOpenAI):
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return self.role_llms.get(role, fallback_llm)
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def _get_continue_handler(self, analyst_type: str):
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specific_handler = getattr(
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self.conditional_logic,
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f"should_continue_{analyst_type}",
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None,
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)
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if specific_handler is not None:
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return specific_handler
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def default_handler(state: AgentState):
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messages = state["messages"]
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last_message = messages[-1]
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if getattr(last_message, "tool_calls", None):
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return f"tools_{analyst_type}"
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return f"Msg Clear {analyst_type.capitalize()}"
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return default_handler
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def setup_graph(
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self, selected_analysts=["market", "social", "news", "fundamentals"]
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):
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"""Set up and compile the agent workflow graph.
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Args:
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selected_analysts (list): List of analyst types to include. Options are:
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- "market": Market analyst
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- "social": Social media analyst
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- "news": News analyst
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- "fundamentals": Fundamentals analyst
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- "macro": Macro analyst
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"""
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if len(selected_analysts) == 0:
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raise ValueError("Trading Agents Graph Setup Error: no analysts selected!")
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# Create analyst nodes
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analyst_nodes = {}
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delete_nodes = {}
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tool_nodes = {}
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if "market" in selected_analysts:
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analyst_nodes["market"] = create_market_analyst(
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self.market_analyst_llm
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)
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delete_nodes["market"] = create_msg_delete()
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tool_nodes["market"] = self.tool_nodes["market"]
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if "social" in selected_analysts:
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analyst_nodes["social"] = create_social_media_analyst(
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self.social_analyst_llm
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)
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delete_nodes["social"] = create_msg_delete()
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tool_nodes["social"] = self.tool_nodes["social"]
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if "news" in selected_analysts:
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analyst_nodes["news"] = create_news_analyst(
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self.news_analyst_llm
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)
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delete_nodes["news"] = create_msg_delete()
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tool_nodes["news"] = self.tool_nodes["news"]
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if "fundamentals" in selected_analysts:
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analyst_nodes["fundamentals"] = create_fundamentals_analyst(
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self.fundamentals_analyst_llm
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)
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delete_nodes["fundamentals"] = create_msg_delete()
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tool_nodes["fundamentals"] = self.tool_nodes["fundamentals"]
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if "macro" in selected_analysts:
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analyst_nodes["macro"] = create_macro_analyst(self.macro_analyst_llm)
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delete_nodes["macro"] = create_msg_delete()
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tool_nodes["macro"] = self.tool_nodes["macro"]
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# Create researcher and manager nodes
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bull_researcher_node = create_bull_researcher(
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self.bull_researcher_llm, self.bull_memory
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)
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bear_researcher_node = create_bear_researcher(
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self.bear_researcher_llm, self.bear_memory
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)
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research_manager_node = create_research_manager(
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self.research_manager_llm, self.invest_judge_memory
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)
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trader_node = create_trader(self.trader_llm, self.trader_memory)
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# Create risk analysis nodes
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aggressive_analyst = create_aggressive_debator(self.aggressive_analyst_llm)
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neutral_analyst = create_neutral_debator(self.neutral_analyst_llm)
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conservative_analyst = create_conservative_debator(
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self.conservative_analyst_llm
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)
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portfolio_manager_node = create_portfolio_manager(
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self.portfolio_manager_llm, self.portfolio_manager_memory
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)
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# Create workflow
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workflow = StateGraph(AgentState)
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# Add analyst nodes to the graph
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for analyst_type, node in analyst_nodes.items():
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workflow.add_node(f"{analyst_type.capitalize()} Analyst", node)
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workflow.add_node(
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f"Msg Clear {analyst_type.capitalize()}", delete_nodes[analyst_type]
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)
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workflow.add_node(f"tools_{analyst_type}", tool_nodes[analyst_type])
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# Add other nodes
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workflow.add_node("Bull Researcher", bull_researcher_node)
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workflow.add_node("Bear Researcher", bear_researcher_node)
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workflow.add_node("Research Manager", research_manager_node)
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workflow.add_node("Trader", trader_node)
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workflow.add_node("Aggressive Analyst", aggressive_analyst)
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workflow.add_node("Neutral Analyst", neutral_analyst)
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workflow.add_node("Conservative Analyst", conservative_analyst)
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workflow.add_node("Portfolio Manager", portfolio_manager_node)
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# Define edges
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# Start with the first analyst
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first_analyst = selected_analysts[0]
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workflow.add_edge(START, f"{first_analyst.capitalize()} Analyst")
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# Connect analysts in sequence
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for i, analyst_type in enumerate(selected_analysts):
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current_analyst = f"{analyst_type.capitalize()} Analyst"
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current_tools = f"tools_{analyst_type}"
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current_clear = f"Msg Clear {analyst_type.capitalize()}"
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# Add conditional edges for current analyst
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workflow.add_conditional_edges(
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current_analyst,
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self._get_continue_handler(analyst_type),
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[current_tools, current_clear],
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)
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workflow.add_edge(current_tools, current_analyst)
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# Connect to next analyst or to Bull Researcher if this is the last analyst
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if i < len(selected_analysts) - 1:
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next_analyst = f"{selected_analysts[i+1].capitalize()} Analyst"
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workflow.add_edge(current_clear, next_analyst)
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else:
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workflow.add_edge(current_clear, "Bull Researcher")
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# Add remaining edges
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workflow.add_conditional_edges(
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"Bull Researcher",
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self.conditional_logic.should_continue_debate,
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{
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"Bear Researcher": "Bear Researcher",
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"Research Manager": "Research Manager",
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},
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)
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workflow.add_conditional_edges(
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"Bear Researcher",
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self.conditional_logic.should_continue_debate,
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{
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"Bull Researcher": "Bull Researcher",
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"Research Manager": "Research Manager",
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},
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)
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workflow.add_edge("Research Manager", "Trader")
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workflow.add_edge("Trader", "Aggressive Analyst")
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workflow.add_conditional_edges(
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"Aggressive Analyst",
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self.conditional_logic.should_continue_risk_analysis,
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{
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"Conservative Analyst": "Conservative Analyst",
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"Portfolio Manager": "Portfolio Manager",
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},
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)
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workflow.add_conditional_edges(
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"Conservative Analyst",
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self.conditional_logic.should_continue_risk_analysis,
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{
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"Neutral Analyst": "Neutral Analyst",
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"Portfolio Manager": "Portfolio Manager",
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},
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)
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workflow.add_conditional_edges(
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"Neutral Analyst",
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self.conditional_logic.should_continue_risk_analysis,
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{
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"Aggressive Analyst": "Aggressive Analyst",
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"Portfolio Manager": "Portfolio Manager",
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},
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)
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workflow.add_edge("Portfolio Manager", END)
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# Compile and return
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return workflow.compile()
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