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https://github.com/TauricResearch/TradingAgents.git
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3e5e99b368
- analyst_execution.py: rename "Social Analyst" / "Msg Clear Social" to "Sentiment Analyst" / "Msg Clear Sentiment" to match v0.2.5. - conditional_logic.should_continue_social returns the renamed route. - TradingAgentsGraph.propagate accepts asset_type and threads through to Propagator.create_initial_state. - Regression test on the Sentiment Analyst label. Verified end-to-end (NVDA stock + BTC-USD crypto) on gpt-5.4-mini.
74 lines
2.9 KiB
Python
74 lines
2.9 KiB
Python
# TradingAgents/graph/conditional_logic.py
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from tradingagents.agents.utils.agent_states import AgentState
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class ConditionalLogic:
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"""Handles conditional logic for determining graph flow."""
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def __init__(self, max_debate_rounds=1, max_risk_discuss_rounds=1):
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"""Initialize with configuration parameters."""
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self.max_debate_rounds = max_debate_rounds
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self.max_risk_discuss_rounds = max_risk_discuss_rounds
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def should_continue_market(self, state: AgentState):
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"""Determine if market analysis should continue."""
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messages = state["messages"]
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last_message = messages[-1]
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if last_message.tool_calls:
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return "tools_market"
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return "Msg Clear Market"
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def should_continue_social(self, state: AgentState):
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"""Determine if sentiment-analyst tool round should continue.
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Method name keeps the legacy ``social`` suffix to match the
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``AnalystType.SOCIAL = "social"`` wire value (saved-config
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back-compat); the returned ``clear_node`` label uses the v0.2.5
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rename so it matches the node registered by the execution plan.
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"""
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messages = state["messages"]
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last_message = messages[-1]
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if last_message.tool_calls:
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return "tools_social"
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return "Msg Clear Sentiment"
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def should_continue_news(self, state: AgentState):
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"""Determine if news analysis should continue."""
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messages = state["messages"]
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last_message = messages[-1]
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if last_message.tool_calls:
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return "tools_news"
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return "Msg Clear News"
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def should_continue_fundamentals(self, state: AgentState):
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"""Determine if fundamentals analysis should continue."""
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messages = state["messages"]
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last_message = messages[-1]
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if last_message.tool_calls:
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return "tools_fundamentals"
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return "Msg Clear Fundamentals"
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def should_continue_debate(self, state: AgentState) -> str:
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"""Determine if debate should continue."""
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if (
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state["investment_debate_state"]["count"] >= 2 * self.max_debate_rounds
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): # 3 rounds of back-and-forth between 2 agents
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return "Research Manager"
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if state["investment_debate_state"]["current_response"].startswith("Bull"):
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return "Bear Researcher"
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return "Bull Researcher"
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def should_continue_risk_analysis(self, state: AgentState) -> str:
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"""Determine if risk analysis should continue."""
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if (
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state["risk_debate_state"]["count"] >= 3 * self.max_risk_discuss_rounds
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): # 3 rounds of back-and-forth between 3 agents
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return "Portfolio Manager"
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if state["risk_debate_state"]["latest_speaker"].startswith("Aggressive"):
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return "Conservative Analyst"
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if state["risk_debate_state"]["latest_speaker"].startswith("Conservative"):
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return "Neutral Analyst"
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return "Aggressive Analyst"
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