refactor: rename risky/safe agents to aggressive/conservative
This commit is contained in:
Yijia Xiao
parent
a3761bdd66
commit
50961b2477
50
cli/main.py
50
cli/main.py
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@ -58,9 +58,9 @@ class MessageBuffer:
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# Trading Team
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"Trader": "pending",
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# Risk Management Team
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"Risky Analyst": "pending",
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"Aggressive Analyst": "pending",
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"Neutral Analyst": "pending",
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"Safe Analyst": "pending",
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"Conservative Analyst": "pending",
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# Portfolio Management Team
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"Portfolio Manager": "pending",
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}
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@ -227,7 +227,7 @@ def update_display(layout, spinner_text=None):
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],
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"Research Team": ["Bull Researcher", "Bear Researcher", "Research Manager"],
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"Trading Team": ["Trader"],
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"Risk Management": ["Risky Analyst", "Neutral Analyst", "Safe Analyst"],
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"Risk Management": ["Aggressive Analyst", "Neutral Analyst", "Conservative Analyst"],
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"Portfolio Management": ["Portfolio Manager"],
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}
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@ -675,10 +675,10 @@ def display_complete_report(final_state):
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risk_state = final_state["risk_debate_state"]
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# Aggressive (Risky) Analyst Analysis
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if risk_state.get("risky_history"):
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if risk_state.get("aggressive_history"):
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risk_reports.append(
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Panel(
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Markdown(risk_state["risky_history"]),
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Markdown(risk_state["aggressive_history"]),
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title="Aggressive Analyst",
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border_style="blue",
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padding=(1, 2),
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@ -686,10 +686,10 @@ def display_complete_report(final_state):
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)
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# Conservative (Safe) Analyst Analysis
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if risk_state.get("safe_history"):
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if risk_state.get("conservative_history"):
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risk_reports.append(
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Panel(
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Markdown(risk_state["safe_history"]),
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Markdown(risk_state["conservative_history"]),
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title="Conservative Analyst",
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border_style="blue",
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padding=(1, 2),
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@ -1009,7 +1009,7 @@ def run_analysis():
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update_research_team_status("completed")
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# Set first risk analyst to in_progress
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message_buffer.update_agent_status(
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"Risky Analyst", "in_progress"
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"Aggressive Analyst", "in_progress"
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)
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# Trading Team
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@ -1021,46 +1021,46 @@ def run_analysis():
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"trader_investment_plan", chunk["trader_investment_plan"]
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)
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# Set first risk analyst to in_progress
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message_buffer.update_agent_status("Risky Analyst", "in_progress")
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message_buffer.update_agent_status("Aggressive Analyst", "in_progress")
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# Risk Management Team - Handle Risk Debate State
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if "risk_debate_state" in chunk and chunk["risk_debate_state"]:
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risk_state = chunk["risk_debate_state"]
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# Update Risky Analyst status and report
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# Update Aggressive Analyst status and report
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if (
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"current_risky_response" in risk_state
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and risk_state["current_risky_response"]
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"current_aggressive_response" in risk_state
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and risk_state["current_aggressive_response"]
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):
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message_buffer.update_agent_status(
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"Risky Analyst", "in_progress"
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"Aggressive Analyst", "in_progress"
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)
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message_buffer.add_message(
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"Reasoning",
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f"Risky Analyst: {risk_state['current_risky_response']}",
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f"Aggressive Analyst: {risk_state['current_aggressive_response']}",
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)
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# Update risk report with risky analyst's latest analysis only
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# Update risk report with aggressive analyst's latest analysis only
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message_buffer.update_report_section(
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"final_trade_decision",
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f"### Risky Analyst Analysis\n{risk_state['current_risky_response']}",
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f"### Aggressive Analyst Analysis\n{risk_state['current_aggressive_response']}",
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)
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# Update Safe Analyst status and report
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# Update Conservative Analyst status and report
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if (
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"current_safe_response" in risk_state
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and risk_state["current_safe_response"]
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"current_conservative_response" in risk_state
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and risk_state["current_conservative_response"]
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):
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message_buffer.update_agent_status(
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"Safe Analyst", "in_progress"
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"Conservative Analyst", "in_progress"
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)
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message_buffer.add_message(
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"Reasoning",
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f"Safe Analyst: {risk_state['current_safe_response']}",
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f"Conservative Analyst: {risk_state['current_conservative_response']}",
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)
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# Update risk report with safe analyst's latest analysis only
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# Update risk report with conservative analyst's latest analysis only
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message_buffer.update_report_section(
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"final_trade_decision",
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f"### Safe Analyst Analysis\n{risk_state['current_safe_response']}",
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f"### Conservative Analyst Analysis\n{risk_state['current_conservative_response']}",
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)
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# Update Neutral Analyst status and report
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@ -1096,8 +1096,8 @@ def run_analysis():
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f"### Portfolio Manager Decision\n{risk_state['judge_decision']}",
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)
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# Mark risk analysts as completed
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message_buffer.update_agent_status("Risky Analyst", "completed")
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message_buffer.update_agent_status("Safe Analyst", "completed")
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message_buffer.update_agent_status("Aggressive Analyst", "completed")
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message_buffer.update_agent_status("Conservative Analyst", "completed")
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message_buffer.update_agent_status(
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"Neutral Analyst", "completed"
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)
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4
main.py
4
main.py
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@ -8,8 +8,8 @@ load_dotenv()
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# Create a custom config
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config = DEFAULT_CONFIG.copy()
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config["deep_think_llm"] = "gpt-4o-mini" # Use a different model
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config["quick_think_llm"] = "gpt-4o-mini" # Use a different model
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config["deep_think_llm"] = "gpt-5-mini" # Use a different model
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config["quick_think_llm"] = "gpt-5-mini" # Use a different model
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config["max_debate_rounds"] = 1 # Increase debate rounds
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# Configure data vendors (default uses yfinance and alpha_vantage)
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@ -10,8 +10,8 @@ from .analysts.social_media_analyst import create_social_media_analyst
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from .researchers.bear_researcher import create_bear_researcher
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from .researchers.bull_researcher import create_bull_researcher
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from .risk_mgmt.aggresive_debator import create_risky_debator
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from .risk_mgmt.conservative_debator import create_safe_debator
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from .risk_mgmt.aggressive_debator import create_aggressive_debator
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from .risk_mgmt.conservative_debator import create_conservative_debator
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from .risk_mgmt.neutral_debator import create_neutral_debator
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from .managers.research_manager import create_research_manager
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@ -32,9 +32,9 @@ __all__ = [
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"create_market_analyst",
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"create_neutral_debator",
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"create_news_analyst",
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"create_risky_debator",
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"create_aggressive_debator",
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"create_risk_manager",
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"create_safe_debator",
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"create_conservative_debator",
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"create_social_media_analyst",
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"create_trader",
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]
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@ -22,7 +22,7 @@ def create_risk_manager(llm, memory):
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for i, rec in enumerate(past_memories, 1):
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past_memory_str += rec["recommendation"] + "\n\n"
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prompt = f"""As the Risk Management Judge and Debate Facilitator, your goal is to evaluate the debate between three risk analysts—Risky, Neutral, and Safe/Conservative—and determine the best course of action for the trader. Your decision must result in a clear recommendation: Buy, Sell, or Hold. Choose Hold only if strongly justified by specific arguments, not as a fallback when all sides seem valid. Strive for clarity and decisiveness.
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prompt = f"""As the Risk Management Judge and Debate Facilitator, your goal is to evaluate the debate between three risk analysts—Aggressive, Neutral, and Conservative—and determine the best course of action for the trader. Your decision must result in a clear recommendation: Buy, Sell, or Hold. Choose Hold only if strongly justified by specific arguments, not as a fallback when all sides seem valid. Strive for clarity and decisiveness.
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Guidelines for Decision-Making:
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1. **Summarize Key Arguments**: Extract the strongest points from each analyst, focusing on relevance to the context.
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@ -48,12 +48,12 @@ Focus on actionable insights and continuous improvement. Build on past lessons,
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new_risk_debate_state = {
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"judge_decision": response.content,
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"history": risk_debate_state["history"],
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"risky_history": risk_debate_state["risky_history"],
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"safe_history": risk_debate_state["safe_history"],
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"aggressive_history": risk_debate_state["aggressive_history"],
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"conservative_history": risk_debate_state["conservative_history"],
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"neutral_history": risk_debate_state["neutral_history"],
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"latest_speaker": "Judge",
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"current_risky_response": risk_debate_state["current_risky_response"],
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"current_safe_response": risk_debate_state["current_safe_response"],
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"current_aggressive_response": risk_debate_state["current_aggressive_response"],
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"current_conservative_response": risk_debate_state["current_conservative_response"],
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"current_neutral_response": risk_debate_state["current_neutral_response"],
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"count": risk_debate_state["count"],
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}
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@ -2,13 +2,13 @@ import time
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import json
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def create_risky_debator(llm):
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def risky_node(state) -> dict:
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def create_aggressive_debator(llm):
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def aggressive_node(state) -> dict:
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risk_debate_state = state["risk_debate_state"]
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history = risk_debate_state.get("history", "")
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risky_history = risk_debate_state.get("risky_history", "")
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aggressive_history = risk_debate_state.get("aggressive_history", "")
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current_safe_response = risk_debate_state.get("current_safe_response", "")
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current_conservative_response = risk_debate_state.get("current_conservative_response", "")
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current_neutral_response = risk_debate_state.get("current_neutral_response", "")
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market_research_report = state["market_report"]
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@ -18,7 +18,7 @@ def create_risky_debator(llm):
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trader_decision = state["trader_investment_plan"]
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prompt = f"""As the Risky Risk Analyst, your role is to actively champion high-reward, high-risk opportunities, emphasizing bold strategies and competitive advantages. When evaluating the trader's decision or plan, focus intently on the potential upside, growth potential, and innovative benefits—even when these come with elevated risk. Use the provided market data and sentiment analysis to strengthen your arguments and challenge the opposing views. Specifically, respond directly to each point made by the conservative and neutral analysts, countering with data-driven rebuttals and persuasive reasoning. Highlight where their caution might miss critical opportunities or where their assumptions may be overly conservative. Here is the trader's decision:
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prompt = f"""As the Aggressive Risk Analyst, your role is to actively champion high-reward, high-risk opportunities, emphasizing bold strategies and competitive advantages. When evaluating the trader's decision or plan, focus intently on the potential upside, growth potential, and innovative benefits—even when these come with elevated risk. Use the provided market data and sentiment analysis to strengthen your arguments and challenge the opposing views. Specifically, respond directly to each point made by the conservative and neutral analysts, countering with data-driven rebuttals and persuasive reasoning. Highlight where their caution might miss critical opportunities or where their assumptions may be overly conservative. Here is the trader's decision:
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{trader_decision}
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@ -28,22 +28,22 @@ Market Research Report: {market_research_report}
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Social Media Sentiment Report: {sentiment_report}
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Latest World Affairs Report: {news_report}
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Company Fundamentals Report: {fundamentals_report}
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Here is the current conversation history: {history} Here are the last arguments from the conservative analyst: {current_safe_response} Here are the last arguments from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not halluncinate and just present your point.
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Here is the current conversation history: {history} Here are the last arguments from the conservative analyst: {current_conservative_response} Here are the last arguments from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not hallucinate and just present your point.
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Engage actively by addressing any specific concerns raised, refuting the weaknesses in their logic, and asserting the benefits of risk-taking to outpace market norms. Maintain a focus on debating and persuading, not just presenting data. Challenge each counterpoint to underscore why a high-risk approach is optimal. Output conversationally as if you are speaking without any special formatting."""
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response = llm.invoke(prompt)
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argument = f"Risky Analyst: {response.content}"
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argument = f"Aggressive Analyst: {response.content}"
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new_risk_debate_state = {
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"history": history + "\n" + argument,
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"risky_history": risky_history + "\n" + argument,
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"safe_history": risk_debate_state.get("safe_history", ""),
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"aggressive_history": aggressive_history + "\n" + argument,
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"conservative_history": risk_debate_state.get("conservative_history", ""),
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"neutral_history": risk_debate_state.get("neutral_history", ""),
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"latest_speaker": "Risky",
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"current_risky_response": argument,
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"current_safe_response": risk_debate_state.get("current_safe_response", ""),
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"latest_speaker": "Aggressive",
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"current_aggressive_response": argument,
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"current_conservative_response": risk_debate_state.get("current_conservative_response", ""),
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"current_neutral_response": risk_debate_state.get(
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"current_neutral_response", ""
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),
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@ -52,4 +52,4 @@ Engage actively by addressing any specific concerns raised, refuting the weaknes
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return {"risk_debate_state": new_risk_debate_state}
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return risky_node
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return aggressive_node
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@ -3,13 +3,13 @@ import time
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import json
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def create_safe_debator(llm):
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def safe_node(state) -> dict:
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def create_conservative_debator(llm):
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def conservative_node(state) -> dict:
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risk_debate_state = state["risk_debate_state"]
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history = risk_debate_state.get("history", "")
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safe_history = risk_debate_state.get("safe_history", "")
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conservative_history = risk_debate_state.get("conservative_history", "")
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current_risky_response = risk_debate_state.get("current_risky_response", "")
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current_aggressive_response = risk_debate_state.get("current_aggressive_response", "")
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current_neutral_response = risk_debate_state.get("current_neutral_response", "")
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market_research_report = state["market_report"]
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@ -19,34 +19,34 @@ def create_safe_debator(llm):
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trader_decision = state["trader_investment_plan"]
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prompt = f"""As the Safe/Conservative Risk Analyst, your primary objective is to protect assets, minimize volatility, and ensure steady, reliable growth. You prioritize stability, security, and risk mitigation, carefully assessing potential losses, economic downturns, and market volatility. When evaluating the trader's decision or plan, critically examine high-risk elements, pointing out where the decision may expose the firm to undue risk and where more cautious alternatives could secure long-term gains. Here is the trader's decision:
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prompt = f"""As the Conservative Risk Analyst, your primary objective is to protect assets, minimize volatility, and ensure steady, reliable growth. You prioritize stability, security, and risk mitigation, carefully assessing potential losses, economic downturns, and market volatility. When evaluating the trader's decision or plan, critically examine high-risk elements, pointing out where the decision may expose the firm to undue risk and where more cautious alternatives could secure long-term gains. Here is the trader's decision:
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{trader_decision}
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Your task is to actively counter the arguments of the Risky and Neutral Analysts, highlighting where their views may overlook potential threats or fail to prioritize sustainability. Respond directly to their points, drawing from the following data sources to build a convincing case for a low-risk approach adjustment to the trader's decision:
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Your task is to actively counter the arguments of the Aggressive and Neutral Analysts, highlighting where their views may overlook potential threats or fail to prioritize sustainability. Respond directly to their points, drawing from the following data sources to build a convincing case for a low-risk approach adjustment to the trader's decision:
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Market Research Report: {market_research_report}
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Social Media Sentiment Report: {sentiment_report}
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Latest World Affairs Report: {news_report}
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Company Fundamentals Report: {fundamentals_report}
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Here is the current conversation history: {history} Here is the last response from the risky analyst: {current_risky_response} Here is the last response from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not halluncinate and just present your point.
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Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not hallucinate and just present your point.
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Engage by questioning their optimism and emphasizing the potential downsides they may have overlooked. Address each of their counterpoints to showcase why a conservative stance is ultimately the safest path for the firm's assets. Focus on debating and critiquing their arguments to demonstrate the strength of a low-risk strategy over their approaches. Output conversationally as if you are speaking without any special formatting."""
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response = llm.invoke(prompt)
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argument = f"Safe Analyst: {response.content}"
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argument = f"Conservative Analyst: {response.content}"
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new_risk_debate_state = {
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"history": history + "\n" + argument,
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"risky_history": risk_debate_state.get("risky_history", ""),
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"safe_history": safe_history + "\n" + argument,
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"aggressive_history": risk_debate_state.get("aggressive_history", ""),
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"conservative_history": conservative_history + "\n" + argument,
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"neutral_history": risk_debate_state.get("neutral_history", ""),
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"latest_speaker": "Safe",
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"current_risky_response": risk_debate_state.get(
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"current_risky_response", ""
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"latest_speaker": "Conservative",
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"current_aggressive_response": risk_debate_state.get(
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"current_aggressive_response", ""
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),
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"current_safe_response": argument,
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"current_conservative_response": argument,
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"current_neutral_response": risk_debate_state.get(
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"current_neutral_response", ""
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),
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@ -55,4 +55,4 @@ Engage by questioning their optimism and emphasizing the potential downsides the
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return {"risk_debate_state": new_risk_debate_state}
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return safe_node
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return conservative_node
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@ -8,8 +8,8 @@ def create_neutral_debator(llm):
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history = risk_debate_state.get("history", "")
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neutral_history = risk_debate_state.get("neutral_history", "")
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current_risky_response = risk_debate_state.get("current_risky_response", "")
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current_safe_response = risk_debate_state.get("current_safe_response", "")
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current_aggressive_response = risk_debate_state.get("current_aggressive_response", "")
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current_conservative_response = risk_debate_state.get("current_conservative_response", "")
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market_research_report = state["market_report"]
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sentiment_report = state["sentiment_report"]
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@ -22,15 +22,15 @@ def create_neutral_debator(llm):
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{trader_decision}
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Your task is to challenge both the Risky and Safe Analysts, pointing out where each perspective may be overly optimistic or overly cautious. Use insights from the following data sources to support a moderate, sustainable strategy to adjust the trader's decision:
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Your task is to challenge both the Aggressive and Conservative Analysts, pointing out where each perspective may be overly optimistic or overly cautious. Use insights from the following data sources to support a moderate, sustainable strategy to adjust the trader's decision:
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Market Research Report: {market_research_report}
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Social Media Sentiment Report: {sentiment_report}
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Latest World Affairs Report: {news_report}
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Company Fundamentals Report: {fundamentals_report}
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Here is the current conversation history: {history} Here is the last response from the risky analyst: {current_risky_response} Here is the last response from the safe analyst: {current_safe_response}. If there are no responses from the other viewpoints, do not halluncinate and just present your point.
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Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the conservative analyst: {current_conservative_response}. If there are no responses from the other viewpoints, do not hallucinate and just present your point.
|
||||
|
||||
Engage actively by analyzing both sides critically, addressing weaknesses in the risky and conservative arguments to advocate for a more balanced approach. Challenge each of their points to illustrate why a moderate risk strategy might offer the best of both worlds, providing growth potential while safeguarding against extreme volatility. Focus on debating rather than simply presenting data, aiming to show that a balanced view can lead to the most reliable outcomes. Output conversationally as if you are speaking without any special formatting."""
|
||||
Engage actively by analyzing both sides critically, addressing weaknesses in the aggressive and conservative arguments to advocate for a more balanced approach. Challenge each of their points to illustrate why a moderate risk strategy might offer the best of both worlds, providing growth potential while safeguarding against extreme volatility. Focus on debating rather than simply presenting data, aiming to show that a balanced view can lead to the most reliable outcomes. Output conversationally as if you are speaking without any special formatting."""
|
||||
|
||||
response = llm.invoke(prompt)
|
||||
|
||||
|
|
@ -38,14 +38,14 @@ Engage actively by analyzing both sides critically, addressing weaknesses in the
|
|||
|
||||
new_risk_debate_state = {
|
||||
"history": history + "\n" + argument,
|
||||
"risky_history": risk_debate_state.get("risky_history", ""),
|
||||
"safe_history": risk_debate_state.get("safe_history", ""),
|
||||
"aggressive_history": risk_debate_state.get("aggressive_history", ""),
|
||||
"conservative_history": risk_debate_state.get("conservative_history", ""),
|
||||
"neutral_history": neutral_history + "\n" + argument,
|
||||
"latest_speaker": "Neutral",
|
||||
"current_risky_response": risk_debate_state.get(
|
||||
"current_risky_response", ""
|
||||
"current_aggressive_response": risk_debate_state.get(
|
||||
"current_aggressive_response", ""
|
||||
),
|
||||
"current_safe_response": risk_debate_state.get("current_safe_response", ""),
|
||||
"current_conservative_response": risk_debate_state.get("current_conservative_response", ""),
|
||||
"current_neutral_response": argument,
|
||||
"count": risk_debate_state["count"] + 1,
|
||||
}
|
||||
|
|
|
|||
|
|
@ -23,22 +23,22 @@ class InvestDebateState(TypedDict):
|
|||
|
||||
# Risk management team state
|
||||
class RiskDebateState(TypedDict):
|
||||
risky_history: Annotated[
|
||||
str, "Risky Agent's Conversation history"
|
||||
aggressive_history: Annotated[
|
||||
str, "Aggressive Agent's Conversation history"
|
||||
] # Conversation history
|
||||
safe_history: Annotated[
|
||||
str, "Safe Agent's Conversation history"
|
||||
conservative_history: Annotated[
|
||||
str, "Conservative Agent's Conversation history"
|
||||
] # Conversation history
|
||||
neutral_history: Annotated[
|
||||
str, "Neutral Agent's Conversation history"
|
||||
] # Conversation history
|
||||
history: Annotated[str, "Conversation history"] # Conversation history
|
||||
latest_speaker: Annotated[str, "Analyst that spoke last"]
|
||||
current_risky_response: Annotated[
|
||||
str, "Latest response by the risky analyst"
|
||||
current_aggressive_response: Annotated[
|
||||
str, "Latest response by the aggressive analyst"
|
||||
] # Last response
|
||||
current_safe_response: Annotated[
|
||||
str, "Latest response by the safe analyst"
|
||||
current_conservative_response: Annotated[
|
||||
str, "Latest response by the conservative analyst"
|
||||
] # Last response
|
||||
current_neutral_response: Annotated[
|
||||
str, "Latest response by the neutral analyst"
|
||||
|
|
|
|||
|
|
@ -60,8 +60,8 @@ class ConditionalLogic:
|
|||
state["risk_debate_state"]["count"] >= 3 * self.max_risk_discuss_rounds
|
||||
): # 3 rounds of back-and-forth between 3 agents
|
||||
return "Risk Judge"
|
||||
if state["risk_debate_state"]["latest_speaker"].startswith("Risky"):
|
||||
return "Safe Analyst"
|
||||
if state["risk_debate_state"]["latest_speaker"].startswith("Safe"):
|
||||
if state["risk_debate_state"]["latest_speaker"].startswith("Aggressive"):
|
||||
return "Conservative Analyst"
|
||||
if state["risk_debate_state"]["latest_speaker"].startswith("Conservative"):
|
||||
return "Neutral Analyst"
|
||||
return "Risky Analyst"
|
||||
return "Aggressive Analyst"
|
||||
|
|
|
|||
|
|
@ -29,8 +29,8 @@ class Propagator:
|
|||
"risk_debate_state": RiskDebateState(
|
||||
{
|
||||
"history": "",
|
||||
"current_risky_response": "",
|
||||
"current_safe_response": "",
|
||||
"current_aggressive_response": "",
|
||||
"current_conservative_response": "",
|
||||
"current_neutral_response": "",
|
||||
"count": 0,
|
||||
}
|
||||
|
|
|
|||
|
|
@ -98,9 +98,9 @@ class GraphSetup:
|
|||
trader_node = create_trader(self.quick_thinking_llm, self.trader_memory)
|
||||
|
||||
# Create risk analysis nodes
|
||||
risky_analyst = create_risky_debator(self.quick_thinking_llm)
|
||||
aggressive_analyst = create_aggressive_debator(self.quick_thinking_llm)
|
||||
neutral_analyst = create_neutral_debator(self.quick_thinking_llm)
|
||||
safe_analyst = create_safe_debator(self.quick_thinking_llm)
|
||||
conservative_analyst = create_conservative_debator(self.quick_thinking_llm)
|
||||
risk_manager_node = create_risk_manager(
|
||||
self.deep_thinking_llm, self.risk_manager_memory
|
||||
)
|
||||
|
|
@ -121,9 +121,9 @@ class GraphSetup:
|
|||
workflow.add_node("Bear Researcher", bear_researcher_node)
|
||||
workflow.add_node("Research Manager", research_manager_node)
|
||||
workflow.add_node("Trader", trader_node)
|
||||
workflow.add_node("Risky Analyst", risky_analyst)
|
||||
workflow.add_node("Aggressive Analyst", aggressive_analyst)
|
||||
workflow.add_node("Neutral Analyst", neutral_analyst)
|
||||
workflow.add_node("Safe Analyst", safe_analyst)
|
||||
workflow.add_node("Conservative Analyst", conservative_analyst)
|
||||
workflow.add_node("Risk Judge", risk_manager_node)
|
||||
|
||||
# Define edges
|
||||
|
|
@ -170,17 +170,17 @@ class GraphSetup:
|
|||
},
|
||||
)
|
||||
workflow.add_edge("Research Manager", "Trader")
|
||||
workflow.add_edge("Trader", "Risky Analyst")
|
||||
workflow.add_edge("Trader", "Aggressive Analyst")
|
||||
workflow.add_conditional_edges(
|
||||
"Risky Analyst",
|
||||
"Aggressive Analyst",
|
||||
self.conditional_logic.should_continue_risk_analysis,
|
||||
{
|
||||
"Safe Analyst": "Safe Analyst",
|
||||
"Conservative Analyst": "Conservative Analyst",
|
||||
"Risk Judge": "Risk Judge",
|
||||
},
|
||||
)
|
||||
workflow.add_conditional_edges(
|
||||
"Safe Analyst",
|
||||
"Conservative Analyst",
|
||||
self.conditional_logic.should_continue_risk_analysis,
|
||||
{
|
||||
"Neutral Analyst": "Neutral Analyst",
|
||||
|
|
@ -191,7 +191,7 @@ class GraphSetup:
|
|||
"Neutral Analyst",
|
||||
self.conditional_logic.should_continue_risk_analysis,
|
||||
{
|
||||
"Risky Analyst": "Risky Analyst",
|
||||
"Aggressive Analyst": "Aggressive Analyst",
|
||||
"Risk Judge": "Risk Judge",
|
||||
},
|
||||
)
|
||||
|
|
|
|||
|
|
@ -234,8 +234,8 @@ class TradingAgentsGraph:
|
|||
},
|
||||
"trader_investment_decision": final_state["trader_investment_plan"],
|
||||
"risk_debate_state": {
|
||||
"risky_history": final_state["risk_debate_state"]["risky_history"],
|
||||
"safe_history": final_state["risk_debate_state"]["safe_history"],
|
||||
"aggressive_history": final_state["risk_debate_state"]["aggressive_history"],
|
||||
"conservative_history": final_state["risk_debate_state"]["conservative_history"],
|
||||
"neutral_history": final_state["risk_debate_state"]["neutral_history"],
|
||||
"history": final_state["risk_debate_state"]["history"],
|
||||
"judge_decision": final_state["risk_debate_state"]["judge_decision"],
|
||||
|
|
|
|||
Loading…
Reference in New Issue