refactor: rename risky/safe agents to aggressive/conservative

tradingagents/agents/risk_mgmt/aggressive_debator.py

@@ -2,13 +2,13 @@ import time
 import json
 
 
-def create_risky_debator(llm):
-    def risky_node(state) -> dict:
+def create_aggressive_debator(llm):
+    def aggressive_node(state) -> dict:
         risk_debate_state = state["risk_debate_state"]
         history = risk_debate_state.get("history", "")
-        risky_history = risk_debate_state.get("risky_history", "")
+        aggressive_history = risk_debate_state.get("aggressive_history", "")
 
-        current_safe_response = risk_debate_state.get("current_safe_response", "")
+        current_conservative_response = risk_debate_state.get("current_conservative_response", "")
         current_neutral_response = risk_debate_state.get("current_neutral_response", "")
 
         market_research_report = state["market_report"]
@@ -18,7 +18,7 @@ def create_risky_debator(llm):
 
         trader_decision = state["trader_investment_plan"]
 
-        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:
+        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:
 
 {trader_decision}
 
@@ -28,22 +28,22 @@ Market Research Report: {market_research_report}
 Social Media Sentiment Report: {sentiment_report}
 Latest World Affairs Report: {news_report}
 Company Fundamentals Report: {fundamentals_report}
-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.
+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.
 
 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."""
 
         response = llm.invoke(prompt)
 
-        argument = f"Risky Analyst: {response.content}"
+        argument = f"Aggressive Analyst: {response.content}"
 
         new_risk_debate_state = {
             "history": history + "\n" + argument,
-            "risky_history": risky_history + "\n" + argument,
-            "safe_history": risk_debate_state.get("safe_history", ""),
+            "aggressive_history": aggressive_history + "\n" + argument,
+            "conservative_history": risk_debate_state.get("conservative_history", ""),
             "neutral_history": risk_debate_state.get("neutral_history", ""),
-            "latest_speaker": "Risky",
-            "current_risky_response": argument,
-            "current_safe_response": risk_debate_state.get("current_safe_response", ""),
+            "latest_speaker": "Aggressive",
+            "current_aggressive_response": argument,
+            "current_conservative_response": risk_debate_state.get("current_conservative_response", ""),
             "current_neutral_response": risk_debate_state.get(
                 "current_neutral_response", ""
             ),
@@ -52,4 +52,4 @@ Engage actively by addressing any specific concerns raised, refuting the weaknes
 
         return {"risk_debate_state": new_risk_debate_state}
 
-    return risky_node
+    return aggressive_node

tradingagents/agents/risk_mgmt/conservative_debator.py

@@ -3,13 +3,13 @@ import time
 import json
 
 
-def create_safe_debator(llm):
-    def safe_node(state) -> dict:
+def create_conservative_debator(llm):
+    def conservative_node(state) -> dict:
         risk_debate_state = state["risk_debate_state"]
         history = risk_debate_state.get("history", "")
-        safe_history = risk_debate_state.get("safe_history", "")
+        conservative_history = risk_debate_state.get("conservative_history", "")
 
-        current_risky_response = risk_debate_state.get("current_risky_response", "")
+        current_aggressive_response = risk_debate_state.get("current_aggressive_response", "")
         current_neutral_response = risk_debate_state.get("current_neutral_response", "")
 
         market_research_report = state["market_report"]
@@ -19,34 +19,34 @@ def create_safe_debator(llm):
 
         trader_decision = state["trader_investment_plan"]
 
-        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:
+        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:
 
 {trader_decision}
 
-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:
+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:
 
 Market Research Report: {market_research_report}
 Social Media Sentiment Report: {sentiment_report}
 Latest World Affairs Report: {news_report}
 Company Fundamentals Report: {fundamentals_report}
-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.
+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.
 
 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."""
 
         response = llm.invoke(prompt)
 
-        argument = f"Safe Analyst: {response.content}"
+        argument = f"Conservative Analyst: {response.content}"
 
         new_risk_debate_state = {
             "history": history + "\n" + argument,
-            "risky_history": risk_debate_state.get("risky_history", ""),
-            "safe_history": safe_history + "\n" + argument,
+            "aggressive_history": risk_debate_state.get("aggressive_history", ""),
+            "conservative_history": conservative_history + "\n" + argument,
             "neutral_history": risk_debate_state.get("neutral_history", ""),
-            "latest_speaker": "Safe",
-            "current_risky_response": risk_debate_state.get(
-                "current_risky_response", ""
+            "latest_speaker": "Conservative",
+            "current_aggressive_response": risk_debate_state.get(
+                "current_aggressive_response", ""
             ),
-            "current_safe_response": argument,
+            "current_conservative_response": argument,
             "current_neutral_response": risk_debate_state.get(
                 "current_neutral_response", ""
             ),
@@ -55,4 +55,4 @@ Engage by questioning their optimism and emphasizing the potential downsides the
 
         return {"risk_debate_state": new_risk_debate_state}
 
-    return safe_node
+    return conservative_node

tradingagents/agents/risk_mgmt/neutral_debator.py

@@ -8,8 +8,8 @@ def create_neutral_debator(llm):
         history = risk_debate_state.get("history", "")
         neutral_history = risk_debate_state.get("neutral_history", "")
 
-        current_risky_response = risk_debate_state.get("current_risky_response", "")
-        current_safe_response = risk_debate_state.get("current_safe_response", "")
+        current_aggressive_response = risk_debate_state.get("current_aggressive_response", "")
+        current_conservative_response = risk_debate_state.get("current_conservative_response", "")
 
         market_research_report = state["market_report"]
         sentiment_report = state["sentiment_report"]
@@ -22,15 +22,15 @@ def create_neutral_debator(llm):
 
 {trader_decision}
 
-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:
+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:
 
 Market Research Report: {market_research_report}
 Social Media Sentiment Report: {sentiment_report}
 Latest World Affairs Report: {news_report}
 Company Fundamentals Report: {fundamentals_report}
-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.
+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,
         }