tradingagents/agents/managers/research_manager.py

@@ -46,14 +46,13 @@ def create_research_manager(llm, memory):
                 return text
             return text[:max_chars] + "\n...(內容已截斷)"
         
+        
         # 為每個報告設置合理的字符限制
-        # 模型 gpt-5-mini 的限制是 8192 tokens
-        # 混合中英文估算: 1 字符 ≈ 1.5-2 tokens (取保守值)
-        # 目標: 總字符數 < 3500 字符 (約 5250-7000 tokens，留足夠 tokens 給 completion)
-        market_research_report = truncate_text(market_research_report, 500)
-        sentiment_report = truncate_text(sentiment_report, 500)
-        news_report = truncate_text(news_report, 600)
-        fundamentals_report = truncate_text(fundamentals_report, 600)
+        # 增加限制以確保 800+ 字的報告不被截斷
+        market_research_report = truncate_text(market_research_report, 2000)
+        sentiment_report = truncate_text(sentiment_report, 2000)
+        news_report = truncate_text(news_report, 2500)
+        fundamentals_report = truncate_text(fundamentals_report, 2000)
         
         # 整合當前情況
         curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
@@ -71,8 +70,8 @@ def create_research_manager(llm, memory):
             past_memory_str += recommendation + "\n\n"
         
         # 截斷辯論歷史 - 這是最容易超過限制的部分
-        # 限制辯論歷史在 1200 字符以內
-        history = truncate_text(history, 1200)
+        # 增加限制以容納更長的辯論內容
+        history = truncate_text(history, 3000)
 
         # 建立提示 (prompt)
         prompt = f"""**重要：您必須使用繁體中文（Traditional Chinese）回覆所有內容。**

tradingagents/agents/managers/risk_manager.py

@@ -48,15 +48,14 @@ def create_risk_manager(llm, memory):
                 return text
             return text[:max_chars] + "\n...(內容已截斷)"
         
+        
         # 為每個報告設置合理的字符限制
-        # 模型 gpt-5-mini 的限制是 8192 tokens
-        # 混合中英文估算: 1 字符 ≈ 1.5-2 tokens (取保守值)
-        # 目標: 總字符數 < 3500 字符 (約 5250-7000 tokens，留足夠 tokens 給 completion)
-        market_research_report = truncate_text(market_research_report, 500)
-        sentiment_report = truncate_text(sentiment_report, 500)
-        news_report = truncate_text(news_report, 600)
-        fundamentals_report = truncate_text(fundamentals_report, 600)
-        trader_plan = truncate_text(trader_plan, 800)
+        # 增加限制以確保 800+ 字的報告不被截斷
+        market_research_report = truncate_text(market_research_report, 2000)
+        sentiment_report = truncate_text(sentiment_report, 2000)
+        news_report = truncate_text(news_report, 2500)
+        fundamentals_report = truncate_text(fundamentals_report, 2000)
+        trader_plan = truncate_text(trader_plan, 2000)
         
         # 整合當前情況
         curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
@@ -74,8 +73,8 @@ def create_risk_manager(llm, memory):
             past_memory_str += recommendation + "\n\n"
         
         # 截斷辯論歷史 - 這是最容易超過限制的部分
-        # 限制辯論歷史在 1000 字符以內（風險辯論通常有3方，比投資辯論更長）
-        history = truncate_text(history, 1000)
+        # 增加限制以容納更長的辯論內容（風險辯論通常有3方，比投資辯論更長）
+        history = truncate_text(history, 3000)
 
         
         # 建立提示 (prompt)

tradingagents/agents/researchers/bear_researcher.py

@@ -58,14 +58,13 @@ def create_bear_researcher(llm, memory):
                     return text[:last_pos + 1] + "\n\n...(為控制長度已精簡)"
             return truncated + "...(為控制長度已精簡)"
         
+        
         # 為每個報告設置合理的字符限制
-        # 模型 gpt-4.1-mini 的限制是 8192 tokens
-        # 混合中英文估算: 1 字符 ≈ 1.5-2 tokens (取保守值)
-        # 目標: 總字符數 < 3000 字符 (約 4500-6000 tokens，留 2000+ tokens 給 completion)
-        market_research_report = truncate_text(market_research_report, 500)
-        sentiment_report = truncate_text(sentiment_report, 500)
-        news_report = truncate_text(news_report, 800)  # 新聞通常較長但也需要控制
-        fundamentals_report = truncate_text(fundamentals_report, 600)
+        # 增加限制以確保 800+ 字的報告不被截斷
+        market_research_report = truncate_text(market_research_report, 2000)
+        sentiment_report = truncate_text(sentiment_report, 2000)
+        news_report = truncate_text(news_report, 2500)
+        fundamentals_report = truncate_text(fundamentals_report, 2000)
         
         curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
         

tradingagents/agents/researchers/bull_researcher.py

@@ -64,13 +64,11 @@ def create_bull_researcher(llm, memory):
             return truncated + "...(為控制長度已精簡)"
         
         # 為每個報告設置合理的字符限制
-        # 模型 gpt-4.1-mini 的限制是 8192 tokens
-        # 混合中英文估算: 1 字符 ≈ 1.5-2 tokens (取保守值)
-        # 目標: 總字符數 < 3000 字符 (約 4500-6000 tokens，留 2000+ tokens 給 completion)
-        market_research_report = truncate_text(market_research_report, 500)
-        sentiment_report = truncate_text(sentiment_report, 500)
-        news_report = truncate_text(news_report, 800)  # 新聞通常較長但也需要控制
-        fundamentals_report = truncate_text(fundamentals_report, 600)
+        # 增加限制以確保 800+ 字的報告不被截斷
+        market_research_report = truncate_text(market_research_report, 2000)
+        sentiment_report = truncate_text(sentiment_report, 2000)
+        news_report = truncate_text(news_report, 2500)
+        fundamentals_report = truncate_text(fundamentals_report, 2000)
         
         curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
         

tradingagents/agents/risk_mgmt/aggresive_debator.py

@@ -53,16 +53,17 @@ def create_risky_debator(llm):
                 return text
             return text[:max_chars] + "\n...(內容已截斷)"
         
+        
         # 截斷各類輸入以控制 token 使用量
-        # 模型限制: 8192 tokens，目標: < 3500 字符
-        market_research_report = truncate_text(market_research_report, 500)
-        sentiment_report = truncate_text(sentiment_report, 500)
-        news_report = truncate_text(news_report, 600)
-        fundamentals_report = truncate_text(fundamentals_report, 600)
-        trader_decision = truncate_text(trader_decision, 800)
-        history = truncate_text(history, 400)
-        current_safe_response = truncate_text(current_safe_response, 300)
-        current_neutral_response = truncate_text(current_neutral_response, 300)
+        # 增加限制以確保 800+ 字的報告不被截斷
+        market_research_report = truncate_text(market_research_report, 2000)
+        sentiment_report = truncate_text(sentiment_report, 2000)
+        news_report = truncate_text(news_report, 2500)
+        fundamentals_report = truncate_text(fundamentals_report, 2000)
+        trader_decision = truncate_text(trader_decision, 2000)
+        history = truncate_text(history, 1500)
+        current_safe_response = truncate_text(current_safe_response, 1000)
+        current_neutral_response = truncate_text(current_neutral_response, 1000)
         
         # 建立提示 (prompt)
         prompt = f"""**重要：您必須使用繁體中文（Traditional Chinese）回覆所有內容。**

tradingagents/agents/risk_mgmt/conservative_debator.py

@@ -54,16 +54,17 @@ def create_safe_debator(llm):
                 return text
             return text[:max_chars] + "\n...(內容已截斷)"
         
+        
         # 截斷各類輸入以控制 token 使用量
-        # 模型限制: 8192 tokens，目標: < 3500 字符
-        market_research_report = truncate_text(market_research_report, 500)
-        sentiment_report = truncate_text(sentiment_report, 500)
-        news_report = truncate_text(news_report, 600)
-        fundamentals_report = truncate_text(fundamentals_report, 600)
-        trader_decision = truncate_text(trader_decision, 800)
-        history = truncate_text(history, 400)
-        current_risky_response = truncate_text(current_risky_response, 300)
-        current_neutral_response = truncate_text(current_neutral_response, 300)
+        # 增加限制以確保 800+ 字的報告不被截斷
+        market_research_report = truncate_text(market_research_report, 2000)
+        sentiment_report = truncate_text(sentiment_report, 2000)
+        news_report = truncate_text(news_report, 2500)
+        fundamentals_report = truncate_text(fundamentals_report, 2000)
+        trader_decision = truncate_text(trader_decision, 2000)
+        history = truncate_text(history, 1500)
+        current_risky_response = truncate_text(current_risky_response, 1000)
+        current_neutral_response = truncate_text(current_neutral_response, 1000)
         
         # 建立提示 (prompt)
         prompt = f"""**重要：您必須使用繁體中文（Traditional Chinese）回覆所有內容。**

tradingagents/agents/risk_mgmt/neutral_debator.py

@@ -53,16 +53,17 @@ def create_neutral_debator(llm):
                 return text
             return text[:max_chars] + "\n...(內容已截斷)"
         
+        
         # 截斷各類輸入以控制 token 使用量
-        # 模型限制: 8192 tokens，目標: < 3500 字符
-        market_research_report = truncate_text(market_research_report, 500)
-        sentiment_report = truncate_text(sentiment_report, 500)
-        news_report = truncate_text(news_report, 600)
-        fundamentals_report = truncate_text(fundamentals_report, 600)
-        trader_decision = truncate_text(trader_decision, 800)
-        history = truncate_text(history, 400)
-        current_risky_response = truncate_text(current_risky_response, 300)
-        current_safe_response = truncate_text(current_safe_response, 300)
+        # 增加限制以確保 800+ 字的報告不被截斷
+        market_research_report = truncate_text(market_research_report, 2000)
+        sentiment_report = truncate_text(sentiment_report, 2000)
+        news_report = truncate_text(news_report, 2500)
+        fundamentals_report = truncate_text(fundamentals_report, 2000)
+        trader_decision = truncate_text(trader_decision, 2000)
+        history = truncate_text(history, 1500)
+        current_risky_response = truncate_text(current_risky_response, 1000)
+        current_safe_response = truncate_text(current_safe_response, 1000)
 
         # 建立提示 (prompt)
         prompt = f"""**重要：您必須使用繁體中文（Traditional Chinese）回覆所有內容。**

tradingagents/agents/trader/trader.py

@@ -57,13 +57,14 @@ def create_trader(llm, memory):
             # 如果找不到合適的分隔符，直接在字符處截斷
             return truncated + "...(為控制長度已精簡)"
         
+        
         # 截斷各類報告以控制 token 使用量
-        # 這些報告將用於記憶檢索（embedding）和 LLM prompt
-        market_research_report_truncated = truncate_text(market_research_report, 500)
-        sentiment_report_truncated = truncate_text(sentiment_report, 500)
-        news_report_truncated = truncate_text(news_report, 600)
-        fundamentals_report_truncated = truncate_text(fundamentals_report, 600)
-        investment_plan_truncated = truncate_text(investment_plan, 800)
+        # 增加限制以確保 800+ 字的報告不被截斷
+        market_research_report_truncated = truncate_text(market_research_report, 2000)
+        sentiment_report_truncated = truncate_text(sentiment_report, 2000)
+        news_report_truncated = truncate_text(news_report, 2500)
+        fundamentals_report_truncated = truncate_text(fundamentals_report, 2000)
+        investment_plan_truncated = truncate_text(investment_plan, 2000)
 
         # 整合當前情況（用於記憶檢索）
         curr_situation = f"{market_research_report_truncated}\n\n{sentiment_report_truncated}\n\n{news_report_truncated}\n\n{fundamentals_report_truncated}"