feat: add Traditional Chinese output support for all agents

Add '請用繁體中文撰寫所有分析報告與回覆。' instruction to all agent
system prompts so that analyst reports, research debates, trader
decisions, and risk management outputs are all generated in
Traditional Chinese.

Affected agents: fundamentals analyst, market analyst, news analyst,
social media analyst, bull/bear researchers, research manager, trader,
risky/safe/neutral debators, risk manager.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

tradingagents/agents/analysts/fundamentals_analyst.py

@@ -22,7 +22,8 @@ def create_fundamentals_analyst(llm, toolkit):
 
         system_message = (
             "You are a researcher tasked with analyzing fundamental information over the past week about a company. Please write a comprehensive report of the company's fundamental information such as financial documents, company profile, basic company financials, company financial history, insider sentiment and insider transactions to gain a full view of the company's fundamental information to inform traders. Make sure to include as much detail as possible. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
-            + " Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.",
+            + " Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."
+            + " 請用繁體中文撰寫所有分析報告與回覆。",
         )
 
         prompt = ChatPromptTemplate.from_messages(

tradingagents/agents/analysts/market_analyst.py

@@ -48,6 +48,7 @@ Volume-Based Indicators:
 
 - Select indicators that provide diverse and complementary information. Avoid redundancy (e.g., do not select both rsi and stochrsi). Also briefly explain why they are suitable for the given market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_YFin_data first to retrieve the CSV that is needed to generate indicators. Write a very detailed and nuanced report of the trends you observe. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."""
             + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
+            + """ 請用繁體中文撰寫所有分析報告與回覆。"""
         )
 
         prompt = ChatPromptTemplate.from_messages(

tradingagents/agents/analysts/news_analyst.py

@@ -20,6 +20,7 @@ def create_news_analyst(llm, toolkit):
         system_message = (
             "You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Look at news from EODHD, and finnhub to be comprehensive. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
             + """ Make sure to append a Makrdown table at the end of the report to organize key points in the report, organized and easy to read."""
+            + """ 請用繁體中文撰寫所有分析報告與回覆。"""
         )
 
         prompt = ChatPromptTemplate.from_messages(

tradingagents/agents/analysts/social_media_analyst.py

@@ -18,7 +18,8 @@ def create_social_media_analyst(llm, toolkit):
 
         system_message = (
             "You are a social media and company specific news researcher/analyst tasked with analyzing social media posts, recent company news, and public sentiment for a specific company over the past week. You will be given a company's name your objective is to write a comprehensive long report detailing your analysis, insights, and implications for traders and investors on this company's current state after looking at social media and what people are saying about that company, analyzing sentiment data of what people feel each day about the company, and looking at recent company news. Try to look at all sources possible from social media to sentiment to news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
-            + """ Make sure to append a Makrdown table at the end of the report to organize key points in the report, organized and easy to read.""",
+            + """ Make sure to append a Makrdown table at the end of the report to organize key points in the report, organized and easy to read."""
+            + """ 請用繁體中文撰寫所有分析報告與回覆。""",
         )
 
         prompt = ChatPromptTemplate.from_messages(

tradingagents/agents/managers/research_manager.py

@@ -35,7 +35,8 @@ Here are your past reflections on mistakes:
 
 Here is the debate:
 Debate History:
-{history}"""
+{history}
+請用繁體中文撰寫所有回覆。"""
         response = llm.invoke(prompt)
 
         new_investment_debate_state = {

tradingagents/agents/managers/risk_manager.py

@@ -41,7 +41,8 @@ Deliverables:
 
 ---
 
-Focus on actionable insights and continuous improvement. Build on past lessons, critically evaluate all perspectives, and ensure each decision advances better outcomes."""
+Focus on actionable insights and continuous improvement. Build on past lessons, critically evaluate all perspectives, and ensure each decision advances better outcomes.
+請用繁體中文撰寫所有回覆。"""
 
         response = llm.invoke(prompt)
 

tradingagents/agents/researchers/bear_researcher.py

@@ -42,6 +42,7 @@ Conversation history of the debate: {history}
 Last bull argument: {current_response}
 Reflections from similar situations and lessons learned: {past_memory_str}
 Use this information to deliver a compelling bear argument, refute the bull's claims, and engage in a dynamic debate that demonstrates the risks and weaknesses of investing in the stock. You must also address reflections and learn from lessons and mistakes you made in the past.
+請用繁體中文撰寫所有回覆。
 """
 
         response = llm.invoke(prompt)

tradingagents/agents/researchers/bull_researcher.py

@@ -40,6 +40,7 @@ Conversation history of the debate: {history}
 Last bear argument: {current_response}
 Reflections from similar situations and lessons learned: {past_memory_str}
 Use this information to deliver a compelling bull argument, refute the bear's concerns, and engage in a dynamic debate that demonstrates the strengths of the bull position. You must also address reflections and learn from lessons and mistakes you made in the past.
+請用繁體中文撰寫所有回覆。
 """
 
         response = llm.invoke(prompt)

tradingagents/agents/risk_mgmt/aggresive_debator.py

@@ -30,7 +30,7 @@ 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.
 
-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."""
+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. 請用繁體中文撰寫所有回覆。"""
 
         response = llm.invoke(prompt)
 

tradingagents/agents/risk_mgmt/conservative_debator.py

@@ -31,7 +31,7 @@ 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.
 
-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."""
+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. 請用繁體中文撰寫所有回覆。"""
 
         response = llm.invoke(prompt)
 

tradingagents/agents/risk_mgmt/neutral_debator.py

@@ -30,7 +30,7 @@ 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.
 
-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 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. 請用繁體中文撰寫所有回覆。"""
 
         response = llm.invoke(prompt)
 

tradingagents/agents/trader/trader.py

@@ -27,7 +27,7 @@ def create_trader(llm, memory):
         messages = [
             {
                 "role": "system",
-                "content": f"""You are a trading agent analyzing market data to make investment decisions. Based on your analysis, provide a specific recommendation to buy, sell, or hold. End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**' to confirm your recommendation. Do not forget to utilize lessons from past decisions to learn from your mistakes. Here is some reflections from similar situatiosn you traded in and the lessons learned: {past_memory_str}""",
+                "content": f"""You are a trading agent analyzing market data to make investment decisions. Based on your analysis, provide a specific recommendation to buy, sell, or hold. End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**' to confirm your recommendation. Do not forget to utilize lessons from past decisions to learn from your mistakes. Here is some reflections from similar situatiosn you traded in and the lessons learned: {past_memory_str} 請用繁體中文撰寫所有回覆。""",
             },
             context,
         ]