feat: add analysis-only crypto asset mode

tradingagents/agents/analysts/market_analyst.py

@@ -10,7 +10,8 @@ def create_market_analyst(llm):
     def market_analyst_node(state):
         current_date = state["trade_date"]
         ticker = state["company_of_interest"]
-        company_name = state["company_of_interest"]
+        asset_type = state.get("asset_type", "stock")
+        asset_label = "stock" if asset_type == "stock" else "crypto asset"
 
         tools = [
             get_stock_data,
@@ -57,7 +58,7 @@ Volume-Based Indicators:
                     " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
                     " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
                     " You have access to the following tools: {tool_names}.\n{system_message}"
-                    "For your reference, the current date is {current_date}. The company we want to look at is {ticker}",
+                    "For your reference, the current date is {current_date}. The {asset_label} we want to look at is {ticker}",
                 ),
                 MessagesPlaceholder(variable_name="messages"),
             ]
@@ -67,6 +68,7 @@ Volume-Based Indicators:
         prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
         prompt = prompt.partial(current_date=current_date)
         prompt = prompt.partial(ticker=ticker)
+        prompt = prompt.partial(asset_label=asset_label)
 
         chain = prompt | llm.bind_tools(tools)
 

tradingagents/agents/analysts/news_analyst.py

@@ -9,6 +9,8 @@ def create_news_analyst(llm):
     def news_analyst_node(state):
         current_date = state["trade_date"]
         ticker = state["company_of_interest"]
+        asset_type = state.get("asset_type", "stock")
+        asset_label = "company" if asset_type == "stock" else "asset"
 
         tools = [
             get_news,
@@ -16,7 +18,7 @@ def create_news_analyst(llm):
         ]
 
         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. Use the available tools: get_news(query, start_date, end_date) for company-specific or targeted news searches, and get_global_news(curr_date, look_back_days, limit) for broader macroeconomic news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
+            f"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. Use the available tools: get_news(query, start_date, end_date) for {asset_label}-specific or targeted news searches, and get_global_news(curr_date, look_back_days, limit) for broader macroeconomic 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 Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
         )
 
@@ -31,7 +33,7 @@ def create_news_analyst(llm):
                     " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
                     " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
                     " You have access to the following tools: {tool_names}.\n{system_message}"
-                    "For your reference, the current date is {current_date}. We are looking at the company {ticker}",
+                    "For your reference, the current date is {current_date}. We are looking at the {asset_label} {ticker}",
                 ),
                 MessagesPlaceholder(variable_name="messages"),
             ]
@@ -41,6 +43,7 @@ def create_news_analyst(llm):
         prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
         prompt = prompt.partial(current_date=current_date)
         prompt = prompt.partial(ticker=ticker)
+        prompt = prompt.partial(asset_label=asset_label)
 
         chain = prompt | llm.bind_tools(tools)
         result = chain.invoke(state["messages"])

tradingagents/agents/analysts/social_media_analyst.py

@@ -9,14 +9,15 @@ def create_social_media_analyst(llm):
     def social_media_analyst_node(state):
         current_date = state["trade_date"]
         ticker = state["company_of_interest"]
-        company_name = state["company_of_interest"]
+        asset_type = state.get("asset_type", "stock")
+        subject_label = "company" if asset_type == "stock" else "asset"
 
         tools = [
             get_news,
         ]
 
         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. Use the get_news(query, start_date, end_date) tool to search for company-specific news and social media discussions. 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."
+            f"You are a social media and targeted news researcher/analyst tasked with analyzing social media posts, recent {subject_label} news, and public sentiment for a specific {subject_label} over the past week. You will be given an asset identifier and your objective is to write a comprehensive long report detailing your analysis, insights, and implications for traders and investors after looking at social media, sentiment, and recent news related to that {subject_label}. Use the get_news(query, start_date, end_date) tool to search for {subject_label}-specific news and social media discussions. 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 Markdown table at the end of the report to organize key points in the report, organized and easy to read.""",
         )
 
@@ -31,7 +32,7 @@ def create_social_media_analyst(llm):
                     " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
                     " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
                     " You have access to the following tools: {tool_names}.\n{system_message}"
-                    "For your reference, the current date is {current_date}. The current company we want to analyze is {ticker}",
+                    "For your reference, the current date is {current_date}. The current {subject_label} we want to analyze is {ticker}",
                 ),
                 MessagesPlaceholder(variable_name="messages"),
             ]
@@ -41,6 +42,7 @@ def create_social_media_analyst(llm):
         prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
         prompt = prompt.partial(current_date=current_date)
         prompt = prompt.partial(ticker=ticker)
+        prompt = prompt.partial(subject_label=subject_label)
 
         chain = prompt | llm.bind_tools(tools)
 

tradingagents/agents/researchers/bear_researcher.py

@@ -14,6 +14,13 @@ def create_bear_researcher(llm, memory):
         sentiment_report = state["sentiment_report"]
         news_report = state["news_report"]
         fundamentals_report = state["fundamentals_report"]
+        asset_type = state.get("asset_type", "stock")
+        target_label = "stock" if asset_type == "stock" else "asset"
+        fundamentals_label = (
+            "Company fundamentals report"
+            if asset_type == "stock"
+            else "Asset fundamentals report (may be unavailable for crypto)"
+        )
 
         curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
         past_memories = memory.get_memories(curr_situation, n_matches=2)
@@ -22,7 +29,7 @@ def create_bear_researcher(llm, memory):
         for i, rec in enumerate(past_memories, 1):
             past_memory_str += rec["recommendation"] + "\n\n"
 
-        prompt = f"""You are a Bear Analyst making the case against investing in the stock. Your goal is to present a well-reasoned argument emphasizing risks, challenges, and negative indicators. Leverage the provided research and data to highlight potential downsides and counter bullish arguments effectively.
+        prompt = f"""You are a Bear Analyst making the case against investing in the {target_label}. Your goal is to present a well-reasoned argument emphasizing risks, challenges, and negative indicators. Leverage the provided research and data to highlight potential downsides and counter bullish arguments effectively.
 
 Key points to focus on:
 
@@ -37,11 +44,11 @@ Resources available:
 Market research report: {market_research_report}
 Social media sentiment report: {sentiment_report}
 Latest world affairs news: {news_report}
-Company fundamentals report: {fundamentals_report}
+{fundamentals_label}: {fundamentals_report}
 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.
+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 {target_label}. You must also address reflections and learn from lessons and mistakes you made in the past.
 """
 
         response = llm.invoke(prompt)

tradingagents/agents/researchers/bull_researcher.py

@@ -14,6 +14,13 @@ def create_bull_researcher(llm, memory):
         sentiment_report = state["sentiment_report"]
         news_report = state["news_report"]
         fundamentals_report = state["fundamentals_report"]
+        asset_type = state.get("asset_type", "stock")
+        target_label = "stock" if asset_type == "stock" else "asset"
+        fundamentals_label = (
+            "Company fundamentals report"
+            if asset_type == "stock"
+            else "Asset fundamentals report (may be unavailable for crypto)"
+        )
 
         curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
         past_memories = memory.get_memories(curr_situation, n_matches=2)
@@ -22,7 +29,7 @@ def create_bull_researcher(llm, memory):
         for i, rec in enumerate(past_memories, 1):
             past_memory_str += rec["recommendation"] + "\n\n"
 
-        prompt = f"""You are a Bull Analyst advocating for investing in the stock. Your task is to build a strong, evidence-based case emphasizing growth potential, competitive advantages, and positive market indicators. Leverage the provided research and data to address concerns and counter bearish arguments effectively.
+        prompt = f"""You are a Bull Analyst advocating for investing in the {target_label}. Your task is to build a strong, evidence-based case emphasizing growth potential, competitive advantages, and positive market indicators. Leverage the provided research and data to address concerns and counter bearish arguments effectively.
 
 Key points to focus on:
 - Growth Potential: Highlight the company's market opportunities, revenue projections, and scalability.
@@ -35,7 +42,7 @@ Resources available:
 Market research report: {market_research_report}
 Social media sentiment report: {sentiment_report}
 Latest world affairs news: {news_report}
-Company fundamentals report: {fundamentals_report}
+{fundamentals_label}: {fundamentals_report}
 Conversation history of the debate: {history}
 Last bear argument: {current_response}
 Reflections from similar situations and lessons learned: {past_memory_str}

tradingagents/agents/trader/trader.py

@@ -6,6 +6,8 @@ import json
 def create_trader(llm, memory):
     def trader_node(state, name):
         company_name = state["company_of_interest"]
+        asset_type = state.get("asset_type", "stock")
+        target_label = "company" if asset_type == "stock" else "asset"
         investment_plan = state["investment_plan"]
         market_research_report = state["market_report"]
         sentiment_report = state["sentiment_report"]
@@ -24,7 +26,7 @@ def create_trader(llm, memory):
 
         context = {
             "role": "user",
-            "content": f"Based on a comprehensive analysis by a team of analysts, here is an investment plan tailored for {company_name}. This plan incorporates insights from current technical market trends, macroeconomic indicators, and social media sentiment. Use this plan as a foundation for evaluating your next trading decision.\n\nProposed Investment Plan: {investment_plan}\n\nLeverage these insights to make an informed and strategic decision.",
+            "content": f"Based on a comprehensive analysis by a team of analysts, here is an investment plan tailored for the {target_label} {company_name}. This plan incorporates insights from current technical market trends, macroeconomic indicators, and social media sentiment. Use this plan as a foundation for evaluating your next trading decision.\n\nProposed Investment Plan: {investment_plan}\n\nLeverage these insights to make an informed and strategic decision.",
         }
 
         messages = [

tradingagents/agents/utils/agent_states.py

@@ -49,6 +49,7 @@ class RiskDebateState(TypedDict):
 
 class AgentState(MessagesState):
     company_of_interest: Annotated[str, "Company that we are interested in trading"]
+    asset_type: Annotated[str, "Asset type under analysis such as stock or crypto"]
     trade_date: Annotated[str, "What date we are trading at"]
 
     sender: Annotated[str, "Agent that sent this message"]

tradingagents/graph/propagation.py

@@ -16,12 +16,13 @@ class Propagator:
         self.max_recur_limit = max_recur_limit
 
     def create_initial_state(
-        self, company_name: str, trade_date: str
+        self, company_name: str, trade_date: str, asset_type: str = "stock"
     ) -> Dict[str, Any]:
         """Create the initial state for the agent graph."""
         return {
             "messages": [("human", company_name)],
             "company_of_interest": company_name,
+            "asset_type": asset_type,
             "trade_date": str(trade_date),
             "investment_debate_state": InvestDebateState(
                 {