Merge #567 — analysis-only crypto asset mode

feat: add analysis-only crypto asset mode

tradingagents/agents/analysts/market_analyst.py

@@ -12,7 +12,10 @@ def create_market_analyst(llm):
 
     def market_analyst_node(state):
         current_date = state["trade_date"]
-        instrument_context = build_instrument_context(state["company_of_interest"])
+        asset_type = state.get("asset_type", "stock")
+        instrument_context = build_instrument_context(
+            state["company_of_interest"], asset_type
+        )
 
         tools = [
             get_stock_data,

tradingagents/agents/analysts/news_analyst.py

@@ -11,7 +11,11 @@ from tradingagents.dataflows.config import get_config
 def create_news_analyst(llm):
     def news_analyst_node(state):
         current_date = state["trade_date"]
-        instrument_context = build_instrument_context(state["company_of_interest"])
+        asset_type = state.get("asset_type", "stock")
+        asset_label = "company" if asset_type == "stock" else "asset"
+        instrument_context = build_instrument_context(
+            state["company_of_interest"], asset_type
+        )
 
         tools = [
             get_news,
@@ -19,7 +23,7 @@ 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. Provide specific, actionable insights with supporting evidence to help traders make informed 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. Provide specific, actionable insights with supporting evidence to help traders make informed 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."""
             + get_language_instruction()
         )

tradingagents/agents/researchers/bear_researcher.py

@@ -12,8 +12,15 @@ def create_bear_researcher(llm):
         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)"
+        )
 
-        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:
 
@@ -28,10 +35,10 @@ 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}
-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.
+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}.
 """ + get_language_instruction()
 
         response = llm.invoke(prompt)

tradingagents/agents/researchers/bull_researcher.py

@@ -12,8 +12,15 @@ def create_bull_researcher(llm):
         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)"
+        )
 
-        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.
@@ -26,7 +33,7 @@ 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}
 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.

tradingagents/agents/trader/trader.py

@@ -22,7 +22,8 @@ def create_trader(llm):
 
     def trader_node(state, name):
         company_name = state["company_of_interest"]
-        instrument_context = build_instrument_context(company_name)
+        asset_type = state.get("asset_type", "stock")
+        instrument_context = build_instrument_context(company_name, asset_type)
         investment_plan = state["investment_plan"]
 
         messages = [

tradingagents/agents/utils/agent_states.py

@@ -45,6 +45,7 @@ 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/agents/utils/agent_utils.py

@@ -36,12 +36,19 @@ def get_language_instruction() -> str:
     return f" Write your entire response in {lang}."
 
 
-def build_instrument_context(ticker: str) -> str:
+def build_instrument_context(ticker: str, asset_type: str = "stock") -> str:
     """Describe the exact instrument so agents preserve exchange-qualified tickers."""
+    instrument_label = "asset" if asset_type == "crypto" else "instrument"
+    extra_hint = (
+        " Treat it as a crypto asset rather than a company, and do not assume company fundamentals are available."
+        if asset_type == "crypto"
+        else ""
+    )
     return (
-        f"The instrument to analyze is `{ticker}`. "
+        f"The {instrument_label} to analyze is `{ticker}`. "
         "Use this exact ticker in every tool call, report, and recommendation, "
-        "preserving any exchange suffix (e.g. `.TO`, `.L`, `.HK`, `.T`)."
+        "preserving any exchange suffix (e.g. `.TO`, `.L`, `.HK`, `.T`, `-USD`)."
+        + extra_hint
     )
 
 def create_msg_delete():

tradingagents/graph/propagation.py

@@ -16,12 +16,17 @@ class Propagator:
         self.max_recur_limit = max_recur_limit
 
     def create_initial_state(
-        self, company_name: str, trade_date: str, past_context: str = ""
+        self,
+        company_name: str,
+        trade_date: str,
+        asset_type: str = "stock",
+        past_context: str = "",
     ) -> 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),
             "past_context": past_context,
             "investment_debate_state": InvestDebateState(