Merge #567 — analysis-only crypto asset mode

feat: add analysis-only crypto asset mode

cli/main.py

@@ -510,6 +510,10 @@ def get_user_selections():
         )
     )
     selected_ticker = get_ticker()
+    asset_type = detect_asset_type(selected_ticker)
+    console.print(
+        f"[green]Detected asset type:[/green] {asset_type.value}"
+    )
 
     # Step 2: Analysis date
     default_date = datetime.datetime.now().strftime("%Y-%m-%d")
@@ -537,7 +541,7 @@ def get_user_selections():
             "Step 4: Analysts Team", "Select your LLM analyst agents for the analysis"
         )
     )
-    selected_analysts = select_analysts()
+    selected_analysts = select_analysts(asset_type)
     console.print(
         f"[green]Selected analysts:[/green] {', '.join(analyst.value for analyst in selected_analysts)}"
     )
@@ -620,6 +624,7 @@ def get_user_selections():
 
     return {
         "ticker": selected_ticker,
+        "asset_type": asset_type.value,
         "analysis_date": analysis_date,
         "analysts": selected_analysts,
         "research_depth": selected_research_depth,
@@ -1071,6 +1076,7 @@ def run_analysis(checkpoint: bool = False):
 
         # Add initial messages
         message_buffer.add_message("System", f"Selected ticker: {selections['ticker']}")
+        message_buffer.add_message("System", f"Detected asset type: {selections['asset_type']}")
         message_buffer.add_message(
             "System", f"Analysis date: {selections['analysis_date']}"
         )
@@ -1094,7 +1100,9 @@ def run_analysis(checkpoint: bool = False):
 
         # Initialize state and get graph args with callbacks
         init_agent_state = graph.propagator.create_initial_state(
-            selections["ticker"], selections["analysis_date"]
+            selections["ticker"],
+            selections["analysis_date"],
+            asset_type=selections["asset_type"],
         )
         # Pass callbacks to graph config for tool execution tracking
         # (LLM tracking is handled separately via LLM constructor)

cli/models.py

@@ -10,3 +10,8 @@ class AnalystType(str, Enum):
     SOCIAL = "social"
     NEWS = "news"
     FUNDAMENTALS = "fundamentals"
+
+
+class AssetType(str, Enum):
+    STOCK = "stock"
+    CRYPTO = "crypto"

cli/utils.py

@@ -6,7 +6,7 @@ import questionary
 from dotenv import find_dotenv, set_key
 from rich.console import Console
 
-from cli.models import AnalystType
+from cli.models import AnalystType, AssetType
 from tradingagents.llm_clients.api_key_env import get_api_key_env
 from tradingagents.llm_clients.model_catalog import get_model_options
 
@@ -21,6 +21,8 @@ ANALYST_ORDER = [
     ("Fundamentals Analyst", AnalystType.FUNDAMENTALS),
 ]
 
+CRYPTO_SUFFIXES = ("-USD", "-USDT", "-USDC", "-BTC", "-ETH")
+
 
 def get_ticker() -> str:
     """Prompt the user to enter a ticker symbol."""
@@ -47,6 +49,25 @@ def normalize_ticker_symbol(ticker: str) -> str:
     return ticker.strip().upper()
 
 
+def detect_asset_type(ticker: str) -> AssetType:
+    normalized_ticker = ticker.strip().upper()
+    if normalized_ticker.endswith(CRYPTO_SUFFIXES):
+        return AssetType.CRYPTO
+    return AssetType.STOCK
+
+
+def filter_analysts_for_asset_type(
+    analysts: List[AnalystType], asset_type: AssetType
+) -> List[AnalystType]:
+    if asset_type != AssetType.CRYPTO:
+        return analysts
+    return [
+        analyst
+        for analyst in analysts
+        if analyst != AnalystType.FUNDAMENTALS
+    ]
+
+
 def get_analysis_date() -> str:
     """Prompt the user to enter a date in YYYY-MM-DD format."""
     import re
@@ -80,12 +101,18 @@ def get_analysis_date() -> str:
     return date.strip()
 
 
-def select_analysts() -> List[AnalystType]:
+def select_analysts(asset_type: AssetType = AssetType.STOCK) -> List[AnalystType]:
     """Select analysts using an interactive checkbox."""
+    available_analysts = filter_analysts_for_asset_type(
+        [value for _, value in ANALYST_ORDER],
+        asset_type,
+    )
     choices = questionary.checkbox(
         "Select Your [Analysts Team]:",
         choices=[
-            questionary.Choice(display, value=value) for display, value in ANALYST_ORDER
+            questionary.Choice(display, value=value)
+            for display, value in ANALYST_ORDER
+            if value in available_analysts
         ],
         instruction="\n- Press Space to select/unselect analysts\n- Press 'a' to select/unselect all\n- Press Enter when done",
         validate=lambda x: len(x) > 0 or "You must select at least one analyst.",

tests/__init__.py

@@ -0,0 +1 @@
+

tests/test_crypto_asset_mode.py

@@ -0,0 +1,56 @@
+import unittest
+
+from cli.models import AnalystType, AssetType
+from cli.utils import detect_asset_type, filter_analysts_for_asset_type
+from tradingagents.graph.propagation import Propagator
+
+
+class CryptoAssetModeTests(unittest.TestCase):
+    def test_detects_crypto_pair_symbols(self):
+        self.assertEqual(detect_asset_type("BTC-USD"), AssetType.CRYPTO)
+        self.assertEqual(detect_asset_type("eth-usd"), AssetType.CRYPTO)
+
+    def test_defaults_non_crypto_symbols_to_stock(self):
+        self.assertEqual(detect_asset_type("AAPL"), AssetType.STOCK)
+        self.assertEqual(detect_asset_type("SPY"), AssetType.STOCK)
+
+    def test_filters_out_fundamentals_analyst_for_crypto(self):
+        analysts = [
+            AnalystType.MARKET,
+            AnalystType.SOCIAL,
+            AnalystType.NEWS,
+            AnalystType.FUNDAMENTALS,
+        ]
+
+        self.assertEqual(
+            filter_analysts_for_asset_type(analysts, AssetType.CRYPTO),
+            [
+                AnalystType.MARKET,
+                AnalystType.SOCIAL,
+                AnalystType.NEWS,
+            ],
+        )
+
+    def test_keeps_all_analysts_for_stock(self):
+        analysts = [
+            AnalystType.MARKET,
+            AnalystType.SOCIAL,
+            AnalystType.NEWS,
+            AnalystType.FUNDAMENTALS,
+        ]
+
+        self.assertEqual(
+            filter_analysts_for_asset_type(analysts, AssetType.STOCK),
+            analysts,
+        )
+
+    def test_propagator_includes_asset_type_in_initial_state(self):
+        state = Propagator().create_initial_state(
+            "BTC-USD", "2026-04-18", asset_type=AssetType.CRYPTO.value
+        )
+
+        self.assertEqual(state["asset_type"], AssetType.CRYPTO.value)
+
+
+if __name__ == "__main__":
+    unittest.main()

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(