feat: add analysis-only crypto asset mode

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}