fix: preserve exchange-qualified tickers across agent prompts

tradingagents/agents/analysts/social_media_analyst.py

@@ -1,7 +1,7 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 import time
 import json
-from tradingagents.agents.utils.agent_utils import get_news
+from tradingagents.agents.utils.agent_utils import build_instrument_context, get_news
 from tradingagents.dataflows.config import get_config
 
 
@@ -9,15 +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"]
+        instrument_context = build_instrument_context(ticker)
 
         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."
-            + """ 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.""",
+            "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. Always preserve the exact ticker symbol provided by the user, including any exchange suffix, and never merge commentary for similarly named companies from other exchanges. 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."""
         )
 
         prompt = ChatPromptTemplate.from_messages(
@@ -31,7 +31,7 @@ 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}. {instrument_context}",
                 ),
                 MessagesPlaceholder(variable_name="messages"),
             ]
@@ -40,7 +40,7 @@ def create_social_media_analyst(llm):
         prompt = prompt.partial(system_message=system_message)
         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(instrument_context=instrument_context)
 
         chain = prompt | llm.bind_tools(tools)