fix(graph): resolve instrument identity to stop wrong-company hallucination

Agents had no ground-truth ticker→company mapping, so the market analyst
could pattern-match a price chart to the wrong company (e.g. TOTDY read as
"TotalEnergies"), and every downstream agent inherited the bad framing.

Resolve identity once at run start via a cached, fail-open yfinance lookup
and inject company/sector/exchange into the shared instrument context that
all twelve agents consume, with an explicit do-not-substitute instruction.
Resolution runs on both the propagate() and CLI entry points.

Also replaces the bare "Continue" message-clear placeholder, which some
OpenAI-compatible providers interpreted as the user task, with a
context-anchored placeholder carrying the resolved identity and date.

#814 #888

tradingagents/agents/analysts/fundamentals_analyst.py

@@ -1,6 +1,6 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from tradingagents.agents.utils.agent_utils import (
-    build_instrument_context,
+    get_instrument_context_from_state,
     get_balance_sheet,
     get_cashflow,
     get_fundamentals,
@@ -14,7 +14,7 @@ from tradingagents.dataflows.config import get_config
 def create_fundamentals_analyst(llm):
     def fundamentals_analyst_node(state):
         current_date = state["trade_date"]
-        instrument_context = build_instrument_context(state["company_of_interest"])
+        instrument_context = get_instrument_context_from_state(state)
 
         tools = [
             get_fundamentals,

tradingagents/agents/analysts/market_analyst.py

@@ -1,6 +1,6 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from tradingagents.agents.utils.agent_utils import (
-    build_instrument_context,
+    get_instrument_context_from_state,
     get_indicators,
     get_language_instruction,
     get_stock_data,
@@ -12,10 +12,7 @@ def create_market_analyst(llm):
 
     def market_analyst_node(state):
         current_date = state["trade_date"]
-        asset_type = state.get("asset_type", "stock")
-        instrument_context = build_instrument_context(
-            state["company_of_interest"], asset_type
-        )
+        instrument_context = get_instrument_context_from_state(state)
 
         tools = [
             get_stock_data,

tradingagents/agents/analysts/news_analyst.py

@@ -1,6 +1,6 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from tradingagents.agents.utils.agent_utils import (
-    build_instrument_context,
+    get_instrument_context_from_state,
     get_global_news,
     get_language_instruction,
     get_news,
@@ -13,9 +13,7 @@ def create_news_analyst(llm):
         current_date = state["trade_date"]
         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
-        )
+        instrument_context = get_instrument_context_from_state(state)
 
         tools = [
             get_news,

tradingagents/agents/analysts/sentiment_analyst.py

@@ -23,7 +23,7 @@ from datetime import datetime, timedelta
 
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from tradingagents.agents.utils.agent_utils import (
-    build_instrument_context,
+    get_instrument_context_from_state,
     get_language_instruction,
     get_news,
 )
@@ -47,7 +47,7 @@ def create_sentiment_analyst(llm):
         ticker = state["company_of_interest"]
         end_date = state["trade_date"]
         start_date = _seven_days_back(end_date)
-        instrument_context = build_instrument_context(ticker)
+        instrument_context = get_instrument_context_from_state(state)
 
         # Pre-fetch all three sources. Each fetcher degrades gracefully and
         # returns a string (no exceptions surface from here), so the LLM