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/researchers/bear_researcher.py

@@ -1,4 +1,7 @@
-from tradingagents.agents.utils.agent_utils import get_language_instruction
+from tradingagents.agents.utils.agent_utils import (
+    get_instrument_context_from_state,
+    get_language_instruction,
+)
 
 
 def create_bear_researcher(llm):
@@ -12,6 +15,7 @@ def create_bear_researcher(llm):
         sentiment_report = state["sentiment_report"]
         news_report = state["news_report"]
         fundamentals_report = state["fundamentals_report"]
+        instrument_context = get_instrument_context_from_state(state)
         asset_type = state.get("asset_type", "stock")
         target_label = "stock" if asset_type == "stock" else "asset"
         fundamentals_label = (
@@ -32,6 +36,7 @@ Key points to focus on:
 
 Resources available:
 
+{instrument_context}
 Market research report: {market_research_report}
 Social media sentiment report: {sentiment_report}
 Latest world affairs news: {news_report}

tradingagents/agents/researchers/bull_researcher.py

@@ -1,4 +1,7 @@
-from tradingagents.agents.utils.agent_utils import get_language_instruction
+from tradingagents.agents.utils.agent_utils import (
+    get_instrument_context_from_state,
+    get_language_instruction,
+)
 
 
 def create_bull_researcher(llm):
@@ -12,6 +15,7 @@ def create_bull_researcher(llm):
         sentiment_report = state["sentiment_report"]
         news_report = state["news_report"]
         fundamentals_report = state["fundamentals_report"]
+        instrument_context = get_instrument_context_from_state(state)
         asset_type = state.get("asset_type", "stock")
         target_label = "stock" if asset_type == "stock" else "asset"
         fundamentals_label = (
@@ -30,6 +34,7 @@ Key points to focus on:
 - Engagement: Present your argument in a conversational style, engaging directly with the bear analyst's points and debating effectively rather than just listing data.
 
 Resources available:
+{instrument_context}
 Market research report: {market_research_report}
 Social media sentiment report: {sentiment_report}
 Latest world affairs news: {news_report}