fix(graph): integrate #487 + #567 — sentiment label, route, propagate asset_type

- analyst_execution.py: rename "Social Analyst" / "Msg Clear Social"
  to "Sentiment Analyst" / "Msg Clear Sentiment" to match v0.2.5.
- conditional_logic.should_continue_social returns the renamed route.
- TradingAgentsGraph.propagate accepts asset_type and threads through
  to Propagator.create_initial_state.
- Regression test on the Sentiment Analyst label.

Verified end-to-end (NVDA stock + BTC-USD crypto) on gpt-5.4-mini.

tradingagents/graph/trading_graph.py

@@ -292,11 +292,14 @@ class TradingAgentsGraph:
         if updates:
             self.memory_log.batch_update_with_outcomes(updates)
 
-    def propagate(self, company_name, trade_date):
+    def propagate(self, company_name, trade_date, asset_type: str = "stock"):
         """Run the trading agents graph for a company on a specific date.
 
-        When ``checkpoint_enabled`` is set in config, the graph is recompiled
-        with a per-ticker SqliteSaver so a crashed run can resume from the last
+        ``asset_type`` selects between the stock pipeline (default) and the
+        crypto pipeline (``"crypto"``) shipped in #567 — the CLI auto-detects
+        from the ticker; programmatic callers pass it explicitly. When
+        ``checkpoint_enabled`` is set in config, the graph is recompiled with
+        a per-ticker SqliteSaver so a crashed run can resume from the last
         successful node on a subsequent invocation with the same ticker+date.
         """
         self.ticker = company_name
@@ -323,19 +326,19 @@ class TradingAgentsGraph:
                 logger.info("Starting fresh for %s on %s", company_name, trade_date)
 
         try:
-            return self._run_graph(company_name, trade_date)
+            return self._run_graph(company_name, trade_date, asset_type=asset_type)
         finally:
             if self._checkpointer_ctx is not None:
                 self._checkpointer_ctx.__exit__(None, None, None)
                 self._checkpointer_ctx = None
                 self.graph = self.workflow.compile()
 
-    def _run_graph(self, company_name, trade_date):
+    def _run_graph(self, company_name, trade_date, asset_type: str = "stock"):
         """Execute the graph and write the resulting state to disk and memory log."""
         # Initialize state — inject memory log context for PM.
         past_context = self.memory_log.get_past_context(company_name)
         init_agent_state = self.propagator.create_initial_state(
-            company_name, trade_date, past_context=past_context
+            company_name, trade_date, asset_type=asset_type, past_context=past_context
         )
         args = self.propagator.get_graph_args()