feat: add footer statistics tracking with LangChain callbacks

- Add StatsCallbackHandler for tracking LLM calls, tool calls, and tokens
- Integrate callbacks into TradingAgentsGraph and all LLM clients
- Dynamic agent/report counts based on selected analysts
- Fix report completion counting (tied to agent completion)

cli/stats_handler.py

@@ -0,0 +1,76 @@
+import threading
+from typing import Any, Dict, List, Union
+
+from langchain_core.callbacks import BaseCallbackHandler
+from langchain_core.outputs import LLMResult
+from langchain_core.messages import AIMessage
+
+
+class StatsCallbackHandler(BaseCallbackHandler):
+    """Callback handler that tracks LLM calls, tool calls, and token usage."""
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._lock = threading.Lock()
+        self.llm_calls = 0
+        self.tool_calls = 0
+        self.tokens_in = 0
+        self.tokens_out = 0
+
+    def on_llm_start(
+        self,
+        serialized: Dict[str, Any],
+        prompts: List[str],
+        **kwargs: Any,
+    ) -> None:
+        """Increment LLM call counter when an LLM starts."""
+        with self._lock:
+            self.llm_calls += 1
+
+    def on_chat_model_start(
+        self,
+        serialized: Dict[str, Any],
+        messages: List[List[Any]],
+        **kwargs: Any,
+    ) -> None:
+        """Increment LLM call counter when a chat model starts."""
+        with self._lock:
+            self.llm_calls += 1
+
+    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
+        """Extract token usage from LLM response."""
+        try:
+            generation = response.generations[0][0]
+        except (IndexError, TypeError):
+            return
+
+        usage_metadata = None
+        if hasattr(generation, "message"):
+            message = generation.message
+            if isinstance(message, AIMessage) and hasattr(message, "usage_metadata"):
+                usage_metadata = message.usage_metadata
+
+        if usage_metadata:
+            with self._lock:
+                self.tokens_in += usage_metadata.get("input_tokens", 0)
+                self.tokens_out += usage_metadata.get("output_tokens", 0)
+
+    def on_tool_start(
+        self,
+        serialized: Dict[str, Any],
+        input_str: str,
+        **kwargs: Any,
+    ) -> None:
+        """Increment tool call counter when a tool starts."""
+        with self._lock:
+            self.tool_calls += 1
+
+    def get_stats(self) -> Dict[str, Any]:
+        """Return current statistics."""
+        with self._lock:
+            return {
+                "llm_calls": self.llm_calls,
+                "tool_calls": self.tool_calls,
+                "tokens_in": self.tokens_in,
+                "tokens_out": self.tokens_out,
+            }