feat: add centralized logging module with dual output support

Add tradingagents/logging.py with JSONFormatter for structured file logs
and RichHandler for colored console output. Migrate 59 print statements
across 13 files to proper logger calls. Configure via environment
variables (TRADINGAGENTS_LOG_LEVEL, etc.) with default_config fallback.

- Rotating file handler: 10MB max, 5 backups to logs/tradingagents.log
- Rich console handler with tracebacks for development
- Logger hierarchy follows module paths (tradingagents.dataflows.*, etc.)
- 20 feature-specific tests covering core, config, migration, integration

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

tests/test_logging.py

@@ -0,0 +1,169 @@
+import json
+import logging
+import os
+import tempfile
+import pytest
+from unittest.mock import patch
+
+
+class TestLoggingModule:
+    @pytest.fixture(autouse=True)
+    def setup_and_teardown(self):
+        for handler in logging.root.handlers[:]:
+            logging.root.removeHandler(handler)
+
+        tradingagents_logger = logging.getLogger("tradingagents")
+        for handler in tradingagents_logger.handlers[:]:
+            tradingagents_logger.removeHandler(handler)
+        tradingagents_logger.setLevel(logging.NOTSET)
+
+        yield
+
+        for handler in logging.root.handlers[:]:
+            logging.root.removeHandler(handler)
+        tradingagents_logger = logging.getLogger("tradingagents")
+        for handler in tradingagents_logger.handlers[:]:
+            tradingagents_logger.removeHandler(handler)
+
+    def test_setup_logging_initializes_handlers_based_on_env_vars(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            env_vars = {
+                "TRADINGAGENTS_LOG_LEVEL": "DEBUG",
+                "TRADINGAGENTS_LOG_DIR": tmpdir,
+                "TRADINGAGENTS_LOG_CONSOLE": "false",
+                "TRADINGAGENTS_LOG_FILE": "true",
+            }
+            with patch.dict(os.environ, env_vars, clear=False):
+                import importlib
+                import tradingagents.logging as log_module
+                importlib.reload(log_module)
+
+                root_logger = log_module.setup_logging()
+
+                assert root_logger is not None
+                assert root_logger.name == "tradingagents"
+                assert root_logger.level == logging.DEBUG
+
+                has_file_handler = any(
+                    hasattr(h, "baseFilename") for h in root_logger.handlers
+                )
+                assert has_file_handler, "File handler should be present when LOG_FILE=true"
+
+    def test_get_logger_returns_properly_configured_logger_with_hierarchy(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            env_vars = {
+                "TRADINGAGENTS_LOG_LEVEL": "INFO",
+                "TRADINGAGENTS_LOG_DIR": tmpdir,
+                "TRADINGAGENTS_LOG_CONSOLE": "false",
+                "TRADINGAGENTS_LOG_FILE": "true",
+            }
+            with patch.dict(os.environ, env_vars, clear=False):
+                import importlib
+                import tradingagents.logging as log_module
+                importlib.reload(log_module)
+
+                log_module.setup_logging()
+                child_logger = log_module.get_logger("tradingagents.dataflows.interface")
+
+                assert child_logger.name == "tradingagents.dataflows.interface"
+                assert child_logger.parent.name == "tradingagents.dataflows" or child_logger.parent.name == "tradingagents"
+
+    def test_json_file_handler_writes_valid_json_with_required_fields(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            env_vars = {
+                "TRADINGAGENTS_LOG_LEVEL": "DEBUG",
+                "TRADINGAGENTS_LOG_DIR": tmpdir,
+                "TRADINGAGENTS_LOG_CONSOLE": "false",
+                "TRADINGAGENTS_LOG_FILE": "true",
+            }
+            with patch.dict(os.environ, env_vars, clear=False):
+                import importlib
+                import tradingagents.logging as log_module
+                importlib.reload(log_module)
+
+                logger = log_module.setup_logging()
+                logger.info("Test message for JSON validation")
+
+                for handler in logger.handlers:
+                    handler.flush()
+
+                log_file_path = os.path.join(tmpdir, "tradingagents.log")
+                assert os.path.exists(log_file_path), f"Log file should exist at {log_file_path}"
+
+                with open(log_file_path, "r") as f:
+                    log_content = f.read().strip()
+
+                assert log_content, "Log file should not be empty"
+
+                log_entry = json.loads(log_content.split("\n")[0])
+
+                required_fields = ["timestamp", "level", "logger", "message", "filename", "funcName", "lineno"]
+                for field in required_fields:
+                    assert field in log_entry, f"JSON log should contain '{field}' field"
+
+                assert "T" in log_entry["timestamp"], "Timestamp should be in ISO 8601 format"
+                assert log_entry["level"] == "INFO"
+                assert log_entry["message"] == "Test message for JSON validation"
+
+    def test_log_rotation_triggers_at_configured_file_size(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            env_vars = {
+                "TRADINGAGENTS_LOG_LEVEL": "DEBUG",
+                "TRADINGAGENTS_LOG_DIR": tmpdir,
+                "TRADINGAGENTS_LOG_CONSOLE": "false",
+                "TRADINGAGENTS_LOG_FILE": "true",
+            }
+            with patch.dict(os.environ, env_vars, clear=False):
+                import importlib
+                import tradingagents.logging as log_module
+                importlib.reload(log_module)
+
+                logger = log_module.setup_logging()
+
+                file_handler = None
+                for handler in logger.handlers:
+                    if hasattr(handler, "maxBytes"):
+                        file_handler = handler
+                        break
+
+                assert file_handler is not None, "RotatingFileHandler should be configured"
+                assert file_handler.maxBytes == 10 * 1024 * 1024, "Max file size should be 10MB"
+                assert file_handler.backupCount == 5, "Backup count should be 5"
+
+    def test_console_handler_disabled_when_env_var_false(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            env_vars = {
+                "TRADINGAGENTS_LOG_LEVEL": "INFO",
+                "TRADINGAGENTS_LOG_DIR": tmpdir,
+                "TRADINGAGENTS_LOG_CONSOLE": "false",
+                "TRADINGAGENTS_LOG_FILE": "true",
+            }
+            with patch.dict(os.environ, env_vars, clear=False):
+                import importlib
+                import tradingagents.logging as log_module
+                importlib.reload(log_module)
+
+                logger = log_module.setup_logging()
+
+                from rich.logging import RichHandler
+                has_rich_handler = any(isinstance(h, RichHandler) for h in logger.handlers)
+                assert not has_rich_handler, "RichHandler should NOT be present when LOG_CONSOLE=false"
+
+    def test_console_handler_enabled_when_env_var_true(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            env_vars = {
+                "TRADINGAGENTS_LOG_LEVEL": "INFO",
+                "TRADINGAGENTS_LOG_DIR": tmpdir,
+                "TRADINGAGENTS_LOG_CONSOLE": "true",
+                "TRADINGAGENTS_LOG_FILE": "false",
+            }
+            with patch.dict(os.environ, env_vars, clear=False):
+                import importlib
+                import tradingagents.logging as log_module
+                importlib.reload(log_module)
+
+                logger = log_module.setup_logging()
+
+                from rich.logging import RichHandler
+                has_rich_handler = any(isinstance(h, RichHandler) for h in logger.handlers)
+                assert has_rich_handler, "RichHandler should be present when LOG_CONSOLE=true"

tests/test_logging_config.py

@@ -0,0 +1,121 @@
+import logging
+import os
+import tempfile
+import pytest
+from unittest.mock import patch
+
+
+class TestLoggingConfigIntegration:
+    @pytest.fixture(autouse=True)
+    def setup_and_teardown(self):
+        for handler in logging.root.handlers[:]:
+            logging.root.removeHandler(handler)
+
+        tradingagents_logger = logging.getLogger("tradingagents")
+        for handler in tradingagents_logger.handlers[:]:
+            tradingagents_logger.removeHandler(handler)
+        tradingagents_logger.setLevel(logging.NOTSET)
+
+        yield
+
+        for handler in logging.root.handlers[:]:
+            logging.root.removeHandler(handler)
+        tradingagents_logger = logging.getLogger("tradingagents")
+        for handler in tradingagents_logger.handlers[:]:
+            tradingagents_logger.removeHandler(handler)
+
+    def test_default_config_values_used_when_env_vars_not_set(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            env_vars_to_remove = [
+                "TRADINGAGENTS_LOG_LEVEL",
+                "TRADINGAGENTS_LOG_DIR",
+                "TRADINGAGENTS_LOG_CONSOLE",
+                "TRADINGAGENTS_LOG_FILE",
+            ]
+            clean_env = {k: v for k, v in os.environ.items() if k not in env_vars_to_remove}
+
+            with patch.dict(os.environ, clean_env, clear=True):
+                import importlib
+                import tradingagents.logging as log_module
+                importlib.reload(log_module)
+
+                from tradingagents.default_config import DEFAULT_CONFIG
+
+                expected_level = getattr(logging, DEFAULT_CONFIG.get("log_level", "INFO").upper())
+
+                logger = log_module.setup_logging()
+
+                assert logger.level == expected_level
+
+    def test_env_vars_override_default_config_values(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            env_vars = {
+                "TRADINGAGENTS_LOG_LEVEL": "WARNING",
+                "TRADINGAGENTS_LOG_DIR": tmpdir,
+                "TRADINGAGENTS_LOG_CONSOLE": "false",
+                "TRADINGAGENTS_LOG_FILE": "true",
+            }
+            with patch.dict(os.environ, env_vars, clear=False):
+                import importlib
+                import tradingagents.logging as log_module
+                importlib.reload(log_module)
+
+                logger = log_module.setup_logging()
+
+                assert logger.level == logging.WARNING
+
+    def test_boolean_parsing_for_log_console_and_file(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_cases = [
+                ("true", True),
+                ("false", False),
+                ("1", True),
+                ("0", False),
+                ("True", True),
+                ("False", False),
+                ("TRUE", True),
+                ("FALSE", False),
+                ("yes", True),
+                ("no", False),
+            ]
+
+            for bool_str, expected in test_cases:
+                env_vars = {
+                    "TRADINGAGENTS_LOG_LEVEL": "INFO",
+                    "TRADINGAGENTS_LOG_DIR": tmpdir,
+                    "TRADINGAGENTS_LOG_CONSOLE": bool_str,
+                    "TRADINGAGENTS_LOG_FILE": "false",
+                }
+
+                tradingagents_logger = logging.getLogger("tradingagents")
+                for handler in tradingagents_logger.handlers[:]:
+                    tradingagents_logger.removeHandler(handler)
+
+                with patch.dict(os.environ, env_vars, clear=False):
+                    import importlib
+                    import tradingagents.logging as log_module
+                    importlib.reload(log_module)
+
+                    logger = log_module.setup_logging()
+
+                    from rich.logging import RichHandler
+                    has_rich_handler = any(isinstance(h, RichHandler) for h in logger.handlers)
+
+                    assert has_rich_handler == expected, f"TRADINGAGENTS_LOG_CONSOLE={bool_str} should result in RichHandler present={expected}"
+
+    def test_invalid_log_level_falls_back_to_info(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            env_vars = {
+                "TRADINGAGENTS_LOG_LEVEL": "INVALID_LEVEL",
+                "TRADINGAGENTS_LOG_DIR": tmpdir,
+                "TRADINGAGENTS_LOG_CONSOLE": "false",
+                "TRADINGAGENTS_LOG_FILE": "true",
+            }
+            with patch.dict(os.environ, env_vars, clear=False):
+                import importlib
+                import tradingagents.logging as log_module
+                importlib.reload(log_module)
+
+                logger = log_module.setup_logging()
+
+                assert logger.level == logging.INFO, "Invalid log level should fall back to INFO"

tests/test_logging_integration.py

@@ -0,0 +1,169 @@
+import logging
+import os
+import tempfile
+import pytest
+from unittest.mock import patch
+
+
+class TestLoggingIntegration:
+    @pytest.fixture(autouse=True)
+    def setup_and_teardown(self):
+        for handler in logging.root.handlers[:]:
+            logging.root.removeHandler(handler)
+
+        tradingagents_logger = logging.getLogger("tradingagents")
+        for handler in tradingagents_logger.handlers[:]:
+            tradingagents_logger.removeHandler(handler)
+        tradingagents_logger.setLevel(logging.NOTSET)
+
+        yield
+
+        for handler in logging.root.handlers[:]:
+            logging.root.removeHandler(handler)
+        tradingagents_logger = logging.getLogger("tradingagents")
+        for handler in tradingagents_logger.handlers[:]:
+            tradingagents_logger.removeHandler(handler)
+
+    def test_logging_initialization_from_module_import(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            env_vars = {
+                "TRADINGAGENTS_LOG_LEVEL": "INFO",
+                "TRADINGAGENTS_LOG_DIR": tmpdir,
+                "TRADINGAGENTS_LOG_CONSOLE": "false",
+                "TRADINGAGENTS_LOG_FILE": "true",
+            }
+            with patch.dict(os.environ, env_vars, clear=False):
+                import importlib
+                import tradingagents.logging as log_module
+                importlib.reload(log_module)
+
+                log_module.setup_logging()
+
+                interface_logger = log_module.get_logger("tradingagents.dataflows.interface")
+
+                assert interface_logger is not None
+                assert interface_logger.name == "tradingagents.dataflows.interface"
+
+                interface_logger.info("Test message from interface logger")
+
+                log_file = os.path.join(tmpdir, "tradingagents.log")
+                assert os.path.exists(log_file)
+
+                with open(log_file, "r") as f:
+                    content = f.read()
+                    assert "Test message from interface logger" in content
+
+    def test_rich_handler_does_not_break_cli_live_displays(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            env_vars = {
+                "TRADINGAGENTS_LOG_LEVEL": "INFO",
+                "TRADINGAGENTS_LOG_DIR": tmpdir,
+                "TRADINGAGENTS_LOG_CONSOLE": "true",
+                "TRADINGAGENTS_LOG_FILE": "false",
+            }
+            with patch.dict(os.environ, env_vars, clear=False):
+                import importlib
+                import tradingagents.logging as log_module
+                importlib.reload(log_module)
+
+                logger = log_module.setup_logging()
+
+                from rich.logging import RichHandler
+                rich_handlers = [h for h in logger.handlers if isinstance(h, RichHandler)]
+                assert len(rich_handlers) == 1
+
+                rich_handler = rich_handlers[0]
+                assert rich_handler.console is not None
+                assert rich_handler.console.file is not None
+
+    def test_log_file_creation_and_format(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            env_vars = {
+                "TRADINGAGENTS_LOG_LEVEL": "DEBUG",
+                "TRADINGAGENTS_LOG_DIR": tmpdir,
+                "TRADINGAGENTS_LOG_CONSOLE": "false",
+                "TRADINGAGENTS_LOG_FILE": "true",
+            }
+            with patch.dict(os.environ, env_vars, clear=False):
+                import importlib
+                import json
+                import tradingagents.logging as log_module
+                importlib.reload(log_module)
+
+                logger = log_module.setup_logging()
+
+                logger.debug("Debug message")
+                logger.info("Info message")
+                logger.warning("Warning message")
+                logger.error("Error message")
+
+                for handler in logger.handlers:
+                    handler.flush()
+
+                log_file = os.path.join(tmpdir, "tradingagents.log")
+                assert os.path.exists(log_file)
+
+                with open(log_file, "r") as f:
+                    lines = f.readlines()
+
+                assert len(lines) >= 4
+
+                for line in lines:
+                    log_entry = json.loads(line)
+                    assert "timestamp" in log_entry
+                    assert "level" in log_entry
+                    assert "logger" in log_entry
+                    assert "message" in log_entry
+
+    def test_logger_hierarchy_inherits_parent_configuration(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            env_vars = {
+                "TRADINGAGENTS_LOG_LEVEL": "WARNING",
+                "TRADINGAGENTS_LOG_DIR": tmpdir,
+                "TRADINGAGENTS_LOG_CONSOLE": "false",
+                "TRADINGAGENTS_LOG_FILE": "true",
+            }
+            with patch.dict(os.environ, env_vars, clear=False):
+                import importlib
+                import tradingagents.logging as log_module
+                importlib.reload(log_module)
+
+                root_logger = log_module.setup_logging()
+
+                child_logger = log_module.get_logger("tradingagents.dataflows.interface")
+                grandchild_logger = log_module.get_logger("tradingagents.dataflows.interface.submodule")
+
+                assert root_logger.level == logging.WARNING
+
+                child_logger.info("This should not be logged")
+                child_logger.warning("This should be logged")
+
+                for handler in root_logger.handlers:
+                    handler.flush()
+
+                log_file = os.path.join(tmpdir, "tradingagents.log")
+                with open(log_file, "r") as f:
+                    content = f.read()
+
+                assert "This should not be logged" not in content
+                assert "This should be logged" in content
+
+    def test_lazy_initialization_pattern(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            env_vars = {
+                "TRADINGAGENTS_LOG_LEVEL": "INFO",
+                "TRADINGAGENTS_LOG_DIR": tmpdir,
+                "TRADINGAGENTS_LOG_CONSOLE": "false",
+                "TRADINGAGENTS_LOG_FILE": "true",
+            }
+            with patch.dict(os.environ, env_vars, clear=False):
+                import importlib
+                import tradingagents.logging as log_module
+                importlib.reload(log_module)
+
+                log_module._logging_initialized = False
+
+                logger = log_module.get_logger("tradingagents.test")
+
+                assert log_module._logging_initialized is True
+                assert logger is not None

tests/test_logging_migration.py

@@ -0,0 +1,124 @@
+import ast
+import os
+import pytest
+
+
+class TestLoggingMigration:
+    def test_no_print_statements_in_interface_py(self):
+        file_path = os.path.join(
+            os.path.dirname(__file__),
+            "..",
+            "tradingagents",
+            "dataflows",
+            "interface.py",
+        )
+        with open(file_path, "r") as f:
+            content = f.read()
+
+        tree = ast.parse(content)
+
+        print_calls = []
+        for node in ast.walk(tree):
+            if isinstance(node, ast.Call):
+                if isinstance(node.func, ast.Name) and node.func.id == "print":
+                    print_calls.append(node.lineno)
+
+        assert len(print_calls) == 0, f"Found print statements at lines: {print_calls}"
+
+    def test_no_print_statements_in_brave_py(self):
+        file_path = os.path.join(
+            os.path.dirname(__file__),
+            "..",
+            "tradingagents",
+            "dataflows",
+            "brave.py",
+        )
+        with open(file_path, "r") as f:
+            content = f.read()
+
+        tree = ast.parse(content)
+
+        print_calls = []
+        for node in ast.walk(tree):
+            if isinstance(node, ast.Call):
+                if isinstance(node.func, ast.Name) and node.func.id == "print":
+                    print_calls.append(node.lineno)
+
+        assert len(print_calls) == 0, f"Found print statements at lines: {print_calls}"
+
+    def test_no_print_statements_in_tavily_py(self):
+        file_path = os.path.join(
+            os.path.dirname(__file__),
+            "..",
+            "tradingagents",
+            "dataflows",
+            "tavily.py",
+        )
+        with open(file_path, "r") as f:
+            content = f.read()
+
+        tree = ast.parse(content)
+
+        print_calls = []
+        for node in ast.walk(tree):
+            if isinstance(node, ast.Call):
+                if isinstance(node.func, ast.Name) and node.func.id == "print":
+                    print_calls.append(node.lineno)
+
+        assert len(print_calls) == 0, f"Found print statements at lines: {print_calls}"
+
+    def test_no_print_statements_in_migrated_dataflow_files(self):
+        dataflow_files = [
+            "alpha_vantage_news.py",
+            "y_finance.py",
+            "local.py",
+            "yfin_utils.py",
+            "googlenews_utils.py",
+            "utils.py",
+            "alpha_vantage_common.py",
+            "alpha_vantage_indicator.py",
+        ]
+
+        dataflows_dir = os.path.join(
+            os.path.dirname(__file__),
+            "..",
+            "tradingagents",
+            "dataflows",
+        )
+
+        all_print_calls = {}
+
+        for filename in dataflow_files:
+            file_path = os.path.join(dataflows_dir, filename)
+            if not os.path.exists(file_path):
+                continue
+
+            with open(file_path, "r") as f:
+                content = f.read()
+
+            tree = ast.parse(content)
+
+            print_calls = []
+            for node in ast.walk(tree):
+                if isinstance(node, ast.Call):
+                    if isinstance(node.func, ast.Name) and node.func.id == "print":
+                        print_calls.append(node.lineno)
+
+            if print_calls:
+                all_print_calls[filename] = print_calls
+
+        assert len(all_print_calls) == 0, f"Found print statements in: {all_print_calls}"
+
+    def test_logger_import_exists_in_interface_py(self):
+        file_path = os.path.join(
+            os.path.dirname(__file__),
+            "..",
+            "tradingagents",
+            "dataflows",
+            "interface.py",
+        )
+        with open(file_path, "r") as f:
+            content = f.read()
+
+        assert "import logging" in content, "interface.py should import logging"
+        assert "logger = logging.getLogger(__name__)" in content, "interface.py should define logger"

tradingagents/agents/utils/memory.py

@@ -1,7 +1,10 @@
+import logging
 import chromadb
 from chromadb.config import Settings
 from openai import OpenAI
 
+logger = logging.getLogger(__name__)
+
 
 class FinancialSituationMemory:
     def __init__(self, name, config):
@@ -14,7 +17,6 @@ class FinancialSituationMemory:
         self.situation_collection = self.chroma_client.get_or_create_collection(name=name)
 
     def get_embedding(self, text):
-        """Get OpenAI embedding for a text"""
 
         response = self.client.embeddings.create(
             model=self.embedding, input=text
@@ -22,7 +24,6 @@ class FinancialSituationMemory:
         return response.data[0].embedding
 
     def add_situations(self, situations_and_advice):
-        """Add financial situations and their corresponding advice. Parameter is a list of tuples (situation, rec)"""
 
         situations = []
         advice = []
@@ -45,7 +46,6 @@ class FinancialSituationMemory:
         )
 
     def get_memories(self, current_situation, n_matches=1):
-        """Find matching recommendations using OpenAI embeddings"""
         query_embedding = self.get_embedding(current_situation)
 
         results = self.situation_collection.query(
@@ -65,47 +65,3 @@ class FinancialSituationMemory:
             )
 
         return matched_results
-
-
-# if __name__ == "__main__":
-#     # Example usage
-#     matcher = FinancialSituationMemory()
-#     example_data = [
-#         (
-#             "High inflation rate with rising interest rates and declining consumer spending",
-#             "Consider defensive sectors like consumer staples and utilities. Review fixed-income portfolio duration.",
-#         ),
-#         (
-#             "Tech sector showing high volatility with increasing institutional selling pressure",
-#             "Reduce exposure to high-growth tech stocks. Look for value opportunities in established tech companies with strong cash flows.",
-#         ),
-#         (
-#             "Strong dollar affecting emerging markets with increasing forex volatility",
-#             "Hedge currency exposure in international positions. Consider reducing allocation to emerging market debt.",
-#         ),
-#         (
-#             "Market showing signs of sector rotation with rising yields",
-#             "Rebalance portfolio to maintain target allocations. Consider increasing exposure to sectors benefiting from higher rates.",
-#         ),
-#     ]
-
-#     # Add the example situations and recommendations
-#     matcher.add_situations(example_data)
-
-#     # Example query
-#     current_situation = """
-#     Market showing increased volatility in tech sector, with institutional investors 
-#     reducing positions and rising interest rates affecting growth stock valuations
-#     """
-
-#     try:
-#         recommendations = matcher.get_memories(current_situation, n_matches=2)
-
-#         for i, rec in enumerate(recommendations, 1):
-#             print(f"\nMatch {i}:")
-#             print(f"Similarity Score: {rec['similarity_score']:.2f}")
-#             print(f"Matched Situation: {rec['matched_situation']}")
-#             print(f"Recommendation: {rec['recommendation']}")
-
-#     except Exception as e:
-#         print(f"Error during recommendation: {str(e)}")

tradingagents/dataflows/alpha_vantage_common.py

@@ -1,3 +1,4 @@
+import logging
 import os
 import requests
 import pandas as pd
@@ -5,22 +6,20 @@ import json
 from datetime import datetime
 from io import StringIO
 
+logger = logging.getLogger(__name__)
+
 API_BASE_URL = "https://www.alphavantage.co/query"
 
 def get_api_key() -> str:
-    """Retrieve the API key for Alpha Vantage from environment variables."""
     api_key = os.getenv("ALPHA_VANTAGE_API_KEY")
     if not api_key:
         raise ValueError("ALPHA_VANTAGE_API_KEY environment variable is not set.")
     return api_key
 
 def format_datetime_for_api(date_input) -> str:
-    """Convert various date formats to YYYYMMDDTHHMM format required by Alpha Vantage API."""
     if isinstance(date_input, str):
-        # If already in correct format, return as-is
         if len(date_input) == 13 and 'T' in date_input:
             return date_input
-        # Try to parse common date formats
         try:
             dt = datetime.strptime(date_input, "%Y-%m-%d")
             return dt.strftime("%Y%m%dT0000")
@@ -36,16 +35,9 @@ def format_datetime_for_api(date_input) -> str:
         raise ValueError(f"Date must be string or datetime object, got {type(date_input)}")
 
 class AlphaVantageRateLimitError(Exception):
-    """Exception raised when Alpha Vantage API rate limit is exceeded."""
     pass
 
 def _make_api_request(function_name: str, params: dict) -> dict | str:
-    """Helper function to make API requests and handle responses.
-    
-    Raises:
-        AlphaVantageRateLimitError: When API rate limit is exceeded
-    """
-    # Create a copy of params to avoid modifying the original
     api_params = params.copy()
     api_params.update({
         "function": function_name,
@@ -53,14 +45,12 @@ def _make_api_request(function_name: str, params: dict) -> dict | str:
         "source": "trading_agents",
     })
 
-    # Handle entitlement parameter if present in params or global variable
     current_entitlement = globals().get('_current_entitlement')
     entitlement = api_params.get("entitlement") or current_entitlement
 
     if entitlement:
         api_params["entitlement"] = entitlement
     elif "entitlement" in api_params:
-        # Remove entitlement if it's None or empty
         api_params.pop("entitlement", None)
 
     response = requests.get(API_BASE_URL, params=api_params)
@@ -68,16 +58,13 @@ def _make_api_request(function_name: str, params: dict) -> dict | str:
 
     response_text = response.text
 
-    # Check if response is JSON (error responses are typically JSON)
     try:
         response_json = json.loads(response_text)
-        # Check for rate limit error
         if "Information" in response_json:
             info_message = response_json["Information"]
             if "rate limit" in info_message.lower() or "api key" in info_message.lower():
                 raise AlphaVantageRateLimitError(f"Alpha Vantage rate limit exceeded: {info_message}")
     except json.JSONDecodeError:
-        # Response is not JSON (likely CSV data), which is normal
         pass
 
     return response_text
@@ -85,38 +72,22 @@ def _make_api_request(function_name: str, params: dict) -> dict | str:
 
 
 def _filter_csv_by_date_range(csv_data: str, start_date: str, end_date: str) -> str:
-    """
-    Filter CSV data to include only rows within the specified date range.
-
-    Args:
-        csv_data: CSV string from Alpha Vantage API
-        start_date: Start date in yyyy-mm-dd format
-        end_date: End date in yyyy-mm-dd format
-
-    Returns:
-        Filtered CSV string
-    """
     if not csv_data or csv_data.strip() == "":
         return csv_data
 
     try:
-        # Parse CSV data
         df = pd.read_csv(StringIO(csv_data))
 
-        # Assume the first column is the date column (timestamp)
         date_col = df.columns[0]
         df[date_col] = pd.to_datetime(df[date_col])
 
-        # Filter by date range
         start_dt = pd.to_datetime(start_date)
         end_dt = pd.to_datetime(end_date)
 
         filtered_df = df[(df[date_col] >= start_dt) & (df[date_col] <= end_dt)]
 
-        # Convert back to CSV string
         return filtered_df.to_csv(index=False)
 
     except Exception as e:
-        # If filtering fails, return original data with a warning
+        logger.warning("Failed to filter CSV data by date range: %s", e)
-        print(f"Warning: Failed to filter CSV data by date range: {e}")
         return csv_data

tradingagents/dataflows/alpha_vantage_indicator.py

@@ -1,5 +1,8 @@
+import logging
 from .alpha_vantage_common import _make_api_request
 
+logger = logging.getLogger(__name__)
+
 def get_indicator(
     symbol: str,
     indicator: str,
@@ -9,21 +12,6 @@ def get_indicator(
     time_period: int = 14,
     series_type: str = "close"
 ) -> str:
-    """
-    Returns Alpha Vantage technical indicator values over a time window.
-
-    Args:
-        symbol: ticker symbol of the company
-        indicator: technical indicator to get the analysis and report of
-        curr_date: The current trading date you are trading on, YYYY-mm-dd
-        look_back_days: how many days to look back
-        interval: Time interval (daily, weekly, monthly)
-        time_period: Number of data points for calculation
-        series_type: The desired price type (close, open, high, low)
-
-    Returns:
-        String containing indicator values and description
-    """
     from datetime import datetime
     from dateutil.relativedelta import relativedelta
 
@@ -65,15 +53,12 @@ def get_indicator(
     curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
     before = curr_date_dt - relativedelta(days=look_back_days)
 
-    # Get the full data for the period instead of making individual calls
     _, required_series_type = supported_indicators[indicator]
 
-    # Use the provided series_type or fall back to the required one
     if required_series_type:
         series_type = required_series_type
 
     try:
-        # Get indicator data for the period
         if indicator == "close_50_sma":
             data = _make_api_request("SMA", {
                 "symbol": symbol,
@@ -143,25 +128,20 @@ def get_indicator(
                 "datatype": "csv"
             })
         elif indicator == "vwma":
-            # Alpha Vantage doesn't have direct VWMA, so we'll return an informative message
-            # In a real implementation, this would need to be calculated from OHLCV data
             return f"## VWMA (Volume Weighted Moving Average) for {symbol}:\n\nVWMA calculation requires OHLCV data and is not directly available from Alpha Vantage API.\nThis indicator would need to be calculated from the raw stock data using volume-weighted price averaging.\n\n{indicator_descriptions.get('vwma', 'No description available.')}"
         else:
             return f"Error: Indicator {indicator} not implemented yet."
 
-        # Parse CSV data and extract values for the date range
         lines = data.strip().split('\n')
         if len(lines) < 2:
             return f"Error: No data returned for {indicator}"
 
-        # Parse header and data
         header = [col.strip() for col in lines[0].split(',')]
         try:
             date_col_idx = header.index('time')
         except ValueError:
             return f"Error: 'time' column not found in data for {indicator}. Available columns: {header}"
 
-        # Map internal indicator names to expected CSV column names from Alpha Vantage
         col_name_map = {
             "macd": "MACD", "macds": "MACD_Signal", "macdh": "MACD_Hist",
             "boll": "Real Middle Band", "boll_ub": "Real Upper Band", "boll_lb": "Real Lower Band",
@@ -172,7 +152,6 @@ def get_indicator(
         target_col_name = col_name_map.get(indicator)
 
         if not target_col_name:
-            # Default to the second column if no specific mapping exists
             value_col_idx = 1
         else:
             try:
@@ -188,17 +167,14 @@ def get_indicator(
             if len(values) > value_col_idx:
                 try:
                     date_str = values[date_col_idx].strip()
-                    # Parse the date
                     date_dt = datetime.strptime(date_str, "%Y-%m-%d")
 
-                    # Check if date is in our range
                     if before <= date_dt <= curr_date_dt:
                         value = values[value_col_idx].strip()
                         result_data.append((date_dt, value))
                 except (ValueError, IndexError):
                     continue
 
-        # Sort by date and format output
         result_data.sort(key=lambda x: x[0])
 
         ind_string = ""
@@ -218,5 +194,5 @@ def get_indicator(
         return result_str
 
     except Exception as e:
-        print(f"Error getting Alpha Vantage indicator data for {indicator}: {e}")
+        logger.error("Error getting Alpha Vantage indicator data for %s: %s", indicator, e)
         return f"Error retrieving {indicator} data: {str(e)}"

tradingagents/dataflows/alpha_vantage_news.py

@@ -1,8 +1,11 @@
 import json
+import logging
 from datetime import datetime, timedelta
 from typing import List, Dict, Any
 from .alpha_vantage_common import _make_api_request, format_datetime_for_api
 
+logger = logging.getLogger(__name__)
+
 def get_news(ticker, start_date, end_date) -> dict[str, str] | str:
     params = {
         "tickers": ticker,
@@ -40,19 +43,19 @@ def get_bulk_news_alpha_vantage(lookback_hours: int) -> List[Dict[str, Any]]:
         try:
             response = json.loads(response)
         except json.JSONDecodeError:
-            print(f"DEBUG: Alpha Vantage JSON decode failed")
+            logger.debug("Alpha Vantage JSON decode failed")
             return []
 
     if not isinstance(response, dict):
-        print(f"DEBUG: Alpha Vantage response not a dict: {type(response)}")
+        logger.debug("Alpha Vantage response not a dict: %s", type(response))
         return []
 
     if "Information" in response:
-        print(f"DEBUG: Alpha Vantage info message: {response.get('Information')}")
+        logger.debug("Alpha Vantage info message: %s", response.get("Information"))
 
     feed = response.get("feed", [])
     if not feed:
-        print(f"DEBUG: Alpha Vantage feed empty. Keys in response: {list(response.keys())}")
+        logger.debug("Alpha Vantage feed empty. Keys in response: %s", list(response.keys()))
 
     articles = []
     for item in feed:

tradingagents/dataflows/brave.py

@@ -1,9 +1,12 @@
+import logging
 import os
 import time
 import requests
 from datetime import datetime, timedelta
 from typing import List, Dict, Any
 
+logger = logging.getLogger(__name__)
+
 BRAVE_SEARCH_URL = "https://api.search.brave.com/res/v1/news/search"
 DEFAULT_TIMEOUT = 30
 MAX_RETRIES = 3
@@ -24,23 +27,23 @@ def _make_request_with_retry(url: str, headers: Dict, params: Dict, max_retries:
             response = requests.get(url, headers=headers, params=params, timeout=DEFAULT_TIMEOUT)
             if response.status_code == 429:
                 retry_after = int(response.headers.get("Retry-After", RETRY_BACKOFF * (attempt + 1)))
-                print(f"DEBUG: Brave rate limited, waiting {retry_after}s before retry {attempt + 1}/{max_retries}")
+                logger.debug("Brave rate limited, waiting %ds before retry %d/%d", retry_after, attempt + 1, max_retries)
                 time.sleep(retry_after)
                 continue
             response.raise_for_status()
             return response
         except requests.exceptions.Timeout as e:
             last_exception = e
-            print(f"DEBUG: Brave request timeout, retry {attempt + 1}/{max_retries}")
+            logger.debug("Brave request timeout, retry %d/%d", attempt + 1, max_retries)
             time.sleep(RETRY_BACKOFF * (attempt + 1))
         except requests.exceptions.ConnectionError as e:
             last_exception = e
-            print(f"DEBUG: Brave connection error, retry {attempt + 1}/{max_retries}")
+            logger.debug("Brave connection error, retry %d/%d", attempt + 1, max_retries)
             time.sleep(RETRY_BACKOFF * (attempt + 1))
         except requests.exceptions.HTTPError as e:
             if e.response is not None and e.response.status_code >= 500:
                 last_exception = e
-                print(f"DEBUG: Brave server error {e.response.status_code}, retry {attempt + 1}/{max_retries}")
+                logger.debug("Brave server error %d, retry %d/%d", e.response.status_code, attempt + 1, max_retries)
                 time.sleep(RETRY_BACKOFF * (attempt + 1))
             else:
                 raise
@@ -51,7 +54,7 @@ def get_bulk_news_brave(lookback_hours: int) -> List[Dict[str, Any]]:
     try:
         api_key = get_api_key()
     except ValueError as e:
-        print(f"DEBUG: Brave API key not configured: {e}")
+        logger.debug("Brave API key not configured: %s", e)
         return []
 
     headers = {
@@ -109,19 +112,19 @@ def get_bulk_news_brave(lookback_hours: int) -> List[Dict[str, Any]]:
                     all_articles.append(article)
 
         except requests.exceptions.HTTPError as e:
-            print(f"DEBUG: Brave search HTTP error for '{query}': {e}")
+            logger.debug("Brave search HTTP error for '%s': %s", query, e)
             continue
         except requests.exceptions.Timeout as e:
-            print(f"DEBUG: Brave search timeout for '{query}': {e}")
+            logger.debug("Brave search timeout for '%s': %s", query, e)
             continue
         except requests.exceptions.RequestException as e:
-            print(f"DEBUG: Brave search request failed for '{query}': {e}")
+            logger.debug("Brave search request failed for '%s': %s", query, e)
             continue
         except Exception as e:
-            print(f"DEBUG: Brave search failed for query '{query}': {e}")
+            logger.debug("Brave search failed for query '%s': %s", query, e)
             continue
 
-    print(f"DEBUG: Brave returned {len(all_articles)} articles")
+    logger.debug("Brave returned %d articles", len(all_articles))
     return all_articles
 
 

tradingagents/dataflows/googlenews_utils.py

@@ -1,3 +1,4 @@
+import logging
 import json
 import requests
 from bs4 import BeautifulSoup
@@ -12,9 +13,10 @@ from tenacity import (
     retry_if_result,
 )
 
+logger = logging.getLogger(__name__)
+
 
 def is_rate_limited(response):
-    """Check if the response indicates rate limiting (status code 429)"""
     return response.status_code == 429
 
 
@@ -24,20 +26,12 @@ def is_rate_limited(response):
     stop=stop_after_attempt(5),
 )
 def make_request(url, headers):
-    """Make a request with retry logic for rate limiting"""
-    # Random delay before each request to avoid detection
     time.sleep(random.uniform(2, 6))
     response = requests.get(url, headers=headers)
     return response
 
 
 def getNewsData(query, start_date, end_date):
-    """
-    Scrape Google News search results for a given query and date range.
-    query: str - search query
-    start_date: str - start date in the format yyyy-mm-dd or mm/dd/yyyy
-    end_date: str - end date in the format yyyy-mm-dd or mm/dd/yyyy
-    """
     if "-" in start_date:
         start_date = datetime.strptime(start_date, "%Y-%m-%d")
         start_date = start_date.strftime("%m/%d/%Y")
@@ -69,7 +63,7 @@ def getNewsData(query, start_date, end_date):
             results_on_page = soup.select("div.SoaBEf")
 
             if not results_on_page:
-                break  # No more results found
+                break
 
             for el in results_on_page:
                 try:
@@ -88,13 +82,9 @@ def getNewsData(query, start_date, end_date):
                         }
                     )
                 except Exception as e:
-                    print(f"Error processing result: {e}")
+                    logger.debug("Error processing result: %s", e)
-                    # If one of the fields is not found, skip this result
                     continue
 
-            # Update the progress bar with the current count of results scraped
-
-            # Check for the "Next" link (pagination)
             next_link = soup.find("a", id="pnnext")
             if not next_link:
                 break
@@ -102,7 +92,7 @@ def getNewsData(query, start_date, end_date):
             page += 1
 
         except Exception as e:
-            print(f"Failed after multiple retries: {e}")
+            logger.debug("Failed after multiple retries: %s", e)
             break
 
     return news_results

tradingagents/dataflows/interface.py

@@ -1,3 +1,4 @@
+import logging
 from typing import Annotated, List, Dict, Any, Optional
 from datetime import datetime, timedelta
 import threading
@@ -25,6 +26,8 @@ from .config import get_config
 
 from tradingagents.agents.discovery import NewsArticle
 
+logger = logging.getLogger(__name__)
+
 TOOLS_CATEGORIES = {
     "core_stock_apis": {
         "description": "OHLCV stock price data",
@@ -206,17 +209,17 @@ def _fetch_bulk_news_from_vendor(lookback_period: str) -> List[Dict[str, Any]]:
         vendor_func = VENDOR_METHODS["get_bulk_news"][vendor]
 
         try:
-            print(f"DEBUG: Attempting bulk news from vendor '{vendor}'...")
+            logger.debug("Attempting bulk news from vendor '%s'...", vendor)
             result = vendor_func(lookback_hours)
             if result:
-                print(f"SUCCESS: Got {len(result)} articles from vendor '{vendor}'")
+                logger.info("Got %d articles from vendor '%s'", len(result), vendor)
                 return result
-            print(f"DEBUG: Vendor '{vendor}' returned empty results, trying next...")
+            logger.debug("Vendor '%s' returned empty results, trying next...", vendor)
         except AlphaVantageRateLimitError as e:
-            print(f"RATE_LIMIT: Alpha Vantage rate limit exceeded: {e}")
+            logger.warning("Alpha Vantage rate limit exceeded: %s", e)
             continue
         except Exception as e:
-            print(f"FAILED: Vendor '{vendor}' failed: {e}")
+            logger.error("Vendor '%s' failed: %s", vendor, e)
             continue
 
     return []
@@ -225,7 +228,7 @@ def _fetch_bulk_news_from_vendor(lookback_period: str) -> List[Dict[str, Any]]:
 def get_bulk_news(lookback_period: str = "24h") -> List[NewsArticle]:
     cached = _get_cached_bulk_news(lookback_period)
     if cached is not None:
-        print(f"DEBUG: Returning cached bulk news for period '{lookback_period}'")
+        logger.debug("Returning cached bulk news for period '%s'", lookback_period)
         return cached
 
     raw_articles = _fetch_bulk_news_from_vendor(lookback_period)
@@ -271,7 +274,7 @@ def route_to_vendor(method: str, *args, **kwargs):
 
     primary_str = " -> ".join(primary_vendors)
     fallback_str = " -> ".join(fallback_vendors)
-    print(f"DEBUG: {method} - Primary: [{primary_str}] | Full fallback order: [{fallback_str}]")
+    logger.debug("%s - Primary: [%s] | Full fallback order: [%s]", method, primary_str, fallback_str)
 
     results = []
     vendor_attempt_count = 0
@@ -281,7 +284,7 @@ def route_to_vendor(method: str, *args, **kwargs):
     for vendor in fallback_vendors:
         if vendor not in VENDOR_METHODS[method]:
             if vendor in primary_vendors:
-                print(f"INFO: Vendor '{vendor}' not supported for method '{method}', falling back to next vendor")
+                logger.info("Vendor '%s' not supported for method '%s', falling back to next vendor", vendor, method)
             continue
 
         vendor_impl = VENDOR_METHODS[method][vendor]
@@ -292,48 +295,48 @@ def route_to_vendor(method: str, *args, **kwargs):
             any_primary_vendor_attempted = True
 
         vendor_type = "PRIMARY" if is_primary_vendor else "FALLBACK"
-        print(f"DEBUG: Attempting {vendor_type} vendor '{vendor}' for {method} (attempt #{vendor_attempt_count})")
+        logger.debug("Attempting %s vendor '%s' for %s (attempt #%d)", vendor_type, vendor, method, vendor_attempt_count)
 
         if isinstance(vendor_impl, list):
             vendor_methods = [(impl, vendor) for impl in vendor_impl]
-            print(f"DEBUG: Vendor '{vendor}' has multiple implementations: {len(vendor_methods)} functions")
+            logger.debug("Vendor '%s' has multiple implementations: %d functions", vendor, len(vendor_methods))
         else:
             vendor_methods = [(vendor_impl, vendor)]
 
         vendor_results = []
         for impl_func, vendor_name in vendor_methods:
             try:
-                print(f"DEBUG: Calling {impl_func.__name__} from vendor '{vendor_name}'...")
+                logger.debug("Calling %s from vendor '%s'...", impl_func.__name__, vendor_name)
                 result = impl_func(*args, **kwargs)
                 vendor_results.append(result)
-                print(f"SUCCESS: {impl_func.__name__} from vendor '{vendor_name}' completed successfully")
+                logger.info("%s from vendor '%s' completed successfully", impl_func.__name__, vendor_name)
 
             except AlphaVantageRateLimitError as e:
                 if vendor == "alpha_vantage":
-                    print(f"RATE_LIMIT: Alpha Vantage rate limit exceeded, falling back to next available vendor")
+                    logger.warning("Alpha Vantage rate limit exceeded, falling back to next available vendor")
-                    print(f"DEBUG: Rate limit details: {e}")
+                    logger.debug("Rate limit details: %s", e)
                 continue
             except Exception as e:
-                print(f"FAILED: {impl_func.__name__} from vendor '{vendor_name}' failed: {e}")
+                logger.error("%s from vendor '%s' failed: %s", impl_func.__name__, vendor_name, e)
                 continue
 
         if vendor_results:
             results.extend(vendor_results)
             successful_vendor = vendor
             result_summary = f"Got {len(vendor_results)} result(s)"
-            print(f"SUCCESS: Vendor '{vendor}' succeeded - {result_summary}")
+            logger.info("Vendor '%s' succeeded - %s", vendor, result_summary)
 
             if len(primary_vendors) == 1:
-                print(f"DEBUG: Stopping after successful vendor '{vendor}' (single-vendor config)")
+                logger.debug("Stopping after successful vendor '%s' (single-vendor config)", vendor)
                 break
         else:
-            print(f"FAILED: Vendor '{vendor}' produced no results")
+            logger.error("Vendor '%s' produced no results", vendor)
 
     if not results:
-        print(f"FAILURE: All {vendor_attempt_count} vendor attempts failed for method '{method}'")
+        logger.error("All %d vendor attempts failed for method '%s'", vendor_attempt_count, method)
         raise RuntimeError(f"All vendor implementations failed for method '{method}'")
     else:
-        print(f"FINAL: Method '{method}' completed with {len(results)} result(s) from {vendor_attempt_count} vendor attempt(s)")
+        logger.info("Method '%s' completed with %d result(s) from %d vendor attempt(s)", method, len(results), vendor_attempt_count)
 
     if len(results) == 1:
         return results[0]

tradingagents/dataflows/local.py

@@ -1,3 +1,4 @@
+import logging
 from typing import Annotated
 import pandas as pd
 import os
@@ -8,17 +9,17 @@ import json
 from .reddit_utils import fetch_top_from_category
 from tqdm import tqdm
 
+logger = logging.getLogger(__name__)
+
 def get_YFin_data_window(
     symbol: Annotated[str, "ticker symbol of the company"],
     curr_date: Annotated[str, "Start date in yyyy-mm-dd format"],
     look_back_days: Annotated[int, "how many days to look back"],
 ) -> str:
-    # calculate past days
     date_obj = datetime.strptime(curr_date, "%Y-%m-%d")
     before = date_obj - relativedelta(days=look_back_days)
     start_date = before.strftime("%Y-%m-%d")
 
-    # read in data
     data = pd.read_csv(
         os.path.join(
             DATA_DIR,
@@ -26,18 +27,14 @@ def get_YFin_data_window(
         )
     )
 
-    # Extract just the date part for comparison
     data["DateOnly"] = data["Date"].str[:10]
 
-    # Filter data between the start and end dates (inclusive)
     filtered_data = data[
         (data["DateOnly"] >= start_date) & (data["DateOnly"] <= curr_date)
     ]
 
-    # Drop the temporary column we created
     filtered_data = filtered_data.drop("DateOnly", axis=1)
 
-    # Set pandas display options to show the full DataFrame
     with pd.option_context(
         "display.max_rows", None, "display.max_columns", None, "display.width", None
     ):
@@ -53,7 +50,6 @@ def get_YFin_data(
     start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
     end_date: Annotated[str, "End date in yyyy-mm-dd format"],
 ) -> str:
-    # read in data
     data = pd.read_csv(
         os.path.join(
             DATA_DIR,
@@ -66,18 +62,14 @@ def get_YFin_data(
             f"Get_YFin_Data: {end_date} is outside of the data range of 2015-01-01 to 2025-03-25"
         )
 
-    # Extract just the date part for comparison
     data["DateOnly"] = data["Date"].str[:10]
 
-    # Filter data between the start and end dates (inclusive)
     filtered_data = data[
         (data["DateOnly"] >= start_date) & (data["DateOnly"] <= end_date)
     ]
 
-    # Drop the temporary column we created
     filtered_data = filtered_data.drop("DateOnly", axis=1)
 
-    # remove the index from the dataframe
     filtered_data = filtered_data.reset_index(drop=True)
 
     return filtered_data
@@ -87,17 +79,6 @@ def get_finnhub_news(
     start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
     end_date: Annotated[str, "End date in yyyy-mm-dd format"],
 ):
-    """
-    Retrieve news about a company within a time frame
-
-    Args
-        query (str): Search query or ticker symbol
-        start_date (str): Start date in yyyy-mm-dd format
-        end_date (str): End date in yyyy-mm-dd format
-    Returns
-        str: dataframe containing the news of the company in the time frame
-
-    """
 
     result = get_data_in_range(query, start_date, end_date, "news_data", DATA_DIR)
 
@@ -121,17 +102,9 @@ def get_finnhub_company_insider_sentiment(
     ticker: Annotated[str, "ticker symbol for the company"],
     curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
 ):
-    """
-    Retrieve insider sentiment about a company (retrieved from public SEC information) for the past 15 days
-    Args:
-        ticker (str): ticker symbol of the company
-        curr_date (str): current date you are trading on, yyyy-mm-dd
-    Returns:
-        str: a report of the sentiment in the past 15 days starting at curr_date
-    """
 
     date_obj = datetime.strptime(curr_date, "%Y-%m-%d")
-    before = date_obj - relativedelta(days=15)  # Default 15 days lookback
+    before = date_obj - relativedelta(days=15)
     before = before.strftime("%Y-%m-%d")
 
     data = get_data_in_range(ticker, before, curr_date, "insider_senti", DATA_DIR)
@@ -158,17 +131,9 @@ def get_finnhub_company_insider_transactions(
     ticker: Annotated[str, "ticker symbol"],
     curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
 ):
-    """
-    Retrieve insider transcaction information about a company (retrieved from public SEC information) for the past 15 days
-    Args:
-        ticker (str): ticker symbol of the company
-        curr_date (str): current date you are trading at, yyyy-mm-dd
-    Returns:
-        str: a report of the company's insider transaction/trading informtaion in the past 15 days
-    """
 
     date_obj = datetime.strptime(curr_date, "%Y-%m-%d")
-    before = date_obj - relativedelta(days=15)  # Default 15 days lookback
+    before = date_obj - relativedelta(days=15)
     before = before.strftime("%Y-%m-%d")
 
     data = get_data_in_range(ticker, before, curr_date, "insider_trans", DATA_DIR)
@@ -192,15 +157,6 @@ def get_finnhub_company_insider_transactions(
     )
 
 def get_data_in_range(ticker, start_date, end_date, data_type, data_dir, period=None):
-    """
-    Gets finnhub data saved and processed on disk.
-    Args:
-        start_date (str): Start date in YYYY-MM-DD format.
-        end_date (str): End date in YYYY-MM-DD format.
-        data_type (str): Type of data from finnhub to fetch. Can be insider_trans, SEC_filings, news_data, insider_senti, or fin_as_reported.
-        data_dir (str): Directory where the data is saved.
-        period (str): Default to none, if there is a period specified, should be annual or quarterly.
-    """
 
     if period:
         data_path = os.path.join(
@@ -217,7 +173,6 @@ def get_data_in_range(ticker, start_date, end_date, data_type, data_dir, period=
     data = open(data_path, "r")
     data = json.load(data)
 
-    # filter keys (date, str in format YYYY-MM-DD) by the date range (str, str in format YYYY-MM-DD)
     filtered_data = {}
     for key, value in data.items():
         if start_date <= key <= end_date and len(value) > 0:
@@ -243,25 +198,19 @@ def get_simfin_balance_sheet(
     )
     df = pd.read_csv(data_path, sep=";")
 
-    # Convert date strings to datetime objects and remove any time components
     df["Report Date"] = pd.to_datetime(df["Report Date"], utc=True).dt.normalize()
     df["Publish Date"] = pd.to_datetime(df["Publish Date"], utc=True).dt.normalize()
 
-    # Convert the current date to datetime and normalize
     curr_date_dt = pd.to_datetime(curr_date, utc=True).normalize()
 
-    # Filter the DataFrame for the given ticker and for reports that were published on or before the current date
     filtered_df = df[(df["Ticker"] == ticker) & (df["Publish Date"] <= curr_date_dt)]
 
-    # Check if there are any available reports; if not, return a notification
     if filtered_df.empty:
-        print("No balance sheet available before the given current date.")
+        logger.info("No balance sheet available before the given current date.")
         return ""
 
-    # Get the most recent balance sheet by selecting the row with the latest Publish Date
     latest_balance_sheet = filtered_df.loc[filtered_df["Publish Date"].idxmax()]
 
-    # drop the SimFinID column
     latest_balance_sheet = latest_balance_sheet.drop("SimFinId")
 
     return (
@@ -290,25 +239,19 @@ def get_simfin_cashflow(
     )
     df = pd.read_csv(data_path, sep=";")
 
-    # Convert date strings to datetime objects and remove any time components
     df["Report Date"] = pd.to_datetime(df["Report Date"], utc=True).dt.normalize()
     df["Publish Date"] = pd.to_datetime(df["Publish Date"], utc=True).dt.normalize()
 
-    # Convert the current date to datetime and normalize
     curr_date_dt = pd.to_datetime(curr_date, utc=True).normalize()
 
-    # Filter the DataFrame for the given ticker and for reports that were published on or before the current date
     filtered_df = df[(df["Ticker"] == ticker) & (df["Publish Date"] <= curr_date_dt)]
 
-    # Check if there are any available reports; if not, return a notification
     if filtered_df.empty:
-        print("No cash flow statement available before the given current date.")
+        logger.info("No cash flow statement available before the given current date.")
         return ""
 
-    # Get the most recent cash flow statement by selecting the row with the latest Publish Date
     latest_cash_flow = filtered_df.loc[filtered_df["Publish Date"].idxmax()]
 
-    # drop the SimFinID column
     latest_cash_flow = latest_cash_flow.drop("SimFinId")
 
     return (
@@ -337,25 +280,19 @@ def get_simfin_income_statements(
     )
     df = pd.read_csv(data_path, sep=";")
 
-    # Convert date strings to datetime objects and remove any time components
     df["Report Date"] = pd.to_datetime(df["Report Date"], utc=True).dt.normalize()
     df["Publish Date"] = pd.to_datetime(df["Publish Date"], utc=True).dt.normalize()
 
-    # Convert the current date to datetime and normalize
     curr_date_dt = pd.to_datetime(curr_date, utc=True).normalize()
 
-    # Filter the DataFrame for the given ticker and for reports that were published on or before the current date
     filtered_df = df[(df["Ticker"] == ticker) & (df["Publish Date"] <= curr_date_dt)]
 
-    # Check if there are any available reports; if not, return a notification
     if filtered_df.empty:
-        print("No income statement available before the given current date.")
+        logger.info("No income statement available before the given current date.")
         return ""
 
-    # Get the most recent income statement by selecting the row with the latest Publish Date
     latest_income = filtered_df.loc[filtered_df["Publish Date"].idxmax()]
 
-    # drop the SimFinID column
     latest_income = latest_income.drop("SimFinId")
 
     return (
@@ -370,22 +307,12 @@ def get_reddit_global_news(
     look_back_days: Annotated[int, "Number of days to look back"] = 7,
     limit: Annotated[int, "Maximum number of articles to return"] = 5,
 ) -> str:
-    """
-    Retrieve the latest top reddit news
-    Args:
-        curr_date: Current date in yyyy-mm-dd format
-        look_back_days: Number of days to look back (default 7)
-        limit: Maximum number of articles to return (default 5)
-    Returns:
-        str: A formatted string containing the latest news articles posts on reddit
-    """
 
     curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
     before = curr_date_dt - relativedelta(days=look_back_days)
     before = before.strftime("%Y-%m-%d")
 
     posts = []
-    # iterate from before to curr_date
     curr_iter_date = datetime.strptime(before, "%Y-%m-%d")
 
     total_iterations = (curr_date_dt - curr_iter_date).days + 1
@@ -423,21 +350,11 @@ def get_reddit_company_news(
     start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
     end_date: Annotated[str, "End date in yyyy-mm-dd format"],
 ) -> str:
-    """
-    Retrieve the latest top reddit news
-    Args:
-        query: Search query or ticker symbol
-        start_date: Start date in yyyy-mm-dd format
-        end_date: End date in yyyy-mm-dd format
-    Returns:
-        str: A formatted string containing news articles posts on reddit
-    """
 
     start_date_dt = datetime.strptime(start_date, "%Y-%m-%d")
     end_date_dt = datetime.strptime(end_date, "%Y-%m-%d")
 
     posts = []
-    # iterate from start_date to end_date
     curr_date = start_date_dt
 
     total_iterations = (end_date_dt - curr_date).days + 1
@@ -451,7 +368,7 @@ def get_reddit_company_news(
         fetch_result = fetch_top_from_category(
             "company_news",
             curr_date_str,
-            10,  # max limit per day
+            10,
             query,
             data_path=os.path.join(DATA_DIR, "reddit_data"),
         )

tradingagents/dataflows/tavily.py

@@ -1,8 +1,11 @@
+import logging
 import os
 import time
 from datetime import datetime, timedelta
 from typing import List, Dict, Any
 
+logger = logging.getLogger(__name__)
+
 try:
     from tavily import TavilyClient
     TAVILY_AVAILABLE = True
@@ -37,17 +40,17 @@ def _search_with_retry(client, query: str, search_depth: str, topic: str, time_r
             error_str = str(e).lower()
             if "rate" in error_str or "limit" in error_str or "429" in error_str:
                 wait_time = RETRY_BACKOFF * (attempt + 1) * 2
-                print(f"DEBUG: Tavily rate limited, waiting {wait_time}s before retry {attempt + 1}/{max_retries}")
+                logger.debug("Tavily rate limited, waiting %ds before retry %d/%d", wait_time, attempt + 1, max_retries)
                 time.sleep(wait_time)
                 last_exception = e
             elif "timeout" in error_str or "timed out" in error_str:
                 wait_time = RETRY_BACKOFF * (attempt + 1)
-                print(f"DEBUG: Tavily timeout, waiting {wait_time}s before retry {attempt + 1}/{max_retries}")
+                logger.debug("Tavily timeout, waiting %ds before retry %d/%d", wait_time, attempt + 1, max_retries)
                 time.sleep(wait_time)
                 last_exception = e
             elif "connection" in error_str or "network" in error_str:
                 wait_time = RETRY_BACKOFF * (attempt + 1)
-                print(f"DEBUG: Tavily connection error, waiting {wait_time}s before retry {attempt + 1}/{max_retries}")
+                logger.debug("Tavily connection error, waiting %ds before retry %d/%d", wait_time, attempt + 1, max_retries)
                 time.sleep(wait_time)
                 last_exception = e
             else:
@@ -57,13 +60,13 @@ def _search_with_retry(client, query: str, search_depth: str, topic: str, time_r
 
 def get_bulk_news_tavily(lookback_hours: int) -> List[Dict[str, Any]]:
     if not TAVILY_AVAILABLE:
-        print("DEBUG: Tavily library not installed")
+        logger.debug("Tavily library not installed")
         return []
 
     try:
         client = TavilyClient(api_key=get_api_key())
     except ValueError as e:
-        print(f"DEBUG: Tavily API key not configured: {e}")
+        logger.debug("Tavily API key not configured: %s", e)
         return []
 
     queries = [
@@ -121,8 +124,8 @@ def get_bulk_news_tavily(lookback_hours: int) -> List[Dict[str, Any]]:
                     all_articles.append(article)
 
         except Exception as e:
-            print(f"DEBUG: Tavily search failed for query '{query}': {e}")
+            logger.debug("Tavily search failed for query '%s': %s", query, e)
             continue
 
-    print(f"DEBUG: Tavily returned {len(all_articles)} articles")
+    logger.debug("Tavily returned %d articles", len(all_articles))
     return all_articles

tradingagents/dataflows/utils.py

@@ -1,15 +1,18 @@
+import logging
 import os
 import json
 import pandas as pd
 from datetime import date, timedelta, datetime
 from typing import Annotated
 
+logger = logging.getLogger(__name__)
+
 SavePathType = Annotated[str, "File path to save data. If None, data is not saved."]
 
 def save_output(data: pd.DataFrame, tag: str, save_path: SavePathType = None) -> None:
     if save_path:
         data.to_csv(save_path)
-        print(f"{tag} saved to {save_path}")
+        logger.info("%s saved to %s", tag, save_path)
 
 
 def get_current_date():

tradingagents/dataflows/y_finance.py

@@ -1,3 +1,4 @@
+import logging
 from typing import Annotated
 from datetime import datetime
 from dateutil.relativedelta import relativedelta
@@ -5,6 +6,8 @@ import yfinance as yf
 import os
 from .stockstats_utils import StockstatsUtils
 
+logger = logging.getLogger(__name__)
+
 def get_YFin_data_online(
     symbol: Annotated[str, "ticker symbol of the company"],
     start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
@@ -14,32 +17,25 @@ def get_YFin_data_online(
     datetime.strptime(start_date, "%Y-%m-%d")
     datetime.strptime(end_date, "%Y-%m-%d")
 
-    # Create ticker object
     ticker = yf.Ticker(symbol.upper())
 
-    # Fetch historical data for the specified date range
     data = ticker.history(start=start_date, end=end_date)
 
-    # Check if data is empty
     if data.empty:
         return (
             f"No data found for symbol '{symbol}' between {start_date} and {end_date}"
         )
 
-    # Remove timezone info from index for cleaner output
     if data.index.tz is not None:
         data.index = data.index.tz_localize(None)
 
-    # Round numerical values to 2 decimal places for cleaner display
     numeric_columns = ["Open", "High", "Low", "Close", "Adj Close"]
     for col in numeric_columns:
         if col in data.columns:
             data[col] = data[col].round(2)
 
-    # Convert DataFrame to CSV string
     csv_string = data.to_csv()
 
-    # Add header information
     header = f"# Stock data for {symbol.upper()} from {start_date} to {end_date}\n"
     header += f"# Total records: {len(data)}\n"
     header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
@@ -56,7 +52,6 @@ def get_stock_stats_indicators_window(
 ) -> str:
 
     best_ind_params = {
-        # Moving Averages
         "close_50_sma": (
             "50 SMA: A medium-term trend indicator. "
             "Usage: Identify trend direction and serve as dynamic support/resistance. "
@@ -72,7 +67,6 @@ def get_stock_stats_indicators_window(
             "Usage: Capture quick shifts in momentum and potential entry points. "
             "Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals."
         ),
-        # MACD Related
         "macd": (
             "MACD: Computes momentum via differences of EMAs. "
             "Usage: Look for crossovers and divergence as signals of trend changes. "
@@ -88,13 +82,11 @@ def get_stock_stats_indicators_window(
             "Usage: Visualize momentum strength and spot divergence early. "
             "Tips: Can be volatile; complement with additional filters in fast-moving markets."
         ),
-        # Momentum Indicators
         "rsi": (
             "RSI: Measures momentum to flag overbought/oversold conditions. "
             "Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. "
             "Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis."
         ),
-        # Volatility Indicators
         "boll": (
             "Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. "
             "Usage: Acts as a dynamic benchmark for price movement. "
@@ -115,7 +107,6 @@ def get_stock_stats_indicators_window(
             "Usage: Set stop-loss levels and adjust position sizes based on current market volatility. "
             "Tips: It's a reactive measure, so use it as part of a broader risk management strategy."
         ),
-        # Volume-Based Indicators
         "vwma": (
             "VWMA: A moving average weighted by volume. "
             "Usage: Confirm trends by integrating price action with volume data. "
@@ -137,18 +128,15 @@ def get_stock_stats_indicators_window(
     curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
     before = curr_date_dt - relativedelta(days=look_back_days)
 
-    # Optimized: Get stock data once and calculate indicators for all dates
     try:
         indicator_data = _get_stock_stats_bulk(symbol, indicator, curr_date)
 
-        # Generate the date range we need
         current_dt = curr_date_dt
         date_values = []
 
         while current_dt >= before:
             date_str = current_dt.strftime('%Y-%m-%d')
 
-            # Look up the indicator value for this date
             if date_str in indicator_data:
                 indicator_value = indicator_data[date_str]
             else:
@@ -157,14 +145,12 @@ def get_stock_stats_indicators_window(
             date_values.append((date_str, indicator_value))
             current_dt = current_dt - relativedelta(days=1)
 
-        # Build the result string
         ind_string = ""
         for date_str, value in date_values:
             ind_string += f"{date_str}: {value}\n"
 
     except Exception as e:
-        print(f"Error getting bulk stockstats data: {e}")
+        logger.error("Error getting bulk stockstats data: %s", e)
-        # Fallback to original implementation if bulk method fails
         ind_string = ""
         curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
         while curr_date_dt >= before:
@@ -189,11 +175,6 @@ def _get_stock_stats_bulk(
     indicator: Annotated[str, "technical indicator to calculate"],
     curr_date: Annotated[str, "current date for reference"]
 ) -> dict:
-    """
-    Optimized bulk calculation of stock stats indicators.
-    Fetches data once and calculates indicator for all available dates.
-    Returns dict mapping date strings to indicator values.
-    """
     from .config import get_config
     import pandas as pd
     from stockstats import wrap
@@ -203,7 +184,6 @@ def _get_stock_stats_bulk(
     online = config["data_vendors"]["technical_indicators"] != "local"
 
     if not online:
-        # Local data path
         try:
             data = pd.read_csv(
                 os.path.join(
@@ -215,7 +195,6 @@ def _get_stock_stats_bulk(
         except FileNotFoundError:
             raise Exception("Stockstats fail: Yahoo Finance data not fetched yet!")
     else:
-        # Online data fetching with caching
         today_date = pd.Timestamp.today()
         curr_date_dt = pd.to_datetime(curr_date)
 
@@ -249,16 +228,13 @@ def _get_stock_stats_bulk(
         df = wrap(data)
         df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
 
-    # Calculate the indicator for all rows at once
+    df[indicator]
-    df[indicator]  # This triggers stockstats to calculate the indicator
 
-    # Create a dictionary mapping date strings to indicator values
     result_dict = {}
     for _, row in df.iterrows():
         date_str = row["Date"]
         indicator_value = row[indicator]
 
-        # Handle NaN/None values
         if pd.isna(indicator_value):
             result_dict[date_str] = "N/A"
         else:
@@ -285,8 +261,9 @@ def get_stockstats_indicator(
             curr_date,
         )
     except Exception as e:
-        print(
+        logger.error(
-            f"Error getting stockstats indicator data for indicator {indicator} on {curr_date}: {e}"
+            "Error getting stockstats indicator data for indicator %s on %s: %s",
+            indicator, curr_date, e
         )
         return ""
 
@@ -298,7 +275,6 @@ def get_balance_sheet(
     freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
     curr_date: Annotated[str, "current date (not used for yfinance)"] = None
 ):
-    """Get balance sheet data from yfinance."""
     try:
         ticker_obj = yf.Ticker(ticker.upper())
 
@@ -310,10 +286,8 @@ def get_balance_sheet(
         if data.empty:
             return f"No balance sheet data found for symbol '{ticker}'"
 
-        # Convert to CSV string for consistency with other functions
         csv_string = data.to_csv()
 
-        # Add header information
         header = f"# Balance Sheet data for {ticker.upper()} ({freq})\n"
         header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
 
@@ -328,7 +302,6 @@ def get_cashflow(
     freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
     curr_date: Annotated[str, "current date (not used for yfinance)"] = None
 ):
-    """Get cash flow data from yfinance."""
     try:
         ticker_obj = yf.Ticker(ticker.upper())
 
@@ -340,10 +313,8 @@ def get_cashflow(
         if data.empty:
             return f"No cash flow data found for symbol '{ticker}'"
 
-        # Convert to CSV string for consistency with other functions
         csv_string = data.to_csv()
 
-        # Add header information
         header = f"# Cash Flow data for {ticker.upper()} ({freq})\n"
         header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
 
@@ -358,7 +329,6 @@ def get_income_statement(
     freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
     curr_date: Annotated[str, "current date (not used for yfinance)"] = None
 ):
-    """Get income statement data from yfinance."""
     try:
         ticker_obj = yf.Ticker(ticker.upper())
 
@@ -370,10 +340,8 @@ def get_income_statement(
         if data.empty:
             return f"No income statement data found for symbol '{ticker}'"
 
-        # Convert to CSV string for consistency with other functions
         csv_string = data.to_csv()
 
-        # Add header information
         header = f"# Income Statement data for {ticker.upper()} ({freq})\n"
         header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
 
@@ -386,7 +354,6 @@ def get_income_statement(
 def get_insider_transactions(
     ticker: Annotated[str, "ticker symbol of the company"]
 ):
-    """Get insider transactions data from yfinance."""
     try:
         ticker_obj = yf.Ticker(ticker.upper())
         data = ticker_obj.insider_transactions
@@ -394,10 +361,8 @@ def get_insider_transactions(
         if data is None or data.empty:
             return f"No insider transactions data found for symbol '{ticker}'"
 
-        # Convert to CSV string for consistency with other functions
         csv_string = data.to_csv()
 
-        # Add header information
         header = f"# Insider Transactions data for {ticker.upper()}\n"
         header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
 

tradingagents/dataflows/yfin_utils.py

@@ -1,5 +1,4 @@
-# gets data/stats
+import logging
-
 import yfinance as yf
 from typing import Annotated, Callable, Any, Optional
 from pandas import DataFrame
@@ -8,9 +7,10 @@ from functools import wraps
 
 from .utils import save_output, SavePathType, decorate_all_methods
 
+logger = logging.getLogger(__name__)
+
 
 def init_ticker(func: Callable) -> Callable:
-    """Decorator to initialize yf.Ticker and pass it to the function."""
 
     @wraps(func)
     def wrapper(symbol: Annotated[str, "ticker symbol"], *args, **kwargs) -> Any:
@@ -33,19 +33,15 @@ class YFinanceUtils:
         ],
         save_path: SavePathType = None,
     ) -> DataFrame:
-        """retrieve stock price data for designated ticker symbol"""
         ticker = symbol
-        # add one day to the end_date so that the data range is inclusive
         end_date = pd.to_datetime(end_date) + pd.DateOffset(days=1)
         end_date = end_date.strftime("%Y-%m-%d")
         stock_data = ticker.history(start=start_date, end=end_date)
-        # save_output(stock_data, f"Stock data for {ticker.ticker}", save_path)
         return stock_data
 
     def get_stock_info(
         symbol: Annotated[str, "ticker symbol"],
     ) -> dict:
-        """Fetches and returns latest stock information."""
         ticker = symbol
         stock_info = ticker.info
         return stock_info
@@ -54,7 +50,6 @@ class YFinanceUtils:
         symbol: Annotated[str, "ticker symbol"],
         save_path: Optional[str] = None,
     ) -> DataFrame:
-        """Fetches and returns company information as a DataFrame."""
         ticker = symbol
         info = ticker.info
         company_info = {
@@ -67,50 +62,43 @@ class YFinanceUtils:
         company_info_df = DataFrame([company_info])
         if save_path:
             company_info_df.to_csv(save_path)
-            print(f"Company info for {ticker.ticker} saved to {save_path}")
+            logger.info("Company info for %s saved to %s", ticker.ticker, save_path)
         return company_info_df
 
     def get_stock_dividends(
         symbol: Annotated[str, "ticker symbol"],
         save_path: Optional[str] = None,
     ) -> DataFrame:
-        """Fetches and returns the latest dividends data as a DataFrame."""
         ticker = symbol
         dividends = ticker.dividends
         if save_path:
             dividends.to_csv(save_path)
-            print(f"Dividends for {ticker.ticker} saved to {save_path}")
+            logger.info("Dividends for %s saved to %s", ticker.ticker, save_path)
         return dividends
 
     def get_income_stmt(symbol: Annotated[str, "ticker symbol"]) -> DataFrame:
-        """Fetches and returns the latest income statement of the company as a DataFrame."""
         ticker = symbol
         income_stmt = ticker.financials
         return income_stmt
 
     def get_balance_sheet(symbol: Annotated[str, "ticker symbol"]) -> DataFrame:
-        """Fetches and returns the latest balance sheet of the company as a DataFrame."""
         ticker = symbol
         balance_sheet = ticker.balance_sheet
         return balance_sheet
 
     def get_cash_flow(symbol: Annotated[str, "ticker symbol"]) -> DataFrame:
-        """Fetches and returns the latest cash flow statement of the company as a DataFrame."""
         ticker = symbol
         cash_flow = ticker.cashflow
         return cash_flow
 
     def get_analyst_recommendations(symbol: Annotated[str, "ticker symbol"]) -> tuple:
-        """Fetches the latest analyst recommendations and returns the most common recommendation and its count."""
         ticker = symbol
         recommendations = ticker.recommendations
         if recommendations.empty:
-            return None, 0  # No recommendations available
+            return None, 0
 
-        # Assuming 'period' column exists and needs to be excluded
+        row_0 = recommendations.iloc[0, 1:]
-        row_0 = recommendations.iloc[0, 1:]  # Exclude 'period' column if necessary
 
-        # Find the maximum voting result
         max_votes = row_0.max()
         majority_voting_result = row_0[row_0 == max_votes].index.tolist()
 

tradingagents/default_config.py

@@ -31,4 +31,8 @@ DEFAULT_CONFIG = {
     "bulk_news_vendor_order": ["tavily", "brave", "alpha_vantage", "openai", "google"],
     "bulk_news_timeout": 30,
     "bulk_news_max_retries": 3,
+    "log_level": os.getenv("TRADINGAGENTS_LOG_LEVEL", "INFO"),
+    "log_dir": os.getenv("TRADINGAGENTS_LOG_DIR", "./logs"),
+    "log_console_enabled": os.getenv("TRADINGAGENTS_LOG_CONSOLE", "true").lower() in ("true", "1", "yes", "on"),
+    "log_file_enabled": os.getenv("TRADINGAGENTS_LOG_FILE", "true").lower() in ("true", "1", "yes", "on"),
 }

tradingagents/graph/trading_graph.py

@@ -1,3 +1,4 @@
+import logging
 import os
 import signal
 import threading
@@ -54,6 +55,8 @@ from .propagation import Propagator
 from .reflection import Reflector
 from .signal_processing import SignalProcessor
 
+logger = logging.getLogger(__name__)
+
 
 class DiscoveryTimeoutException(Exception):
     pass
@@ -164,7 +167,7 @@ class TradingAgentsGraph:
                 if len(chunk["messages"]) == 0:
                     pass
                 else:
-                    chunk["messages"][-1].pretty_print()
+                    logger.debug("Agent message: %s", chunk["messages"][-1])
                     trace.append(chunk)
 
             final_state = trace[-1]

tradingagents/logging.py

@@ -0,0 +1,121 @@
+import logging
+import logging.handlers
+import os
+import json
+from datetime import datetime
+
+LOG_LEVEL_DEFAULT = "INFO"
+LOG_DIR_DEFAULT = "./logs"
+LOG_FILE_NAME = "tradingagents.log"
+LOG_MAX_BYTES = 10 * 1024 * 1024
+LOG_BACKUP_COUNT = 5
+
+_logging_initialized = False
+
+
+class JSONFormatter(logging.Formatter):
+    def format(self, record):
+        log_record = {
+            "timestamp": datetime.utcnow().isoformat() + "Z",
+            "level": record.levelname,
+            "logger": record.name,
+            "message": record.getMessage(),
+            "filename": record.filename,
+            "funcName": record.funcName,
+            "lineno": record.lineno,
+        }
+
+        if record.exc_info:
+            log_record["exception"] = self.formatException(record.exc_info)
+
+        return json.dumps(log_record)
+
+
+def _parse_bool(value):
+    if isinstance(value, bool):
+        return value
+    if isinstance(value, str):
+        return value.lower() in ("true", "1", "yes", "on")
+    return bool(value)
+
+
+def _get_config_value(key, default):
+    try:
+        from tradingagents.default_config import DEFAULT_CONFIG
+        return DEFAULT_CONFIG.get(key, default)
+    except ImportError:
+        return default
+
+
+def setup_logging():
+    global _logging_initialized
+
+    log_level_str = os.getenv("TRADINGAGENTS_LOG_LEVEL")
+    if log_level_str is None:
+        log_level_str = _get_config_value("log_level", LOG_LEVEL_DEFAULT)
+
+    log_dir = os.getenv("TRADINGAGENTS_LOG_DIR")
+    if log_dir is None:
+        log_dir = _get_config_value("log_dir", LOG_DIR_DEFAULT)
+
+    console_enabled_env = os.getenv("TRADINGAGENTS_LOG_CONSOLE")
+    if console_enabled_env is not None:
+        console_enabled = _parse_bool(console_enabled_env)
+    else:
+        console_enabled = _get_config_value("log_console_enabled", True)
+
+    file_enabled_env = os.getenv("TRADINGAGENTS_LOG_FILE")
+    if file_enabled_env is not None:
+        file_enabled = _parse_bool(file_enabled_env)
+    else:
+        file_enabled = _get_config_value("log_file_enabled", True)
+
+    log_level = getattr(logging, log_level_str.upper(), logging.INFO)
+
+    root_logger = logging.getLogger("tradingagents")
+
+    for handler in root_logger.handlers[:]:
+        root_logger.removeHandler(handler)
+
+    root_logger.setLevel(log_level)
+
+    if file_enabled:
+        os.makedirs(log_dir, exist_ok=True)
+        log_file_path = os.path.join(log_dir, LOG_FILE_NAME)
+
+        file_handler = logging.handlers.RotatingFileHandler(
+            log_file_path,
+            maxBytes=LOG_MAX_BYTES,
+            backupCount=LOG_BACKUP_COUNT,
+        )
+        file_handler.setLevel(log_level)
+        file_handler.setFormatter(JSONFormatter())
+        root_logger.addHandler(file_handler)
+
+    if console_enabled:
+        from rich.logging import RichHandler
+        from rich.console import Console
+
+        console = Console(stderr=True)
+        rich_handler = RichHandler(
+            console=console,
+            show_time=True,
+            show_level=True,
+            show_path=True,
+            rich_tracebacks=True,
+        )
+        rich_handler.setLevel(log_level)
+        root_logger.addHandler(rich_handler)
+
+    _logging_initialized = True
+
+    return root_logger
+
+
+def get_logger(name):
+    global _logging_initialized
+
+    if not _logging_initialized:
+        setup_logging()
+
+    return logging.getLogger(name)