docs: drop comments that narrate the next line

- about seventy '# Create/Initialize/Add ...' lines across graph, cli and dataflows, and the file-path headers; comments that give a reason stay

cli/display.py

@@ -20,8 +20,6 @@ from tradingagents.graph.analyst_execution import (
     AnalystExecutionPlan,
 )
 
-# Create a deque to store recent messages with a maximum length
-
 console = Console()
 
 
@@ -72,20 +70,16 @@ class MessageBuffer:
         """
         self.selected_analysts = [a.lower() for a in selected_analysts]
 
-        # Build agent_status dynamically
         self.agent_status = {}
 
-        # Add selected analysts
         for analyst_key in self.selected_analysts:
             if analyst_key in self.ANALYST_MAPPING:
                 self.agent_status[self.ANALYST_MAPPING[analyst_key]] = "pending"
 
-        # Add fixed teams
         for team_agents in self.FIXED_AGENTS.values():
             for agent in team_agents:
                 self.agent_status[agent] = "pending"
 
-        # Build report_sections dynamically
         self.report_sections = {}
         for section, (analyst_key, _) in self.REPORT_SECTIONS.items():
             if analyst_key is None or analyst_key in self.selected_analysts:
@@ -140,14 +134,12 @@ class MessageBuffer:
         latest_section = None
         latest_content = None
 
-        # Find the most recently updated section
         for section, content in self.report_sections.items():
             if content is not None:
                 latest_section = section
                 latest_content = content
 
         if latest_section and latest_content:
-            # Format the current section for display
             section_titles = {
                 "market_report": "Market Analysis",
                 "sentiment_report": "Social Sentiment",
@@ -229,7 +221,6 @@ def update_display(layout, spinner_text=None, stats_handler=None, start_time=Non
         "Portfolio Management": ["Portfolio Manager"],
     }
 
-    # Filter teams to only include agents that are in agent_status
     teams = {}
     for team, agents in all_teams.items():
         active_agents = [a for a in agents if a in message_buffer.agent_status]
@@ -237,7 +228,6 @@ def update_display(layout, spinner_text=None, stats_handler=None, start_time=Non
             teams[team] = active_agents
 
     for team, agents in teams.items():
-        # Add first agent with team name
         first_agent = agents[0]
         status = message_buffer.agent_status.get(first_agent, "pending")
         if status == "in_progress":
@@ -254,7 +244,6 @@ def update_display(layout, spinner_text=None, stats_handler=None, start_time=Non
             status_cell = f"[{status_color}]{status}[/{status_color}]"
         progress_table.add_row(team, first_agent, status_cell)
 
-        # Add remaining agents in team
         for agent in agents[1:]:
             status = message_buffer.agent_status.get(agent, "pending")
             if status == "in_progress":
@@ -271,7 +260,6 @@ def update_display(layout, spinner_text=None, stats_handler=None, start_time=Non
                 status_cell = f"[{status_color}]{status}[/{status_color}]"
             progress_table.add_row("", agent, status_cell)
 
-        # Add horizontal line after each team
         progress_table.add_row("─" * 20, "─" * 20, "─" * 20, style="dim")
 
     layout["progress"].update(
@@ -297,12 +285,10 @@ def update_display(layout, spinner_text=None, stats_handler=None, start_time=Non
     # Combine tool calls and messages
     all_messages = []
 
-    # Add tool calls
     for timestamp, tool_name, args in message_buffer.tool_calls:
         formatted_args = format_tool_args(args)
         all_messages.append((timestamp, "Tool", f"{tool_name}: {formatted_args}"))
 
-    # Add regular messages
     for timestamp, msg_type, content in message_buffer.messages:
         content_str = str(content) if content else ""
         if len(content_str) > 200:
@@ -312,15 +298,11 @@ def update_display(layout, spinner_text=None, stats_handler=None, start_time=Non
     # Sort by timestamp descending (newest first)
     all_messages.sort(key=lambda x: x[0], reverse=True)
 
-    # Calculate how many messages we can show based on available space
     max_messages = 12
 
-    # Get the first N messages (newest ones)
     recent_messages = all_messages[:max_messages]
 
-    # Add messages to table (already in newest-first order)
     for timestamp, msg_type, content in recent_messages:
-        # Format content with word wrapping
         wrapped_content = Text(content, overflow="fold")
         messages_table.add_row(timestamp, msg_type, wrapped_content)
 
@@ -364,7 +346,6 @@ def update_display(layout, spinner_text=None, stats_handler=None, start_time=Non
     reports_completed = message_buffer.get_completed_reports_count()
     reports_total = len(message_buffer.report_sections)
 
-    # Build stats parts
     stats_parts = [f"Agents: {agents_completed}/{agents_total}"]
 
     # LLM and tool stats from callback handler

cli/prompts.py

@@ -140,7 +140,6 @@ def select_analysts(asset_type: AssetType = AssetType.STOCK, default=None) -> li
 def select_research_depth(default=None) -> int:
     """Select research depth using an interactive selection."""
 
-    # Define research depth options with their corresponding values
     DEPTH_OPTIONS = [
         ("Shallow - Quick research, few debate and strategy discussion rounds", 1),
         ("Medium - Middle ground, moderate debate rounds and strategy discussion", 3),

cli/run.py

@@ -99,7 +99,6 @@ def run_analysis(checkpoint: bool | None = None, portfolio=None):
 
     config = _build_run_config(selections, checkpoint)
 
-    # Create stats callback handler for tracking LLM/tool calls
     stats_handler = StatsCallbackHandler()
 
     # Normalize analyst selection to predefined order (selection is a 'set', order is fixed)
@@ -108,7 +107,6 @@ def run_analysis(checkpoint: bool | None = None, portfolio=None):
     analyst_execution_plan = build_analyst_execution_plan(selected_analyst_keys)
     analyst_wall_time_tracker = AnalystWallTimeTracker(analyst_execution_plan)
 
-    # Initialize the graph with callbacks bound to LLMs
     graph = TradingAgentsGraph(
         selected_analyst_keys,
         config=config,
@@ -116,13 +114,11 @@ def run_analysis(checkpoint: bool | None = None, portfolio=None):
         callbacks=[stats_handler],
     )
 
-    # Initialize message buffer with selected analysts
     message_buffer.init_for_analysis(selected_analyst_keys)
 
     # Track start time for elapsed display
     start_time = time.time()
 
-    # Create result directory
     results_dir = _run_directory(config, selections["ticker"], selections["analysis_date"])
     results_dir.mkdir(parents=True, exist_ok=True)
     report_dir = results_dir / "reports"
@@ -173,7 +169,6 @@ def run_analysis(checkpoint: bool | None = None, portfolio=None):
     message_buffer.add_tool_call = save_tool_call_decorator(message_buffer, "add_tool_call")
     message_buffer.update_report_section = save_report_section_decorator(message_buffer, "update_report_section")
 
-    # Now start the display layout
     layout = create_layout()
 
     # The alternate screen keeps a layout taller than the window from redrawing
@@ -182,7 +177,6 @@ def run_analysis(checkpoint: bool | None = None, portfolio=None):
         # Initial display
         update_display(layout, stats_handler=stats_handler, start_time=start_time)
 
-        # Add initial messages
         message_buffer.add_message("System", f"Selected ticker: {selections['ticker']}")
         if selections["asset_type"] != "stock":
             message_buffer.add_message("System", f"Detected asset type: {selections['asset_type']}")
@@ -195,13 +189,11 @@ def run_analysis(checkpoint: bool | None = None, portfolio=None):
         )
         update_display(layout, stats_handler=stats_handler, start_time=start_time)
 
-        # Update agent status to in_progress for the first analyst
         first_analyst = analyst_execution_plan.specs[0].agent_node
         message_buffer.update_agent_status(first_analyst, "in_progress")
         analyst_wall_time_tracker.mark_started(selected_analyst_keys[0])
         update_display(layout, stats_handler=stats_handler, start_time=start_time)
 
-        # Create spinner text
         spinner_text = (
             f"Analyzing {selections['ticker']} on {selections['analysis_date']}..."
         )
@@ -232,7 +224,6 @@ def run_analysis(checkpoint: bool | None = None, portfolio=None):
         trace = []
         try:
             for chunk in graph.graph.stream(graph.checkpoint_input(init_agent_state), **args):
-                # Process all messages in chunk, deduplicating by message ID
                 for message in chunk.get("messages", []):
                     msg_id = getattr(message, "id", None)
                     if msg_id is not None:
@@ -251,7 +242,6 @@ def run_analysis(checkpoint: bool | None = None, portfolio=None):
                             else:
                                 message_buffer.add_tool_call(tool_call.name, tool_call.args)
 
-                # Update analyst statuses based on report state (runs on every chunk)
                 update_analyst_statuses(
                     message_buffer,
                     chunk,
@@ -328,7 +318,6 @@ def run_analysis(checkpoint: bool | None = None, portfolio=None):
                         message_buffer.update_agent_status("Neutral Analyst", "completed")
                         message_buffer.update_agent_status("Portfolio Manager", "completed")
 
-                # Update the display
                 update_display(layout, stats_handler=stats_handler, start_time=start_time)
 
                 trace.append(chunk)
@@ -350,7 +339,6 @@ def run_analysis(checkpoint: bool | None = None, portfolio=None):
             # Always restore the plain uncheckpointed graph, even on failure.
             graph.end_checkpoint()
 
-        # Update all agent statuses to completed
         for agent in message_buffer.agent_status:
             message_buffer.update_agent_status(agent, "completed")
 
@@ -359,7 +347,6 @@ def run_analysis(checkpoint: bool | None = None, portfolio=None):
         )
         message_buffer.add_message("System", analyst_wall_time_tracker.format_summary())
 
-        # Update final report sections
         for section in message_buffer.report_sections:
             if section in final_state:
                 message_buffer.update_report_section(section, final_state[section])

cli/selections.py

@@ -45,11 +45,9 @@ def get_user_selections():
 
 def _prompt_selections(prefs):
     """Walk the selection steps. ``prefs`` prefills, the environment skips."""
-    # Display ASCII art welcome message
     with open(Path(__file__).parent / "static" / "welcome.txt", encoding="utf-8") as f:
         welcome_ascii = f.read()
 
-    # Create welcome box content
     welcome_content = f"{welcome_ascii}\n"
     welcome_content += "[bold green]TradingAgents: Multi-Agents LLM Financial Trading Framework - CLI[/bold green]\n\n"
     welcome_content += "[bold]Workflow Steps:[/bold]\n"
@@ -58,7 +56,6 @@ def _prompt_selections(prefs):
         "[dim]Built by [Tauric Research](https://github.com/TauricResearch)[/dim]"
     )
 
-    # Create and center the welcome box
     welcome_box = Panel(
         welcome_content,
         border_style="green",
@@ -74,7 +71,6 @@ def _prompt_selections(prefs):
     announcements = fetch_announcements()
     display_announcements(console, announcements)
 
-    # Create a boxed questionnaire for each step
     def create_question_box(title, prompt, default=None):
         box_content = f"[bold]{title}[/bold]\n"
         box_content += f"[dim]{prompt}[/dim]"

tradingagents/dataflows/config.py

@@ -61,5 +61,4 @@ def get_config() -> dict:
     return deepcopy(_config)
 
 
-# Initialize with default config
 initialize_config()

tradingagents/dataflows/vendors/alpha_vantage/indicator.py

@@ -83,7 +83,6 @@ def get_indicator(
         series_type = required_series_type
 
     try:
-        # Get indicator data for the period
         if indicator == "close_50_sma":
             data = _make_api_request("SMA", {
                 "symbol": symbol,
@@ -146,12 +145,10 @@ def get_indicator(
                 symbol, symbol, f"Alpha Vantage does not serve the {indicator} indicator"
             )
 
-        # 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')
@@ -185,10 +182,8 @@ 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))

tradingagents/dataflows/vendors/alpha_vantage/stock.py

@@ -23,7 +23,6 @@ def get_stock(
     Returns:
         CSV string containing the daily adjusted time series data filtered to the date range.
     """
-    # Parse dates to determine the range
     start_dt = datetime.strptime(start_date, "%Y-%m-%d")
     today = datetime.now()
 

tradingagents/dataflows/vendors/yahoo/market.py

@@ -180,7 +180,6 @@ 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"
@@ -225,16 +224,13 @@ 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]  # 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:
@@ -274,7 +270,6 @@ def get_stockstats_indicator(
 
 
 
-
 def get_closes(symbol: str, start_date: str, end_date: str) -> pd.Series:
     """Daily closes from ``start_date`` up to, not including, ``end_date``."""
     canonical = normalize_symbol(symbol)

tradingagents/dataflows/vendors/yahoo/news.py

@@ -15,7 +15,6 @@ from tradingagents.dataflows.vendors.yahoo.ohlcv import yf_retry
 
 def _extract_article_data(article: dict) -> dict:
     """Extract article data from yfinance news format (handles nested 'content' structure)."""
-    # Handle nested content structure
     if "content" in article:
         content = article["content"]
         title = content.get("title", "No title")
@@ -23,11 +22,9 @@ def _extract_article_data(article: dict) -> dict:
         provider = content.get("provider", {})
         publisher = provider.get("displayName", "Unknown")
 
-        # Get URL from canonicalUrl or clickThroughUrl
         url_obj = content.get("canonicalUrl") or content.get("clickThroughUrl") or {}
         link = url_obj.get("url", "")
 
-        # Get publish date
         pub_date_str = content.get("pubDate", "")
         pub_date = None
         if pub_date_str:
@@ -87,7 +84,6 @@ def get_news_yfinance(
         stock = yf.Ticker(canonical)
         news = yf_retry(lambda: stock.get_news(count=article_limit)) or []
 
-        # Parse date range for filtering
         start_dt = datetime.strptime(start_date, "%Y-%m-%d")
         end_dt = datetime.strptime(end_date, "%Y-%m-%d")
 

tradingagents/decision_log.py

@@ -154,7 +154,6 @@ class TradingMemoryLog:
                 and tag_line.startswith(pending_prefix)
                 and tag_line.endswith("| pending]")
             ):
-                # Parse rating from the existing pending tag
                 fields = [f.strip() for f in tag_line[1:-1].split("|")]
                 rating = fields[2]
                 new_tag = self._resolved_tag(
@@ -189,7 +188,6 @@ class TradingMemoryLog:
         text = self._log_path.read_text(encoding="utf-8")
         blocks = text.split(self._SEPARATOR)
 
-        # Build lookup keyed by (trade_date, ticker) for O(1) dispatch
         update_map = {(u["trade_date"], u["ticker"]): u for u in updates}
 
         new_blocks = []

tradingagents/graph/__init__.py

@@ -1,5 +1,3 @@
-# TradingAgents/graph/__init__.py
-
 from .conditional_logic import ConditionalLogic
 from .propagation import Propagator
 from .reflection import Reflector

tradingagents/graph/conditional_logic.py

@@ -1,5 +1,3 @@
-# TradingAgents/graph/conditional_logic.py
-
 from tradingagents.agents.state import AgentState
 
 

tradingagents/graph/propagation.py

@@ -1,5 +1,3 @@
-# TradingAgents/graph/propagation.py
-
 from typing import Any
 
 from tradingagents.agents.state import InvestDebateState, RiskDebateState

tradingagents/graph/reflection.py

@@ -1,5 +1,3 @@
-# TradingAgents/graph/reflection.py
-
 from typing import Any
 
 

tradingagents/graph/setup.py

@@ -1,5 +1,3 @@
-# TradingAgents/graph/setup.py
-
 from typing import Any
 
 from langgraph.graph import END, START, StateGraph
@@ -84,29 +82,24 @@ class GraphSetup:
             "fundamentals": lambda: create_fundamentals_analyst(self.quick_thinking_llm),
         }
 
-        # Create researcher and manager nodes
         bull_researcher_node = create_bull_researcher(self.quick_thinking_llm)
         bear_researcher_node = create_bear_researcher(self.quick_thinking_llm)
         research_manager_node = create_research_manager(self.deep_thinking_llm)
         trader_node = create_trader(self.quick_thinking_llm)
 
-        # Create risk analysis nodes
         aggressive_analyst = create_aggressive_debator(self.quick_thinking_llm)
         neutral_analyst = create_neutral_debator(self.quick_thinking_llm)
         conservative_analyst = create_conservative_debator(self.quick_thinking_llm)
         portfolio_manager_node = create_portfolio_manager(self.deep_thinking_llm)
 
-        # Create workflow
         workflow = StateGraph(AgentState)
 
-        # Add analyst nodes to the graph
         for spec in plan.specs:
             workflow.add_node(spec.agent_node, analyst_factories[spec.key]())
             workflow.add_node(spec.clear_node, create_msg_delete())
             if spec.tools:
                 workflow.add_node(spec.tool_node, ToolNode(list(spec.tools)))
 
-        # Add other nodes
         workflow.add_node("Bull Researcher", bull_researcher_node)
         workflow.add_node("Bear Researcher", bear_researcher_node)
         workflow.add_node("Research Manager", research_manager_node)
@@ -116,11 +109,8 @@ class GraphSetup:
         workflow.add_node("Conservative Analyst", conservative_analyst)
         workflow.add_node("Portfolio Manager", portfolio_manager_node)
 
-        # Define edges
-        # Start with the first analyst
         workflow.add_edge(START, plan.specs[0].agent_node)
 
-        # Connect analysts in sequence
         for i, spec in enumerate(plan.specs):
             if spec.tools:
                 workflow.add_conditional_edges(

tradingagents/graph/trading_graph.py

@@ -1,5 +1,3 @@
-# TradingAgents/graph/trading_graph.py
-
 import json
 import logging
 import os
@@ -64,17 +62,13 @@ class TradingAgentsGraph:
         self.config = config or DEFAULT_CONFIG
         self.callbacks = callbacks or []
 
-        # Update the interface's config
         set_config(self.config)
 
-        # Create necessary directories
         os.makedirs(self.config["data_cache_dir"], exist_ok=True)
         os.makedirs(self.config["results_dir"], exist_ok=True)
 
-        # Initialize LLMs with provider-specific thinking configuration
         llm_kwargs = build_llm_kwargs(self.config)
 
-        # Add callbacks to kwargs if provided (passed to LLM constructor)
         if self.callbacks:
             llm_kwargs["callbacks"] = self.callbacks
 
@@ -96,7 +90,6 @@ class TradingAgentsGraph:
 
         self.memory_log = TradingMemoryLog(self.config)
 
-        # Initialize components
         self.conditional_logic = ConditionalLogic(
             max_debate_rounds=self.config["max_debate_rounds"],
             max_risk_discuss_rounds=self.config["max_risk_discuss_rounds"],