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

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"],