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docs: drop comments that narrate the next line

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- about seventy '# Create/Initialize/Add ...' lines across graph, cli and dataflows, and the file-path headers; comments that give a reason stay
This commit is contained in:
Yijia-Xiao
2026-09-24 05:00:36 +00:00
parent 4a30cb1c0a
commit 825e6321ae
16 changed files with 0 additions and 80 deletions
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cli/display.py
-19
View File
@@ -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
-1
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@@ -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
-13
View File
@@ -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
-4
View File
@@ -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
-1
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@@ -61,5 +61,4 @@ def get_config() -> dict:
return deepcopy(_config)
# Initialize with default config
initialize_config()
tradingagents/dataflows/vendors/alpha_vantage/indicator.py
Vendored
-5
View File
@@ -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
Vendored
-1
View File
@@ -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
Vendored
-5
View File
@@ -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
Vendored
-4
View File
@@ -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
-2
View File
@@ -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
-2
View File
@@ -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
-2
View File
@@ -1,5 +1,3 @@
# TradingAgents/graph/conditional_logic.py
from tradingagents.agents.state import AgentState
tradingagents/graph/propagation.py
-2
View File
@@ -1,5 +1,3 @@
# TradingAgents/graph/propagation.py
from typing import Any
from tradingagents.agents.state import InvestDebateState, RiskDebateState
tradingagents/graph/reflection.py
-2
View File
@@ -1,5 +1,3 @@
# TradingAgents/graph/reflection.py
from typing import Any
tradingagents/graph/setup.py
-10
View File
@@ -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
-7
View File
@@ -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"],