from typing import Optional import datetime import typer from pathlib import Path from functools import wraps from rich.console import Console from dotenv import load_dotenv # Load environment variables from .env file load_dotenv() from rich.panel import Panel from rich.spinner import Spinner from rich.live import Live from rich.columns import Columns from rich.markdown import Markdown from rich.layout import Layout from rich.text import Text from rich.table import Table from collections import deque import time from rich.tree import Tree from rich import box from rich.align import Align from rich.rule import Rule from tradingagents.graph.trading_graph import TradingAgentsGraph from tradingagents.default_config import DEFAULT_CONFIG from cli.models import AnalystType from cli.utils import * from cli.announcements import fetch_announcements, display_announcements from cli.stats_handler import StatsCallbackHandler console = Console() app = typer.Typer( name="TradingAgents", help="TradingAgents CLI: Multi-Agents LLM Financial Trading Framework", add_completion=True, # Enable shell completion ) # Create a deque to store recent messages with a maximum length class MessageBuffer: # Fixed teams that always run (not user-selectable) FIXED_AGENTS = { "Research Team": ["Bull Researcher", "Bear Researcher", "Research Manager"], "Trading Team": ["Trader"], "Risk Management": ["Aggressive Analyst", "Neutral Analyst", "Conservative Analyst"], "Portfolio Management": ["Portfolio Manager"], } # Analyst name mapping ANALYST_MAPPING = { "market": "Market Analyst", "social": "Social Analyst", "news": "News Analyst", "fundamentals": "Fundamentals Analyst", } # Report section mapping: section -> (analyst_key for filtering, finalizing_agent) # analyst_key: which analyst selection controls this section (None = always included) # finalizing_agent: which agent must be "completed" for this report to count as done REPORT_SECTIONS = { "market_report": ("market", "Market Analyst"), "sentiment_report": ("social", "Social Analyst"), "news_report": ("news", "News Analyst"), "fundamentals_report": ("fundamentals", "Fundamentals Analyst"), "investment_plan": (None, "Research Manager"), "trader_investment_plan": (None, "Trader"), "final_trade_decision": (None, "Portfolio Manager"), } def __init__(self, max_length=100): self.messages = deque(maxlen=max_length) self.tool_calls = deque(maxlen=max_length) self.current_report = None self.final_report = None # Store the complete final report self.agent_status = {} self.current_agent = None self.report_sections = {} self.selected_analysts = [] self._last_message_id = None def init_for_analysis(self, selected_analysts): """Initialize agent status and report sections based on selected analysts. Args: selected_analysts: List of analyst type strings (e.g., ["market", "news"]) """ 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: self.report_sections[section] = None # Reset other state self.current_report = None self.final_report = None self.current_agent = None self.messages.clear() self.tool_calls.clear() self._last_message_id = None def get_completed_reports_count(self): """Count reports that are finalized (their finalizing agent is completed). A report is considered complete when: 1. The report section has content (not None), AND 2. The agent responsible for finalizing that report has status "completed" This prevents interim updates (like debate rounds) from counting as completed. """ count = 0 for section in self.report_sections: if section not in self.REPORT_SECTIONS: continue _, finalizing_agent = self.REPORT_SECTIONS[section] # Report is complete if it has content AND its finalizing agent is done has_content = self.report_sections.get(section) is not None agent_done = self.agent_status.get(finalizing_agent) == "completed" if has_content and agent_done: count += 1 return count def add_message(self, message_type, content): timestamp = datetime.datetime.now().strftime("%H:%M:%S") self.messages.append((timestamp, message_type, content)) def add_tool_call(self, tool_name, args): timestamp = datetime.datetime.now().strftime("%H:%M:%S") self.tool_calls.append((timestamp, tool_name, args)) def update_agent_status(self, agent, status): if agent in self.agent_status: self.agent_status[agent] = status self.current_agent = agent def update_report_section(self, section_name, content): if section_name in self.report_sections: self.report_sections[section_name] = content self._update_current_report() def _update_current_report(self): # For the panel display, only show the most recently updated section 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", "news_report": "News Analysis", "fundamentals_report": "Fundamentals Analysis", "investment_plan": "Research Team Decision", "trader_investment_plan": "Trading Team Plan", "final_trade_decision": "Portfolio Management Decision", } self.current_report = ( f"### {section_titles[latest_section]}\n{latest_content}" ) # Update the final complete report self._update_final_report() def _update_final_report(self): report_parts = [] # Analyst Team Reports - use .get() to handle missing sections analyst_sections = ["market_report", "sentiment_report", "news_report", "fundamentals_report"] if any(self.report_sections.get(section) for section in analyst_sections): report_parts.append("## Analyst Team Reports") if self.report_sections.get("market_report"): report_parts.append( f"### Market Analysis\n{self.report_sections['market_report']}" ) if self.report_sections.get("sentiment_report"): report_parts.append( f"### Social Sentiment\n{self.report_sections['sentiment_report']}" ) if self.report_sections.get("news_report"): report_parts.append( f"### News Analysis\n{self.report_sections['news_report']}" ) if self.report_sections.get("fundamentals_report"): report_parts.append( f"### Fundamentals Analysis\n{self.report_sections['fundamentals_report']}" ) # Research Team Reports if self.report_sections.get("investment_plan"): report_parts.append("## Research Team Decision") report_parts.append(f"{self.report_sections['investment_plan']}") # Trading Team Reports if self.report_sections.get("trader_investment_plan"): report_parts.append("## Trading Team Plan") report_parts.append(f"{self.report_sections['trader_investment_plan']}") # Portfolio Management Decision if self.report_sections.get("final_trade_decision"): report_parts.append("## Portfolio Management Decision") report_parts.append(f"{self.report_sections['final_trade_decision']}") self.final_report = "\n\n".join(report_parts) if report_parts else None message_buffer = MessageBuffer() def create_layout(): layout = Layout() layout.split_column( Layout(name="header", size=3), Layout(name="main"), Layout(name="footer", size=3), ) layout["main"].split_column( Layout(name="upper", ratio=3), Layout(name="analysis", ratio=5) ) layout["upper"].split_row( Layout(name="progress", ratio=2), Layout(name="messages", ratio=3) ) return layout def format_tokens(n): """Format token count for display.""" if n >= 1000: return f"{n/1000:.1f}k" return str(n) def update_display(layout, spinner_text=None, stats_handler=None, start_time=None): # Header with welcome message layout["header"].update( Panel( "[bold green]Welcome to TradingAgents CLI[/bold green]\n" "[dim]© [Tauric Research](https://github.com/TauricResearch)[/dim]", title="Welcome to TradingAgents", border_style="green", padding=(1, 2), expand=True, ) ) # Progress panel showing agent status progress_table = Table( show_header=True, header_style="bold magenta", show_footer=False, box=box.SIMPLE_HEAD, # Use simple header with horizontal lines title=None, # Remove the redundant Progress title padding=(0, 2), # Add horizontal padding expand=True, # Make table expand to fill available space ) progress_table.add_column("Team", style="cyan", justify="center", width=20) progress_table.add_column("Agent", style="green", justify="center", width=20) progress_table.add_column("Status", style="yellow", justify="center", width=20) # Group agents by team - filter to only include agents in agent_status all_teams = { "Analyst Team": [ "Market Analyst", "Social Analyst", "News Analyst", "Fundamentals Analyst", ], "Research Team": ["Bull Researcher", "Bear Researcher", "Research Manager"], "Trading Team": ["Trader"], "Risk Management": ["Aggressive Analyst", "Neutral Analyst", "Conservative Analyst"], "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] if active_agents: 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": spinner = Spinner( "dots", text="[blue]in_progress[/blue]", style="bold cyan" ) status_cell = spinner else: status_color = { "pending": "yellow", "completed": "green", "error": "red", }.get(status, "white") 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": spinner = Spinner( "dots", text="[blue]in_progress[/blue]", style="bold cyan" ) status_cell = spinner else: status_color = { "pending": "yellow", "completed": "green", "error": "red", }.get(status, "white") 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( Panel(progress_table, title="Progress", border_style="cyan", padding=(1, 2)) ) # Messages panel showing recent messages and tool calls messages_table = Table( show_header=True, header_style="bold magenta", show_footer=False, expand=True, # Make table expand to fill available space box=box.MINIMAL, # Use minimal box style for a lighter look show_lines=True, # Keep horizontal lines padding=(0, 1), # Add some padding between columns ) messages_table.add_column("Time", style="cyan", width=8, justify="center") messages_table.add_column("Type", style="green", width=10, justify="center") messages_table.add_column( "Content", style="white", no_wrap=False, ratio=1 ) # Make content column expand # 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: content_str = content_str[:197] + "..." all_messages.append((timestamp, msg_type, content_str)) # 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) layout["messages"].update( Panel( messages_table, title="Messages & Tools", border_style="blue", padding=(1, 2), ) ) # Analysis panel showing current report if message_buffer.current_report: layout["analysis"].update( Panel( Markdown(message_buffer.current_report), title="Current Report", border_style="green", padding=(1, 2), ) ) else: layout["analysis"].update( Panel( "[italic]Waiting for analysis report...[/italic]", title="Current Report", border_style="green", padding=(1, 2), ) ) # Footer with statistics # Agent progress - derived from agent_status dict agents_completed = sum( 1 for status in message_buffer.agent_status.values() if status == "completed" ) agents_total = len(message_buffer.agent_status) # Report progress - based on agent completion (not just content existence) 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 if stats_handler: stats = stats_handler.get_stats() stats_parts.append(f"LLM: {stats['llm_calls']}") stats_parts.append(f"Tools: {stats['tool_calls']}") # Token display with graceful fallback if stats["tokens_in"] > 0 or stats["tokens_out"] > 0: tokens_str = f"Tokens: {format_tokens(stats['tokens_in'])}\u2191 {format_tokens(stats['tokens_out'])}\u2193" else: tokens_str = "Tokens: --" stats_parts.append(tokens_str) stats_parts.append(f"Reports: {reports_completed}/{reports_total}") # Elapsed time if start_time: elapsed = time.time() - start_time elapsed_str = f"\u23f1 {int(elapsed // 60):02d}:{int(elapsed % 60):02d}" stats_parts.append(elapsed_str) stats_table = Table(show_header=False, box=None, padding=(0, 2), expand=True) stats_table.add_column("Stats", justify="center") stats_table.add_row(" | ".join(stats_parts)) layout["footer"].update(Panel(stats_table, border_style="grey50")) def get_user_selections(): """Get all user selections before starting the analysis display.""" # Display ASCII art welcome message with open(Path(__file__).parent / "static" / "welcome.txt", "r") 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" welcome_content += "I. Analyst Team → II. Research Team → III. Trader → IV. Risk Management → V. Portfolio Management\n\n" welcome_content += ( "[dim]Built by [Tauric Research](https://github.com/TauricResearch)[/dim]" ) # Create and center the welcome box welcome_box = Panel( welcome_content, border_style="green", padding=(1, 2), title="Welcome to TradingAgents", subtitle="Multi-Agents LLM Financial Trading Framework", ) console.print(Align.center(welcome_box)) console.print() console.print() # Add vertical space before announcements # Fetch and display announcements (silent on failure) 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]" if default: box_content += f"\n[dim]Default: {default}[/dim]" return Panel(box_content, border_style="blue", padding=(1, 2)) # Step 1: Ticker symbol console.print( create_question_box( "Step 1: Ticker Symbol", "Enter the exact ticker symbol to analyze, including exchange suffix when needed (examples: SPY, CNC.TO, 7203.T, 0700.HK)", "SPY", ) ) selected_ticker = get_ticker() # Step 2: Analysis date default_date = datetime.datetime.now().strftime("%Y-%m-%d") console.print( create_question_box( "Step 2: Analysis Date", "Enter the analysis date (YYYY-MM-DD)", default_date, ) ) analysis_date = get_analysis_date() # Step 3: Select analysts console.print( create_question_box( "Step 3: Analysts Team", "Select your LLM analyst agents for the analysis" ) ) selected_analysts = select_analysts() console.print( f"[green]Selected analysts:[/green] {', '.join(analyst.value for analyst in selected_analysts)}" ) # Step 4: Research depth console.print( create_question_box( "Step 4: Research Depth", "Select your research depth level" ) ) selected_research_depth = select_research_depth() # Step 5: OpenAI backend console.print( create_question_box( "Step 5: OpenAI backend", "Select which service to talk to" ) ) selected_llm_provider, backend_url = select_llm_provider() # Step 6: Thinking agents console.print( create_question_box( "Step 6: Thinking Agents", "Select your thinking agents for analysis" ) ) selected_shallow_thinker = select_shallow_thinking_agent(selected_llm_provider) selected_deep_thinker = select_deep_thinking_agent(selected_llm_provider) # Step 7: Provider-specific thinking configuration thinking_level = None reasoning_effort = None anthropic_effort = None provider_lower = selected_llm_provider.lower() if provider_lower == "google": console.print( create_question_box( "Step 7: Thinking Mode", "Configure Gemini thinking mode" ) ) thinking_level = ask_gemini_thinking_config() elif provider_lower == "openai": console.print( create_question_box( "Step 7: Reasoning Effort", "Configure OpenAI reasoning effort level" ) ) reasoning_effort = ask_openai_reasoning_effort() elif provider_lower == "anthropic": console.print( create_question_box( "Step 7: Effort Level", "Configure Claude effort level" ) ) anthropic_effort = ask_anthropic_effort() return { "ticker": selected_ticker, "analysis_date": analysis_date, "analysts": selected_analysts, "research_depth": selected_research_depth, "llm_provider": selected_llm_provider.lower(), "backend_url": backend_url, "shallow_thinker": selected_shallow_thinker, "deep_thinker": selected_deep_thinker, "google_thinking_level": thinking_level, "openai_reasoning_effort": reasoning_effort, "anthropic_effort": anthropic_effort, } def get_ticker(): """Get ticker symbol from user input.""" return typer.prompt("", default="SPY") def get_analysis_date(): """Get the analysis date from user input.""" while True: date_str = typer.prompt( "", default=datetime.datetime.now().strftime("%Y-%m-%d") ) try: # Validate date format and ensure it's not in the future analysis_date = datetime.datetime.strptime(date_str, "%Y-%m-%d") if analysis_date.date() > datetime.datetime.now().date(): console.print("[red]Error: Analysis date cannot be in the future[/red]") continue return date_str except ValueError: console.print( "[red]Error: Invalid date format. Please use YYYY-MM-DD[/red]" ) def save_report_to_disk(final_state, ticker: str, save_path: Path): """Save complete analysis report to disk with organized subfolders.""" save_path.mkdir(parents=True, exist_ok=True) sections = [] # 1. Analysts analysts_dir = save_path / "1_analysts" analyst_parts = [] if final_state.get("market_report"): analysts_dir.mkdir(exist_ok=True) (analysts_dir / "market.md").write_text(final_state["market_report"]) analyst_parts.append(("Market Analyst", final_state["market_report"])) if final_state.get("sentiment_report"): analysts_dir.mkdir(exist_ok=True) (analysts_dir / "sentiment.md").write_text(final_state["sentiment_report"]) analyst_parts.append(("Social Analyst", final_state["sentiment_report"])) if final_state.get("news_report"): analysts_dir.mkdir(exist_ok=True) (analysts_dir / "news.md").write_text(final_state["news_report"]) analyst_parts.append(("News Analyst", final_state["news_report"])) if final_state.get("fundamentals_report"): analysts_dir.mkdir(exist_ok=True) (analysts_dir / "fundamentals.md").write_text(final_state["fundamentals_report"]) analyst_parts.append(("Fundamentals Analyst", final_state["fundamentals_report"])) if analyst_parts: content = "\n\n".join(f"### {name}\n{text}" for name, text in analyst_parts) sections.append(f"## I. Analyst Team Reports\n\n{content}") # 2. Research if final_state.get("investment_debate_state"): research_dir = save_path / "2_research" debate = final_state["investment_debate_state"] research_parts = [] if debate.get("bull_history"): research_dir.mkdir(exist_ok=True) (research_dir / "bull.md").write_text(debate["bull_history"]) research_parts.append(("Bull Researcher", debate["bull_history"])) if debate.get("bear_history"): research_dir.mkdir(exist_ok=True) (research_dir / "bear.md").write_text(debate["bear_history"]) research_parts.append(("Bear Researcher", debate["bear_history"])) if debate.get("judge_decision"): research_dir.mkdir(exist_ok=True) (research_dir / "manager.md").write_text(debate["judge_decision"]) research_parts.append(("Research Manager", debate["judge_decision"])) if research_parts: content = "\n\n".join(f"### {name}\n{text}" for name, text in research_parts) sections.append(f"## II. Research Team Decision\n\n{content}") # 3. Trading if final_state.get("trader_investment_plan"): trading_dir = save_path / "3_trading" trading_dir.mkdir(exist_ok=True) (trading_dir / "trader.md").write_text(final_state["trader_investment_plan"]) sections.append(f"## III. Trading Team Plan\n\n### Trader\n{final_state['trader_investment_plan']}") # 4. Risk Management if final_state.get("risk_debate_state"): risk_dir = save_path / "4_risk" risk = final_state["risk_debate_state"] risk_parts = [] if risk.get("aggressive_history"): risk_dir.mkdir(exist_ok=True) (risk_dir / "aggressive.md").write_text(risk["aggressive_history"]) risk_parts.append(("Aggressive Analyst", risk["aggressive_history"])) if risk.get("conservative_history"): risk_dir.mkdir(exist_ok=True) (risk_dir / "conservative.md").write_text(risk["conservative_history"]) risk_parts.append(("Conservative Analyst", risk["conservative_history"])) if risk.get("neutral_history"): risk_dir.mkdir(exist_ok=True) (risk_dir / "neutral.md").write_text(risk["neutral_history"]) risk_parts.append(("Neutral Analyst", risk["neutral_history"])) if risk_parts: content = "\n\n".join(f"### {name}\n{text}" for name, text in risk_parts) sections.append(f"## IV. Risk Management Team Decision\n\n{content}") # 5. Portfolio Manager if risk.get("judge_decision"): portfolio_dir = save_path / "5_portfolio" portfolio_dir.mkdir(exist_ok=True) (portfolio_dir / "decision.md").write_text(risk["judge_decision"]) sections.append(f"## V. Portfolio Manager Decision\n\n### Portfolio Manager\n{risk['judge_decision']}") # Write consolidated report header = f"# Trading Analysis Report: {ticker}\n\nGenerated: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n" (save_path / "complete_report.md").write_text(header + "\n\n".join(sections)) return save_path / "complete_report.md" def display_complete_report(final_state): """Display the complete analysis report sequentially (avoids truncation).""" console.print() console.print(Rule("Complete Analysis Report", style="bold green")) # I. Analyst Team Reports analysts = [] if final_state.get("market_report"): analysts.append(("Market Analyst", final_state["market_report"])) if final_state.get("sentiment_report"): analysts.append(("Social Analyst", final_state["sentiment_report"])) if final_state.get("news_report"): analysts.append(("News Analyst", final_state["news_report"])) if final_state.get("fundamentals_report"): analysts.append(("Fundamentals Analyst", final_state["fundamentals_report"])) if analysts: console.print(Panel("[bold]I. Analyst Team Reports[/bold]", border_style="cyan")) for title, content in analysts: console.print(Panel(Markdown(content), title=title, border_style="blue", padding=(1, 2))) # II. Research Team Reports if final_state.get("investment_debate_state"): debate = final_state["investment_debate_state"] research = [] if debate.get("bull_history"): research.append(("Bull Researcher", debate["bull_history"])) if debate.get("bear_history"): research.append(("Bear Researcher", debate["bear_history"])) if debate.get("judge_decision"): research.append(("Research Manager", debate["judge_decision"])) if research: console.print(Panel("[bold]II. Research Team Decision[/bold]", border_style="magenta")) for title, content in research: console.print(Panel(Markdown(content), title=title, border_style="blue", padding=(1, 2))) # III. Trading Team if final_state.get("trader_investment_plan"): console.print(Panel("[bold]III. Trading Team Plan[/bold]", border_style="yellow")) console.print(Panel(Markdown(final_state["trader_investment_plan"]), title="Trader", border_style="blue", padding=(1, 2))) # IV. Risk Management Team if final_state.get("risk_debate_state"): risk = final_state["risk_debate_state"] risk_reports = [] if risk.get("aggressive_history"): risk_reports.append(("Aggressive Analyst", risk["aggressive_history"])) if risk.get("conservative_history"): risk_reports.append(("Conservative Analyst", risk["conservative_history"])) if risk.get("neutral_history"): risk_reports.append(("Neutral Analyst", risk["neutral_history"])) if risk_reports: console.print(Panel("[bold]IV. Risk Management Team Decision[/bold]", border_style="red")) for title, content in risk_reports: console.print(Panel(Markdown(content), title=title, border_style="blue", padding=(1, 2))) # V. Portfolio Manager Decision if risk.get("judge_decision"): console.print(Panel("[bold]V. Portfolio Manager Decision[/bold]", border_style="green")) console.print(Panel(Markdown(risk["judge_decision"]), title="Portfolio Manager", border_style="blue", padding=(1, 2))) def update_research_team_status(status): """Update status for research team members (not Trader).""" research_team = ["Bull Researcher", "Bear Researcher", "Research Manager"] for agent in research_team: message_buffer.update_agent_status(agent, status) # Ordered list of analysts for status transitions ANALYST_ORDER = ["market", "social", "news", "fundamentals"] ANALYST_AGENT_NAMES = { "market": "Market Analyst", "social": "Social Analyst", "news": "News Analyst", "fundamentals": "Fundamentals Analyst", } ANALYST_REPORT_MAP = { "market": "market_report", "social": "sentiment_report", "news": "news_report", "fundamentals": "fundamentals_report", } def update_analyst_statuses(message_buffer, chunk): """Update analyst statuses based on accumulated report state. Logic: - Store new report content from the current chunk if present - Check accumulated report_sections (not just current chunk) for status - Analysts with reports = completed - First analyst without report = in_progress - Remaining analysts without reports = pending - When all analysts done, set Bull Researcher to in_progress """ selected = message_buffer.selected_analysts found_active = False for analyst_key in ANALYST_ORDER: if analyst_key not in selected: continue agent_name = ANALYST_AGENT_NAMES[analyst_key] report_key = ANALYST_REPORT_MAP[analyst_key] # Capture new report content from current chunk if chunk.get(report_key): message_buffer.update_report_section(report_key, chunk[report_key]) # Determine status from accumulated sections, not just current chunk has_report = bool(message_buffer.report_sections.get(report_key)) if has_report: message_buffer.update_agent_status(agent_name, "completed") elif not found_active: message_buffer.update_agent_status(agent_name, "in_progress") found_active = True else: message_buffer.update_agent_status(agent_name, "pending") # When all analysts complete, transition research team to in_progress if not found_active and selected: if message_buffer.agent_status.get("Bull Researcher") == "pending": message_buffer.update_agent_status("Bull Researcher", "in_progress") def extract_content_string(content): """Extract string content from various message formats. Returns None if no meaningful text content is found. """ import ast def is_empty(val): """Check if value is empty using Python's truthiness.""" if val is None or val == '': return True if isinstance(val, str): s = val.strip() if not s: return True try: return not bool(ast.literal_eval(s)) except (ValueError, SyntaxError): return False # Can't parse = real text return not bool(val) if is_empty(content): return None if isinstance(content, str): return content.strip() if isinstance(content, dict): text = content.get('text', '') return text.strip() if not is_empty(text) else None if isinstance(content, list): text_parts = [ item.get('text', '').strip() if isinstance(item, dict) and item.get('type') == 'text' else (item.strip() if isinstance(item, str) else '') for item in content ] result = ' '.join(t for t in text_parts if t and not is_empty(t)) return result if result else None return str(content).strip() if not is_empty(content) else None def classify_message_type(message) -> tuple[str, str | None]: """Classify LangChain message into display type and extract content. Returns: (type, content) - type is one of: User, Agent, Data, Control - content is extracted string or None """ from langchain_core.messages import AIMessage, HumanMessage, ToolMessage content = extract_content_string(getattr(message, 'content', None)) if isinstance(message, HumanMessage): if content and content.strip() == "Continue": return ("Control", content) return ("User", content) if isinstance(message, ToolMessage): return ("Data", content) if isinstance(message, AIMessage): return ("Agent", content) # Fallback for unknown types return ("System", content) def format_tool_args(args, max_length=80) -> str: """Format tool arguments for terminal display.""" result = str(args) if len(result) > max_length: return result[:max_length - 3] + "..." return result def run_analysis(): # First get all user selections selections = get_user_selections() # Create config with selected research depth config = DEFAULT_CONFIG.copy() config["max_debate_rounds"] = selections["research_depth"] config["max_risk_discuss_rounds"] = selections["research_depth"] config["quick_think_llm"] = selections["shallow_thinker"] config["deep_think_llm"] = selections["deep_thinker"] config["backend_url"] = selections["backend_url"] config["llm_provider"] = selections["llm_provider"].lower() # Provider-specific thinking configuration config["google_thinking_level"] = selections.get("google_thinking_level") config["openai_reasoning_effort"] = selections.get("openai_reasoning_effort") config["anthropic_effort"] = selections.get("anthropic_effort") # 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) selected_set = {analyst.value for analyst in selections["analysts"]} selected_analyst_keys = [a for a in ANALYST_ORDER if a in selected_set] # Initialize the graph with callbacks bound to LLMs graph = TradingAgentsGraph( selected_analyst_keys, config=config, debug=True, 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 = Path(config["results_dir"]) / selections["ticker"] / selections["analysis_date"] results_dir.mkdir(parents=True, exist_ok=True) report_dir = results_dir / "reports" report_dir.mkdir(parents=True, exist_ok=True) log_file = results_dir / "message_tool.log" log_file.touch(exist_ok=True) def save_message_decorator(obj, func_name): func = getattr(obj, func_name) @wraps(func) def wrapper(*args, **kwargs): func(*args, **kwargs) timestamp, message_type, content = obj.messages[-1] content = content.replace("\n", " ") # Replace newlines with spaces with open(log_file, "a") as f: f.write(f"{timestamp} [{message_type}] {content}\n") return wrapper def save_tool_call_decorator(obj, func_name): func = getattr(obj, func_name) @wraps(func) def wrapper(*args, **kwargs): func(*args, **kwargs) timestamp, tool_name, args = obj.tool_calls[-1] args_str = ", ".join(f"{k}={v}" for k, v in args.items()) with open(log_file, "a") as f: f.write(f"{timestamp} [Tool Call] {tool_name}({args_str})\n") return wrapper def save_report_section_decorator(obj, func_name): func = getattr(obj, func_name) @wraps(func) def wrapper(section_name, content): func(section_name, content) if section_name in obj.report_sections and obj.report_sections[section_name] is not None: content = obj.report_sections[section_name] if content: file_name = f"{section_name}.md" text = "\n".join(str(item) for item in content) if isinstance(content, list) else content with open(report_dir / file_name, "w") as f: f.write(text) return wrapper message_buffer.add_message = save_message_decorator(message_buffer, "add_message") 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() with Live(layout, refresh_per_second=4) as live: # 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']}") message_buffer.add_message( "System", f"Analysis date: {selections['analysis_date']}" ) message_buffer.add_message( "System", f"Selected analysts: {', '.join(analyst.value for analyst in selections['analysts'])}", ) update_display(layout, stats_handler=stats_handler, start_time=start_time) # Update agent status to in_progress for the first analyst first_analyst = f"{selections['analysts'][0].value.capitalize()} Analyst" message_buffer.update_agent_status(first_analyst, "in_progress") update_display(layout, stats_handler=stats_handler, start_time=start_time) # Create spinner text spinner_text = ( f"Analyzing {selections['ticker']} on {selections['analysis_date']}..." ) update_display(layout, spinner_text, stats_handler=stats_handler, start_time=start_time) # Initialize state and get graph args with callbacks init_agent_state = graph.propagator.create_initial_state( selections["ticker"], selections["analysis_date"] ) # Pass callbacks to graph config for tool execution tracking # (LLM tracking is handled separately via LLM constructor) args = graph.propagator.get_graph_args(callbacks=[stats_handler]) # Stream the analysis trace = [] for chunk in graph.graph.stream(init_agent_state, **args): # Process messages if present (skip duplicates via message ID) if len(chunk["messages"]) > 0: last_message = chunk["messages"][-1] msg_id = getattr(last_message, "id", None) if msg_id != message_buffer._last_message_id: message_buffer._last_message_id = msg_id # Add message to buffer msg_type, content = classify_message_type(last_message) if content and content.strip(): message_buffer.add_message(msg_type, content) # Handle tool calls if hasattr(last_message, "tool_calls") and last_message.tool_calls: for tool_call in last_message.tool_calls: if isinstance(tool_call, dict): message_buffer.add_tool_call( tool_call["name"], tool_call["args"] ) 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) # Research Team - Handle Investment Debate State if chunk.get("investment_debate_state"): debate_state = chunk["investment_debate_state"] bull_hist = debate_state.get("bull_history", "").strip() bear_hist = debate_state.get("bear_history", "").strip() judge = debate_state.get("judge_decision", "").strip() # Only update status when there's actual content if bull_hist or bear_hist: update_research_team_status("in_progress") if bull_hist: message_buffer.update_report_section( "investment_plan", f"### Bull Researcher Analysis\n{bull_hist}" ) if bear_hist: message_buffer.update_report_section( "investment_plan", f"### Bear Researcher Analysis\n{bear_hist}" ) if judge: message_buffer.update_report_section( "investment_plan", f"### Research Manager Decision\n{judge}" ) update_research_team_status("completed") message_buffer.update_agent_status("Trader", "in_progress") # Trading Team if chunk.get("trader_investment_plan"): message_buffer.update_report_section( "trader_investment_plan", chunk["trader_investment_plan"] ) if message_buffer.agent_status.get("Trader") != "completed": message_buffer.update_agent_status("Trader", "completed") message_buffer.update_agent_status("Aggressive Analyst", "in_progress") # Risk Management Team - Handle Risk Debate State if chunk.get("risk_debate_state"): risk_state = chunk["risk_debate_state"] agg_hist = risk_state.get("aggressive_history", "").strip() con_hist = risk_state.get("conservative_history", "").strip() neu_hist = risk_state.get("neutral_history", "").strip() judge = risk_state.get("judge_decision", "").strip() if agg_hist: if message_buffer.agent_status.get("Aggressive Analyst") != "completed": message_buffer.update_agent_status("Aggressive Analyst", "in_progress") message_buffer.update_report_section( "final_trade_decision", f"### Aggressive Analyst Analysis\n{agg_hist}" ) if con_hist: if message_buffer.agent_status.get("Conservative Analyst") != "completed": message_buffer.update_agent_status("Conservative Analyst", "in_progress") message_buffer.update_report_section( "final_trade_decision", f"### Conservative Analyst Analysis\n{con_hist}" ) if neu_hist: if message_buffer.agent_status.get("Neutral Analyst") != "completed": message_buffer.update_agent_status("Neutral Analyst", "in_progress") message_buffer.update_report_section( "final_trade_decision", f"### Neutral Analyst Analysis\n{neu_hist}" ) if judge: if message_buffer.agent_status.get("Portfolio Manager") != "completed": message_buffer.update_agent_status("Portfolio Manager", "in_progress") message_buffer.update_report_section( "final_trade_decision", f"### Portfolio Manager Decision\n{judge}" ) message_buffer.update_agent_status("Aggressive Analyst", "completed") message_buffer.update_agent_status("Conservative Analyst", "completed") 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) # Get final state and decision final_state = trace[-1] decision = graph.process_signal(final_state["final_trade_decision"]) # Update all agent statuses to completed for agent in message_buffer.agent_status: message_buffer.update_agent_status(agent, "completed") message_buffer.add_message( "System", f"Completed analysis for {selections['analysis_date']}" ) # Update final report sections for section in message_buffer.report_sections.keys(): if section in final_state: message_buffer.update_report_section(section, final_state[section]) update_display(layout, stats_handler=stats_handler, start_time=start_time) # Post-analysis prompts (outside Live context for clean interaction) console.print("\n[bold cyan]Analysis Complete![/bold cyan]\n") # Prompt to save report save_choice = typer.prompt("Save report?", default="Y").strip().upper() if save_choice in ("Y", "YES", ""): timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") default_path = Path.cwd() / "reports" / f"{selections['ticker']}_{timestamp}" save_path_str = typer.prompt( "Save path (press Enter for default)", default=str(default_path) ).strip() save_path = Path(save_path_str) try: report_file = save_report_to_disk(final_state, selections["ticker"], save_path) console.print(f"\n[green]✓ Report saved to:[/green] {save_path.resolve()}") console.print(f" [dim]Complete report:[/dim] {report_file.name}") except Exception as e: console.print(f"[red]Error saving report: {e}[/red]") # Prompt to display full report display_choice = typer.prompt("\nDisplay full report on screen?", default="Y").strip().upper() if display_choice in ("Y", "YES", ""): display_complete_report(final_state) @app.command() def analyze(): run_analysis() if __name__ == "__main__": app()