from typing import Optional import datetime import typer from pathlib import Path from functools import wraps from rich.console import Console 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.live import Live 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 # Load environment variables from .env file from dotenv import load_dotenv load_dotenv() from tradingagents.graph.trading_graph import TradingAgentsGraph from tradingagents.default_config import DEFAULT_CONFIG from cli.models import AnalystType from cli.utils import * 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: 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 = { # Analyst Team "Market Analyst": "pending", "Social Analyst": "pending", "News Analyst": "pending", "Fundamentals Analyst": "pending", # Research Team "Bull Researcher": "pending", "Bear Researcher": "pending", "Research Manager": "pending", # Trading Team "Trader": "pending", # Risk Management Team "Risky Analyst": "pending", "Neutral Analyst": "pending", "Safe Analyst": "pending", # Portfolio Management Team "Portfolio Manager": "pending", } self.current_agent = None self.report_sections = { "market_report": None, "sentiment_report": None, "news_report": None, "fundamentals_report": None, "investment_plan": None, "trader_investment_plan": None, "final_trade_decision": None, } 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 if any( self.report_sections[section] for section in [ "market_report", "sentiment_report", "news_report", "fundamentals_report", ] ): report_parts.append("## Analyst Team Reports") if self.report_sections["market_report"]: report_parts.append( f"### Market Analysis\n{self.report_sections['market_report']}" ) if self.report_sections["sentiment_report"]: report_parts.append( f"### Social Sentiment\n{self.report_sections['sentiment_report']}" ) if self.report_sections["news_report"]: report_parts.append( f"### News Analysis\n{self.report_sections['news_report']}" ) if self.report_sections["fundamentals_report"]: report_parts.append( f"### Fundamentals Analysis\n{self.report_sections['fundamentals_report']}" ) # Research Team Reports if self.report_sections["investment_plan"]: report_parts.append("## Research Team Decision") report_parts.append(f"{self.report_sections['investment_plan']}") # Trading Team Reports if self.report_sections["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["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 update_display(layout, spinner_text=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 teams = { "Analyst Team": [ "Market Analyst", "Social Analyst", "News Analyst", "Fundamentals Analyst", ], "Research Team": ["Bull Researcher", "Bear Researcher", "Research Manager"], "Trading Team": ["Trader"], "Risk Management": ["Risky Analyst", "Neutral Analyst", "Safe Analyst"], "Portfolio Management": ["Portfolio Manager"], } for team, agents in teams.items(): # Add first agent with team name first_agent = agents[0] status = message_buffer.agent_status[first_agent] 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[agent] 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: # Truncate tool call args if too long if isinstance(args, str) and len(args) > 100: args = args[:97] + "..." all_messages.append((timestamp, "Tool", f"{tool_name}: {args}")) # Add regular messages for timestamp, msg_type, content in message_buffer.messages: # Convert content to string if it's not already content_str = content if isinstance(content, list): # Handle list of content blocks (Anthropic format) text_parts = [] for item in content: if isinstance(item, dict): if item.get('type') == 'text': text_parts.append(item.get('text', '')) elif item.get('type') == 'tool_use': text_parts.append(f"[Tool: {item.get('name', 'unknown')}]") else: text_parts.append(str(item)) content_str = ' '.join(text_parts) elif not isinstance(content_str, str): content_str = str(content) # Truncate message content if too long if len(content_str) > 200: content_str = content_str[:197] + "..." all_messages.append((timestamp, msg_type, content_str)) # Sort by timestamp all_messages.sort(key=lambda x: x[0]) # Calculate how many messages we can show based on available space # Start with a reasonable number and adjust based on content length max_messages = 12 # Increased from 8 to better fill the space # Get the last N messages that will fit in the panel recent_messages = all_messages[-max_messages:] # Add messages to table 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) if spinner_text: messages_table.add_row("", "Spinner", spinner_text) # Add a footer to indicate if messages were truncated if len(all_messages) > max_messages: messages_table.footer = ( f"[dim]Showing last {max_messages} of {len(all_messages)} messages[/dim]" ) 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 tool_calls_count = len(message_buffer.tool_calls) llm_calls_count = sum( 1 for _, msg_type, _ in message_buffer.messages if msg_type == "Reasoning" ) reports_count = sum( 1 for content in message_buffer.report_sections.values() if content is not None ) stats_table = Table(show_header=False, box=None, padding=(0, 2), expand=True) stats_table.add_column("Stats", justify="center") stats_table.add_row( f"Tool Calls: {tool_calls_count} | LLM Calls: {llm_calls_count} | Generated Reports: {reports_count}" ) layout["footer"].update(Panel(stats_table, border_style="grey50")) def get_user_selections(): """Get user ticker selection with simplified interface and sensible defaults.""" # Display ASCII art welcome message with open("./cli/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() # Add a blank line after the welcome box # Simplified input - only ask for ticker symbol ticker_box = Panel( "[bold]Enter Ticker Symbol[/bold]\n[dim]Enter the stock ticker you want to analyze (e.g., AAPL, TSLA, SPY)[/dim]\n[dim]Default: SPY[/dim]", border_style="blue", padding=(1, 2) ) console.print(ticker_box) selected_ticker = get_ticker() # Use sensible defaults for all other parameters analysis_date = datetime.datetime.now().strftime("%Y-%m-%d") selected_analysts = [AnalystType.MARKET, AnalystType.SOCIAL, AnalystType.NEWS, AnalystType.FUNDAMENTALS] selected_research_depth = 5 selected_llm_provider = "openai" backend_url = "https://api.openai.com/v1" selected_shallow_thinker = "gpt-4o" selected_deep_thinker = "o3" # Display the configuration being used config_info = f"""[bold green]Configuration:[/bold green] • [bold]Ticker:[/bold] {selected_ticker} • [bold]Date:[/bold] {analysis_date} (latest trading day) • [bold]Analysts:[/bold] All analysts (Market, Social, News, Fundamentals) • [bold]Research Depth:[/bold] Deep (5 rounds of debate) • [bold]LLM Provider:[/bold] OpenAI • [bold]Quick Thinking:[/bold] GPT-4o • [bold]Deep Thinking:[/bold] o3 [dim]Starting analysis with optimized settings...[/dim]""" console.print(Panel(config_info, border_style="green", padding=(1, 2), title="Analysis Configuration")) console.print() 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, } def get_ticker(): """Get ticker symbol from user input.""" ticker = typer.prompt("", default="SPY") return ticker.strip().upper() 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 display_complete_report(final_state): """Display the complete analysis report with team-based panels.""" console.print("\n[bold green]Complete Analysis Report[/bold green]\n") # I. Analyst Team Reports analyst_reports = [] # Market Analyst Report if final_state.get("market_report"): analyst_reports.append( Panel( Markdown(final_state["market_report"]), title="Market Analyst", border_style="blue", padding=(1, 2), ) ) # Social Analyst Report if final_state.get("sentiment_report"): analyst_reports.append( Panel( Markdown(final_state["sentiment_report"]), title="Social Analyst", border_style="blue", padding=(1, 2), ) ) # News Analyst Report if final_state.get("news_report"): analyst_reports.append( Panel( Markdown(final_state["news_report"]), title="News Analyst", border_style="blue", padding=(1, 2), ) ) # Fundamentals Analyst Report if final_state.get("fundamentals_report"): analyst_reports.append( Panel( Markdown(final_state["fundamentals_report"]), title="Fundamentals Analyst", border_style="blue", padding=(1, 2), ) ) if analyst_reports: console.print( Panel( Columns(analyst_reports, equal=True, expand=True), title="I. Analyst Team Reports", border_style="cyan", padding=(1, 2), ) ) # II. Research Team Reports if final_state.get("investment_debate_state"): research_reports = [] debate_state = final_state["investment_debate_state"] # Bull Researcher Analysis if debate_state.get("bull_history"): research_reports.append( Panel( Markdown(debate_state["bull_history"]), title="Bull Researcher", border_style="blue", padding=(1, 2), ) ) # Bear Researcher Analysis if debate_state.get("bear_history"): research_reports.append( Panel( Markdown(debate_state["bear_history"]), title="Bear Researcher", border_style="blue", padding=(1, 2), ) ) # Research Manager Decision if debate_state.get("judge_decision"): research_reports.append( Panel( Markdown(debate_state["judge_decision"]), title="Research Manager", border_style="blue", padding=(1, 2), ) ) if research_reports: console.print( Panel( Columns(research_reports, equal=True, expand=True), title="II. Research Team Decision", border_style="magenta", padding=(1, 2), ) ) # III. Trading Team Reports if final_state.get("trader_investment_plan"): console.print( Panel( Panel( Markdown(final_state["trader_investment_plan"]), title="Trader", border_style="blue", padding=(1, 2), ), title="III. Trading Team Plan", border_style="yellow", padding=(1, 2), ) ) # IV. Risk Management Team Reports if final_state.get("risk_debate_state"): risk_reports = [] risk_state = final_state["risk_debate_state"] # Aggressive (Risky) Analyst Analysis if risk_state.get("risky_history"): risk_reports.append( Panel( Markdown(risk_state["risky_history"]), title="Aggressive Analyst", border_style="blue", padding=(1, 2), ) ) # Conservative (Safe) Analyst Analysis if risk_state.get("safe_history"): risk_reports.append( Panel( Markdown(risk_state["safe_history"]), title="Conservative Analyst", border_style="blue", padding=(1, 2), ) ) # Neutral Analyst Analysis if risk_state.get("neutral_history"): risk_reports.append( Panel( Markdown(risk_state["neutral_history"]), title="Neutral Analyst", border_style="blue", padding=(1, 2), ) ) if risk_reports: console.print( Panel( Columns(risk_reports, equal=True, expand=True), title="IV. Risk Management Team Decision", border_style="red", padding=(1, 2), ) ) # V. Portfolio Manager Decision if risk_state.get("judge_decision"): console.print( Panel( Panel( Markdown(risk_state["judge_decision"]), title="Portfolio Manager", border_style="blue", padding=(1, 2), ), title="V. Portfolio Manager Decision", border_style="green", padding=(1, 2), ) ) def update_research_team_status(status): """Update status for all research team members and trader.""" research_team = ["Bull Researcher", "Bear Researcher", "Research Manager", "Trader"] for agent in research_team: message_buffer.update_agent_status(agent, status) def extract_content_string(content): """Extract string content from various message formats.""" if isinstance(content, str): return content elif isinstance(content, list): # Handle Anthropic's list format text_parts = [] for item in content: if isinstance(item, dict): if item.get('type') == 'text': text_parts.append(item.get('text', '')) elif item.get('type') == 'tool_use': text_parts.append(f"[Tool: {item.get('name', 'unknown')}]") else: text_parts.append(str(item)) return ' '.join(text_parts) else: return str(content) def get_user_selections_advanced(): """Get all user selections with advanced configuration options.""" # Display ASCII art welcome message with open("./cli/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 (Advanced Mode)[/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 - Advanced Mode", subtitle="Multi-Agents LLM Financial Trading Framework", ) console.print(Align.center(welcome_box)) console.print() # Add a blank line after the welcome box # 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 ticker symbol to analyze", "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: LLM Provider", "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) 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, } def run_analysis(advanced_mode=False): # Get user selections based on mode if advanced_mode: selections = get_user_selections_advanced() else: 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() # Initialize the graph graph = TradingAgentsGraph( [analyst.value for analyst in selections["analysts"]], config=config, debug=True ) # 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" with open(report_dir / file_name, "w") as f: f.write(content) 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) # 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) # Reset agent statuses for agent in message_buffer.agent_status: message_buffer.update_agent_status(agent, "pending") # Reset report sections for section in message_buffer.report_sections: message_buffer.report_sections[section] = None message_buffer.current_report = None message_buffer.final_report = None # 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) # Create spinner text spinner_text = ( f"Analyzing {selections['ticker']} on {selections['analysis_date']}..." ) update_display(layout, spinner_text) # Initialize state and get graph args init_agent_state = graph.propagator.create_initial_state( selections["ticker"], selections["analysis_date"] ) args = graph.propagator.get_graph_args() # Stream the analysis trace = [] for chunk in graph.graph.stream(init_agent_state, **args): if len(chunk["messages"]) > 0: # Get the last message from the chunk last_message = chunk["messages"][-1] # Extract message content and type if hasattr(last_message, "content"): content = extract_content_string(last_message.content) # Use the helper function msg_type = "Reasoning" else: content = str(last_message) msg_type = "System" # Add message to buffer message_buffer.add_message(msg_type, content) # If it's a tool call, add it to tool calls if hasattr(last_message, "tool_calls"): for tool_call in last_message.tool_calls: # Handle both dictionary and object 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 reports and agent status based on chunk content # Analyst Team Reports if "market_report" in chunk and chunk["market_report"]: message_buffer.update_report_section( "market_report", chunk["market_report"] ) message_buffer.update_agent_status("Market Analyst", "completed") # Set next analyst to in_progress if "social" in selections["analysts"]: message_buffer.update_agent_status( "Social Analyst", "in_progress" ) if "sentiment_report" in chunk and chunk["sentiment_report"]: message_buffer.update_report_section( "sentiment_report", chunk["sentiment_report"] ) message_buffer.update_agent_status("Social Analyst", "completed") # Set next analyst to in_progress if "news" in selections["analysts"]: message_buffer.update_agent_status( "News Analyst", "in_progress" ) if "news_report" in chunk and chunk["news_report"]: message_buffer.update_report_section( "news_report", chunk["news_report"] ) message_buffer.update_agent_status("News Analyst", "completed") # Set next analyst to in_progress if "fundamentals" in selections["analysts"]: message_buffer.update_agent_status( "Fundamentals Analyst", "in_progress" ) if "fundamentals_report" in chunk and chunk["fundamentals_report"]: message_buffer.update_report_section( "fundamentals_report", chunk["fundamentals_report"] ) message_buffer.update_agent_status( "Fundamentals Analyst", "completed" ) # Set all research team members to in_progress update_research_team_status("in_progress") # Research Team - Handle Investment Debate State if ( "investment_debate_state" in chunk and chunk["investment_debate_state"] ): debate_state = chunk["investment_debate_state"] # Update Bull Researcher status and report if "bull_history" in debate_state and debate_state["bull_history"]: # Keep all research team members in progress update_research_team_status("in_progress") # Extract latest bull response bull_responses = debate_state["bull_history"].split("\n") latest_bull = bull_responses[-1] if bull_responses else "" if latest_bull: message_buffer.add_message("Reasoning", latest_bull) # Update research report with bull's latest analysis message_buffer.update_report_section( "investment_plan", f"### Bull Researcher Analysis\n{latest_bull}", ) # Update Bear Researcher status and report if "bear_history" in debate_state and debate_state["bear_history"]: # Keep all research team members in progress update_research_team_status("in_progress") # Extract latest bear response bear_responses = debate_state["bear_history"].split("\n") latest_bear = bear_responses[-1] if bear_responses else "" if latest_bear: message_buffer.add_message("Reasoning", latest_bear) # Update research report with bear's latest analysis message_buffer.update_report_section( "investment_plan", f"{message_buffer.report_sections['investment_plan']}\n\n### Bear Researcher Analysis\n{latest_bear}", ) # Update Research Manager status and final decision if ( "judge_decision" in debate_state and debate_state["judge_decision"] ): # Keep all research team members in progress until final decision update_research_team_status("in_progress") message_buffer.add_message( "Reasoning", f"Research Manager: {debate_state['judge_decision']}", ) # Update research report with final decision message_buffer.update_report_section( "investment_plan", f"{message_buffer.report_sections['investment_plan']}\n\n### Research Manager Decision\n{debate_state['judge_decision']}", ) # Mark all research team members as completed update_research_team_status("completed") # Set first risk analyst to in_progress message_buffer.update_agent_status( "Risky Analyst", "in_progress" ) # Trading Team if ( "trader_investment_plan" in chunk and chunk["trader_investment_plan"] ): message_buffer.update_report_section( "trader_investment_plan", chunk["trader_investment_plan"] ) # Set first risk analyst to in_progress message_buffer.update_agent_status("Risky Analyst", "in_progress") # Risk Management Team - Handle Risk Debate State if "risk_debate_state" in chunk and chunk["risk_debate_state"]: risk_state = chunk["risk_debate_state"] # Update Risky Analyst status and report if ( "current_risky_response" in risk_state and risk_state["current_risky_response"] ): message_buffer.update_agent_status( "Risky Analyst", "in_progress" ) message_buffer.add_message( "Reasoning", f"Risky Analyst: {risk_state['current_risky_response']}", ) # Update risk report with risky analyst's latest analysis only message_buffer.update_report_section( "final_trade_decision", f"### Risky Analyst Analysis\n{risk_state['current_risky_response']}", ) # Update Safe Analyst status and report if ( "current_safe_response" in risk_state and risk_state["current_safe_response"] ): message_buffer.update_agent_status( "Safe Analyst", "in_progress" ) message_buffer.add_message( "Reasoning", f"Safe Analyst: {risk_state['current_safe_response']}", ) # Update risk report with safe analyst's latest analysis only message_buffer.update_report_section( "final_trade_decision", f"### Safe Analyst Analysis\n{risk_state['current_safe_response']}", ) # Update Neutral Analyst status and report if ( "current_neutral_response" in risk_state and risk_state["current_neutral_response"] ): message_buffer.update_agent_status( "Neutral Analyst", "in_progress" ) message_buffer.add_message( "Reasoning", f"Neutral Analyst: {risk_state['current_neutral_response']}", ) # Update risk report with neutral analyst's latest analysis only message_buffer.update_report_section( "final_trade_decision", f"### Neutral Analyst Analysis\n{risk_state['current_neutral_response']}", ) # Update Portfolio Manager status and final decision if "judge_decision" in risk_state and risk_state["judge_decision"]: message_buffer.update_agent_status( "Portfolio Manager", "in_progress" ) message_buffer.add_message( "Reasoning", f"Portfolio Manager: {risk_state['judge_decision']}", ) # Update risk report with final decision only message_buffer.update_report_section( "final_trade_decision", f"### Portfolio Manager Decision\n{risk_state['judge_decision']}", ) # Mark risk analysts as completed message_buffer.update_agent_status("Risky Analyst", "completed") message_buffer.update_agent_status("Safe Analyst", "completed") message_buffer.update_agent_status( "Neutral Analyst", "completed" ) message_buffer.update_agent_status( "Portfolio Manager", "completed" ) # Update the display update_display(layout) 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( "Analysis", 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]) # Display the complete final report display_complete_report(final_state) update_display(layout) @app.command() def analyze( advanced: bool = typer.Option( False, "--advanced", "-a", help="Use advanced configuration mode with full customization options" ), streaming: bool = typer.Option( False, "--streaming", "-s", help="Enable real-time streaming of analysis reports as they're generated" ) ): """Run trading analysis with simplified or advanced configuration.""" if streaming: run_analysis_streaming(advanced_mode=advanced) else: run_analysis(advanced_mode=advanced) @app.command() def stream( advanced: bool = typer.Option( False, "--advanced", "-a", help="Use advanced configuration mode with full customization options" ) ): """Run real-time streaming trading analysis.""" run_analysis_streaming(advanced_mode=advanced) @app.callback(invoke_without_command=True) def main( ctx: typer.Context, advanced: bool = typer.Option( False, "--advanced", "-a", help="Use advanced configuration mode with full customization options" ), streaming: bool = typer.Option( False, "--streaming", "-s", help="Enable real-time streaming of analysis reports as they're generated" ) ): """TradingAgents CLI: Multi-Agents LLM Financial Trading Framework""" if ctx.invoked_subcommand is None: # Default behavior - run analysis if streaming: run_analysis_streaming(advanced_mode=advanced) else: run_analysis(advanced_mode=advanced) class StreamingMessageBuffer(MessageBuffer): """Enhanced MessageBuffer for real-time content streaming""" def __init__(self, max_length=100): super().__init__(max_length) self.streaming_content = { "current_agent": None, "current_content": "", "content_buffer": "", "last_streamed_length": 0 } self.content_callbacks = [] def add_content_callback(self, callback): """Add a callback to be called when new content is streamed""" self.content_callbacks.append(callback) def stream_content(self, agent_name, content_chunk): """Stream content in real-time""" self.streaming_content["current_agent"] = agent_name self.streaming_content["content_buffer"] += content_chunk # Call registered callbacks with new content for callback in self.content_callbacks: callback(agent_name, content_chunk, self.streaming_content["content_buffer"]) def finalize_streaming_content(self, section_name): """Finalize the streaming content into a report section""" if self.streaming_content["content_buffer"]: self.update_report_section(section_name, self.streaming_content["content_buffer"]) self.streaming_content["content_buffer"] = "" self.streaming_content["last_streamed_length"] = 0 def create_streaming_layout(): """Create layout optimized for streaming content""" 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=2), Layout(name="streaming_content", ratio=4), Layout(name="analysis", ratio=3) ) layout["upper"].split_row( Layout(name="progress", ratio=2), Layout(name="messages", ratio=3) ) return layout def update_streaming_display(layout, streaming_buffer, spinner_text=None): """Update display with streaming content""" # Update header 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", ) ) # Update progress panel using streaming_buffer progress_table = Table(show_header=False, box=box.MINIMAL) progress_table.add_column("Agent", style="cyan", no_wrap=True) progress_table.add_column("Status", style="magenta") for agent, status in streaming_buffer.agent_status.items(): if status == "completed": status_icon = "✅" elif status == "in_progress": status_icon = "🔄" else: status_icon = "⏳" progress_table.add_row(agent, f"{status_icon} {status.title()}") layout["progress"].update( Panel( progress_table, title="Agent Progress", border_style="blue" ) ) # Update messages panel using streaming_buffer messages_content = [] for timestamp, msg_type, content in list(streaming_buffer.messages)[-10:]: # Show last 10 messages messages_content.append(f"[dim]{timestamp}[/dim] [{msg_type}] {content}") if spinner_text: messages_content.append(f"[yellow]⚡ {spinner_text}[/yellow]") layout["messages"].update( Panel( "\n".join(messages_content), title="Recent Messages", border_style="yellow" ) ) # Add streaming content panel if streaming_buffer.streaming_content["current_agent"] and streaming_buffer.streaming_content["content_buffer"]: agent_name = streaming_buffer.streaming_content["current_agent"] content = streaming_buffer.streaming_content["content_buffer"] # Limit display content to prevent overwhelming the terminal display_content = content[-2000:] if len(content) > 2000 else content if len(content) > 2000: display_content = "...\n" + display_content streaming_panel = Panel( Markdown(display_content), title=f"🔴 Live: {agent_name}", border_style="red", expand=True ) layout["streaming_content"].update(streaming_panel) else: layout["streaming_content"].update( Panel( "[dim]Waiting for content to stream...[/dim]", title="📡 Streaming Content", border_style="dim" ) ) # Update analysis panel using streaming_buffer if streaming_buffer.current_report: layout["analysis"].update( Panel( Markdown(streaming_buffer.current_report), title="Latest Report Section", border_style="green" ) ) else: layout["analysis"].update( Panel( "[dim]Analysis reports will appear here...[/dim]", title="Analysis Reports", border_style="dim" ) ) # Footer with instructions layout["footer"].update( Panel( "[bold]TradingAgents Streaming Analysis[/bold] | Press Ctrl+C to stop", style="bold white on blue" ) ) def update_research_team_status_streaming(streaming_buffer, status): """Update all research team agent statuses for streaming""" research_agents = ["Bull Researcher", "Bear Researcher", "Research Manager"] for agent in research_agents: streaming_buffer.update_agent_status(agent, status) def run_analysis_streaming(advanced_mode=False): """ Streaming version of run_analysis that delivers reports in real-time """ # Get user selections based on mode if advanced_mode: selections = get_user_selections_advanced() else: 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() # Initialize the graph graph = TradingAgentsGraph( [analyst.value for analyst in selections["analysts"]], config=config, debug=True ) # 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) # Use streaming message buffer instead of regular one streaming_buffer = StreamingMessageBuffer() 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" with open(report_dir / file_name, "w") as f: f.write(content) return wrapper streaming_buffer.add_message = save_message_decorator(streaming_buffer, "add_message") streaming_buffer.add_tool_call = save_tool_call_decorator(streaming_buffer, "add_tool_call") streaming_buffer.update_report_section = save_report_section_decorator(streaming_buffer, "update_report_section") # Create streaming layout layout = create_streaming_layout() # Agent mapping for streaming agent_mapping = { "market": "Market Analyst", "social": "Social Media Analyst", "news": "News Analyst", "fundamentals": "Fundamentals Analyst", "bull": "Bull Researcher", "bear": "Bear Researcher", "research_manager": "Research Manager", "trader": "Trading Team", "risky": "Risky Analyst", "safe": "Safe Analyst", "neutral": "Neutral Analyst", "portfolio": "Portfolio Manager" } with Live(layout, refresh_per_second=8) as live: # Higher refresh rate for streaming # Initial display update_streaming_display(layout, streaming_buffer) # Add initial messages streaming_buffer.add_message("System", f"Selected ticker: {selections['ticker']}") streaming_buffer.add_message( "System", f"Analysis date: {selections['analysis_date']}" ) streaming_buffer.add_message( "System", f"Selected analysts: {', '.join(analyst.value for analyst in selections['analysts'])}", ) update_streaming_display(layout, streaming_buffer) # Reset agent statuses for agent in streaming_buffer.agent_status: streaming_buffer.update_agent_status(agent, "pending") # Reset report sections for section in streaming_buffer.report_sections: streaming_buffer.report_sections[section] = None streaming_buffer.current_report = None streaming_buffer.final_report = None # Update agent status to in_progress for the first analyst first_analyst = f"{selections['analysts'][0].value.capitalize()} Analyst" streaming_buffer.update_agent_status(first_analyst, "in_progress") update_streaming_display(layout, streaming_buffer) # Create spinner text spinner_text = ( f"Analyzing {selections['ticker']} on {selections['analysis_date']}..." ) update_streaming_display(layout, streaming_buffer, spinner_text) # Initialize state and get graph args init_agent_state = graph.propagator.create_initial_state( selections["ticker"], selections["analysis_date"] ) args = graph.propagator.get_graph_args() # Stream the analysis with real-time content delivery trace = [] current_streaming_agent = None for chunk in graph.graph.stream(init_agent_state, **args): if len(chunk["messages"]) > 0: # Get the last message from the chunk last_message = chunk["messages"][-1] # Extract message content and type if hasattr(last_message, "content"): content = extract_content_string(last_message.content) msg_type = "Reasoning" # Detect which agent is currently speaking and stream content agent_detected = None for key, agent_name in agent_mapping.items(): if any(keyword in content.lower() for keyword in [key, agent_name.lower()]): agent_detected = agent_name break # If we detected an agent or have ongoing streaming if agent_detected or current_streaming_agent: if agent_detected and agent_detected != current_streaming_agent: # New agent started - finalize previous and start new if current_streaming_agent: section_map = { "Market Analyst": "market_report", "Social Media Analyst": "sentiment_report", "News Analyst": "news_report", "Fundamentals Analyst": "fundamentals_report", "Research Manager": "investment_plan", "Trading Team": "trader_investment_plan", "Portfolio Manager": "final_trade_decision" } if current_streaming_agent in section_map: streaming_buffer.finalize_streaming_content(section_map[current_streaming_agent]) current_streaming_agent = agent_detected streaming_buffer.update_agent_status(agent_detected, "in_progress") # Stream the content in real-time if current_streaming_agent: streaming_buffer.stream_content(current_streaming_agent, content + "\n") else: content = str(last_message) msg_type = "System" # Add message to buffer streaming_buffer.add_message(msg_type, content[:200] + "..." if len(content) > 200 else content) # Handle tool calls if hasattr(last_message, "tool_calls"): for tool_call in last_message.tool_calls: if isinstance(tool_call, dict): streaming_buffer.add_tool_call( tool_call["name"], tool_call["args"] ) else: streaming_buffer.add_tool_call(tool_call.name, tool_call.args) # Handle section completions and agent status updates # Analyst Team Reports if "market_report" in chunk and chunk["market_report"]: streaming_buffer.update_report_section("market_report", chunk["market_report"]) streaming_buffer.update_agent_status("Market Analyst", "completed") current_streaming_agent = None if "social" in [a.value for a in selections["analysts"]]: streaming_buffer.update_agent_status("Social Media Analyst", "in_progress") if "sentiment_report" in chunk and chunk["sentiment_report"]: streaming_buffer.update_report_section("sentiment_report", chunk["sentiment_report"]) streaming_buffer.update_agent_status("Social Media Analyst", "completed") current_streaming_agent = None if "news" in [a.value for a in selections["analysts"]]: streaming_buffer.update_agent_status("News Analyst", "in_progress") if "news_report" in chunk and chunk["news_report"]: streaming_buffer.update_report_section("news_report", chunk["news_report"]) streaming_buffer.update_agent_status("News Analyst", "completed") current_streaming_agent = None if "fundamentals" in [a.value for a in selections["analysts"]]: streaming_buffer.update_agent_status("Fundamentals Analyst", "in_progress") if "fundamentals_report" in chunk and chunk["fundamentals_report"]: streaming_buffer.update_report_section("fundamentals_report", chunk["fundamentals_report"]) streaming_buffer.update_agent_status("Fundamentals Analyst", "completed") current_streaming_agent = None update_research_team_status_streaming(streaming_buffer, "in_progress") # Research Team - Handle Investment Debate State with streaming if "investment_debate_state" in chunk and chunk["investment_debate_state"]: debate_state = chunk["investment_debate_state"] if "bull_history" in debate_state and debate_state["bull_history"]: update_research_team_status_streaming(streaming_buffer, "in_progress") bull_responses = debate_state["bull_history"].split("\n") latest_bull = bull_responses[-1] if bull_responses else "" if latest_bull: streaming_buffer.stream_content("Bull Researcher", latest_bull + "\n") if "bear_history" in debate_state and debate_state["bear_history"]: update_research_team_status_streaming(streaming_buffer, "in_progress") bear_responses = debate_state["bear_history"].split("\n") latest_bear = bear_responses[-1] if bear_responses else "" if latest_bear: streaming_buffer.stream_content("Bear Researcher", latest_bear + "\n") if "judge_decision" in debate_state and debate_state["judge_decision"]: streaming_buffer.stream_content("Research Manager", debate_state["judge_decision"] + "\n") streaming_buffer.finalize_streaming_content("investment_plan") update_research_team_status_streaming(streaming_buffer, "completed") streaming_buffer.update_agent_status("Risky Analyst", "in_progress") current_streaming_agent = None # Trading Team with streaming if "trader_investment_plan" in chunk and chunk["trader_investment_plan"]: streaming_buffer.update_report_section("trader_investment_plan", chunk["trader_investment_plan"]) streaming_buffer.update_agent_status("Risky Analyst", "in_progress") current_streaming_agent = None # Risk Management Team with streaming if "risk_debate_state" in chunk and chunk["risk_debate_state"]: risk_state = chunk["risk_debate_state"] if "current_risky_response" in risk_state and risk_state["current_risky_response"]: streaming_buffer.update_agent_status("Risky Analyst", "in_progress") streaming_buffer.stream_content("Risky Analyst", risk_state["current_risky_response"] + "\n") if "current_safe_response" in risk_state and risk_state["current_safe_response"]: streaming_buffer.update_agent_status("Safe Analyst", "in_progress") streaming_buffer.stream_content("Safe Analyst", risk_state["current_safe_response"] + "\n") if "current_neutral_response" in risk_state and risk_state["current_neutral_response"]: streaming_buffer.update_agent_status("Neutral Analyst", "in_progress") streaming_buffer.stream_content("Neutral Analyst", risk_state["current_neutral_response"] + "\n") if "judge_decision" in risk_state and risk_state["judge_decision"]: streaming_buffer.stream_content("Portfolio Manager", risk_state["judge_decision"] + "\n") streaming_buffer.finalize_streaming_content("final_trade_decision") # Mark all risk team as completed streaming_buffer.update_agent_status("Risky Analyst", "completed") streaming_buffer.update_agent_status("Safe Analyst", "completed") streaming_buffer.update_agent_status("Neutral Analyst", "completed") streaming_buffer.update_agent_status("Portfolio Manager", "completed") current_streaming_agent = None # Update the display with streaming content update_streaming_display(layout, streaming_buffer) trace.append(chunk) # Finalize any remaining streaming content if current_streaming_agent: section_map = { "Market Analyst": "market_report", "Social Media Analyst": "sentiment_report", "News Analyst": "news_report", "Fundamentals Analyst": "fundamentals_report", "Research Manager": "investment_plan", "Trading Team": "trader_investment_plan", "Portfolio Manager": "final_trade_decision" } if current_streaming_agent in section_map: streaming_buffer.finalize_streaming_content(section_map[current_streaming_agent]) # 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 streaming_buffer.agent_status: streaming_buffer.update_agent_status(agent, "completed") streaming_buffer.add_message( "Analysis", f"Completed streaming analysis for {selections['analysis_date']}" ) # Update final report sections for section in streaming_buffer.report_sections.keys(): if section in final_state: streaming_buffer.update_report_section(section, final_state[section]) # Display the complete final report display_complete_report(final_state) update_streaming_display(layout, streaming_buffer) if __name__ == "__main__": app()