# TradingAgents命令行界面主文件 # 这个文件提供了用户友好的命令行界面来运行交易代理分析 # 导入类型提示模块 from typing import Optional # 导入日期时间处理模块 import datetime # 导入命令行界面框架 import typer # 尝试导入dotenv from dotenv import load_dotenv load_dotenv() # 加载.env文件中的环境变量 # 导入路径处理模块 from pathlib import Path # 导入装饰器工具 from functools import wraps # 导入Rich库的各种组件，用于美化终端输出 from rich.console import Console # 控制台输出 from rich.panel import Panel # 面板显示 from rich.spinner import Spinner # 加载动画 from rich.live import Live # 实时更新显示 from rich.columns import Columns # 列布局 from rich.markdown import Markdown # Markdown渲染 from rich.layout import Layout # 布局管理 from rich.text import Text # 文本处理 from rich.table import Table # 表格显示 from rich.tree import Tree # 树形结构 from rich import box # 边框样式 from rich.align import Align # 对齐 from rich.rule import Rule # 分隔线 # 导入集合模块 from collections import deque # 导入时间模块 import time # 导入TradingAgents核心模块 from tradingagents.graph.trading_graph import TradingAgentsGraph # 交易代理图 from tradingagents.default_config import DEFAULT_CONFIG # 默认配置 # 导入CLI相关模块 from cli.models import AnalystType # 分析师类型枚举 from cli.utils import * # CLI工具函数 # 创建控制台对象，用于输出 console = Console() # 创建Typer应用，这是命令行界面的主入口 app = typer.Typer( name="TradingAgents", # 应用名称 help="TradingAgents CLI: Multi-Agents LLM Financial Trading Framework", # 帮助信息 add_completion=True, # 启用shell自动补全功能 ) # 消息缓冲区类 # 用于存储最近的消息，限制最大长度以节省内存 class MessageBuffer: def __init__(self, max_length=100): """ 初始化消息缓冲区 参数: max_length (int): 最大消息数量，默认100条 """ self.messages = deque(maxlen=max_length) # 存储普通消息的双端队列 self.tool_calls = deque(maxlen=max_length) # 存储工具调用的双端队列 self.current_report = None # 当前报告 self.final_report = None # 最终完整报告 # 代理状态字典，跟踪各个代理的工作状态 self.agent_status = { # 分析师团队 "Market Analyst": "pending", # 市场分析师：待处理 "Social Analyst": "pending", # 社交媒体分析师：待处理 "News Analyst": "pending", # 新闻分析师：待处理 "Fundamentals Analyst": "pending", # 基本面分析师：待处理 # 研究团队 "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 all user selections before starting the analysis display.""" # 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 # 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: 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) 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.""" 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 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 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() # 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", encoding="utf-8") 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", encoding="utf-8") 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", encoding="utf-8") 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(): run_analysis() if __name__ == "__main__": app()