2033 lines
83 KiB
Python
2033 lines
83 KiB
Python
from typing import Optional
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import datetime
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import typer
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from pathlib import Path
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from functools import wraps
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from rich.console import Console
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from rich.panel import Panel
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from rich.spinner import Spinner
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from rich.live import Live
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from rich.columns import Columns
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from rich.markdown import Markdown
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from rich.layout import Layout
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from rich.text import Text
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from rich.live import Live
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from rich.table import Table
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from collections import deque
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import time
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from rich.tree import Tree
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from rich import box
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from rich.align import Align
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from rich.rule import Rule
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# Load environment variables from .env file
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from dotenv import load_dotenv
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load_dotenv()
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from tradingagents.graph.trading_graph import TradingAgentsGraph
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from tradingagents.default_config import DEFAULT_CONFIG
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from cli.models import AnalystType
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from cli.utils import *
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console = Console()
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app = typer.Typer(
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name="TradingAgents",
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help="TradingAgents CLI: Multi-Agents LLM Financial Trading Framework",
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add_completion=True, # Enable shell completion
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)
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# Create a deque to store recent messages with a maximum length
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class MessageBuffer:
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def __init__(self, max_length=100):
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self.messages = deque(maxlen=max_length)
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self.tool_calls = deque(maxlen=max_length)
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self.current_report = None
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self.final_report = None # Store the complete final report
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self.agent_status = {
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# Analyst Team
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"Market Analyst": "pending",
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"Social Analyst": "pending",
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"News Analyst": "pending",
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"Fundamentals Analyst": "pending",
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# Research Team
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"Bull Researcher": "pending",
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"Bear Researcher": "pending",
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"Research Manager": "pending",
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# Trading Team
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"Trader": "pending",
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# Risk Management Team
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"Risky Analyst": "pending",
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"Neutral Analyst": "pending",
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"Safe Analyst": "pending",
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# Portfolio Management Team
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"Portfolio Manager": "pending",
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}
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self.current_agent = None
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self.report_sections = {
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"market_report": None,
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"sentiment_report": None,
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"news_report": None,
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"fundamentals_report": None,
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"investment_plan": None,
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"trader_investment_plan": None,
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"final_trade_decision": None,
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}
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def add_message(self, message_type, content):
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timestamp = datetime.datetime.now().strftime("%H:%M:%S")
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self.messages.append((timestamp, message_type, content))
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def add_tool_call(self, tool_name, args):
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timestamp = datetime.datetime.now().strftime("%H:%M:%S")
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self.tool_calls.append((timestamp, tool_name, args))
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def update_agent_status(self, agent, status):
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if agent in self.agent_status:
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self.agent_status[agent] = status
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self.current_agent = agent
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def update_report_section(self, section_name, content):
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if section_name in self.report_sections:
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self.report_sections[section_name] = content
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self._update_current_report()
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def _update_current_report(self):
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# For the panel display, only show the most recently updated section
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latest_section = None
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latest_content = None
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# Find the most recently updated section
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for section, content in self.report_sections.items():
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if content is not None:
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latest_section = section
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latest_content = content
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if latest_section and latest_content:
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# Format the current section for display
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section_titles = {
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"market_report": "Market Analysis",
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"sentiment_report": "Social Sentiment",
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"news_report": "News Analysis",
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"fundamentals_report": "Fundamentals Analysis",
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"investment_plan": "Research Team Decision",
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"trader_investment_plan": "Trading Team Plan",
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"final_trade_decision": "Portfolio Management Decision",
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}
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self.current_report = (
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f"### {section_titles[latest_section]}\n{latest_content}"
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)
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# Update the final complete report
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self._update_final_report()
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def _update_final_report(self):
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report_parts = []
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# Analyst Team Reports
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if any(
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self.report_sections[section]
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for section in [
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"market_report",
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"sentiment_report",
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"news_report",
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"fundamentals_report",
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]
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):
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report_parts.append("## Analyst Team Reports")
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if self.report_sections["market_report"]:
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report_parts.append(
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f"### Market Analysis\n{self.report_sections['market_report']}"
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)
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if self.report_sections["sentiment_report"]:
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report_parts.append(
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f"### Social Sentiment\n{self.report_sections['sentiment_report']}"
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)
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if self.report_sections["news_report"]:
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report_parts.append(
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f"### News Analysis\n{self.report_sections['news_report']}"
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)
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if self.report_sections["fundamentals_report"]:
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report_parts.append(
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f"### Fundamentals Analysis\n{self.report_sections['fundamentals_report']}"
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)
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# Research Team Reports
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if self.report_sections["investment_plan"]:
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report_parts.append("## Research Team Decision")
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report_parts.append(f"{self.report_sections['investment_plan']}")
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# Trading Team Reports
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if self.report_sections["trader_investment_plan"]:
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report_parts.append("## Trading Team Plan")
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report_parts.append(f"{self.report_sections['trader_investment_plan']}")
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# Portfolio Management Decision
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if self.report_sections["final_trade_decision"]:
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report_parts.append("## Portfolio Management Decision")
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report_parts.append(f"{self.report_sections['final_trade_decision']}")
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self.final_report = "\n\n".join(report_parts) if report_parts else None
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message_buffer = MessageBuffer()
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def create_layout():
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layout = Layout()
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layout.split_column(
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Layout(name="header", size=3),
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Layout(name="main"),
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Layout(name="footer", size=3),
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)
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layout["main"].split_column(
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Layout(name="upper", ratio=3), Layout(name="analysis", ratio=5)
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)
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layout["upper"].split_row(
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Layout(name="progress", ratio=2), Layout(name="messages", ratio=3)
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)
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return layout
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def update_display(layout, spinner_text=None):
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# Header with welcome message
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layout["header"].update(
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Panel(
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"[bold green]Welcome to TradingAgents CLI[/bold green]\n"
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"[dim]© [Tauric Research](https://github.com/TauricResearch)[/dim]",
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title="Welcome to TradingAgents",
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border_style="green",
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padding=(1, 2),
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expand=True,
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)
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)
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# Progress panel showing agent status
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progress_table = Table(
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show_header=True,
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header_style="bold magenta",
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show_footer=False,
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box=box.SIMPLE_HEAD, # Use simple header with horizontal lines
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title=None, # Remove the redundant Progress title
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padding=(0, 2), # Add horizontal padding
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expand=True, # Make table expand to fill available space
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)
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progress_table.add_column("Team", style="cyan", justify="center", width=20)
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progress_table.add_column("Agent", style="green", justify="center", width=20)
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progress_table.add_column("Status", style="yellow", justify="center", width=20)
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# Group agents by team
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teams = {
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"Analyst Team": [
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"Market Analyst",
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"Social Analyst",
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"News Analyst",
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"Fundamentals Analyst",
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],
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"Research Team": ["Bull Researcher", "Bear Researcher", "Research Manager"],
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"Trading Team": ["Trader"],
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"Risk Management": ["Risky Analyst", "Neutral Analyst", "Safe Analyst"],
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"Portfolio Management": ["Portfolio Manager"],
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}
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for team, agents in teams.items():
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# Add first agent with team name
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first_agent = agents[0]
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status = message_buffer.agent_status[first_agent]
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if status == "in_progress":
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spinner = Spinner(
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"dots", text="[blue]in_progress[/blue]", style="bold cyan"
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)
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status_cell = spinner
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else:
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status_color = {
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"pending": "yellow",
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"completed": "green",
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"error": "red",
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}.get(status, "white")
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status_cell = f"[{status_color}]{status}[/{status_color}]"
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progress_table.add_row(team, first_agent, status_cell)
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# Add remaining agents in team
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for agent in agents[1:]:
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status = message_buffer.agent_status[agent]
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if status == "in_progress":
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spinner = Spinner(
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"dots", text="[blue]in_progress[/blue]", style="bold cyan"
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)
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status_cell = spinner
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else:
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status_color = {
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"pending": "yellow",
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"completed": "green",
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"error": "red",
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}.get(status, "white")
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status_cell = f"[{status_color}]{status}[/{status_color}]"
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progress_table.add_row("", agent, status_cell)
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# Add horizontal line after each team
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progress_table.add_row("─" * 20, "─" * 20, "─" * 20, style="dim")
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layout["progress"].update(
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Panel(progress_table, title="Progress", border_style="cyan", padding=(1, 2))
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)
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# Messages panel showing recent messages and tool calls
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messages_table = Table(
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show_header=True,
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header_style="bold magenta",
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show_footer=False,
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expand=True, # Make table expand to fill available space
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box=box.MINIMAL, # Use minimal box style for a lighter look
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show_lines=True, # Keep horizontal lines
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padding=(0, 1), # Add some padding between columns
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)
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messages_table.add_column("Time", style="cyan", width=8, justify="center")
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messages_table.add_column("Type", style="green", width=10, justify="center")
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messages_table.add_column(
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"Content", style="white", no_wrap=False, ratio=1
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) # Make content column expand
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# Combine tool calls and messages
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all_messages = []
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# Add tool calls
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for timestamp, tool_name, args in message_buffer.tool_calls:
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# Truncate tool call args if too long
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if isinstance(args, str) and len(args) > 100:
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args = args[:97] + "..."
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all_messages.append((timestamp, "Tool", f"{tool_name}: {args}"))
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# Add regular messages
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for timestamp, msg_type, content in message_buffer.messages:
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# Convert content to string if it's not already
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content_str = content
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if isinstance(content, list):
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# Handle list of content blocks (Anthropic format)
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text_parts = []
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for item in content:
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if isinstance(item, dict):
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if item.get('type') == 'text':
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text_parts.append(item.get('text', ''))
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elif item.get('type') == 'tool_use':
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text_parts.append(f"[Tool: {item.get('name', 'unknown')}]")
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else:
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text_parts.append(str(item))
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content_str = ' '.join(text_parts)
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elif not isinstance(content_str, str):
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content_str = str(content)
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# Truncate message content if too long
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if len(content_str) > 200:
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content_str = content_str[:197] + "..."
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all_messages.append((timestamp, msg_type, content_str))
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# Sort by timestamp
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all_messages.sort(key=lambda x: x[0])
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# Calculate how many messages we can show based on available space
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# Start with a reasonable number and adjust based on content length
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max_messages = 12 # Increased from 8 to better fill the space
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# Get the last N messages that will fit in the panel
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recent_messages = all_messages[-max_messages:]
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# Add messages to table
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for timestamp, msg_type, content in recent_messages:
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# Format content with word wrapping
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wrapped_content = Text(content, overflow="fold")
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messages_table.add_row(timestamp, msg_type, wrapped_content)
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if spinner_text:
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messages_table.add_row("", "Spinner", spinner_text)
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# Add a footer to indicate if messages were truncated
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if len(all_messages) > max_messages:
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messages_table.footer = (
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f"[dim]Showing last {max_messages} of {len(all_messages)} messages[/dim]"
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)
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layout["messages"].update(
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Panel(
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messages_table,
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title="Messages & Tools",
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border_style="blue",
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padding=(1, 2),
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)
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)
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# Analysis panel showing current report
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if message_buffer.current_report:
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layout["analysis"].update(
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Panel(
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Markdown(message_buffer.current_report),
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title="Current Report",
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border_style="green",
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padding=(1, 2),
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)
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)
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else:
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layout["analysis"].update(
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Panel(
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"[italic]Waiting for analysis report...[/italic]",
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title="Current Report",
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border_style="green",
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padding=(1, 2),
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)
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)
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# Footer with statistics
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tool_calls_count = len(message_buffer.tool_calls)
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llm_calls_count = sum(
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1 for _, msg_type, _ in message_buffer.messages if msg_type == "Reasoning"
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)
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reports_count = sum(
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1 for content in message_buffer.report_sections.values() if content is not None
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)
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stats_table = Table(show_header=False, box=None, padding=(0, 2), expand=True)
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stats_table.add_column("Stats", justify="center")
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stats_table.add_row(
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f"Tool Calls: {tool_calls_count} | LLM Calls: {llm_calls_count} | Generated Reports: {reports_count}"
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)
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layout["footer"].update(Panel(stats_table, border_style="grey50"))
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def get_user_selections():
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"""Get user ticker selection with simplified interface and sensible defaults."""
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# Display ASCII art welcome message
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with open("./cli/static/welcome.txt", "r") as f:
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welcome_ascii = f.read()
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# Create welcome box content
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welcome_content = f"{welcome_ascii}\n"
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welcome_content += "[bold green]TradingAgents: Multi-Agents LLM Financial Trading Framework - CLI[/bold green]\n\n"
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welcome_content += "[bold]Workflow Steps:[/bold]\n"
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welcome_content += "I. Analyst Team → II. Research Team → III. Trader → IV. Risk Management → V. Portfolio Management\n\n"
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welcome_content += (
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"[dim]Built by [Tauric Research](https://github.com/TauricResearch)[/dim]"
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)
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# Create and center the welcome box
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welcome_box = Panel(
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welcome_content,
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border_style="green",
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padding=(1, 2),
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title="Welcome to TradingAgents",
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subtitle="Multi-Agents LLM Financial Trading Framework",
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)
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console.print(Align.center(welcome_box))
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console.print() # Add a blank line after the welcome box
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# Simplified input - only ask for ticker symbol
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ticker_box = Panel(
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"[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]",
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border_style="blue",
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padding=(1, 2)
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)
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console.print(ticker_box)
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selected_ticker = get_ticker()
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# Use sensible defaults for all other parameters
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analysis_date = datetime.datetime.now().strftime("%Y-%m-%d")
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selected_analysts = [AnalystType.MARKET, AnalystType.SOCIAL, AnalystType.NEWS, AnalystType.FUNDAMENTALS]
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selected_research_depth = 5
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selected_llm_provider = "openai"
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backend_url = "https://api.openai.com/v1"
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selected_shallow_thinker = "gpt-4o"
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selected_deep_thinker = "o3"
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# Display the configuration being used
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config_info = f"""[bold green]Configuration:[/bold green]
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• [bold]Ticker:[/bold] {selected_ticker}
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• [bold]Date:[/bold] {analysis_date} (latest trading day)
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• [bold]Analysts:[/bold] All analysts (Market, Social, News, Fundamentals)
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• [bold]Research Depth:[/bold] Deep (5 rounds of debate)
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• [bold]LLM Provider:[/bold] OpenAI
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• [bold]Quick Thinking:[/bold] GPT-4o
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• [bold]Deep Thinking:[/bold] o3
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[dim]Starting analysis with optimized settings...[/dim]"""
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|
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console.print(Panel(config_info, border_style="green", padding=(1, 2), title="Analysis Configuration"))
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console.print()
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|
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return {
|
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"ticker": selected_ticker,
|
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"analysis_date": analysis_date,
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"analysts": selected_analysts,
|
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"research_depth": selected_research_depth,
|
|
"llm_provider": selected_llm_provider.lower(),
|
|
"backend_url": backend_url,
|
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"shallow_thinker": selected_shallow_thinker,
|
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"deep_thinker": selected_deep_thinker,
|
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}
|
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|
|
|
|
def get_ticker():
|
|
"""Get ticker symbol from user input."""
|
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ticker = typer.prompt("", default="SPY")
|
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return ticker.strip().upper()
|
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|
|
|
|
def get_analysis_date():
|
|
"""Get the analysis date from user input."""
|
|
while True:
|
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date_str = typer.prompt(
|
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"", default=datetime.datetime.now().strftime("%Y-%m-%d")
|
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)
|
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try:
|
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# Validate date format and ensure it's not in the future
|
|
analysis_date = datetime.datetime.strptime(date_str, "%Y-%m-%d")
|
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if analysis_date.date() > datetime.datetime.now().date():
|
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console.print("[red]Error: Analysis date cannot be in the future[/red]")
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continue
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return date_str
|
|
except ValueError:
|
|
console.print(
|
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"[red]Error: Invalid date format. Please use YYYY-MM-DD[/red]"
|
|
)
|
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|
|
|
|
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),
|
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)
|
|
)
|
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|
|
# 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):
|
|
# Handle the new parallel execution structure
|
|
messages_found = False
|
|
|
|
# Check for messages in the old format (for backward compatibility)
|
|
if "messages" in chunk and len(chunk["messages"]) > 0:
|
|
messages_found = True
|
|
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)
|
|
|
|
# Check for messages in the new parallel execution format
|
|
else:
|
|
# Look for messages in analyst channels
|
|
message_channels = ["market_messages", "social_messages", "news_messages", "fundamentals_messages"]
|
|
|
|
for node_name, node_data in chunk.items():
|
|
if isinstance(node_data, dict):
|
|
for channel in message_channels:
|
|
if channel in node_data and node_data[channel]:
|
|
messages_found = True
|
|
# Get the last message from this channel
|
|
last_message = node_data[channel][-1]
|
|
|
|
# Extract message content and type
|
|
if hasattr(last_message, "content"):
|
|
content = extract_content_string(last_message.content)
|
|
msg_type = f"{channel.replace('_messages', '').title()} Analyst"
|
|
else:
|
|
content = str(last_message)
|
|
msg_type = f"{channel.replace('_messages', '').title()} 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)
|
|
|
|
# Only process the first message channel found to avoid duplicates
|
|
break
|
|
if messages_found:
|
|
break
|
|
|
|
# Continue with the rest of the processing (reports, etc.)
|
|
if True: # Always process chunk for reports regardless of messages
|
|
|
|
# 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]
|
|
|
|
# Extract the final trade decision from the correct location
|
|
final_trade_decision = None
|
|
if "Risk Judge" in final_state and "final_trade_decision" in final_state["Risk Judge"]:
|
|
final_trade_decision = final_state["Risk Judge"]["final_trade_decision"]
|
|
elif "final_trade_decision" in final_state:
|
|
final_trade_decision = final_state["final_trade_decision"]
|
|
|
|
if final_trade_decision:
|
|
decision = graph.process_signal(final_trade_decision)
|
|
else:
|
|
decision = "No trade decision available"
|
|
|
|
# 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)
|
|
|
|
# Save the final complete report and decision
|
|
# Save the final trade decision
|
|
if final_trade_decision:
|
|
decision_file = results_dir / "final_trade_decision.md"
|
|
with open(decision_file, "w") as f:
|
|
f.write(f"# Final Trading Decision\n\n")
|
|
f.write(f"**Ticker:** {selections['ticker']}\n")
|
|
f.write(f"**Analysis Date:** {selections['analysis_date']}\n")
|
|
f.write(f"**Decision:** {decision}\n\n")
|
|
f.write("## Raw Decision Text\n\n")
|
|
f.write(final_trade_decision)
|
|
|
|
# Save the complete final report
|
|
complete_report_file = results_dir / "complete_analysis_report.md"
|
|
with open(complete_report_file, "w") as f:
|
|
f.write(f"# Complete Analysis Report\n\n")
|
|
f.write(f"**Ticker:** {selections['ticker']}\n")
|
|
f.write(f"**Analysis Date:** {selections['analysis_date']}\n")
|
|
f.write(f"**Analysis Time:** {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n")
|
|
|
|
# Add analyst reports
|
|
if final_state.get("market_report"):
|
|
f.write("## Market Analysis\n\n")
|
|
f.write(final_state["market_report"])
|
|
f.write("\n\n")
|
|
|
|
if final_state.get("sentiment_report"):
|
|
f.write("## Social Media Sentiment Analysis\n\n")
|
|
f.write(final_state["sentiment_report"])
|
|
f.write("\n\n")
|
|
|
|
if final_state.get("news_report"):
|
|
f.write("## News Analysis\n\n")
|
|
f.write(final_state["news_report"])
|
|
f.write("\n\n")
|
|
|
|
if final_state.get("fundamentals_report"):
|
|
f.write("## Fundamentals Analysis\n\n")
|
|
f.write(final_state["fundamentals_report"])
|
|
f.write("\n\n")
|
|
|
|
# Add research team analysis
|
|
if final_state.get("investment_debate_state"):
|
|
debate_state = final_state["investment_debate_state"]
|
|
f.write("## Investment Research Analysis\n\n")
|
|
|
|
if debate_state.get("bull_history"):
|
|
f.write("### Bull Researcher Analysis\n\n")
|
|
f.write(debate_state["bull_history"])
|
|
f.write("\n\n")
|
|
|
|
if debate_state.get("bear_history"):
|
|
f.write("### Bear Researcher Analysis\n\n")
|
|
f.write(debate_state["bear_history"])
|
|
f.write("\n\n")
|
|
|
|
if debate_state.get("judge_decision"):
|
|
f.write("### Research Manager Decision\n\n")
|
|
f.write(debate_state["judge_decision"])
|
|
f.write("\n\n")
|
|
|
|
# Add trading analysis
|
|
if final_state.get("trader_investment_plan"):
|
|
f.write("## Trading Plan\n\n")
|
|
f.write(final_state["trader_investment_plan"])
|
|
f.write("\n\n")
|
|
|
|
# Add risk analysis
|
|
if final_state.get("risk_debate_state"):
|
|
risk_state = final_state["risk_debate_state"]
|
|
f.write("## Risk Management Analysis\n\n")
|
|
|
|
if risk_state.get("risky_history"):
|
|
f.write("### Aggressive Risk Analysis\n\n")
|
|
f.write(risk_state["risky_history"])
|
|
f.write("\n\n")
|
|
|
|
if risk_state.get("safe_history"):
|
|
f.write("### Conservative Risk Analysis\n\n")
|
|
f.write(risk_state["safe_history"])
|
|
f.write("\n\n")
|
|
|
|
if risk_state.get("neutral_history"):
|
|
f.write("### Neutral Risk Analysis\n\n")
|
|
f.write(risk_state["neutral_history"])
|
|
f.write("\n\n")
|
|
|
|
if risk_state.get("judge_decision"):
|
|
f.write("### Risk Manager Final Decision\n\n")
|
|
f.write(risk_state["judge_decision"])
|
|
f.write("\n\n")
|
|
|
|
# Add final decision
|
|
if final_trade_decision:
|
|
f.write("## Final Trading Decision\n\n")
|
|
f.write(f"**Decision:** {decision}\n\n")
|
|
f.write("### Detailed Decision\n\n")
|
|
f.write(final_trade_decision)
|
|
|
|
# Save final state as JSON for programmatic access
|
|
final_state_file = results_dir / "final_state.json"
|
|
with open(final_state_file, "w") as f:
|
|
import json
|
|
# Convert final_state to JSON-serializable format
|
|
json_state = {}
|
|
for key, value in final_state.items():
|
|
try:
|
|
json.dumps(value) # Test if it's JSON serializable
|
|
json_state[key] = value
|
|
except:
|
|
json_state[key] = str(value) # Convert to string if not serializable
|
|
json.dump(json_state, f, indent=2)
|
|
|
|
print(f"\n✅ Analysis complete! Results saved to: {results_dir}")
|
|
print(f"📄 Complete report: {complete_report_file}")
|
|
print(f"🎯 Final decision: {decision_file}")
|
|
print(f"📊 Final state: {final_state_file}")
|
|
|
|
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]
|
|
# Extract the final trade decision from the correct location
|
|
final_trade_decision = None
|
|
if "Risk Judge" in final_state and "final_trade_decision" in final_state["Risk Judge"]:
|
|
final_trade_decision = final_state["Risk Judge"]["final_trade_decision"]
|
|
elif "final_trade_decision" in final_state:
|
|
final_trade_decision = final_state["final_trade_decision"]
|
|
|
|
if final_trade_decision:
|
|
decision = graph.process_signal(final_trade_decision)
|
|
else:
|
|
decision = "No trade decision available"
|
|
|
|
# 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)
|
|
|
|
# Save the final complete report and decision
|
|
# Save the final trade decision
|
|
if final_trade_decision:
|
|
decision_file = results_dir / "final_trade_decision.md"
|
|
with open(decision_file, "w") as f:
|
|
f.write(f"# Final Trading Decision\n\n")
|
|
f.write(f"**Ticker:** {selections['ticker']}\n")
|
|
f.write(f"**Analysis Date:** {selections['analysis_date']}\n")
|
|
f.write(f"**Decision:** {decision}\n\n")
|
|
f.write("## Raw Decision Text\n\n")
|
|
f.write(final_trade_decision)
|
|
|
|
# Save the complete final report
|
|
complete_report_file = results_dir / "complete_analysis_report.md"
|
|
with open(complete_report_file, "w") as f:
|
|
f.write(f"# Complete Analysis Report\n\n")
|
|
f.write(f"**Ticker:** {selections['ticker']}\n")
|
|
f.write(f"**Analysis Date:** {selections['analysis_date']}\n")
|
|
f.write(f"**Analysis Time:** {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n")
|
|
|
|
# Add analyst reports
|
|
if final_state.get("market_report"):
|
|
f.write("## Market Analysis\n\n")
|
|
f.write(final_state["market_report"])
|
|
f.write("\n\n")
|
|
|
|
if final_state.get("sentiment_report"):
|
|
f.write("## Social Media Sentiment Analysis\n\n")
|
|
f.write(final_state["sentiment_report"])
|
|
f.write("\n\n")
|
|
|
|
if final_state.get("news_report"):
|
|
f.write("## News Analysis\n\n")
|
|
f.write(final_state["news_report"])
|
|
f.write("\n\n")
|
|
|
|
if final_state.get("fundamentals_report"):
|
|
f.write("## Fundamentals Analysis\n\n")
|
|
f.write(final_state["fundamentals_report"])
|
|
f.write("\n\n")
|
|
|
|
# Add research team analysis
|
|
if final_state.get("investment_debate_state"):
|
|
debate_state = final_state["investment_debate_state"]
|
|
f.write("## Investment Research Analysis\n\n")
|
|
|
|
if debate_state.get("bull_history"):
|
|
f.write("### Bull Researcher Analysis\n\n")
|
|
f.write(debate_state["bull_history"])
|
|
f.write("\n\n")
|
|
|
|
if debate_state.get("bear_history"):
|
|
f.write("### Bear Researcher Analysis\n\n")
|
|
f.write(debate_state["bear_history"])
|
|
f.write("\n\n")
|
|
|
|
if debate_state.get("judge_decision"):
|
|
f.write("### Research Manager Decision\n\n")
|
|
f.write(debate_state["judge_decision"])
|
|
f.write("\n\n")
|
|
|
|
# Add trading analysis
|
|
if final_state.get("trader_investment_plan"):
|
|
f.write("## Trading Plan\n\n")
|
|
f.write(final_state["trader_investment_plan"])
|
|
f.write("\n\n")
|
|
|
|
# Add risk analysis
|
|
if final_state.get("risk_debate_state"):
|
|
risk_state = final_state["risk_debate_state"]
|
|
f.write("## Risk Management Analysis\n\n")
|
|
|
|
if risk_state.get("risky_history"):
|
|
f.write("### Aggressive Risk Analysis\n\n")
|
|
f.write(risk_state["risky_history"])
|
|
f.write("\n\n")
|
|
|
|
if risk_state.get("safe_history"):
|
|
f.write("### Conservative Risk Analysis\n\n")
|
|
f.write(risk_state["safe_history"])
|
|
f.write("\n\n")
|
|
|
|
if risk_state.get("neutral_history"):
|
|
f.write("### Neutral Risk Analysis\n\n")
|
|
f.write(risk_state["neutral_history"])
|
|
f.write("\n\n")
|
|
|
|
if risk_state.get("judge_decision"):
|
|
f.write("### Risk Manager Final Decision\n\n")
|
|
f.write(risk_state["judge_decision"])
|
|
f.write("\n\n")
|
|
|
|
# Add final decision
|
|
if final_trade_decision:
|
|
f.write("## Final Trading Decision\n\n")
|
|
f.write(f"**Decision:** {decision}\n\n")
|
|
f.write("### Detailed Decision\n\n")
|
|
f.write(final_trade_decision)
|
|
|
|
# Save final state as JSON for programmatic access
|
|
final_state_file = results_dir / "final_state.json"
|
|
with open(final_state_file, "w") as f:
|
|
import json
|
|
# Convert final_state to JSON-serializable format
|
|
json_state = {}
|
|
for key, value in final_state.items():
|
|
try:
|
|
json.dumps(value) # Test if it's JSON serializable
|
|
json_state[key] = value
|
|
except:
|
|
json_state[key] = str(value) # Convert to string if not serializable
|
|
json.dump(json_state, f, indent=2)
|
|
|
|
print(f"\n✅ Analysis complete! Results saved to: {results_dir}")
|
|
print(f"📄 Complete report: {complete_report_file}")
|
|
print(f"🎯 Final decision: {decision_file}")
|
|
print(f"📊 Final state: {final_state_file}")
|
|
|
|
update_streaming_display(layout, streaming_buffer)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
app()
|