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Repository: TauricResearch/TradingAgents
Branch: main
Commit: f362a160c309
Files: 65
Total size: 246.5 KB
Directory structure:
gitextract_e7yyz3y3/
├── .gitignore
├── LICENSE
├── README.md
├── cli/
│ ├── __init__.py
│ ├── announcements.py
│ ├── config.py
│ ├── main.py
│ ├── models.py
│ ├── static/
│ │ └── welcome.txt
│ ├── stats_handler.py
│ └── utils.py
├── main.py
├── pyproject.toml
├── requirements.txt
├── test.py
└── tradingagents/
├── __init__.py
├── agents/
│ ├── __init__.py
│ ├── analysts/
│ │ ├── fundamentals_analyst.py
│ │ ├── market_analyst.py
│ │ ├── news_analyst.py
│ │ └── social_media_analyst.py
│ ├── managers/
│ │ ├── research_manager.py
│ │ └── risk_manager.py
│ ├── researchers/
│ │ ├── bear_researcher.py
│ │ └── bull_researcher.py
│ ├── risk_mgmt/
│ │ ├── aggressive_debator.py
│ │ ├── conservative_debator.py
│ │ └── neutral_debator.py
│ ├── trader/
│ │ └── trader.py
│ └── utils/
│ ├── agent_states.py
│ ├── agent_utils.py
│ ├── core_stock_tools.py
│ ├── fundamental_data_tools.py
│ ├── memory.py
│ ├── news_data_tools.py
│ └── technical_indicators_tools.py
├── dataflows/
│ ├── __init__.py
│ ├── alpha_vantage.py
│ ├── alpha_vantage_common.py
│ ├── alpha_vantage_fundamentals.py
│ ├── alpha_vantage_indicator.py
│ ├── alpha_vantage_news.py
│ ├── alpha_vantage_stock.py
│ ├── config.py
│ ├── interface.py
│ ├── stockstats_utils.py
│ ├── utils.py
│ ├── y_finance.py
│ └── yfinance_news.py
├── default_config.py
├── graph/
│ ├── __init__.py
│ ├── conditional_logic.py
│ ├── propagation.py
│ ├── reflection.py
│ ├── setup.py
│ ├── signal_processing.py
│ └── trading_graph.py
└── llm_clients/
├── TODO.md
├── __init__.py
├── anthropic_client.py
├── base_client.py
├── factory.py
├── google_client.py
├── openai_client.py
└── validators.py
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FILE: README.md
================================================
<p align="center">
<img src="assets/TauricResearch.png" style="width: 60%; height: auto;">
</p>
<div align="center" style="line-height: 1;">
<a href="https://arxiv.org/abs/2412.20138" target="_blank"><img alt="arXiv" src="https://img.shields.io/badge/arXiv-2412.20138-B31B1B?logo=arxiv"/></a>
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</div>
---
# TradingAgents: Multi-Agents LLM Financial Trading Framework
## News
- [2026-03] **TradingAgents v0.2.1** released with GPT-5.4, Gemini 3.1, Claude 4.6 model coverage and improved system stability.
- [2026-02] **TradingAgents v0.2.0** released with multi-provider LLM support (GPT-5.x, Gemini 3.x, Claude 4.x, Grok 4.x) and improved system architecture.
- [2026-01] **Trading-R1** [Technical Report](https://arxiv.org/abs/2509.11420) released, with [Terminal](https://github.com/TauricResearch/Trading-R1) expected to land soon.
<div align="center">
<a href="https://www.star-history.com/#TauricResearch/TradingAgents&Date">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date&theme=dark" />
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date" />
<img alt="TradingAgents Star History" src="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date" style="width: 80%; height: auto;" />
</picture>
</a>
</div>
> 🎉 **TradingAgents** officially released! We have received numerous inquiries about the work, and we would like to express our thanks for the enthusiasm in our community.
>
> So we decided to fully open-source the framework. Looking forward to building impactful projects with you!
<div align="center">
🚀 [TradingAgents](#tradingagents-framework) | ⚡ [Installation & CLI](#installation-and-cli) | 🎬 [Demo](https://www.youtube.com/watch?v=90gr5lwjIho) | 📦 [Package Usage](#tradingagents-package) | 🤝 [Contributing](#contributing) | 📄 [Citation](#citation)
</div>
## TradingAgents Framework
TradingAgents is a multi-agent trading framework that mirrors the dynamics of real-world trading firms. By deploying specialized LLM-powered agents: from fundamental analysts, sentiment experts, and technical analysts, to trader, risk management team, the platform collaboratively evaluates market conditions and informs trading decisions. Moreover, these agents engage in dynamic discussions to pinpoint the optimal strategy.
<p align="center">
<img src="assets/schema.png" style="width: 100%; height: auto;">
</p>
> TradingAgents framework is designed for research purposes. Trading performance may vary based on many factors, including the chosen backbone language models, model temperature, trading periods, the quality of data, and other non-deterministic factors. [It is not intended as financial, investment, or trading advice.](https://tauric.ai/disclaimer/)
Our framework decomposes complex trading tasks into specialized roles. This ensures the system achieves a robust, scalable approach to market analysis and decision-making.
### Analyst Team
- Fundamentals Analyst: Evaluates company financials and performance metrics, identifying intrinsic values and potential red flags.
- Sentiment Analyst: Analyzes social media and public sentiment using sentiment scoring algorithms to gauge short-term market mood.
- News Analyst: Monitors global news and macroeconomic indicators, interpreting the impact of events on market conditions.
- Technical Analyst: Utilizes technical indicators (like MACD and RSI) to detect trading patterns and forecast price movements.
<p align="center">
<img src="assets/analyst.png" width="100%" style="display: inline-block; margin: 0 2%;">
</p>
### Researcher Team
- Comprises both bullish and bearish researchers who critically assess the insights provided by the Analyst Team. Through structured debates, they balance potential gains against inherent risks.
<p align="center">
<img src="assets/researcher.png" width="70%" style="display: inline-block; margin: 0 2%;">
</p>
### Trader Agent
- Composes reports from the analysts and researchers to make informed trading decisions. It determines the timing and magnitude of trades based on comprehensive market insights.
<p align="center">
<img src="assets/trader.png" width="70%" style="display: inline-block; margin: 0 2%;">
</p>
### Risk Management and Portfolio Manager
- Continuously evaluates portfolio risk by assessing market volatility, liquidity, and other risk factors. The risk management team evaluates and adjusts trading strategies, providing assessment reports to the Portfolio Manager for final decision.
- The Portfolio Manager approves/rejects the transaction proposal. If approved, the order will be sent to the simulated exchange and executed.
<p align="center">
<img src="assets/risk.png" width="70%" style="display: inline-block; margin: 0 2%;">
</p>
## Installation and CLI
### Installation
Clone TradingAgents:
```bash
git clone https://github.com/TauricResearch/TradingAgents.git
cd TradingAgents
```
Create a virtual environment in any of your favorite environment managers:
```bash
conda create -n tradingagents python=3.13
conda activate tradingagents
```
Install dependencies:
```bash
pip install -r requirements.txt
```
### Required APIs
TradingAgents supports multiple LLM providers. Set the API key for your chosen provider:
```bash
export OPENAI_API_KEY=... # OpenAI (GPT)
export GOOGLE_API_KEY=... # Google (Gemini)
export ANTHROPIC_API_KEY=... # Anthropic (Claude)
export XAI_API_KEY=... # xAI (Grok)
export OPENROUTER_API_KEY=... # OpenRouter
export ALPHA_VANTAGE_API_KEY=... # Alpha Vantage
```
For local models, configure Ollama with `llm_provider: "ollama"` in your config.
Alternatively, copy `.env.example` to `.env` and fill in your keys:
```bash
cp .env.example .env
```
### CLI Usage
You can also try out the CLI directly by running:
```bash
python -m cli.main
```
You will see a screen where you can select your desired tickers, date, LLMs, research depth, etc.
<p align="center">
<img src="assets/cli/cli_init.png" width="100%" style="display: inline-block; margin: 0 2%;">
</p>
An interface will appear showing results as they load, letting you track the agent's progress as it runs.
<p align="center">
<img src="assets/cli/cli_news.png" width="100%" style="display: inline-block; margin: 0 2%;">
</p>
<p align="center">
<img src="assets/cli/cli_transaction.png" width="100%" style="display: inline-block; margin: 0 2%;">
</p>
## TradingAgents Package
### Implementation Details
We built TradingAgents with LangGraph to ensure flexibility and modularity. The framework supports multiple LLM providers: OpenAI, Google, Anthropic, xAI, OpenRouter, and Ollama.
### Python Usage
To use TradingAgents inside your code, you can import the `tradingagents` module and initialize a `TradingAgentsGraph()` object. The `.propagate()` function will return a decision. You can run `main.py`, here's also a quick example:
```python
from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG
ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy())
# forward propagate
_, decision = ta.propagate("NVDA", "2026-01-15")
print(decision)
```
You can also adjust the default configuration to set your own choice of LLMs, debate rounds, etc.
```python
from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG
config = DEFAULT_CONFIG.copy()
config["llm_provider"] = "openai" # openai, google, anthropic, xai, openrouter, ollama
config["deep_think_llm"] = "gpt-5.2" # Model for complex reasoning
config["quick_think_llm"] = "gpt-5-mini" # Model for quick tasks
config["max_debate_rounds"] = 2
ta = TradingAgentsGraph(debug=True, config=config)
_, decision = ta.propagate("NVDA", "2026-01-15")
print(decision)
```
See `tradingagents/default_config.py` for all configuration options.
## Contributing
We welcome contributions from the community! Whether it's fixing a bug, improving documentation, or suggesting a new feature, your input helps make this project better. If you are interested in this line of research, please consider joining our open-source financial AI research community [Tauric Research](https://tauric.ai/).
## Citation
Please reference our work if you find *TradingAgents* provides you with some help :)
```
@misc{xiao2025tradingagentsmultiagentsllmfinancial,
title={TradingAgents: Multi-Agents LLM Financial Trading Framework},
author={Yijia Xiao and Edward Sun and Di Luo and Wei Wang},
year={2025},
eprint={2412.20138},
archivePrefix={arXiv},
primaryClass={q-fin.TR},
url={https://arxiv.org/abs/2412.20138},
}
```
================================================
FILE: cli/__init__.py
================================================
================================================
FILE: cli/announcements.py
================================================
import getpass
import requests
from rich.console import Console
from rich.panel import Panel
from cli.config import CLI_CONFIG
def fetch_announcements(url: str = None, timeout: float = None) -> dict:
"""Fetch announcements from endpoint. Returns dict with announcements and settings."""
endpoint = url or CLI_CONFIG["announcements_url"]
timeout = timeout or CLI_CONFIG["announcements_timeout"]
fallback = CLI_CONFIG["announcements_fallback"]
try:
response = requests.get(endpoint, timeout=timeout)
response.raise_for_status()
data = response.json()
return {
"announcements": data.get("announcements", [fallback]),
"require_attention": data.get("require_attention", False),
}
except Exception:
return {
"announcements": [fallback],
"require_attention": False,
}
def display_announcements(console: Console, data: dict) -> None:
"""Display announcements panel. Prompts for Enter if require_attention is True."""
announcements = data.get("announcements", [])
require_attention = data.get("require_attention", False)
if not announcements:
return
content = "\n".join(announcements)
panel = Panel(
content,
border_style="cyan",
padding=(1, 2),
title="Announcements",
)
console.print(panel)
if require_attention:
getpass.getpass("Press Enter to continue...")
else:
console.print()
================================================
FILE: cli/config.py
================================================
CLI_CONFIG = {
# Announcements
"announcements_url": "https://api.tauric.ai/v1/announcements",
"announcements_timeout": 1.0,
"announcements_fallback": "[cyan]For more information, please visit[/cyan] [link=https://github.com/TauricResearch]https://github.com/TauricResearch[/link]",
}
================================================
FILE: cli/main.py
================================================
from typing import Optional
import datetime
import typer
from pathlib import Path
from functools import wraps
from rich.console import Console
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
from rich.panel import Panel
from rich.spinner import Spinner
from rich.live import Live
from rich.columns import Columns
from rich.markdown import Markdown
from rich.layout import Layout
from rich.text import Text
from rich.table import Table
from collections import deque
import time
from rich.tree import Tree
from rich import box
from rich.align import Align
from rich.rule import Rule
from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG
from cli.models import AnalystType
from cli.utils import *
from cli.announcements import fetch_announcements, display_announcements
from cli.stats_handler import StatsCallbackHandler
console = Console()
app = typer.Typer(
name="TradingAgents",
help="TradingAgents CLI: Multi-Agents LLM Financial Trading Framework",
add_completion=True, # Enable shell completion
)
# Create a deque to store recent messages with a maximum length
class MessageBuffer:
# Fixed teams that always run (not user-selectable)
FIXED_AGENTS = {
"Research Team": ["Bull Researcher", "Bear Researcher", "Research Manager"],
"Trading Team": ["Trader"],
"Risk Management": ["Aggressive Analyst", "Neutral Analyst", "Conservative Analyst"],
"Portfolio Management": ["Portfolio Manager"],
}
# Analyst name mapping
ANALYST_MAPPING = {
"market": "Market Analyst",
"social": "Social Analyst",
"news": "News Analyst",
"fundamentals": "Fundamentals Analyst",
}
# Report section mapping: section -> (analyst_key for filtering, finalizing_agent)
# analyst_key: which analyst selection controls this section (None = always included)
# finalizing_agent: which agent must be "completed" for this report to count as done
REPORT_SECTIONS = {
"market_report": ("market", "Market Analyst"),
"sentiment_report": ("social", "Social Analyst"),
"news_report": ("news", "News Analyst"),
"fundamentals_report": ("fundamentals", "Fundamentals Analyst"),
"investment_plan": (None, "Research Manager"),
"trader_investment_plan": (None, "Trader"),
"final_trade_decision": (None, "Portfolio Manager"),
}
def __init__(self, max_length=100):
self.messages = deque(maxlen=max_length)
self.tool_calls = deque(maxlen=max_length)
self.current_report = None
self.final_report = None # Store the complete final report
self.agent_status = {}
self.current_agent = None
self.report_sections = {}
self.selected_analysts = []
self._last_message_id = None
def init_for_analysis(self, selected_analysts):
"""Initialize agent status and report sections based on selected analysts.
Args:
selected_analysts: List of analyst type strings (e.g., ["market", "news"])
"""
self.selected_analysts = [a.lower() for a in selected_analysts]
# Build agent_status dynamically
self.agent_status = {}
# Add selected analysts
for analyst_key in self.selected_analysts:
if analyst_key in self.ANALYST_MAPPING:
self.agent_status[self.ANALYST_MAPPING[analyst_key]] = "pending"
# Add fixed teams
for team_agents in self.FIXED_AGENTS.values():
for agent in team_agents:
self.agent_status[agent] = "pending"
# Build report_sections dynamically
self.report_sections = {}
for section, (analyst_key, _) in self.REPORT_SECTIONS.items():
if analyst_key is None or analyst_key in self.selected_analysts:
self.report_sections[section] = None
# Reset other state
self.current_report = None
self.final_report = None
self.current_agent = None
self.messages.clear()
self.tool_calls.clear()
self._last_message_id = None
def get_completed_reports_count(self):
"""Count reports that are finalized (their finalizing agent is completed).
A report is considered complete when:
1. The report section has content (not None), AND
2. The agent responsible for finalizing that report has status "completed"
This prevents interim updates (like debate rounds) from counting as completed.
"""
count = 0
for section in self.report_sections:
if section not in self.REPORT_SECTIONS:
continue
_, finalizing_agent = self.REPORT_SECTIONS[section]
# Report is complete if it has content AND its finalizing agent is done
has_content = self.report_sections.get(section) is not None
agent_done = self.agent_status.get(finalizing_agent) == "completed"
if has_content and agent_done:
count += 1
return count
def add_message(self, message_type, content):
timestamp = datetime.datetime.now().strftime("%H:%M:%S")
self.messages.append((timestamp, message_type, content))
def add_tool_call(self, tool_name, args):
timestamp = datetime.datetime.now().strftime("%H:%M:%S")
self.tool_calls.append((timestamp, tool_name, args))
def update_agent_status(self, agent, status):
if agent in self.agent_status:
self.agent_status[agent] = status
self.current_agent = agent
def update_report_section(self, section_name, content):
if section_name in self.report_sections:
self.report_sections[section_name] = content
self._update_current_report()
def _update_current_report(self):
# For the panel display, only show the most recently updated section
latest_section = None
latest_content = None
# Find the most recently updated section
for section, content in self.report_sections.items():
if content is not None:
latest_section = section
latest_content = content
if latest_section and latest_content:
# Format the current section for display
section_titles = {
"market_report": "Market Analysis",
"sentiment_report": "Social Sentiment",
"news_report": "News Analysis",
"fundamentals_report": "Fundamentals Analysis",
"investment_plan": "Research Team Decision",
"trader_investment_plan": "Trading Team Plan",
"final_trade_decision": "Portfolio Management Decision",
}
self.current_report = (
f"### {section_titles[latest_section]}\n{latest_content}"
)
# Update the final complete report
self._update_final_report()
def _update_final_report(self):
report_parts = []
# Analyst Team Reports - use .get() to handle missing sections
analyst_sections = ["market_report", "sentiment_report", "news_report", "fundamentals_report"]
if any(self.report_sections.get(section) for section in analyst_sections):
report_parts.append("## Analyst Team Reports")
if self.report_sections.get("market_report"):
report_parts.append(
f"### Market Analysis\n{self.report_sections['market_report']}"
)
if self.report_sections.get("sentiment_report"):
report_parts.append(
f"### Social Sentiment\n{self.report_sections['sentiment_report']}"
)
if self.report_sections.get("news_report"):
report_parts.append(
f"### News Analysis\n{self.report_sections['news_report']}"
)
if self.report_sections.get("fundamentals_report"):
report_parts.append(
f"### Fundamentals Analysis\n{self.report_sections['fundamentals_report']}"
)
# Research Team Reports
if self.report_sections.get("investment_plan"):
report_parts.append("## Research Team Decision")
report_parts.append(f"{self.report_sections['investment_plan']}")
# Trading Team Reports
if self.report_sections.get("trader_investment_plan"):
report_parts.append("## Trading Team Plan")
report_parts.append(f"{self.report_sections['trader_investment_plan']}")
# Portfolio Management Decision
if self.report_sections.get("final_trade_decision"):
report_parts.append("## Portfolio Management Decision")
report_parts.append(f"{self.report_sections['final_trade_decision']}")
self.final_report = "\n\n".join(report_parts) if report_parts else None
message_buffer = MessageBuffer()
def create_layout():
layout = Layout()
layout.split_column(
Layout(name="header", size=3),
Layout(name="main"),
Layout(name="footer", size=3),
)
layout["main"].split_column(
Layout(name="upper", ratio=3), Layout(name="analysis", ratio=5)
)
layout["upper"].split_row(
Layout(name="progress", ratio=2), Layout(name="messages", ratio=3)
)
return layout
def format_tokens(n):
"""Format token count for display."""
if n >= 1000:
return f"{n/1000:.1f}k"
return str(n)
def update_display(layout, spinner_text=None, stats_handler=None, start_time=None):
# Header with welcome message
layout["header"].update(
Panel(
"[bold green]Welcome to TradingAgents CLI[/bold green]\n"
"[dim]© [Tauric Research](https://github.com/TauricResearch)[/dim]",
title="Welcome to TradingAgents",
border_style="green",
padding=(1, 2),
expand=True,
)
)
# Progress panel showing agent status
progress_table = Table(
show_header=True,
header_style="bold magenta",
show_footer=False,
box=box.SIMPLE_HEAD, # Use simple header with horizontal lines
title=None, # Remove the redundant Progress title
padding=(0, 2), # Add horizontal padding
expand=True, # Make table expand to fill available space
)
progress_table.add_column("Team", style="cyan", justify="center", width=20)
progress_table.add_column("Agent", style="green", justify="center", width=20)
progress_table.add_column("Status", style="yellow", justify="center", width=20)
# Group agents by team - filter to only include agents in agent_status
all_teams = {
"Analyst Team": [
"Market Analyst",
"Social Analyst",
"News Analyst",
"Fundamentals Analyst",
],
"Research Team": ["Bull Researcher", "Bear Researcher", "Research Manager"],
"Trading Team": ["Trader"],
"Risk Management": ["Aggressive Analyst", "Neutral Analyst", "Conservative Analyst"],
"Portfolio Management": ["Portfolio Manager"],
}
# Filter teams to only include agents that are in agent_status
teams = {}
for team, agents in all_teams.items():
active_agents = [a for a in agents if a in message_buffer.agent_status]
if active_agents:
teams[team] = active_agents
for team, agents in teams.items():
# Add first agent with team name
first_agent = agents[0]
status = message_buffer.agent_status.get(first_agent, "pending")
if status == "in_progress":
spinner = Spinner(
"dots", text="[blue]in_progress[/blue]", style="bold cyan"
)
status_cell = spinner
else:
status_color = {
"pending": "yellow",
"completed": "green",
"error": "red",
}.get(status, "white")
status_cell = f"[{status_color}]{status}[/{status_color}]"
progress_table.add_row(team, first_agent, status_cell)
# Add remaining agents in team
for agent in agents[1:]:
status = message_buffer.agent_status.get(agent, "pending")
if status == "in_progress":
spinner = Spinner(
"dots", text="[blue]in_progress[/blue]", style="bold cyan"
)
status_cell = spinner
else:
status_color = {
"pending": "yellow",
"completed": "green",
"error": "red",
}.get(status, "white")
status_cell = f"[{status_color}]{status}[/{status_color}]"
progress_table.add_row("", agent, status_cell)
# Add horizontal line after each team
progress_table.add_row("─" * 20, "─" * 20, "─" * 20, style="dim")
layout["progress"].update(
Panel(progress_table, title="Progress", border_style="cyan", padding=(1, 2))
)
# Messages panel showing recent messages and tool calls
messages_table = Table(
show_header=True,
header_style="bold magenta",
show_footer=False,
expand=True, # Make table expand to fill available space
box=box.MINIMAL, # Use minimal box style for a lighter look
show_lines=True, # Keep horizontal lines
padding=(0, 1), # Add some padding between columns
)
messages_table.add_column("Time", style="cyan", width=8, justify="center")
messages_table.add_column("Type", style="green", width=10, justify="center")
messages_table.add_column(
"Content", style="white", no_wrap=False, ratio=1
) # Make content column expand
# Combine tool calls and messages
all_messages = []
# Add tool calls
for timestamp, tool_name, args in message_buffer.tool_calls:
formatted_args = format_tool_args(args)
all_messages.append((timestamp, "Tool", f"{tool_name}: {formatted_args}"))
# Add regular messages
for timestamp, msg_type, content in message_buffer.messages:
content_str = str(content) if content else ""
if len(content_str) > 200:
content_str = content_str[:197] + "..."
all_messages.append((timestamp, msg_type, content_str))
# Sort by timestamp descending (newest first)
all_messages.sort(key=lambda x: x[0], reverse=True)
# Calculate how many messages we can show based on available space
max_messages = 12
# Get the first N messages (newest ones)
recent_messages = all_messages[:max_messages]
# Add messages to table (already in newest-first order)
for timestamp, msg_type, content in recent_messages:
# Format content with word wrapping
wrapped_content = Text(content, overflow="fold")
messages_table.add_row(timestamp, msg_type, wrapped_content)
layout["messages"].update(
Panel(
messages_table,
title="Messages & Tools",
border_style="blue",
padding=(1, 2),
)
)
# Analysis panel showing current report
if message_buffer.current_report:
layout["analysis"].update(
Panel(
Markdown(message_buffer.current_report),
title="Current Report",
border_style="green",
padding=(1, 2),
)
)
else:
layout["analysis"].update(
Panel(
"[italic]Waiting for analysis report...[/italic]",
title="Current Report",
border_style="green",
padding=(1, 2),
)
)
# Footer with statistics
# Agent progress - derived from agent_status dict
agents_completed = sum(
1 for status in message_buffer.agent_status.values() if status == "completed"
)
agents_total = len(message_buffer.agent_status)
# Report progress - based on agent completion (not just content existence)
reports_completed = message_buffer.get_completed_reports_count()
reports_total = len(message_buffer.report_sections)
# Build stats parts
stats_parts = [f"Agents: {agents_completed}/{agents_total}"]
# LLM and tool stats from callback handler
if stats_handler:
stats = stats_handler.get_stats()
stats_parts.append(f"LLM: {stats['llm_calls']}")
stats_parts.append(f"Tools: {stats['tool_calls']}")
# Token display with graceful fallback
if stats["tokens_in"] > 0 or stats["tokens_out"] > 0:
tokens_str = f"Tokens: {format_tokens(stats['tokens_in'])}\u2191 {format_tokens(stats['tokens_out'])}\u2193"
else:
tokens_str = "Tokens: --"
stats_parts.append(tokens_str)
stats_parts.append(f"Reports: {reports_completed}/{reports_total}")
# Elapsed time
if start_time:
elapsed = time.time() - start_time
elapsed_str = f"\u23f1 {int(elapsed // 60):02d}:{int(elapsed % 60):02d}"
stats_parts.append(elapsed_str)
stats_table = Table(show_header=False, box=None, padding=(0, 2), expand=True)
stats_table.add_column("Stats", justify="center")
stats_table.add_row(" | ".join(stats_parts))
layout["footer"].update(Panel(stats_table, border_style="grey50"))
def get_user_selections():
"""Get all user selections before starting the analysis display."""
# Display ASCII art welcome message
with open("./cli/static/welcome.txt", "r", encoding="utf-8") as f:
welcome_ascii = f.read()
# Create welcome box content
welcome_content = f"{welcome_ascii}\n"
welcome_content += "[bold green]TradingAgents: Multi-Agents LLM Financial Trading Framework - CLI[/bold green]\n\n"
welcome_content += "[bold]Workflow Steps:[/bold]\n"
welcome_content += "I. Analyst Team → II. Research Team → III. Trader → IV. Risk Management → V. Portfolio Management\n\n"
welcome_content += (
"[dim]Built by [Tauric Research](https://github.com/TauricResearch)[/dim]"
)
# Create and center the welcome box
welcome_box = Panel(
welcome_content,
border_style="green",
padding=(1, 2),
title="Welcome to TradingAgents",
subtitle="Multi-Agents LLM Financial Trading Framework",
)
console.print(Align.center(welcome_box))
console.print()
console.print() # Add vertical space before announcements
# Fetch and display announcements (silent on failure)
announcements = fetch_announcements()
display_announcements(console, announcements)
# Create a boxed questionnaire for each step
def create_question_box(title, prompt, default=None):
box_content = f"[bold]{title}[/bold]\n"
box_content += f"[dim]{prompt}[/dim]"
if default:
box_content += f"\n[dim]Default: {default}[/dim]"
return Panel(box_content, border_style="blue", padding=(1, 2))
# Step 1: Ticker symbol
console.print(
create_question_box(
"Step 1: Ticker Symbol", "Enter the 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)
# Step 7: Provider-specific thinking configuration
thinking_level = None
reasoning_effort = None
provider_lower = selected_llm_provider.lower()
if provider_lower == "google":
console.print(
create_question_box(
"Step 7: Thinking Mode",
"Configure Gemini thinking mode"
)
)
thinking_level = ask_gemini_thinking_config()
elif provider_lower == "openai":
console.print(
create_question_box(
"Step 7: Reasoning Effort",
"Configure OpenAI reasoning effort level"
)
)
reasoning_effort = ask_openai_reasoning_effort()
return {
"ticker": selected_ticker,
"analysis_date": analysis_date,
"analysts": selected_analysts,
"research_depth": selected_research_depth,
"llm_provider": selected_llm_provider.lower(),
"backend_url": backend_url,
"shallow_thinker": selected_shallow_thinker,
"deep_thinker": selected_deep_thinker,
"google_thinking_level": thinking_level,
"openai_reasoning_effort": reasoning_effort,
}
def get_ticker():
"""Get ticker symbol from user input."""
return typer.prompt("", default="SPY")
def get_analysis_date():
"""Get the analysis date from user input."""
while True:
date_str = typer.prompt(
"", default=datetime.datetime.now().strftime("%Y-%m-%d")
)
try:
# Validate date format and ensure it's not in the future
analysis_date = datetime.datetime.strptime(date_str, "%Y-%m-%d")
if analysis_date.date() > datetime.datetime.now().date():
console.print("[red]Error: Analysis date cannot be in the future[/red]")
continue
return date_str
except ValueError:
console.print(
"[red]Error: Invalid date format. Please use YYYY-MM-DD[/red]"
)
def save_report_to_disk(final_state, ticker: str, save_path: Path):
"""Save complete analysis report to disk with organized subfolders."""
save_path.mkdir(parents=True, exist_ok=True)
sections = []
# 1. Analysts
analysts_dir = save_path / "1_analysts"
analyst_parts = []
if final_state.get("market_report"):
analysts_dir.mkdir(exist_ok=True)
(analysts_dir / "market.md").write_text(final_state["market_report"])
analyst_parts.append(("Market Analyst", final_state["market_report"]))
if final_state.get("sentiment_report"):
analysts_dir.mkdir(exist_ok=True)
(analysts_dir / "sentiment.md").write_text(final_state["sentiment_report"])
analyst_parts.append(("Social Analyst", final_state["sentiment_report"]))
if final_state.get("news_report"):
analysts_dir.mkdir(exist_ok=True)
(analysts_dir / "news.md").write_text(final_state["news_report"])
analyst_parts.append(("News Analyst", final_state["news_report"]))
if final_state.get("fundamentals_report"):
analysts_dir.mkdir(exist_ok=True)
(analysts_dir / "fundamentals.md").write_text(final_state["fundamentals_report"])
analyst_parts.append(("Fundamentals Analyst", final_state["fundamentals_report"]))
if analyst_parts:
content = "\n\n".join(f"### {name}\n{text}" for name, text in analyst_parts)
sections.append(f"## I. Analyst Team Reports\n\n{content}")
# 2. Research
if final_state.get("investment_debate_state"):
research_dir = save_path / "2_research"
debate = final_state["investment_debate_state"]
research_parts = []
if debate.get("bull_history"):
research_dir.mkdir(exist_ok=True)
(research_dir / "bull.md").write_text(debate["bull_history"])
research_parts.append(("Bull Researcher", debate["bull_history"]))
if debate.get("bear_history"):
research_dir.mkdir(exist_ok=True)
(research_dir / "bear.md").write_text(debate["bear_history"])
research_parts.append(("Bear Researcher", debate["bear_history"]))
if debate.get("judge_decision"):
research_dir.mkdir(exist_ok=True)
(research_dir / "manager.md").write_text(debate["judge_decision"])
research_parts.append(("Research Manager", debate["judge_decision"]))
if research_parts:
content = "\n\n".join(f"### {name}\n{text}" for name, text in research_parts)
sections.append(f"## II. Research Team Decision\n\n{content}")
# 3. Trading
if final_state.get("trader_investment_plan"):
trading_dir = save_path / "3_trading"
trading_dir.mkdir(exist_ok=True)
(trading_dir / "trader.md").write_text(final_state["trader_investment_plan"])
sections.append(f"## III. Trading Team Plan\n\n### Trader\n{final_state['trader_investment_plan']}")
# 4. Risk Management
if final_state.get("risk_debate_state"):
risk_dir = save_path / "4_risk"
risk = final_state["risk_debate_state"]
risk_parts = []
if risk.get("aggressive_history"):
risk_dir.mkdir(exist_ok=True)
(risk_dir / "aggressive.md").write_text(risk["aggressive_history"])
risk_parts.append(("Aggressive Analyst", risk["aggressive_history"]))
if risk.get("conservative_history"):
risk_dir.mkdir(exist_ok=True)
(risk_dir / "conservative.md").write_text(risk["conservative_history"])
risk_parts.append(("Conservative Analyst", risk["conservative_history"]))
if risk.get("neutral_history"):
risk_dir.mkdir(exist_ok=True)
(risk_dir / "neutral.md").write_text(risk["neutral_history"])
risk_parts.append(("Neutral Analyst", risk["neutral_history"]))
if risk_parts:
content = "\n\n".join(f"### {name}\n{text}" for name, text in risk_parts)
sections.append(f"## IV. Risk Management Team Decision\n\n{content}")
# 5. Portfolio Manager
if risk.get("judge_decision"):
portfolio_dir = save_path / "5_portfolio"
portfolio_dir.mkdir(exist_ok=True)
(portfolio_dir / "decision.md").write_text(risk["judge_decision"])
sections.append(f"## V. Portfolio Manager Decision\n\n### Portfolio Manager\n{risk['judge_decision']}")
# Write consolidated report
header = f"# Trading Analysis Report: {ticker}\n\nGenerated: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
(save_path / "complete_report.md").write_text(header + "\n\n".join(sections))
return save_path / "complete_report.md"
def display_complete_report(final_state):
"""Display the complete analysis report sequentially (avoids truncation)."""
console.print()
console.print(Rule("Complete Analysis Report", style="bold green"))
# I. Analyst Team Reports
analysts = []
if final_state.get("market_report"):
analysts.append(("Market Analyst", final_state["market_report"]))
if final_state.get("sentiment_report"):
analysts.append(("Social Analyst", final_state["sentiment_report"]))
if final_state.get("news_report"):
analysts.append(("News Analyst", final_state["news_report"]))
if final_state.get("fundamentals_report"):
analysts.append(("Fundamentals Analyst", final_state["fundamentals_report"]))
if analysts:
console.print(Panel("[bold]I. Analyst Team Reports[/bold]", border_style="cyan"))
for title, content in analysts:
console.print(Panel(Markdown(content), title=title, border_style="blue", padding=(1, 2)))
# II. Research Team Reports
if final_state.get("investment_debate_state"):
debate = final_state["investment_debate_state"]
research = []
if debate.get("bull_history"):
research.append(("Bull Researcher", debate["bull_history"]))
if debate.get("bear_history"):
research.append(("Bear Researcher", debate["bear_history"]))
if debate.get("judge_decision"):
research.append(("Research Manager", debate["judge_decision"]))
if research:
console.print(Panel("[bold]II. Research Team Decision[/bold]", border_style="magenta"))
for title, content in research:
console.print(Panel(Markdown(content), title=title, border_style="blue", padding=(1, 2)))
# III. Trading Team
if final_state.get("trader_investment_plan"):
console.print(Panel("[bold]III. Trading Team Plan[/bold]", border_style="yellow"))
console.print(Panel(Markdown(final_state["trader_investment_plan"]), title="Trader", border_style="blue", padding=(1, 2)))
# IV. Risk Management Team
if final_state.get("risk_debate_state"):
risk = final_state["risk_debate_state"]
risk_reports = []
if risk.get("aggressive_history"):
risk_reports.append(("Aggressive Analyst", risk["aggressive_history"]))
if risk.get("conservative_history"):
risk_reports.append(("Conservative Analyst", risk["conservative_history"]))
if risk.get("neutral_history"):
risk_reports.append(("Neutral Analyst", risk["neutral_history"]))
if risk_reports:
console.print(Panel("[bold]IV. Risk Management Team Decision[/bold]", border_style="red"))
for title, content in risk_reports:
console.print(Panel(Markdown(content), title=title, border_style="blue", padding=(1, 2)))
# V. Portfolio Manager Decision
if risk.get("judge_decision"):
console.print(Panel("[bold]V. Portfolio Manager Decision[/bold]", border_style="green"))
console.print(Panel(Markdown(risk["judge_decision"]), title="Portfolio Manager", border_style="blue", padding=(1, 2)))
def update_research_team_status(status):
"""Update status for research team members (not Trader)."""
research_team = ["Bull Researcher", "Bear Researcher", "Research Manager"]
for agent in research_team:
message_buffer.update_agent_status(agent, status)
# Ordered list of analysts for status transitions
ANALYST_ORDER = ["market", "social", "news", "fundamentals"]
ANALYST_AGENT_NAMES = {
"market": "Market Analyst",
"social": "Social Analyst",
"news": "News Analyst",
"fundamentals": "Fundamentals Analyst",
}
ANALYST_REPORT_MAP = {
"market": "market_report",
"social": "sentiment_report",
"news": "news_report",
"fundamentals": "fundamentals_report",
}
def update_analyst_statuses(message_buffer, chunk):
"""Update all analyst statuses based on current report state.
Logic:
- Analysts with reports = completed
- First analyst without report = in_progress
- Remaining analysts without reports = pending
- When all analysts done, set Bull Researcher to in_progress
"""
selected = message_buffer.selected_analysts
found_active = False
for analyst_key in ANALYST_ORDER:
if analyst_key not in selected:
continue
agent_name = ANALYST_AGENT_NAMES[analyst_key]
report_key = ANALYST_REPORT_MAP[analyst_key]
has_report = bool(chunk.get(report_key))
if has_report:
message_buffer.update_agent_status(agent_name, "completed")
message_buffer.update_report_section(report_key, chunk[report_key])
elif not found_active:
message_buffer.update_agent_status(agent_name, "in_progress")
found_active = True
else:
message_buffer.update_agent_status(agent_name, "pending")
# When all analysts complete, transition research team to in_progress
if not found_active and selected:
if message_buffer.agent_status.get("Bull Researcher") == "pending":
message_buffer.update_agent_status("Bull Researcher", "in_progress")
def extract_content_string(content):
"""Extract string content from various message formats.
Returns None if no meaningful text content is found.
"""
import ast
def is_empty(val):
"""Check if value is empty using Python's truthiness."""
if val is None or val == '':
return True
if isinstance(val, str):
s = val.strip()
if not s:
return True
try:
return not bool(ast.literal_eval(s))
except (ValueError, SyntaxError):
return False # Can't parse = real text
return not bool(val)
if is_empty(content):
return None
if isinstance(content, str):
return content.strip()
if isinstance(content, dict):
text = content.get('text', '')
return text.strip() if not is_empty(text) else None
if isinstance(content, list):
text_parts = [
item.get('text', '').strip() if isinstance(item, dict) and item.get('type') == 'text'
else (item.strip() if isinstance(item, str) else '')
for item in content
]
result = ' '.join(t for t in text_parts if t and not is_empty(t))
return result if result else None
return str(content).strip() if not is_empty(content) else None
def classify_message_type(message) -> tuple[str, str | None]:
"""Classify LangChain message into display type and extract content.
Returns:
(type, content) - type is one of: User, Agent, Data, Control
- content is extracted string or None
"""
from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
content = extract_content_string(getattr(message, 'content', None))
if isinstance(message, HumanMessage):
if content and content.strip() == "Continue":
return ("Control", content)
return ("User", content)
if isinstance(message, ToolMessage):
return ("Data", content)
if isinstance(message, AIMessage):
return ("Agent", content)
# Fallback for unknown types
return ("System", content)
def format_tool_args(args, max_length=80) -> str:
"""Format tool arguments for terminal display."""
result = str(args)
if len(result) > max_length:
return result[:max_length - 3] + "..."
return result
def run_analysis():
# First get all user selections
selections = get_user_selections()
# Create config with selected research depth
config = DEFAULT_CONFIG.copy()
config["max_debate_rounds"] = selections["research_depth"]
config["max_risk_discuss_rounds"] = selections["research_depth"]
config["quick_think_llm"] = selections["shallow_thinker"]
config["deep_think_llm"] = selections["deep_thinker"]
config["backend_url"] = selections["backend_url"]
config["llm_provider"] = selections["llm_provider"].lower()
# Provider-specific thinking configuration
config["google_thinking_level"] = selections.get("google_thinking_level")
config["openai_reasoning_effort"] = selections.get("openai_reasoning_effort")
# Create stats callback handler for tracking LLM/tool calls
stats_handler = StatsCallbackHandler()
# Normalize analyst selection to predefined order (selection is a 'set', order is fixed)
selected_set = {analyst.value for analyst in selections["analysts"]}
selected_analyst_keys = [a for a in ANALYST_ORDER if a in selected_set]
# Initialize the graph with callbacks bound to LLMs
graph = TradingAgentsGraph(
selected_analyst_keys,
config=config,
debug=True,
callbacks=[stats_handler],
)
# Initialize message buffer with selected analysts
message_buffer.init_for_analysis(selected_analyst_keys)
# Track start time for elapsed display
start_time = time.time()
# Create result directory
results_dir = Path(config["results_dir"]) / selections["ticker"] / selections["analysis_date"]
results_dir.mkdir(parents=True, exist_ok=True)
report_dir = results_dir / "reports"
report_dir.mkdir(parents=True, exist_ok=True)
log_file = results_dir / "message_tool.log"
log_file.touch(exist_ok=True)
def save_message_decorator(obj, func_name):
func = getattr(obj, func_name)
@wraps(func)
def wrapper(*args, **kwargs):
func(*args, **kwargs)
timestamp, message_type, content = obj.messages[-1]
content = content.replace("\n", " ") # Replace newlines with spaces
with open(log_file, "a", 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, stats_handler=stats_handler, start_time=start_time)
# Add initial messages
message_buffer.add_message("System", f"Selected ticker: {selections['ticker']}")
message_buffer.add_message(
"System", f"Analysis date: {selections['analysis_date']}"
)
message_buffer.add_message(
"System",
f"Selected analysts: {', '.join(analyst.value for analyst in selections['analysts'])}",
)
update_display(layout, stats_handler=stats_handler, start_time=start_time)
# Update agent status to in_progress for the first analyst
first_analyst = f"{selections['analysts'][0].value.capitalize()} Analyst"
message_buffer.update_agent_status(first_analyst, "in_progress")
update_display(layout, stats_handler=stats_handler, start_time=start_time)
# Create spinner text
spinner_text = (
f"Analyzing {selections['ticker']} on {selections['analysis_date']}..."
)
update_display(layout, spinner_text, stats_handler=stats_handler, start_time=start_time)
# Initialize state and get graph args with callbacks
init_agent_state = graph.propagator.create_initial_state(
selections["ticker"], selections["analysis_date"]
)
# Pass callbacks to graph config for tool execution tracking
# (LLM tracking is handled separately via LLM constructor)
args = graph.propagator.get_graph_args(callbacks=[stats_handler])
# Stream the analysis
trace = []
for chunk in graph.graph.stream(init_agent_state, **args):
# Process messages if present (skip duplicates via message ID)
if len(chunk["messages"]) > 0:
last_message = chunk["messages"][-1]
msg_id = getattr(last_message, "id", None)
if msg_id != message_buffer._last_message_id:
message_buffer._last_message_id = msg_id
# Add message to buffer
msg_type, content = classify_message_type(last_message)
if content and content.strip():
message_buffer.add_message(msg_type, content)
# Handle tool calls
if hasattr(last_message, "tool_calls") and last_message.tool_calls:
for tool_call in last_message.tool_calls:
if isinstance(tool_call, dict):
message_buffer.add_tool_call(
tool_call["name"], tool_call["args"]
)
else:
message_buffer.add_tool_call(tool_call.name, tool_call.args)
# Update analyst statuses based on report state (runs on every chunk)
update_analyst_statuses(message_buffer, chunk)
# Research Team - Handle Investment Debate State
if chunk.get("investment_debate_state"):
debate_state = chunk["investment_debate_state"]
bull_hist = debate_state.get("bull_history", "").strip()
bear_hist = debate_state.get("bear_history", "").strip()
judge = debate_state.get("judge_decision", "").strip()
# Only update status when there's actual content
if bull_hist or bear_hist:
update_research_team_status("in_progress")
if bull_hist:
message_buffer.update_report_section(
"investment_plan", f"### Bull Researcher Analysis\n{bull_hist}"
)
if bear_hist:
message_buffer.update_report_section(
"investment_plan", f"### Bear Researcher Analysis\n{bear_hist}"
)
if judge:
message_buffer.update_report_section(
"investment_plan", f"### Research Manager Decision\n{judge}"
)
update_research_team_status("completed")
message_buffer.update_agent_status("Trader", "in_progress")
# Trading Team
if chunk.get("trader_investment_plan"):
message_buffer.update_report_section(
"trader_investment_plan", chunk["trader_investment_plan"]
)
if message_buffer.agent_status.get("Trader") != "completed":
message_buffer.update_agent_status("Trader", "completed")
message_buffer.update_agent_status("Aggressive Analyst", "in_progress")
# Risk Management Team - Handle Risk Debate State
if chunk.get("risk_debate_state"):
risk_state = chunk["risk_debate_state"]
agg_hist = risk_state.get("aggressive_history", "").strip()
con_hist = risk_state.get("conservative_history", "").strip()
neu_hist = risk_state.get("neutral_history", "").strip()
judge = risk_state.get("judge_decision", "").strip()
if agg_hist:
if message_buffer.agent_status.get("Aggressive Analyst") != "completed":
message_buffer.update_agent_status("Aggressive Analyst", "in_progress")
message_buffer.update_report_section(
"final_trade_decision", f"### Aggressive Analyst Analysis\n{agg_hist}"
)
if con_hist:
if message_buffer.agent_status.get("Conservative Analyst") != "completed":
message_buffer.update_agent_status("Conservative Analyst", "in_progress")
message_buffer.update_report_section(
"final_trade_decision", f"### Conservative Analyst Analysis\n{con_hist}"
)
if neu_hist:
if message_buffer.agent_status.get("Neutral Analyst") != "completed":
message_buffer.update_agent_status("Neutral Analyst", "in_progress")
message_buffer.update_report_section(
"final_trade_decision", f"### Neutral Analyst Analysis\n{neu_hist}"
)
if judge:
if message_buffer.agent_status.get("Portfolio Manager") != "completed":
message_buffer.update_agent_status("Portfolio Manager", "in_progress")
message_buffer.update_report_section(
"final_trade_decision", f"### Portfolio Manager Decision\n{judge}"
)
message_buffer.update_agent_status("Aggressive Analyst", "completed")
message_buffer.update_agent_status("Conservative Analyst", "completed")
message_buffer.update_agent_status("Neutral Analyst", "completed")
message_buffer.update_agent_status("Portfolio Manager", "completed")
# Update the display
update_display(layout, stats_handler=stats_handler, start_time=start_time)
trace.append(chunk)
# Get final state and decision
final_state = trace[-1]
decision = graph.process_signal(final_state["final_trade_decision"])
# Update all agent statuses to completed
for agent in message_buffer.agent_status:
message_buffer.update_agent_status(agent, "completed")
message_buffer.add_message(
"System", f"Completed analysis for {selections['analysis_date']}"
)
# Update final report sections
for section in message_buffer.report_sections.keys():
if section in final_state:
message_buffer.update_report_section(section, final_state[section])
update_display(layout, stats_handler=stats_handler, start_time=start_time)
# Post-analysis prompts (outside Live context for clean interaction)
console.print("\n[bold cyan]Analysis Complete![/bold cyan]\n")
# Prompt to save report
save_choice = typer.prompt("Save report?", default="Y").strip().upper()
if save_choice in ("Y", "YES", ""):
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
default_path = Path.cwd() / "reports" / f"{selections['ticker']}_{timestamp}"
save_path_str = typer.prompt(
"Save path (press Enter for default)",
default=str(default_path)
).strip()
save_path = Path(save_path_str)
try:
report_file = save_report_to_disk(final_state, selections["ticker"], save_path)
console.print(f"\n[green]✓ Report saved to:[/green] {save_path.resolve()}")
console.print(f" [dim]Complete report:[/dim] {report_file.name}")
except Exception as e:
console.print(f"[red]Error saving report: {e}[/red]")
# Prompt to display full report
display_choice = typer.prompt("\nDisplay full report on screen?", default="Y").strip().upper()
if display_choice in ("Y", "YES", ""):
display_complete_report(final_state)
@app.command()
def analyze():
run_analysis()
if __name__ == "__main__":
app()
================================================
FILE: cli/models.py
================================================
from enum import Enum
from typing import List, Optional, Dict
from pydantic import BaseModel
class AnalystType(str, Enum):
MARKET = "market"
SOCIAL = "social"
NEWS = "news"
FUNDAMENTALS = "fundamentals"
================================================
FILE: cli/static/welcome.txt
================================================
______ ___ ___ __
/_ __/________ _____/ (_)___ ____ _/ | ____ ____ ____ / /______
/ / / ___/ __ `/ __ / / __ \/ __ `/ /| |/ __ `/ _ \/ __ \/ __/ ___/
/ / / / / /_/ / /_/ / / / / / /_/ / ___ / /_/ / __/ / / / /_(__ )
/_/ /_/ \__,_/\__,_/_/_/ /_/\__, /_/ |_\__, /\___/_/ /_/\__/____/
/____/ /____/
================================================
FILE: cli/stats_handler.py
================================================
import threading
from typing import Any, Dict, List, Union
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import LLMResult
from langchain_core.messages import AIMessage
class StatsCallbackHandler(BaseCallbackHandler):
"""Callback handler that tracks LLM calls, tool calls, and token usage."""
def __init__(self) -> None:
super().__init__()
self._lock = threading.Lock()
self.llm_calls = 0
self.tool_calls = 0
self.tokens_in = 0
self.tokens_out = 0
def on_llm_start(
self,
serialized: Dict[str, Any],
prompts: List[str],
**kwargs: Any,
) -> None:
"""Increment LLM call counter when an LLM starts."""
with self._lock:
self.llm_calls += 1
def on_chat_model_start(
self,
serialized: Dict[str, Any],
messages: List[List[Any]],
**kwargs: Any,
) -> None:
"""Increment LLM call counter when a chat model starts."""
with self._lock:
self.llm_calls += 1
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Extract token usage from LLM response."""
try:
generation = response.generations[0][0]
except (IndexError, TypeError):
return
usage_metadata = None
if hasattr(generation, "message"):
message = generation.message
if isinstance(message, AIMessage) and hasattr(message, "usage_metadata"):
usage_metadata = message.usage_metadata
if usage_metadata:
with self._lock:
self.tokens_in += usage_metadata.get("input_tokens", 0)
self.tokens_out += usage_metadata.get("output_tokens", 0)
def on_tool_start(
self,
serialized: Dict[str, Any],
input_str: str,
**kwargs: Any,
) -> None:
"""Increment tool call counter when a tool starts."""
with self._lock:
self.tool_calls += 1
def get_stats(self) -> Dict[str, Any]:
"""Return current statistics."""
with self._lock:
return {
"llm_calls": self.llm_calls,
"tool_calls": self.tool_calls,
"tokens_in": self.tokens_in,
"tokens_out": self.tokens_out,
}
================================================
FILE: cli/utils.py
================================================
import questionary
from typing import List, Optional, Tuple, Dict
from rich.console import Console
from cli.models import AnalystType
console = Console()
ANALYST_ORDER = [
("Market Analyst", AnalystType.MARKET),
("Social Media Analyst", AnalystType.SOCIAL),
("News Analyst", AnalystType.NEWS),
("Fundamentals Analyst", AnalystType.FUNDAMENTALS),
]
def get_ticker() -> str:
"""Prompt the user to enter a ticker symbol."""
ticker = questionary.text(
"Enter the ticker symbol to analyze:",
validate=lambda x: len(x.strip()) > 0 or "Please enter a valid ticker symbol.",
style=questionary.Style(
[
("text", "fg:green"),
("highlighted", "noinherit"),
]
),
).ask()
if not ticker:
console.print("\n[red]No ticker symbol provided. Exiting...[/red]")
exit(1)
return ticker.strip().upper()
def get_analysis_date() -> str:
"""Prompt the user to enter a date in YYYY-MM-DD format."""
import re
from datetime import datetime
def validate_date(date_str: str) -> bool:
if not re.match(r"^\d{4}-\d{2}-\d{2}$", date_str):
return False
try:
datetime.strptime(date_str, "%Y-%m-%d")
return True
except ValueError:
return False
date = questionary.text(
"Enter the analysis date (YYYY-MM-DD):",
validate=lambda x: validate_date(x.strip())
or "Please enter a valid date in YYYY-MM-DD format.",
style=questionary.Style(
[
("text", "fg:green"),
("highlighted", "noinherit"),
]
),
).ask()
if not date:
console.print("\n[red]No date provided. Exiting...[/red]")
exit(1)
return date.strip()
def select_analysts() -> List[AnalystType]:
"""Select analysts using an interactive checkbox."""
choices = questionary.checkbox(
"Select Your [Analysts Team]:",
choices=[
questionary.Choice(display, value=value) for display, value in ANALYST_ORDER
],
instruction="\n- Press Space to select/unselect analysts\n- Press 'a' to select/unselect all\n- Press Enter when done",
validate=lambda x: len(x) > 0 or "You must select at least one analyst.",
style=questionary.Style(
[
("checkbox-selected", "fg:green"),
("selected", "fg:green noinherit"),
("highlighted", "noinherit"),
("pointer", "noinherit"),
]
),
).ask()
if not choices:
console.print("\n[red]No analysts selected. Exiting...[/red]")
exit(1)
return choices
def select_research_depth() -> int:
"""Select research depth using an interactive selection."""
# Define research depth options with their corresponding values
DEPTH_OPTIONS = [
("Shallow - Quick research, few debate and strategy discussion rounds", 1),
("Medium - Middle ground, moderate debate rounds and strategy discussion", 3),
("Deep - Comprehensive research, in depth debate and strategy discussion", 5),
]
choice = questionary.select(
"Select Your [Research Depth]:",
choices=[
questionary.Choice(display, value=value) for display, value in DEPTH_OPTIONS
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
style=questionary.Style(
[
("selected", "fg:yellow noinherit"),
("highlighted", "fg:yellow noinherit"),
("pointer", "fg:yellow noinherit"),
]
),
).ask()
if choice is None:
console.print("\n[red]No research depth selected. Exiting...[/red]")
exit(1)
return choice
def select_shallow_thinking_agent(provider) -> str:
"""Select shallow thinking llm engine using an interactive selection."""
# Define shallow thinking llm engine options with their corresponding model names
# Ordering: medium → light → heavy (balanced first for quick tasks)
# Within same tier, newer models first
SHALLOW_AGENT_OPTIONS = {
"openai": [
("GPT-5 Mini - Balanced speed, cost, and capability", "gpt-5-mini"),
("GPT-5 Nano - High-throughput, simple tasks", "gpt-5-nano"),
("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
("GPT-4.1 - Smartest non-reasoning model", "gpt-4.1"),
],
"anthropic": [
("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
("Claude Haiku 4.5 - Fast, near-instant responses", "claude-haiku-4-5"),
("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
],
"google": [
("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
("Gemini 3.1 Flash Lite - Most cost-efficient", "gemini-3.1-flash-lite-preview"),
("Gemini 2.5 Flash Lite - Fast, low-cost", "gemini-2.5-flash-lite"),
],
"xai": [
("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"),
("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
],
"openrouter": [
("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),
],
"ollama": [
("Qwen3:latest (8B, local)", "qwen3:latest"),
("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
],
}
choice = questionary.select(
"Select Your [Quick-Thinking LLM Engine]:",
choices=[
questionary.Choice(display, value=value)
for display, value in SHALLOW_AGENT_OPTIONS[provider.lower()]
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
style=questionary.Style(
[
("selected", "fg:magenta noinherit"),
("highlighted", "fg:magenta noinherit"),
("pointer", "fg:magenta noinherit"),
]
),
).ask()
if choice is None:
console.print(
"\n[red]No shallow thinking llm engine selected. Exiting...[/red]"
)
exit(1)
return choice
def select_deep_thinking_agent(provider) -> str:
"""Select deep thinking llm engine using an interactive selection."""
# Define deep thinking llm engine options with their corresponding model names
# Ordering: heavy → medium → light (most capable first for deep tasks)
# Within same tier, newer models first
DEEP_AGENT_OPTIONS = {
"openai": [
("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
("GPT-5.2 - Strong reasoning, cost-effective", "gpt-5.2"),
("GPT-5 Mini - Balanced speed, cost, and capability", "gpt-5-mini"),
("GPT-5.4 Pro - Most capable, expensive ($30/$180 per 1M tokens)", "gpt-5.4-pro"),
],
"anthropic": [
("Claude Opus 4.6 - Most intelligent, agents and coding", "claude-opus-4-6"),
("Claude Opus 4.5 - Premium, max intelligence", "claude-opus-4-5"),
("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
],
"google": [
("Gemini 3.1 Pro - Reasoning-first, complex workflows", "gemini-3.1-pro-preview"),
("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
("Gemini 2.5 Pro - Stable pro model", "gemini-2.5-pro"),
("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
],
"xai": [
("Grok 4 - Flagship model", "grok-4-0709"),
("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
],
"openrouter": [
("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),
("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
],
"ollama": [
("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
("Qwen3:latest (8B, local)", "qwen3:latest"),
],
}
choice = questionary.select(
"Select Your [Deep-Thinking LLM Engine]:",
choices=[
questionary.Choice(display, value=value)
for display, value in DEEP_AGENT_OPTIONS[provider.lower()]
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
style=questionary.Style(
[
("selected", "fg:magenta noinherit"),
("highlighted", "fg:magenta noinherit"),
("pointer", "fg:magenta noinherit"),
]
),
).ask()
if choice is None:
console.print("\n[red]No deep thinking llm engine selected. Exiting...[/red]")
exit(1)
return choice
def select_llm_provider() -> tuple[str, str]:
"""Select the OpenAI api url using interactive selection."""
# Define OpenAI api options with their corresponding endpoints
BASE_URLS = [
("OpenAI", "https://api.openai.com/v1"),
("Google", "https://generativelanguage.googleapis.com/v1"),
("Anthropic", "https://api.anthropic.com/"),
("xAI", "https://api.x.ai/v1"),
("Openrouter", "https://openrouter.ai/api/v1"),
("Ollama", "http://localhost:11434/v1"),
]
choice = questionary.select(
"Select your LLM Provider:",
choices=[
questionary.Choice(display, value=(display, value))
for display, value in BASE_URLS
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
style=questionary.Style(
[
("selected", "fg:magenta noinherit"),
("highlighted", "fg:magenta noinherit"),
("pointer", "fg:magenta noinherit"),
]
),
).ask()
if choice is None:
console.print("\n[red]no OpenAI backend selected. Exiting...[/red]")
exit(1)
display_name, url = choice
print(f"You selected: {display_name}\tURL: {url}")
return display_name, url
def ask_openai_reasoning_effort() -> str:
"""Ask for OpenAI reasoning effort level."""
choices = [
questionary.Choice("Medium (Default)", "medium"),
questionary.Choice("High (More thorough)", "high"),
questionary.Choice("Low (Faster)", "low"),
]
return questionary.select(
"Select Reasoning Effort:",
choices=choices,
style=questionary.Style([
("selected", "fg:cyan noinherit"),
("highlighted", "fg:cyan noinherit"),
("pointer", "fg:cyan noinherit"),
]),
).ask()
def ask_gemini_thinking_config() -> str | None:
"""Ask for Gemini thinking configuration.
Returns thinking_level: "high" or "minimal".
Client maps to appropriate API param based on model series.
"""
return questionary.select(
"Select Thinking Mode:",
choices=[
questionary.Choice("Enable Thinking (recommended)", "high"),
questionary.Choice("Minimal/Disable Thinking", "minimal"),
],
style=questionary.Style([
("selected", "fg:green noinherit"),
("highlighted", "fg:green noinherit"),
("pointer", "fg:green noinherit"),
]),
).ask()
================================================
FILE: main.py
================================================
from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
# Create a custom config
config = DEFAULT_CONFIG.copy()
config["deep_think_llm"] = "gpt-5-mini" # Use a different model
config["quick_think_llm"] = "gpt-5-mini" # Use a different model
config["max_debate_rounds"] = 1 # Increase debate rounds
# Configure data vendors (default uses yfinance, no extra API keys needed)
config["data_vendors"] = {
"core_stock_apis": "yfinance", # Options: alpha_vantage, yfinance
"technical_indicators": "yfinance", # Options: alpha_vantage, yfinance
"fundamental_data": "yfinance", # Options: alpha_vantage, yfinance
"news_data": "yfinance", # Options: alpha_vantage, yfinance
}
# Initialize with custom config
ta = TradingAgentsGraph(debug=True, config=config)
# forward propagate
_, decision = ta.propagate("NVDA", "2024-05-10")
print(decision)
# Memorize mistakes and reflect
# ta.reflect_and_remember(1000) # parameter is the position returns
================================================
FILE: pyproject.toml
================================================
[build-system]
requires = ["setuptools>=61.0"]
build-backend = "setuptools.build_meta"
[project]
name = "tradingagents"
version = "0.2.1"
description = "TradingAgents: Multi-Agents LLM Financial Trading Framework"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"langchain-core>=0.3.81",
"backtrader>=1.9.78.123",
"langchain-anthropic>=0.3.15",
"langchain-experimental>=0.3.4",
"langchain-google-genai>=2.1.5",
"langchain-openai>=0.3.23",
"langgraph>=0.4.8",
"pandas>=2.3.0",
"parsel>=1.10.0",
"pytz>=2025.2",
"questionary>=2.1.0",
"rank-bm25>=0.2.2",
"redis>=6.2.0",
"requests>=2.32.4",
"rich>=14.0.0",
"typer>=0.21.0",
"setuptools>=80.9.0",
"stockstats>=0.6.5",
"tqdm>=4.67.1",
"typing-extensions>=4.14.0",
"yfinance>=0.2.63",
]
[project.scripts]
tradingagents = "cli.main:app"
[tool.setuptools.packages.find]
include = ["tradingagents*", "cli*"]
================================================
FILE: requirements.txt
================================================
typing-extensions
langchain-core
langchain-openai
langchain-experimental
pandas
yfinance
stockstats
langgraph
rank-bm25
setuptools
backtrader
parsel
requests
tqdm
pytz
redis
rich
typer
questionary
langchain_anthropic
langchain-google-genai
================================================
FILE: test.py
================================================
import time
from tradingagents.dataflows.y_finance import get_YFin_data_online, get_stock_stats_indicators_window, get_balance_sheet as get_yfinance_balance_sheet, get_cashflow as get_yfinance_cashflow, get_income_statement as get_yfinance_income_statement, get_insider_transactions as get_yfinance_insider_transactions
print("Testing optimized implementation with 30-day lookback:")
start_time = time.time()
result = get_stock_stats_indicators_window("AAPL", "macd", "2024-11-01", 30)
end_time = time.time()
print(f"Execution time: {end_time - start_time:.2f} seconds")
print(f"Result length: {len(result)} characters")
print(result)
================================================
FILE: tradingagents/__init__.py
================================================
================================================
FILE: tradingagents/agents/__init__.py
================================================
from .utils.agent_utils import create_msg_delete
from .utils.agent_states import AgentState, InvestDebateState, RiskDebateState
from .utils.memory import FinancialSituationMemory
from .analysts.fundamentals_analyst import create_fundamentals_analyst
from .analysts.market_analyst import create_market_analyst
from .analysts.news_analyst import create_news_analyst
from .analysts.social_media_analyst import create_social_media_analyst
from .researchers.bear_researcher import create_bear_researcher
from .researchers.bull_researcher import create_bull_researcher
from .risk_mgmt.aggressive_debator import create_aggressive_debator
from .risk_mgmt.conservative_debator import create_conservative_debator
from .risk_mgmt.neutral_debator import create_neutral_debator
from .managers.research_manager import create_research_manager
from .managers.risk_manager import create_risk_manager
from .trader.trader import create_trader
__all__ = [
"FinancialSituationMemory",
"AgentState",
"create_msg_delete",
"InvestDebateState",
"RiskDebateState",
"create_bear_researcher",
"create_bull_researcher",
"create_research_manager",
"create_fundamentals_analyst",
"create_market_analyst",
"create_neutral_debator",
"create_news_analyst",
"create_aggressive_debator",
"create_risk_manager",
"create_conservative_debator",
"create_social_media_analyst",
"create_trader",
]
================================================
FILE: tradingagents/agents/analysts/fundamentals_analyst.py
================================================
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import time
import json
from tradingagents.agents.utils.agent_utils import get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement, get_insider_transactions
from tradingagents.dataflows.config import get_config
def create_fundamentals_analyst(llm):
def fundamentals_analyst_node(state):
current_date = state["trade_date"]
ticker = state["company_of_interest"]
company_name = state["company_of_interest"]
tools = [
get_fundamentals,
get_balance_sheet,
get_cashflow,
get_income_statement,
]
system_message = (
"You are a researcher tasked with analyzing fundamental information over the past week about a company. Please write a comprehensive report of the company's fundamental information such as financial documents, company profile, basic company financials, and company financial history to gain a full view of the company's fundamental information to inform traders. Make sure to include as much detail as possible. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
+ " Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."
+ " Use the available tools: `get_fundamentals` for comprehensive company analysis, `get_balance_sheet`, `get_cashflow`, and `get_income_statement` for specific financial statements.",
)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK; another assistant with different tools"
" will help where you left off. Execute what you can to make progress."
" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
" You have access to the following tools: {tool_names}.\n{system_message}"
"For your reference, the current date is {current_date}. The company we want to look at is {ticker}",
),
MessagesPlaceholder(variable_name="messages"),
]
)
prompt = prompt.partial(system_message=system_message)
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
prompt = prompt.partial(current_date=current_date)
prompt = prompt.partial(ticker=ticker)
chain = prompt | llm.bind_tools(tools)
result = chain.invoke(state["messages"])
report = ""
if len(result.tool_calls) == 0:
report = result.content
return {
"messages": [result],
"fundamentals_report": report,
}
return fundamentals_analyst_node
================================================
FILE: tradingagents/agents/analysts/market_analyst.py
================================================
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import time
import json
from tradingagents.agents.utils.agent_utils import get_stock_data, get_indicators
from tradingagents.dataflows.config import get_config
def create_market_analyst(llm):
def market_analyst_node(state):
current_date = state["trade_date"]
ticker = state["company_of_interest"]
company_name = state["company_of_interest"]
tools = [
get_stock_data,
get_indicators,
]
system_message = (
"""You are a trading assistant tasked with analyzing financial markets. Your role is to select the **most relevant indicators** for a given market condition or trading strategy from the following list. The goal is to choose up to **8 indicators** that provide complementary insights without redundancy. Categories and each category's indicators are:
Moving Averages:
- close_50_sma: 50 SMA: A medium-term trend indicator. Usage: Identify trend direction and serve as dynamic support/resistance. Tips: It lags price; combine with faster indicators for timely signals.
- close_200_sma: 200 SMA: A long-term trend benchmark. Usage: Confirm overall market trend and identify golden/death cross setups. Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries.
- close_10_ema: 10 EMA: A responsive short-term average. Usage: Capture quick shifts in momentum and potential entry points. Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals.
MACD Related:
- macd: MACD: Computes momentum via differences of EMAs. Usage: Look for crossovers and divergence as signals of trend changes. Tips: Confirm with other indicators in low-volatility or sideways markets.
- macds: MACD Signal: An EMA smoothing of the MACD line. Usage: Use crossovers with the MACD line to trigger trades. Tips: Should be part of a broader strategy to avoid false positives.
- macdh: MACD Histogram: Shows the gap between the MACD line and its signal. Usage: Visualize momentum strength and spot divergence early. Tips: Can be volatile; complement with additional filters in fast-moving markets.
Momentum Indicators:
- rsi: RSI: Measures momentum to flag overbought/oversold conditions. Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis.
Volatility Indicators:
- boll: Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. Usage: Acts as a dynamic benchmark for price movement. Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals.
- boll_ub: Bollinger Upper Band: Typically 2 standard deviations above the middle line. Usage: Signals potential overbought conditions and breakout zones. Tips: Confirm signals with other tools; prices may ride the band in strong trends.
- boll_lb: Bollinger Lower Band: Typically 2 standard deviations below the middle line. Usage: Indicates potential oversold conditions. Tips: Use additional analysis to avoid false reversal signals.
- atr: ATR: Averages true range to measure volatility. Usage: Set stop-loss levels and adjust position sizes based on current market volatility. Tips: It's a reactive measure, so use it as part of a broader risk management strategy.
Volume-Based Indicators:
- vwma: VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses.
- Select indicators that provide diverse and complementary information. Avoid redundancy (e.g., do not select both rsi and stochrsi). Also briefly explain why they are suitable for the given market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_stock_data first to retrieve the CSV that is needed to generate indicators. Then use get_indicators with the specific indicator names. Write a very detailed and nuanced report of the trends you observe. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."""
+ """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK; another assistant with different tools"
" will help where you left off. Execute what you can to make progress."
" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
" You have access to the following tools: {tool_names}.\n{system_message}"
"For your reference, the current date is {current_date}. The company we want to look at is {ticker}",
),
MessagesPlaceholder(variable_name="messages"),
]
)
prompt = prompt.partial(system_message=system_message)
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
prompt = prompt.partial(current_date=current_date)
prompt = prompt.partial(ticker=ticker)
chain = prompt | llm.bind_tools(tools)
result = chain.invoke(state["messages"])
report = ""
if len(result.tool_calls) == 0:
report = result.content
return {
"messages": [result],
"market_report": report,
}
return market_analyst_node
================================================
FILE: tradingagents/agents/analysts/news_analyst.py
================================================
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import time
import json
from tradingagents.agents.utils.agent_utils import get_news, get_global_news
from tradingagents.dataflows.config import get_config
def create_news_analyst(llm):
def news_analyst_node(state):
current_date = state["trade_date"]
ticker = state["company_of_interest"]
tools = [
get_news,
get_global_news,
]
system_message = (
"You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the available tools: get_news(query, start_date, end_date) for company-specific or targeted news searches, and get_global_news(curr_date, look_back_days, limit) for broader macroeconomic news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
+ """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK; another assistant with different tools"
" will help where you left off. Execute what you can to make progress."
" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
" You have access to the following tools: {tool_names}.\n{system_message}"
"For your reference, the current date is {current_date}. We are looking at the company {ticker}",
),
MessagesPlaceholder(variable_name="messages"),
]
)
prompt = prompt.partial(system_message=system_message)
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
prompt = prompt.partial(current_date=current_date)
prompt = prompt.partial(ticker=ticker)
chain = prompt | llm.bind_tools(tools)
result = chain.invoke(state["messages"])
report = ""
if len(result.tool_calls) == 0:
report = result.content
return {
"messages": [result],
"news_report": report,
}
return news_analyst_node
================================================
FILE: tradingagents/agents/analysts/social_media_analyst.py
================================================
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import time
import json
from tradingagents.agents.utils.agent_utils import get_news
from tradingagents.dataflows.config import get_config
def create_social_media_analyst(llm):
def social_media_analyst_node(state):
current_date = state["trade_date"]
ticker = state["company_of_interest"]
company_name = state["company_of_interest"]
tools = [
get_news,
]
system_message = (
"You are a social media and company specific news researcher/analyst tasked with analyzing social media posts, recent company news, and public sentiment for a specific company over the past week. You will be given a company's name your objective is to write a comprehensive long report detailing your analysis, insights, and implications for traders and investors on this company's current state after looking at social media and what people are saying about that company, analyzing sentiment data of what people feel each day about the company, and looking at recent company news. Use the get_news(query, start_date, end_date) tool to search for company-specific news and social media discussions. Try to look at all sources possible from social media to sentiment to news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
+ """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.""",
)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK; another assistant with different tools"
" will help where you left off. Execute what you can to make progress."
" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
" You have access to the following tools: {tool_names}.\n{system_message}"
"For your reference, the current date is {current_date}. The current company we want to analyze is {ticker}",
),
MessagesPlaceholder(variable_name="messages"),
]
)
prompt = prompt.partial(system_message=system_message)
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
prompt = prompt.partial(current_date=current_date)
prompt = prompt.partial(ticker=ticker)
chain = prompt | llm.bind_tools(tools)
result = chain.invoke(state["messages"])
report = ""
if len(result.tool_calls) == 0:
report = result.content
return {
"messages": [result],
"sentiment_report": report,
}
return social_media_analyst_node
================================================
FILE: tradingagents/agents/managers/research_manager.py
================================================
import time
import json
def create_research_manager(llm, memory):
def research_manager_node(state) -> dict:
history = state["investment_debate_state"].get("history", "")
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
investment_debate_state = state["investment_debate_state"]
curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
past_memories = memory.get_memories(curr_situation, n_matches=2)
past_memory_str = ""
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
prompt = f"""As the portfolio manager and debate facilitator, your role is to critically evaluate this round of debate and make a definitive decision: align with the bear analyst, the bull analyst, or choose Hold only if it is strongly justified based on the arguments presented.
Summarize the key points from both sides concisely, focusing on the most compelling evidence or reasoning. Your recommendation—Buy, Sell, or Hold—must be clear and actionable. Avoid defaulting to Hold simply because both sides have valid points; commit to a stance grounded in the debate's strongest arguments.
Additionally, develop a detailed investment plan for the trader. This should include:
Your Recommendation: A decisive stance supported by the most convincing arguments.
Rationale: An explanation of why these arguments lead to your conclusion.
Strategic Actions: Concrete steps for implementing the recommendation.
Take into account your past mistakes on similar situations. Use these insights to refine your decision-making and ensure you are learning and improving. Present your analysis conversationally, as if speaking naturally, without special formatting.
Here are your past reflections on mistakes:
\"{past_memory_str}\"
Here is the debate:
Debate History:
{history}"""
response = llm.invoke(prompt)
new_investment_debate_state = {
"judge_decision": response.content,
"history": investment_debate_state.get("history", ""),
"bear_history": investment_debate_state.get("bear_history", ""),
"bull_history": investment_debate_state.get("bull_history", ""),
"current_response": response.content,
"count": investment_debate_state["count"],
}
return {
"investment_debate_state": new_investment_debate_state,
"investment_plan": response.content,
}
return research_manager_node
================================================
FILE: tradingagents/agents/managers/risk_manager.py
================================================
import time
import json
def create_risk_manager(llm, memory):
def risk_manager_node(state) -> dict:
company_name = state["company_of_interest"]
history = state["risk_debate_state"]["history"]
risk_debate_state = state["risk_debate_state"]
market_research_report = state["market_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
sentiment_report = state["sentiment_report"]
trader_plan = state["investment_plan"]
curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
past_memories = memory.get_memories(curr_situation, n_matches=2)
past_memory_str = ""
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
prompt = f"""As the Risk Management Judge and Debate Facilitator, your goal is to evaluate the debate between three risk analysts—Aggressive, Neutral, and Conservative—and determine the best course of action for the trader. Your decision must result in a clear recommendation: Buy, Sell, or Hold. Choose Hold only if strongly justified by specific arguments, not as a fallback when all sides seem valid. Strive for clarity and decisiveness.
Guidelines for Decision-Making:
1. **Summarize Key Arguments**: Extract the strongest points from each analyst, focusing on relevance to the context.
2. **Provide Rationale**: Support your recommendation with direct quotes and counterarguments from the debate.
3. **Refine the Trader's Plan**: Start with the trader's original plan, **{trader_plan}**, and adjust it based on the analysts' insights.
4. **Learn from Past Mistakes**: Use lessons from **{past_memory_str}** to address prior misjudgments and improve the decision you are making now to make sure you don't make a wrong BUY/SELL/HOLD call that loses money.
Deliverables:
- A clear and actionable recommendation: Buy, Sell, or Hold.
- Detailed reasoning anchored in the debate and past reflections.
---
**Analysts Debate History:**
{history}
---
Focus on actionable insights and continuous improvement. Build on past lessons, critically evaluate all perspectives, and ensure each decision advances better outcomes."""
response = llm.invoke(prompt)
new_risk_debate_state = {
"judge_decision": response.content,
"history": risk_debate_state["history"],
"aggressive_history": risk_debate_state["aggressive_history"],
"conservative_history": risk_debate_state["conservative_history"],
"neutral_history": risk_debate_state["neutral_history"],
"latest_speaker": "Judge",
"current_aggressive_response": risk_debate_state["current_aggressive_response"],
"current_conservative_response": risk_debate_state["current_conservative_response"],
"current_neutral_response": risk_debate_state["current_neutral_response"],
"count": risk_debate_state["count"],
}
return {
"risk_debate_state": new_risk_debate_state,
"final_trade_decision": response.content,
}
return risk_manager_node
================================================
FILE: tradingagents/agents/researchers/bear_researcher.py
================================================
from langchain_core.messages import AIMessage
import time
import json
def create_bear_researcher(llm, memory):
def bear_node(state) -> dict:
investment_debate_state = state["investment_debate_state"]
history = investment_debate_state.get("history", "")
bear_history = investment_debate_state.get("bear_history", "")
current_response = investment_debate_state.get("current_response", "")
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
past_memories = memory.get_memories(curr_situation, n_matches=2)
past_memory_str = ""
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
prompt = f"""You are a Bear Analyst making the case against investing in the stock. Your goal is to present a well-reasoned argument emphasizing risks, challenges, and negative indicators. Leverage the provided research and data to highlight potential downsides and counter bullish arguments effectively.
Key points to focus on:
- Risks and Challenges: Highlight factors like market saturation, financial instability, or macroeconomic threats that could hinder the stock's performance.
- Competitive Weaknesses: Emphasize vulnerabilities such as weaker market positioning, declining innovation, or threats from competitors.
- Negative Indicators: Use evidence from financial data, market trends, or recent adverse news to support your position.
- Bull Counterpoints: Critically analyze the bull argument with specific data and sound reasoning, exposing weaknesses or over-optimistic assumptions.
- Engagement: Present your argument in a conversational style, directly engaging with the bull analyst's points and debating effectively rather than simply listing facts.
Resources available:
Market research report: {market_research_report}
Social media sentiment report: {sentiment_report}
Latest world affairs news: {news_report}
Company fundamentals report: {fundamentals_report}
Conversation history of the debate: {history}
Last bull argument: {current_response}
Reflections from similar situations and lessons learned: {past_memory_str}
Use this information to deliver a compelling bear argument, refute the bull's claims, and engage in a dynamic debate that demonstrates the risks and weaknesses of investing in the stock. You must also address reflections and learn from lessons and mistakes you made in the past.
"""
response = llm.invoke(prompt)
argument = f"Bear Analyst: {response.content}"
new_investment_debate_state = {
"history": history + "\n" + argument,
"bear_history": bear_history + "\n" + argument,
"bull_history": investment_debate_state.get("bull_history", ""),
"current_response": argument,
"count": investment_debate_state["count"] + 1,
}
return {"investment_debate_state": new_investment_debate_state}
return bear_node
================================================
FILE: tradingagents/agents/researchers/bull_researcher.py
================================================
from langchain_core.messages import AIMessage
import time
import json
def create_bull_researcher(llm, memory):
def bull_node(state) -> dict:
investment_debate_state = state["investment_debate_state"]
history = investment_debate_state.get("history", "")
bull_history = investment_debate_state.get("bull_history", "")
current_response = investment_debate_state.get("current_response", "")
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
past_memories = memory.get_memories(curr_situation, n_matches=2)
past_memory_str = ""
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
prompt = f"""You are a Bull Analyst advocating for investing in the stock. Your task is to build a strong, evidence-based case emphasizing growth potential, competitive advantages, and positive market indicators. Leverage the provided research and data to address concerns and counter bearish arguments effectively.
Key points to focus on:
- Growth Potential: Highlight the company's market opportunities, revenue projections, and scalability.
- Competitive Advantages: Emphasize factors like unique products, strong branding, or dominant market positioning.
- Positive Indicators: Use financial health, industry trends, and recent positive news as evidence.
- Bear Counterpoints: Critically analyze the bear argument with specific data and sound reasoning, addressing concerns thoroughly and showing why the bull perspective holds stronger merit.
- Engagement: Present your argument in a conversational style, engaging directly with the bear analyst's points and debating effectively rather than just listing data.
Resources available:
Market research report: {market_research_report}
Social media sentiment report: {sentiment_report}
Latest world affairs news: {news_report}
Company fundamentals report: {fundamentals_report}
Conversation history of the debate: {history}
Last bear argument: {current_response}
Reflections from similar situations and lessons learned: {past_memory_str}
Use this information to deliver a compelling bull argument, refute the bear's concerns, and engage in a dynamic debate that demonstrates the strengths of the bull position. You must also address reflections and learn from lessons and mistakes you made in the past.
"""
response = llm.invoke(prompt)
argument = f"Bull Analyst: {response.content}"
new_investment_debate_state = {
"history": history + "\n" + argument,
"bull_history": bull_history + "\n" + argument,
"bear_history": investment_debate_state.get("bear_history", ""),
"current_response": argument,
"count": investment_debate_state["count"] + 1,
}
return {"investment_debate_state": new_investment_debate_state}
return bull_node
================================================
FILE: tradingagents/agents/risk_mgmt/aggressive_debator.py
================================================
import time
import json
def create_aggressive_debator(llm):
def aggressive_node(state) -> dict:
risk_debate_state = state["risk_debate_state"]
history = risk_debate_state.get("history", "")
aggressive_history = risk_debate_state.get("aggressive_history", "")
current_conservative_response = risk_debate_state.get("current_conservative_response", "")
current_neutral_response = risk_debate_state.get("current_neutral_response", "")
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
trader_decision = state["trader_investment_plan"]
prompt = f"""As the Aggressive Risk Analyst, your role is to actively champion high-reward, high-risk opportunities, emphasizing bold strategies and competitive advantages. When evaluating the trader's decision or plan, focus intently on the potential upside, growth potential, and innovative benefits—even when these come with elevated risk. Use the provided market data and sentiment analysis to strengthen your arguments and challenge the opposing views. Specifically, respond directly to each point made by the conservative and neutral analysts, countering with data-driven rebuttals and persuasive reasoning. Highlight where their caution might miss critical opportunities or where their assumptions may be overly conservative. Here is the trader's decision:
{trader_decision}
Your task is to create a compelling case for the trader's decision by questioning and critiquing the conservative and neutral stances to demonstrate why your high-reward perspective offers the best path forward. Incorporate insights from the following sources into your arguments:
Market Research Report: {market_research_report}
Social Media Sentiment Report: {sentiment_report}
Latest World Affairs Report: {news_report}
Company Fundamentals Report: {fundamentals_report}
Here is the current conversation history: {history} Here are the last arguments from the conservative analyst: {current_conservative_response} Here are the last arguments from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not hallucinate and just present your point.
Engage actively by addressing any specific concerns raised, refuting the weaknesses in their logic, and asserting the benefits of risk-taking to outpace market norms. Maintain a focus on debating and persuading, not just presenting data. Challenge each counterpoint to underscore why a high-risk approach is optimal. Output conversationally as if you are speaking without any special formatting."""
response = llm.invoke(prompt)
argument = f"Aggressive Analyst: {response.content}"
new_risk_debate_state = {
"history": history + "\n" + argument,
"aggressive_history": aggressive_history + "\n" + argument,
"conservative_history": risk_debate_state.get("conservative_history", ""),
"neutral_history": risk_debate_state.get("neutral_history", ""),
"latest_speaker": "Aggressive",
"current_aggressive_response": argument,
"current_conservative_response": risk_debate_state.get("current_conservative_response", ""),
"current_neutral_response": risk_debate_state.get(
"current_neutral_response", ""
),
"count": risk_debate_state["count"] + 1,
}
return {"risk_debate_state": new_risk_debate_state}
return aggressive_node
================================================
FILE: tradingagents/agents/risk_mgmt/conservative_debator.py
================================================
from langchain_core.messages import AIMessage
import time
import json
def create_conservative_debator(llm):
def conservative_node(state) -> dict:
risk_debate_state = state["risk_debate_state"]
history = risk_debate_state.get("history", "")
conservative_history = risk_debate_state.get("conservative_history", "")
current_aggressive_response = risk_debate_state.get("current_aggressive_response", "")
current_neutral_response = risk_debate_state.get("current_neutral_response", "")
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
trader_decision = state["trader_investment_plan"]
prompt = f"""As the Conservative Risk Analyst, your primary objective is to protect assets, minimize volatility, and ensure steady, reliable growth. You prioritize stability, security, and risk mitigation, carefully assessing potential losses, economic downturns, and market volatility. When evaluating the trader's decision or plan, critically examine high-risk elements, pointing out where the decision may expose the firm to undue risk and where more cautious alternatives could secure long-term gains. Here is the trader's decision:
{trader_decision}
Your task is to actively counter the arguments of the Aggressive and Neutral Analysts, highlighting where their views may overlook potential threats or fail to prioritize sustainability. Respond directly to their points, drawing from the following data sources to build a convincing case for a low-risk approach adjustment to the trader's decision:
Market Research Report: {market_research_report}
Social Media Sentiment Report: {sentiment_report}
Latest World Affairs Report: {news_report}
Company Fundamentals Report: {fundamentals_report}
Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not hallucinate and just present your point.
Engage by questioning their optimism and emphasizing the potential downsides they may have overlooked. Address each of their counterpoints to showcase why a conservative stance is ultimately the safest path for the firm's assets. Focus on debating and critiquing their arguments to demonstrate the strength of a low-risk strategy over their approaches. Output conversationally as if you are speaking without any special formatting."""
response = llm.invoke(prompt)
argument = f"Conservative Analyst: {response.content}"
new_risk_debate_state = {
"history": history + "\n" + argument,
"aggressive_history": risk_debate_state.get("aggressive_history", ""),
"conservative_history": conservative_history + "\n" + argument,
"neutral_history": risk_debate_state.get("neutral_history", ""),
"latest_speaker": "Conservative",
"current_aggressive_response": risk_debate_state.get(
"current_aggressive_response", ""
),
"current_conservative_response": argument,
"current_neutral_response": risk_debate_state.get(
"current_neutral_response", ""
),
"count": risk_debate_state["count"] + 1,
}
return {"risk_debate_state": new_risk_debate_state}
return conservative_node
================================================
FILE: tradingagents/agents/risk_mgmt/neutral_debator.py
================================================
import time
import json
def create_neutral_debator(llm):
def neutral_node(state) -> dict:
risk_debate_state = state["risk_debate_state"]
history = risk_debate_state.get("history", "")
neutral_history = risk_debate_state.get("neutral_history", "")
current_aggressive_response = risk_debate_state.get("current_aggressive_response", "")
current_conservative_response = risk_debate_state.get("current_conservative_response", "")
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
trader_decision = state["trader_investment_plan"]
prompt = f"""As the Neutral Risk Analyst, your role is to provide a balanced perspective, weighing both the potential benefits and risks of the trader's decision or plan. You prioritize a well-rounded approach, evaluating the upsides and downsides while factoring in broader market trends, potential economic shifts, and diversification strategies.Here is the trader's decision:
{trader_decision}
Your task is to challenge both the Aggressive and Conservative Analysts, pointing out where each perspective may be overly optimistic or overly cautious. Use insights from the following data sources to support a moderate, sustainable strategy to adjust the trader's decision:
Market Research Report: {market_research_report}
Social Media Sentiment Report: {sentiment_report}
Latest World Affairs Report: {news_report}
Company Fundamentals Report: {fundamentals_report}
Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the conservative analyst: {current_conservative_response}. If there are no responses from the other viewpoints, do not hallucinate and just present your point.
Engage actively by analyzing both sides critically, addressing weaknesses in the aggressive and conservative arguments to advocate for a more balanced approach. Challenge each of their points to illustrate why a moderate risk strategy might offer the best of both worlds, providing growth potential while safeguarding against extreme volatility. Focus on debating rather than simply presenting data, aiming to show that a balanced view can lead to the most reliable outcomes. Output conversationally as if you are speaking without any special formatting."""
response = llm.invoke(prompt)
argument = f"Neutral Analyst: {response.content}"
new_risk_debate_state = {
"history": history + "\n" + argument,
"aggressive_history": risk_debate_state.get("aggressive_history", ""),
"conservative_history": risk_debate_state.get("conservative_history", ""),
"neutral_history": neutral_history + "\n" + argument,
"latest_speaker": "Neutral",
"current_aggressive_response": risk_debate_state.get(
"current_aggressive_response", ""
),
"current_conservative_response": risk_debate_state.get("current_conservative_response", ""),
"current_neutral_response": argument,
"count": risk_debate_state["count"] + 1,
}
return {"risk_debate_state": new_risk_debate_state}
return neutral_node
================================================
FILE: tradingagents/agents/trader/trader.py
================================================
import functools
import time
import json
def create_trader(llm, memory):
def trader_node(state, name):
company_name = state["company_of_interest"]
investment_plan = state["investment_plan"]
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
past_memories = memory.get_memories(curr_situation, n_matches=2)
past_memory_str = ""
if past_memories:
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
else:
past_memory_str = "No past memories found."
context = {
"role": "user",
"content": f"Based on a comprehensive analysis by a team of analysts, here is an investment plan tailored for {company_name}. This plan incorporates insights from current technical market trends, macroeconomic indicators, and social media sentiment. Use this plan as a foundation for evaluating your next trading decision.\n\nProposed Investment Plan: {investment_plan}\n\nLeverage these insights to make an informed and strategic decision.",
}
messages = [
{
"role": "system",
"content": f"""You are a trading agent analyzing market data to make investment decisions. Based on your analysis, provide a specific recommendation to buy, sell, or hold. End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**' to confirm your recommendation. Do not forget to utilize lessons from past decisions to learn from your mistakes. Here is some reflections from similar situatiosn you traded in and the lessons learned: {past_memory_str}""",
},
context,
]
result = llm.invoke(messages)
return {
"messages": [result],
"trader_investment_plan": result.content,
"sender": name,
}
return functools.partial(trader_node, name="Trader")
================================================
FILE: tradingagents/agents/utils/agent_states.py
================================================
from typing import Annotated, Sequence
from datetime import date, timedelta, datetime
from typing_extensions import TypedDict, Optional
from langchain_openai import ChatOpenAI
from tradingagents.agents import *
from langgraph.prebuilt import ToolNode
from langgraph.graph import END, StateGraph, START, MessagesState
# Researcher team state
class InvestDebateState(TypedDict):
bull_history: Annotated[
str, "Bullish Conversation history"
] # Bullish Conversation history
bear_history: Annotated[
str, "Bearish Conversation history"
] # Bullish Conversation history
history: Annotated[str, "Conversation history"] # Conversation history
current_response: Annotated[str, "Latest response"] # Last response
judge_decision: Annotated[str, "Final judge decision"] # Last response
count: Annotated[int, "Length of the current conversation"] # Conversation length
# Risk management team state
class RiskDebateState(TypedDict):
aggressive_history: Annotated[
str, "Aggressive Agent's Conversation history"
] # Conversation history
conservative_history: Annotated[
str, "Conservative Agent's Conversation history"
] # Conversation history
neutral_history: Annotated[
str, "Neutral Agent's Conversation history"
] # Conversation history
history: Annotated[str, "Conversation history"] # Conversation history
latest_speaker: Annotated[str, "Analyst that spoke last"]
current_aggressive_response: Annotated[
str, "Latest response by the aggressive analyst"
] # Last response
current_conservative_response: Annotated[
str, "Latest response by the conservative analyst"
] # Last response
current_neutral_response: Annotated[
str, "Latest response by the neutral analyst"
] # Last response
judge_decision: Annotated[str, "Judge's decision"]
count: Annotated[int, "Length of the current conversation"] # Conversation length
class AgentState(MessagesState):
company_of_interest: Annotated[str, "Company that we are interested in trading"]
trade_date: Annotated[str, "What date we are trading at"]
sender: Annotated[str, "Agent that sent this message"]
# research step
market_report: Annotated[str, "Report from the Market Analyst"]
sentiment_report: Annotated[str, "Report from the Social Media Analyst"]
news_report: Annotated[
str, "Report from the News Researcher of current world affairs"
]
fundamentals_report: Annotated[str, "Report from the Fundamentals Researcher"]
# researcher team discussion step
investment_debate_state: Annotated[
InvestDebateState, "Current state of the debate on if to invest or not"
]
investment_plan: Annotated[str, "Plan generated by the Analyst"]
trader_investment_plan: Annotated[str, "Plan generated by the Trader"]
# risk management team discussion step
risk_debate_state: Annotated[
RiskDebateState, "Current state of the debate on evaluating risk"
]
final_trade_decision: Annotated[str, "Final decision made by the Risk Analysts"]
================================================
FILE: tradingagents/agents/utils/agent_utils.py
================================================
from langchain_core.messages import HumanMessage, RemoveMessage
# Import tools from separate utility files
from tradingagents.agents.utils.core_stock_tools import (
get_stock_data
)
from tradingagents.agents.utils.technical_indicators_tools import (
get_indicators
)
from tradingagents.agents.utils.fundamental_data_tools import (
get_fundamentals,
get_balance_sheet,
get_cashflow,
get_income_statement
)
from tradingagents.agents.utils.news_data_tools import (
get_news,
get_insider_transactions,
get_global_news
)
def create_msg_delete():
def delete_messages(state):
"""Clear messages and add placeholder for Anthropic compatibility"""
messages = state["messages"]
# Remove all messages
removal_operations = [RemoveMessage(id=m.id) for m in messages]
# Add a minimal placeholder message
placeholder = HumanMessage(content="Continue")
return {"messages": removal_operations + [placeholder]}
return delete_messages
================================================
FILE: tradingagents/agents/utils/core_stock_tools.py
================================================
from langchain_core.tools import tool
from typing import Annotated
from tradingagents.dataflows.interface import route_to_vendor
@tool
def get_stock_data(
symbol: Annotated[str, "ticker symbol of the company"],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
) -> str:
"""
Retrieve stock price data (OHLCV) for a given ticker symbol.
Uses the configured core_stock_apis vendor.
Args:
symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
start_date (str): Start date in yyyy-mm-dd format
end_date (str): End date in yyyy-mm-dd format
Returns:
str: A formatted dataframe containing the stock price data for the specified ticker symbol in the specified date range.
"""
return route_to_vendor("get_stock_data", symbol, start_date, end_date)
================================================
FILE: tradingagents/agents/utils/fundamental_data_tools.py
================================================
from langchain_core.tools import tool
from typing import Annotated
from tradingagents.dataflows.interface import route_to_vendor
@tool
def get_fundamentals(
ticker: Annotated[str, "ticker symbol"],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
) -> str:
"""
Retrieve comprehensive fundamental data for a given ticker symbol.
Uses the configured fundamental_data vendor.
Args:
ticker (str): Ticker symbol of the company
curr_date (str): Current date you are trading at, yyyy-mm-dd
Returns:
str: A formatted report containing comprehensive fundamental data
"""
return route_to_vendor("get_fundamentals", ticker, curr_date)
@tool
def get_balance_sheet(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[str, "reporting frequency: annual/quarterly"] = "quarterly",
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"] = None,
) -> str:
"""
Retrieve balance sheet data for a given ticker symbol.
Uses the configured fundamental_data vendor.
Args:
ticker (str): Ticker symbol of the company
freq (str): Reporting frequency: annual/quarterly (default quarterly)
curr_date (str): Current date you are trading at, yyyy-mm-dd
Returns:
str: A formatted report containing balance sheet data
"""
return route_to_vendor("get_balance_sheet", ticker, freq, curr_date)
@tool
def get_cashflow(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[str, "reporting frequency: annual/quarterly"] = "quarterly",
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"] = None,
) -> str:
"""
Retrieve cash flow statement data for a given ticker symbol.
Uses the configured fundamental_data vendor.
Args:
ticker (str): Ticker symbol of the company
freq (str): Reporting frequency: annual/quarterly (default quarterly)
curr_date (str): Current date you are trading at, yyyy-mm-dd
Returns:
str: A formatted report containing cash flow statement data
"""
return route_to_vendor("get_cashflow", ticker, freq, curr_date)
@tool
def get_income_statement(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[str, "reporting frequency: annual/quarterly"] = "quarterly",
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"] = None,
) -> str:
"""
Retrieve income statement data for a given ticker symbol.
Uses the configured fundamental_data vendor.
Args:
ticker (str): Ticker symbol of the company
freq (str): Reporting frequency: annual/quarterly (default quarterly)
curr_date (str): Current date you are trading at, yyyy-mm-dd
Returns:
str: A formatted report containing income statement data
"""
return route_to_vendor("get_income_statement", ticker, freq, curr_date)
================================================
FILE: tradingagents/agents/utils/memory.py
================================================
"""Financial situation memory using BM25 for lexical similarity matching.
Uses BM25 (Best Matching 25) algorithm for retrieval - no API calls,
no token limits, works offline with any LLM provider.
"""
from rank_bm25 import BM25Okapi
from typing import List, Tuple
import re
class FinancialSituationMemory:
"""Memory system for storing and retrieving financial situations using BM25."""
def __init__(self, name: str, config: dict = None):
"""Initialize the memory system.
Args:
name: Name identifier for this memory instance
config: Configuration dict (kept for API compatibility, not used for BM25)
"""
self.name = name
self.documents: List[str] = []
self.recommendations: List[str] = []
self.bm25 = None
def _tokenize(self, text: str) -> List[str]:
"""Tokenize text for BM25 indexing.
Simple whitespace + punctuation tokenization with lowercasing.
"""
# Lowercase and split on non-alphanumeric characters
tokens = re.findall(r'\b\w+\b', text.lower())
return tokens
def _rebuild_index(self):
"""Rebuild the BM25 index after adding documents."""
if self.documents:
tokenized_docs = [self._tokenize(doc) for doc in self.documents]
self.bm25 = BM25Okapi(tokenized_docs)
else:
self.bm25 = None
def add_situations(self, situations_and_advice: List[Tuple[str, str]]):
"""Add financial situations and their corresponding advice.
Args:
situations_and_advice: List of tuples (situation, recommendation)
"""
for situation, recommendation in situations_and_advice:
self.documents.append(situation)
self.recommendations.append(recommendation)
# Rebuild BM25 index with new documents
self._rebuild_index()
def get_memories(self, current_situation: str, n_matches: int = 1) -> List[dict]:
"""Find matching recommendations using BM25 similarity.
Args:
current_situation: The current financial situation to match against
n_matches: Number of top matches to return
Returns:
List of dicts with matched_situation, recommendation, and similarity_score
"""
if not self.documents or self.bm25 is None:
return []
# Tokenize query
query_tokens = self._tokenize(current_situation)
# Get BM25 scores for all documents
scores = self.bm25.get_scores(query_tokens)
# Get top-n indices sorted by score (descending)
top_indices = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:n_matches]
# Build results
results = []
max_score = max(scores) if max(scores) > 0 else 1 # Normalize scores
for idx in top_indices:
# Normalize score to 0-1 range for consistency
normalized_score = scores[idx] / max_score if max_score > 0 else 0
results.append({
"matched_situation": self.documents[idx],
"recommendation": self.recommendations[idx],
"similarity_score": normalized_score,
})
return results
def clear(self):
"""Clear all stored memories."""
self.documents = []
self.recommendations = []
self.bm25 = None
if __name__ == "__main__":
# Example usage
matcher = FinancialSituationMemory("test_memory")
# Example data
example_data = [
(
"High inflation rate with rising interest rates and declining consumer spending",
"Consider defensive sectors like consumer staples and utilities. Review fixed-income portfolio duration.",
),
(
"Tech sector showing high volatility with increasing institutional selling pressure",
"Reduce exposure to high-growth tech stocks. Look for value opportunities in established tech companies with strong cash flows.",
),
(
"Strong dollar affecting emerging markets with increasing forex volatility",
"Hedge currency exposure in international positions. Consider reducing allocation to emerging market debt.",
),
(
"Market showing signs of sector rotation with rising yields",
"Rebalance portfolio to maintain target allocations. Consider increasing exposure to sectors benefiting from higher rates.",
),
]
# Add the example situations and recommendations
matcher.add_situations(example_data)
# Example query
current_situation = """
Market showing increased volatility in tech sector, with institutional investors
reducing positions and rising interest rates affecting growth stock valuations
"""
try:
recommendations = matcher.get_memories(current_situation, n_matches=2)
for i, rec in enumerate(recommendations, 1):
print(f"\nMatch {i}:")
print(f"Similarity Score: {rec['similarity_score']:.2f}")
print(f"Matched Situation: {rec['matched_situation']}")
print(f"Recommendation: {rec['recommendation']}")
except Exception as e:
print(f"Error during recommendation: {str(e)}")
================================================
FILE: tradingagents/agents/utils/news_data_tools.py
================================================
from langchain_core.tools import tool
from typing import Annotated
from tradingagents.dataflows.interface import route_to_vendor
@tool
def get_news(
ticker: Annotated[str, "Ticker symbol"],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
) -> str:
"""
Retrieve news data for a given ticker symbol.
Uses the configured news_data vendor.
Args:
ticker (str): Ticker symbol
start_date (str): Start date in yyyy-mm-dd format
end_date (str): End date in yyyy-mm-dd format
Returns:
str: A formatted string containing news data
"""
return route_to_vendor("get_news", ticker, start_date, end_date)
@tool
def get_global_news(
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
look_back_days: Annotated[int, "Number of days to look back"] = 7,
limit: Annotated[int, "Maximum number of articles to return"] = 5,
) -> str:
"""
Retrieve global news data.
Uses the configured news_data vendor.
Args:
curr_date (str): Current date in yyyy-mm-dd format
look_back_days (int): Number of days to look back (default 7)
limit (int): Maximum number of articles to return (default 5)
Returns:
str: A formatted string containing global news data
"""
return route_to_vendor("get_global_news", curr_date, look_back_days, limit)
@tool
def get_insider_transactions(
ticker: Annotated[str, "ticker symbol"],
) -> str:
"""
Retrieve insider transaction information about a company.
Uses the configured news_data vendor.
Args:
ticker (str): Ticker symbol of the company
Returns:
str: A report of insider transaction data
"""
return route_to_vendor("get_insider_transactions", ticker)
================================================
FILE: tradingagents/agents/utils/technical_indicators_tools.py
================================================
from langchain_core.tools import tool
from typing import Annotated
from tradingagents.dataflows.interface import route_to_vendor
@tool
def get_indicators(
symbol: Annotated[str, "ticker symbol of the company"],
indicator: Annotated[str, "technical indicator to get the analysis and report of"],
curr_date: Annotated[str, "The current trading date you are trading on, YYYY-mm-dd"],
look_back_days: Annotated[int, "how many days to look back"] = 30,
) -> str:
"""
Retrieve a single technical indicator for a given ticker symbol.
Uses the configured technical_indicators vendor.
Args:
symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
indicator (str): A single technical indicator name, e.g. 'rsi', 'macd'. Call this tool once per indicator.
curr_date (str): The current trading date you are trading on, YYYY-mm-dd
look_back_days (int): How many days to look back, default is 30
Returns:
str: A formatted dataframe containing the technical indicators for the specified ticker symbol and indicator.
"""
# LLMs sometimes pass multiple indicators as a comma-separated string;
# split and process each individually.
indicators = [i.strip() for i in indicator.split(",") if i.strip()]
if len(indicators) > 1:
results = []
for ind in indicators:
results.append(route_to_vendor("get_indicators", symbol, ind, curr_date, look_back_days))
return "\n\n".join(results)
return route_to_vendor("get_indicators", symbol, indicator.strip(), curr_date, look_back_days)
================================================
FILE: tradingagents/dataflows/__init__.py
================================================
================================================
FILE: tradingagents/dataflows/alpha_vantage.py
================================================
# Import functions from specialized modules
from .alpha_vantage_stock import get_stock
from .alpha_vantage_indicator import get_indicator
from .alpha_vantage_fundamentals import get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement
from .alpha_vantage_news import get_news, get_global_news, get_insider_transactions
================================================
FILE: tradingagents/dataflows/alpha_vantage_common.py
================================================
import os
import requests
import pandas as pd
import json
from datetime import datetime
from io import StringIO
API_BASE_URL = "https://www.alphavantage.co/query"
def get_api_key() -> str:
"""Retrieve the API key for Alpha Vantage from environment variables."""
api_key = os.getenv("ALPHA_VANTAGE_API_KEY")
if not api_key:
raise ValueError("ALPHA_VANTAGE_API_KEY environment variable is not set.")
return api_key
def format_datetime_for_api(date_input) -> str:
"""Convert various date formats to YYYYMMDDTHHMM format required by Alpha Vantage API."""
if isinstance(date_input, str):
# If already in correct format, return as-is
if len(date_input) == 13 and 'T' in date_input:
return date_input
# Try to parse common date formats
try:
dt = datetime.strptime(date_input, "%Y-%m-%d")
return dt.strftime("%Y%m%dT0000")
except ValueError:
try:
dt = datetime.strptime(date_input, "%Y-%m-%d %H:%M")
return dt.strftime("%Y%m%dT%H%M")
except ValueError:
raise ValueError(f"Unsupported date format: {date_input}")
elif isinstance(date_input, datetime):
return date_input.strftime("%Y%m%dT%H%M")
else:
raise ValueError(f"Date must be string or datetime object, got {type(date_input)}")
class AlphaVantageRateLimitError(Exception):
"""Exception raised when Alpha Vantage API rate limit is exceeded."""
pass
def _make_api_request(function_name: str, params: dict) -> dict | str:
"""Helper function to make API requests and handle responses.
Raises:
AlphaVantageRateLimitError: When API rate limit is exceeded
"""
# Create a copy of params to avoid modifying the original
api_params = params.copy()
api_params.update({
"function": function_name,
"apikey": get_api_key(),
"source": "trading_agents",
})
# Handle entitlement parameter if present in params or global variable
current_entitlement = globals().get('_current_entitlement')
entitlement = api_params.get("entitlement") or current_entitlement
if entitlement:
api_params["entitlement"] = entitlement
elif "entitlement" in api_params:
# Remove entitlement if it's None or empty
api_params.pop("entitlement", None)
response = requests.get(API_BASE_URL, params=api_params)
response.raise_for_status()
response_text = response.text
# Check if response is JSON (error responses are typically JSON)
try:
response_json = json.loads(response_text)
# Check for rate limit error
if "Information" in response_json:
info_message = response_json["Information"]
if "rate limit" in info_message.lower() or "api key" in info_message.lower():
raise AlphaVantageRateLimitError(f"Alpha Vantage rate limit exceeded: {info_message}")
except json.JSONDecodeError:
# Response is not JSON (likely CSV data), which is normal
pass
return response_text
def _filter_csv_by_date_range(csv_data: str, start_date: str, end_date: str) -> str:
"""
Filter CSV data to include only rows within the specified date range.
Args:
csv_data: CSV string from Alpha Vantage API
start_date: Start date in yyyy-mm-dd format
end_date: End date in yyyy-mm-dd format
Returns:
Filtered CSV string
"""
if not csv_data or csv_data.strip() == "":
return csv_data
try:
# Parse CSV data
df = pd.read_csv(StringIO(csv_data))
# Assume the first column is the date column (timestamp)
date_col = df.columns[0]
df[date_col] = pd.to_datetime(df[date_col])
# Filter by date range
start_dt = pd.to_datetime(start_date)
end_dt = pd.to_datetime(end_date)
filtered_df = df[(df[date_col] >= start_dt) & (df[date_col] <= end_dt)]
# Convert back to CSV string
return filtered_df.to_csv(index=False)
except Exception as e:
# If filtering fails, return original data with a warning
print(f"Warning: Failed to filter CSV data by date range: {e}")
return csv_data
================================================
FILE: tradingagents/dataflows/alpha_vantage_fundamentals.py
================================================
from .alpha_vantage_common import _make_api_request
def get_fundamentals(ticker: str, curr_date: str = None) -> str:
"""
Retrieve comprehensive fundamental data for a given ticker symbol using Alpha Vantage.
Args:
ticker (str): Ticker symbol of the company
curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage)
Returns:
str: Company overview data including financial ratios and key metrics
"""
params = {
"symbol": ticker,
}
return _make_api_request("OVERVIEW", params)
def get_balance_sheet(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str:
"""
Retrieve balance sheet data for a given ticker symbol using Alpha Vantage.
Args:
ticker (str): Ticker symbol of the company
freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage
curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage)
Returns:
str: Balance sheet data with normalized fields
"""
params = {
"symbol": ticker,
}
return _make_api_request("BALANCE_SHEET", params)
def get_cashflow(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str:
"""
Retrieve cash flow statement data for a given ticker symbol using Alpha Vantage.
Args:
ticker (str): Ticker symbol of the company
freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage
curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage)
Returns:
str: Cash flow statement data with normalized fields
"""
params = {
"symbol": ticker,
}
return _make_api_request("CASH_FLOW", params)
def get_income_statement(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str:
"""
Retrieve income statement data for a given ticker symbol using Alpha Vantage.
Args:
ticker (str): Ticker symbol of the company
freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage
curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage)
Returns:
str: Income statement data with normalized fields
"""
params = {
"symbol": ticker,
}
return _make_api_request("INCOME_STATEMENT", params)
================================================
FILE: tradingagents/dataflows/alpha_vantage_indicator.py
================================================
from .alpha_vantage_common import _make_api_request
def get_indicator(
symbol: str,
indicator: str,
curr_date: str,
look_back_days: int,
interval: str = "daily",
time_period: int = 14,
series_type: str = "close"
) -> str:
"""
Returns Alpha Vantage technical indicator values over a time window.
Args:
symbol: ticker symbol of the company
indicator: technical indicator to get the analysis and report of
curr_date: The current trading date you are trading on, YYYY-mm-dd
look_back_days: how many days to look back
interval: Time interval (daily, weekly, monthly)
time_period: Number of data points for calculation
series_type: The desired price type (close, open, high, low)
Returns:
String containing indicator values and description
"""
from datetime import datetime
from dateutil.relativedelta import relativedelta
supported_indicators = {
"close_50_sma": ("50 SMA", "close"),
"close_200_sma": ("200 SMA", "close"),
"close_10_ema": ("10 EMA", "close"),
"macd": ("MACD", "close"),
"macds": ("MACD Signal", "close"),
"macdh": ("MACD Histogram", "close"),
"rsi": ("RSI", "close"),
"boll": ("Bollinger Middle", "close"),
"boll_ub": ("Bollinger Upper Band", "close"),
"boll_lb": ("Bollinger Lower Band", "close"),
"atr": ("ATR", None),
"vwma": ("VWMA", "close")
}
indicator_descriptions = {
"close_50_sma": "50 SMA: A medium-term trend indicator. Usage: Identify trend direction and serve as dynamic support/resistance. Tips: It lags price; combine with faster indicators for timely signals.",
"close_200_sma": "200 SMA: A long-term trend benchmark. Usage: Confirm overall market trend and identify golden/death cross setups. Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries.",
"close_10_ema": "10 EMA: A responsive short-term average. Usage: Capture quick shifts in momentum and potential entry points. Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals.",
"macd": "MACD: Computes momentum via differences of EMAs. Usage: Look for crossovers and divergence as signals of trend changes. Tips: Confirm with other indicators in low-volatility or sideways markets.",
"macds": "MACD Signal: An EMA smoothing of the MACD line. Usage: Use crossovers with the MACD line to trigger trades. Tips: Should be part of a broader strategy to avoid false positives.",
"macdh": "MACD Histogram: Shows the gap between the MACD line and its signal. Usage: Visualize momentum strength and spot divergence early. Tips: Can be volatile; complement with additional filters in fast-moving markets.",
"rsi": "RSI: Measures momentum to flag overbought/oversold conditions. Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis.",
"boll": "Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. Usage: Acts as a dynamic benchmark for price movement. Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals.",
"boll_ub": "Bollinger Upper Band: Typically 2 standard deviations above the middle line. Usage: Signals potential overbought conditions and breakout zones. Tips: Confirm signals with other tools; prices may ride the band in strong trends.",
"boll_lb": "Bollinger Lower Band: Typically 2 standard deviations below the middle line. Usage: Indicates potential oversold conditions. Tips: Use additional analysis to avoid false reversal signals.",
"atr": "ATR: Averages true range to measure volatility. Usage: Set stop-loss levels and adjust position sizes based on current market volatility. Tips: It's a reactive measure, so use it as part of a broader risk management strategy.",
"vwma": "VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses."
}
if indicator not in supported_indicators:
raise ValueError(
f"Indicator {indicator} is not supported. Please choose from: {list(supported_indicators.keys())}"
)
curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
before = curr_date_dt - relativedelta(days=look_back_days)
# Get the full data for the period instead of making individual calls
_, required_series_type = supported_indicators[indicator]
# Use the provided series_type or fall back to the required one
if required_series_type:
series_type = required_series_type
try:
# Get indicator data for the period
if indicator == "close_50_sma":
data = _make_api_request("SMA", {
"symbol": symbol,
"interval": interval,
"time_period": "50",
"series_type": series_type,
"datatype": "csv"
})
elif indicator == "close_200_sma":
data = _make_api_request("SMA", {
"symbol": symbol,
"interval": interval,
"time_period": "200",
"series_type": series_type,
"datatype": "csv"
})
elif indicator == "close_10_ema":
data = _make_api_request("EMA", {
"symbol": symbol,
"interval": interval,
"time_period": "10",
"series_type": series_type,
"datatype": "csv"
})
elif indicator == "macd":
data = _make_api_request("MACD", {
"symbol": symbol,
"interval": interval,
"series_type": series_type,
"datatype": "csv"
})
elif indicator == "macds":
data = _make_api_request("MACD", {
"symbol": symbol,
"interval": interval,
"series_type": series_type,
"datatype": "csv"
})
elif indicator == "macdh":
data = _make_api_request("MACD", {
"symbol": symbol,
"interval": interval,
"series_type": series_type,
"datatype": "csv"
})
elif indicator == "rsi":
data = _make_api_request("RSI", {
"symbol": symbol,
"interval": interval,
"time_period": str(time_period),
"series_type": series_type,
"datatype": "csv"
})
elif indicator in ["boll", "boll_ub", "boll_lb"]:
data = _make_api_request("BBANDS", {
"symbol": symbol,
"interval": interval,
"time_period": "20",
"series_type": series_type,
"datatype": "csv"
})
elif indicator == "atr":
data = _make_api_request("ATR", {
"symbol": symbol,
"interval": interval,
"time_period": str(time_period),
"datatype": "csv"
})
elif indicator == "vwma":
# Alpha Vantage doesn't have direct VWMA, so we'll return an informative message
# In a real implementation, this would need to be calculated from OHLCV data
return f"## VWMA (Volume Weighted Moving Average) for {symbol}:\n\nVWMA calculation requires OHLCV data and is not directly available from Alpha Vantage API.\nThis indicator would need to be calculated from the raw stock data using volume-weighted price averaging.\n\n{indicator_descriptions.get('vwma', 'No description available.')}"
else:
return f"Error: Indicator {indicator} not implemented yet."
# Parse CSV data and extract values for the date range
lines = data.strip().split('\n')
if len(lines) < 2:
return f"Error: No data returned for {indicator}"
# Parse header and data
header = [col.strip() for col in lines[0].split(',')]
try:
date_col_idx = header.index('time')
except ValueError:
return f"Error: 'time' column not found in data for {indicator}. Available columns: {header}"
# Map internal indicator names to expected CSV column names from Alpha Vantage
col_name_map = {
"macd": "MACD", "macds": "MACD_Signal", "macdh": "MACD_Hist",
"boll": "Real Middle Band", "boll_ub": "Real Upper Band", "boll_lb": "Real Lower Band",
"rsi": "RSI", "atr": "ATR", "close_10_ema": "EMA",
"close_50_sma": "SMA", "close_200_sma": "SMA"
}
target_col_name = col_name_map.get(indicator)
if not target_col_name:
# Default to the second column if no specific mapping exists
value_col_idx = 1
else:
try:
value_col_idx = header.index(target_col_name)
except ValueError:
return f"Error: Column '{target_col_name}' not found for indicator '{indicator}'. Available columns: {header}"
result_data = []
for line in lines[1:]:
if not line.strip():
continue
values = line.split(',')
if len(values) > value_col_idx:
try:
date_str = values[date_col_idx].strip()
# Parse the date
date_dt = datetime.strptime(date_str, "%Y-%m-%d")
# Check if date is in our range
if before <= date_dt <= curr_date_dt:
value = values[value_col_idx].strip()
result_data.append((date_dt, value))
except (ValueError, IndexError):
continue
# Sort by date and format output
result_data.sort(key=lambda x: x[0])
ind_string = ""
for date_dt, value in result_data:
ind_string += f"{date_dt.strftime('%Y-%m-%d')}: {value}\n"
if not ind_string:
ind_string = "No data available for the specified date range.\n"
result_str = (
f"## {indicator.upper()} values from {before.strftime('%Y-%m-%d')} to {curr_date}:\n\n"
+ ind_string
+ "\n\n"
+ indicator_descriptions.get(indicator, "No description available.")
)
return result_str
except Exception as e:
print(f"Error getting Alpha Vantage indicator data for {indicator}: {e}")
return f"Error retrieving {indicator} data: {str(e)}"
================================================
FILE: tradingagents/dataflows/alpha_vantage_news.py
================================================
from .alpha_vantage_common import _make_api_request, format_datetime_for_api
def get_news(ticker, start_date, end_date) -> dict[str, str] | str:
"""Returns live and historical market news & sentiment data from premier news outlets worldwide.
Covers stocks, cryptocurrencies, forex, and topics like fiscal policy, mergers & acquisitions, IPOs.
Args:
ticker: Stock symbol for news articles.
start_date: Start date for news search.
end_date: End date for news search.
Returns:
Dictionary containing news sentiment data or JSON string.
"""
params = {
"tickers": ticker,
"time_from": format_datetime_for_api(start_date),
"time_to": format_datetime_for_api(end_date),
}
return _make_api_request("NEWS_SENTIMENT", params)
def get_global_news(curr_date, look_back_days: int = 7, limit: int = 50) -> dict[str, str] | str:
"""Returns global market news & sentiment data without ticker-specific filtering.
Covers broad market topics like financial markets, economy, and more.
Args:
curr_date: Current date in yyyy-mm-dd format.
look_back_days: Number of days to look back (default 7).
limit: Maximum number of articles (default 50).
Returns:
Dictionary containing global news sentiment data or JSON string.
"""
from datetime import datetime, timedelta
# Calculate start date
curr_dt = datetime.strptime(curr_date, "%Y-%m-%d")
start_dt = curr_dt - timedelta(days=look_back_days)
start_date = start_dt.strftime("%Y-%m-%d")
params = {
"topics": "financial_markets,economy_macro,economy_monetary",
"time_from": format_datetime_for_api(start_date),
"time_to": format_datetime_for_api(curr_date),
"limit": str(limit),
}
return _make_api_request("NEWS_SENTIMENT", params)
def get_insider_transactions(symbol: str) -> dict[str, str] | str:
"""Returns latest and historical insider transactions by key stakeholders.
Covers transactions by founders, executives, board members, etc.
Args:
symbol: Ticker symbol. Example: "IBM".
Returns:
Dictionary containing insider transaction data or JSON string.
"""
params = {
"symbol": symbol,
}
return _make_api_request("INSIDER_TRANSACTIONS", params)
================================================
FILE: tradingagents/dataflows/alpha_vantage_stock.py
================================================
from datetime import datetime
from .alpha_vantage_common import _make_api_request, _filter_csv_by_date_range
def get_stock(
symbol: str,
start_date: str,
end_date: str
) -> str:
"""
Returns raw daily OHLCV values, adjusted close values, and historical split/dividend events
filtered to the specified date range.
Args:
symbol: The name of the equity. For example: symbol=IBM
start_date: Start date in yyyy-mm-dd format
end_date: End date in yyyy-mm-dd format
Returns:
CSV string containing the daily adjusted time series data filtered to the date range.
"""
# Parse dates to determine the range
start_dt = datetime.strptime(start_date, "%Y-%m-%d")
today = datetime.now()
# Choose outputsize based on whether the requested range is within the latest 100 days
# Compact returns latest 100 data points, so check if start_date is recent enough
days_from_today_to_start = (today - start_dt).days
outputsize = "compact" if days_from_today_to_start < 100 else "full"
params = {
"symbol": symbol,
"outputsize": outputsize,
"datatype": "csv",
}
response = _make_api_request("TIME_SERIES_DAILY_ADJUSTED", params)
return _filter_csv_by_date_range(response, start_date, end_date)
================================================
FILE: tradingagents/dataflows/config.py
================================================
import tradingagents.default_config as default_config
from typing import Dict, Optional
# Use default config but allow it to be overridden
_config: Optional[Dict] = None
def initialize_config():
"""Initialize the configuration with default values."""
global _config
if _config is None:
_config = default_config.DEFAULT_CONFIG.copy()
def set_config(config: Dict):
"""Update the configuration with custom values."""
global _config
if _config is None:
_config = default_config.DEFAULT_CONFIG.copy()
_config.update(config)
def get_config() -> Dict:
"""Get the current configuration."""
if _config is None:
initialize_config()
return _config.copy()
# Initialize with default config
initialize_config()
================================================
FILE: tradingagents/dataflows/interface.py
================================================
from typing import Annotated
# Import from vendor-specific modules
from .y_finance import (
get_YFin_data_online,
get_stock_stats_indicators_window,
get_fundamentals as get_yfinance_fundamentals,
get_balance_sheet as get_yfinance_balance_sheet,
get_cashflow as get_yfinance_cashflow,
get_income_statement as get_yfinance_income_statement,
get_insider_transactions as get_yfinance_insider_transactions,
)
from .yfinance_news import get_news_yfinance, get_global_news_yfinance
from .alpha_vantage import (
get_stock as get_alpha_vantage_stock,
get_indicator as get_alpha_vantage_indicator,
get_fundamentals as get_alpha_vantage_fundamentals,
get_balance_sheet as get_alpha_vantage_balance_sheet,
get_cashflow as get_alpha_vantage_cashflow,
get_income_statement as get_alpha_vantage_income_statement,
get_insider_transactions as get_alpha_vantage_insider_transactions,
get_news as get_alpha_vantage_news,
get_global_news as get_alpha_vantage_global_news,
)
from .alpha_vantage_common import AlphaVantageRateLimitError
# Configuration and routing logic
from .config import get_config
# Tools organized by category
TOOLS_CATEGORIES = {
"core_stock_apis": {
"description": "OHLCV stock price data",
"tools": [
"get_stock_data"
]
},
"technical_indicators": {
"description": "Technical analysis indicators",
"tools": [
"get_indicators"
]
},
"fundamental_data": {
"description": "Company fundamentals",
"tools": [
"get_fundamentals",
"get_balance_sheet",
"get_cashflow",
"get_income_statement"
]
},
"news_data": {
"description": "News and insider data",
"tools": [
"get_news",
"get_global_news",
"get_insider_transactions",
]
}
}
VENDOR_LIST = [
"yfinance",
"alpha_vantage",
]
# Mapping of methods to their vendor-specific implementations
VENDOR_METHODS = {
# core_stock_apis
"get_stock_data": {
"alpha_vantage": get_alpha_vantage_stock,
"yfinance": get_YFin_data_online,
},
# technical_indicators
"get_indicators": {
"alpha_vantage": get_alpha_vantage_indicator,
"yfinance": get_stock_stats_indicators_window,
},
# fundamental_data
"get_fundamentals": {
"alpha_vantage": get_alpha_vantage_fundamentals,
"yfinance": get_yfinance_fundamentals,
},
"get_balance_sheet": {
"alpha_vantage": get_alpha_vantage_balance_sheet,
"yfinance": get_yfinance_balance_sheet,
},
"get_cashflow": {
"alpha_vantage": get_alpha_vantage_cashflow,
"yfinance": get_yfinance_cashflow,
},
"get_income_statement": {
"alpha_vantage": get_alpha_vantage_income_statement,
"yfinance": get_yfinance_income_statement,
},
# news_data
"get_news": {
"alpha_vantage": get_alpha_vantage_news,
"yfinance": get_news_yfinance,
},
"get_global_news": {
"yfinance": get_global_news_yfinance,
"alpha_vantage": get_alpha_vantage_global_news,
},
"get_insider_transactions": {
"alpha_vantage": get_alpha_vantage_insider_transactions,
"yfinance": get_yfinance_insider_transactions,
},
}
def get_category_for_method(method: str) -> str:
"""Get the category that contains the specified method."""
for category, info in TOOLS_CATEGORIES.items():
if method in info["tools"]:
return category
raise ValueError(f"Method '{method}' not found in any category")
def get_vendor(category: str, method: str = None) -> str:
"""Get the configured vendor for a data category or specific tool method.
Tool-level configuration takes precedence over category-level.
"""
config = get_config()
# Check tool-level configuration first (if method provided)
if method:
tool_vendors = config.get("tool_vendors", {})
if method in tool_vendors:
return tool_vendors[method]
# Fall back to category-level configuration
return config.get("data_vendors", {}).get(category, "default")
def route_to_vendor(method: str, *args, **kwargs):
"""Route method calls to appropriate vendor implementation with fallback support."""
category = get_category_for_method(method)
vendor_config = get_vendor(category, method)
primary_vendors = [v.strip() for v in vendor_config.split(',')]
if method not in VENDOR_METHODS:
raise ValueError(f"Method '{method}' not supported")
# Build fallback chain: primary vendors first, then remaining available vendors
all_available_vendors = list(VENDOR_METHODS[method].keys())
fallback_vendors = primary_vendors.copy()
for vendor in all_available_vendors:
if vendor not in fallback_vendors:
fallback_vendors.append(vendor)
for vendor in fallback_vendors:
if vendor not in VENDOR_METHODS[method]:
continue
vendor_impl = VENDOR_METHODS[method][vendor]
impl_func = vendor_impl[0] if isinstance(vendor_impl, list) else vendor_impl
try:
return impl_func(*args, **kwargs)
except AlphaVantageRateLimitError:
continue # Only rate limits trigger fallback
raise RuntimeError(f"No available vendor for '{method}'")
================================================
FILE: tradingagents/dataflows/stockstats_utils.py
================================================
import pandas as pd
import yfinance as yf
from stockstats import wrap
from typing import Annotated
import os
from .config import get_config
def _clean_dataframe(data: pd.DataFrame) -> pd.DataFrame:
"""Normalize a stock DataFrame for stockstats: parse dates, drop invalid rows, fill price gaps."""
data["Date"] = pd.to_datetime(data["Date"], errors="coerce")
data = data.dropna(subset=["Date"])
price_cols = [c for c in ["Open", "High", "Low", "Close", "Volume"] if c in data.columns]
data[price_cols] = data[price_cols].apply(pd.to_numeric, errors="coerce")
data = data.dropna(subset=["Close"])
data[price_cols] = data[price_cols].ffill().bfill()
return data
class StockstatsUtils:
@staticmethod
def get_stock_stats(
symbol: Annotated[str, "ticker symbol for the company"],
indicator: Annotated[
str, "quantitative indicators based off of the stock data for the company"
],
curr_date: Annotated[
str, "curr date for retrieving stock price data, YYYY-mm-dd"
],
):
config = get_config()
today_date = pd.Timestamp.today()
curr_date_dt = pd.to_datetime(curr_date)
end_date = today_date
start_date = today_date - pd.DateOffset(years=15)
start_date_str = start_date.strftime("%Y-%m-%d")
end_date_str = end_date.strftime("%Y-%m-%d")
# Ensure cache directory exists
os.makedirs(config["data_cache_dir"], exist_ok=True)
data_file = os.path.join(
config["data_cache_dir"],
f"{symbol}-YFin-data-{start_date_str}-{end_date_str}.csv",
)
if os.path.exists(data_file):
data = pd.read_csv(data_file, on_bad_lines="skip")
else:
data = yf.download(
symbol,
start=start_date_str,
end=end_date_str,
multi_level_index=False,
progress=False,
auto_adjust=True,
)
data = data.reset_index()
data.to_csv(data_file, index=False)
data = _clean_dataframe(data)
df = wrap(data)
df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
curr_date_str = curr_date_dt.strftime("%Y-%m-%d")
df[indicator] # trigger stockstats to calculate the indicator
matching_rows = df[df["Date"].str.startswith(curr_date_str)]
if not matching_rows.empty:
indicator_value = matching_rows[indicator].values[0]
return indicator_value
else:
return "N/A: Not a trading day (weekend or holiday)"
================================================
FILE: tradingagents/dataflows/utils.py
================================================
import os
import json
import pandas as pd
from datetime import date, timedelta, datetime
from typing import Annotated
SavePathType = Annotated[str, "File path to save data. If None, data is not saved."]
def save_output(data: pd.DataFrame, tag: str, save_path: SavePathType = None) -> None:
if save_path:
data.to_csv(save_path)
print(f"{tag} saved to {save_path}")
def get_current_date():
return date.today().strftime("%Y-%m-%d")
def decorate_all_methods(decorator):
def class_decorator(cls):
for attr_name, attr_value in cls.__dict__.items():
if callable(attr_value):
setattr(cls, attr_name, decorator(attr_value))
return cls
return class_decorator
def get_next_weekday(date):
if not isinstance(date, datetime):
date = datetime.strptime(date, "%Y-%m-%d")
if date.weekday() >= 5:
days_to_add = 7 - date.weekday()
next_weekday = date + timedelta(days=days_to_add)
return next_weekday
else:
return date
================================================
FILE: tradingagents/dataflows/y_finance.py
================================================
from typing import Annotated
from datetime import datetime
from dateutil.relativedelta import relativedelta
import yfinance as yf
import os
from .stockstats_utils import StockstatsUtils, _clean_dataframe
def get_YFin_data_online(
symbol: Annotated[str, "ticker symbol of the company"],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
):
datetime.strptime(start_date, "%Y-%m-%d")
datetime.strptime(end_date, "%Y-%m-%d")
# Create ticker object
ticker = yf.Ticker(symbol.upper())
# Fetch historical data for the specified date range
data = ticker.history(start=start_date, end=end_date)
# Check if data is empty
if data.empty:
return (
f"No data found for symbol '{symbol}' between {start_date} and {end_date}"
)
# Remove timezone info from index for cleaner output
if data.index.tz is not None:
data.index = data.index.tz_localize(None)
# Round numerical values to 2 decimal places for cleaner display
numeric_columns = ["Open", "High", "Low", "Close", "Adj Close"]
for col in numeric_columns:
if col in data.columns:
data[col] = data[col].round(2)
# Convert DataFrame to CSV string
csv_string = data.to_csv()
# Add header information
header = f"# Stock data for {symbol.upper()} from {start_date} to {end_date}\n"
header += f"# Total records: {len(data)}\n"
header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
return header + csv_string
def get_stock_stats_indicators_window(
symbol: Annotated[str, "ticker symbol of the company"],
indicator: Annotated[str, "technical indicator to get the analysis and report of"],
curr_date: Annotated[
str, "The current trading date you are trading on, YYYY-mm-dd"
],
look_back_days: Annotated[int, "how many days to look back"],
) -> str:
best_ind_params = {
# Moving Averages
"close_50_sma": (
"50 SMA: A medium-term trend indicator. "
"Usage: Identify trend direction and serve as dynamic support/resistance. "
"Tips: It lags price; combine with faster indicators for timely signals."
),
"close_200_sma": (
"200 SMA: A long-term trend benchmark. "
"Usage: Confirm overall market trend and identify golden/death cross setups. "
"Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries."
),
"close_10_ema": (
"10 EMA: A responsive short-term average. "
"Usage: Capture quick shifts in momentum and potential entry points. "
"Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals."
),
# MACD Related
"macd": (
"MACD: Computes momentum via differences of EMAs. "
"Usage: Look for crossovers and divergence as signals of trend changes. "
"Tips: Confirm with other indicators in low-volatility or sideways markets."
),
"macds": (
"MACD Signal: An EMA smoothing of the MACD line. "
"Usage: Use crossovers with the MACD line to trigger trades. "
"Tips: Should be part of a broader strategy to avoid false positives."
),
"macdh": (
"MACD Histogram: Shows the gap between the MACD line and its signal. "
"Usage: Visualize momentum strength and spot divergence early. "
"Tips: Can be volatile; complement with additional filters in fast-moving markets."
),
# Momentum Indicators
"rsi": (
"RSI: Measures momentum to flag overbought/oversold conditions. "
"Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. "
"Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis."
),
# Volatility Indicators
"boll": (
"Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. "
"Usage: Acts as a dynamic benchmark for price movement. "
"Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals."
),
"boll_ub": (
"Bollinger Upper Band: Typically 2 standard deviations above the middle line. "
"Usage: Signals potential overbought conditions and breakout zones. "
"Tips: Confirm signals with other tools; prices may ride the band in strong trends."
),
"boll_lb": (
"Bollinger Lower Band: Typically 2 standard deviations below the middle line. "
"Usage: Indicates potential oversold conditions. "
"Tips: Use additional analysis to avoid false reversal signals."
),
"atr": (
"ATR: Averages true range to measure volatility. "
"Usage: Set stop-loss levels and adjust position sizes based on current market volatility. "
"Tips: It's a reactive measure, so use it as part of a broader risk management strategy."
),
# Volume-Based Indicators
"vwma": (
"VWMA: A moving average weighted by volume. "
"Usage: Confirm trends by integrating price action with volume data. "
"Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses."
),
"mfi": (
"MFI: The Money Flow Index is a momentum indicator that uses both price and volume to measure buying and selling pressure. "
"Usage: Identify overbought (>80) or oversold (<20) conditions and confirm the strength of trends or reversals. "
"Tips: Use alongside RSI or MACD to confirm signals; divergence between price and MFI can indicate potential reversals."
),
}
if indicator not in best_ind_params:
raise ValueError(
f"Indicator {indicator} is not supported. Please choose from: {list(best_ind_params.keys())}"
)
end_date = curr_date
curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
before = curr_date_dt - relativedelta(days=look_back_days)
# Optimized: Get stock data once and calculate indicators for all dates
try:
indicator_data = _get_stock_stats_bulk(symbol, indicator, curr_date)
# Generate the date range we need
current_dt = curr_date_dt
date_values = []
while current_dt >= before:
date_str = current_dt.strftime('%Y-%m-%d')
# Look up the indicator value for this date
if date_str in indicator_data:
indicator_value = indicator_data[date_str]
else:
indicator_value = "N/A: Not a trading day (weekend or holiday)"
date_values.append((date_str, indicator_value))
current_dt = current_dt - relativedelta(days=1)
# Build the result string
ind_string = ""
for date_str, value in date_values:
ind_string += f"{date_str}: {value}\n"
except Exception as e:
print(f"Error getting bulk stockstats data: {e}")
# Fallback to original implementation if bulk method fails
ind_string = ""
curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
while curr_date_dt >= before:
indicator_value = get_stockstats_indicator(
symbol, indicator, curr_date_dt.strftime("%Y-%m-%d")
)
ind_string += f"{curr_date_dt.strftime('%Y-%m-%d')}: {indicator_value}\n"
curr_date_dt = curr_date_dt - relativedelta(days=1)
result_str = (
f"## {indicator} values from {before.strftime('%Y-%m-%d')} to {end_date}:\n\n"
+ ind_string
+ "\n\n"
+ best_ind_params.get(indicator, "No description available.")
)
return result_str
def _get_stock_stats_bulk(
symbol: Annotated[str, "ticker symbol of the company"],
indicator: Annotated[str, "technical indicator to calculate"],
gitextract_e7yyz3y3/
├── .gitignore
├── LICENSE
├── README.md
├── cli/
│ ├── __init__.py
│ ├── announcements.py
│ ├── config.py
│ ├── main.py
│ ├── models.py
│ ├── static/
│ │ └── welcome.txt
│ ├── stats_handler.py
│ └── utils.py
├── main.py
├── pyproject.toml
├── requirements.txt
├── test.py
└── tradingagents/
├── __init__.py
├── agents/
│ ├── __init__.py
│ ├── analysts/
│ │ ├── fundamentals_analyst.py
│ │ ├── market_analyst.py
│ │ ├── news_analyst.py
│ │ └── social_media_analyst.py
│ ├── managers/
│ │ ├── research_manager.py
│ │ └── risk_manager.py
│ ├── researchers/
│ │ ├── bear_researcher.py
│ │ └── bull_researcher.py
│ ├── risk_mgmt/
│ │ ├── aggressive_debator.py
│ │ ├── conservative_debator.py
│ │ └── neutral_debator.py
│ ├── trader/
│ │ └── trader.py
│ └── utils/
│ ├── agent_states.py
│ ├── agent_utils.py
│ ├── core_stock_tools.py
│ ├── fundamental_data_tools.py
│ ├── memory.py
│ ├── news_data_tools.py
│ └── technical_indicators_tools.py
├── dataflows/
│ ├── __init__.py
│ ├── alpha_vantage.py
│ ├── alpha_vantage_common.py
│ ├── alpha_vantage_fundamentals.py
│ ├── alpha_vantage_indicator.py
│ ├── alpha_vantage_news.py
│ ├── alpha_vantage_stock.py
│ ├── config.py
│ ├── interface.py
│ ├── stockstats_utils.py
│ ├── utils.py
│ ├── y_finance.py
│ └── yfinance_news.py
├── default_config.py
├── graph/
│ ├── __init__.py
│ ├── conditional_logic.py
│ ├── propagation.py
│ ├── reflection.py
│ ├── setup.py
│ ├── signal_processing.py
│ └── trading_graph.py
└── llm_clients/
├── TODO.md
├── __init__.py
├── anthropic_client.py
├── base_client.py
├── factory.py
├── google_client.py
├── openai_client.py
└── validators.py
SYMBOL INDEX (174 symbols across 47 files)
FILE: cli/announcements.py
function fetch_announcements (line 9) | def fetch_announcements(url: str = None, timeout: float = None) -> dict:
function display_announcements (line 30) | def display_announcements(console: Console, data: dict) -> None:
FILE: cli/main.py
class MessageBuffer (line 43) | class MessageBuffer:
method __init__ (line 73) | def __init__(self, max_length=100):
method init_for_analysis (line 84) | def init_for_analysis(self, selected_analysts):
method get_completed_reports_count (line 119) | def get_completed_reports_count(self):
method add_message (line 140) | def add_message(self, message_type, content):
method add_tool_call (line 144) | def add_tool_call(self, tool_name, args):
method update_agent_status (line 148) | def update_agent_status(self, agent, status):
method update_report_section (line 153) | def update_report_section(self, section_name, content):
method _update_current_report (line 158) | def _update_current_report(self):
method _update_final_report (line 187) | def _update_final_report(self):
function create_layout (line 232) | def create_layout():
function format_tokens (line 248) | def format_tokens(n):
function update_display (line 255) | def update_display(layout, spinner_text=None, stats_handler=None, start_...
function get_user_selections (line 462) | def get_user_selections():
function get_ticker (line 592) | def get_ticker():
function get_analysis_date (line 597) | def get_analysis_date():
function save_report_to_disk (line 616) | def save_report_to_disk(final_state, ticker: str, save_path: Path):
function display_complete_report (line 706) | def display_complete_report(final_state):
function update_research_team_status (line 767) | def update_research_team_status(status):
function update_analyst_statuses (line 790) | def update_analyst_statuses(message_buffer, chunk):
function extract_content_string (line 824) | def extract_content_string(content):
function classify_message_type (line 866) | def classify_message_type(message) -> tuple[str, str | None]:
function format_tool_args (line 892) | def format_tool_args(args, max_length=80) -> str:
function run_analysis (line 899) | def run_analysis():
function analyze (line 1171) | def analyze():
FILE: cli/models.py
class AnalystType (line 6) | class AnalystType(str, Enum):
FILE: cli/stats_handler.py
class StatsCallbackHandler (line 9) | class StatsCallbackHandler(BaseCallbackHandler):
method __init__ (line 12) | def __init__(self) -> None:
method on_llm_start (line 20) | def on_llm_start(
method on_chat_model_start (line 30) | def on_chat_model_start(
method on_llm_end (line 40) | def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
method on_tool_start (line 58) | def on_tool_start(
method get_stats (line 68) | def get_stats(self) -> Dict[str, Any]:
FILE: cli/utils.py
function get_ticker (line 18) | def get_ticker() -> str:
function get_analysis_date (line 38) | def get_analysis_date() -> str:
function select_analysts (line 71) | def select_analysts() -> List[AnalystType]:
function select_research_depth (line 97) | def select_research_depth() -> int:
function select_shallow_thinking_agent (line 129) | def select_shallow_thinking_agent(provider) -> str:
function select_deep_thinking_agent (line 194) | def select_deep_thinking_agent(provider) -> str:
function select_llm_provider (line 258) | def select_llm_provider() -> tuple[str, str]:
function ask_openai_reasoning_effort (line 296) | def ask_openai_reasoning_effort() -> str:
function ask_gemini_thinking_config (line 314) | def ask_gemini_thinking_config() -> str | None:
FILE: tradingagents/agents/analysts/fundamentals_analyst.py
function create_fundamentals_analyst (line 8) | def create_fundamentals_analyst(llm):
FILE: tradingagents/agents/analysts/market_analyst.py
function create_market_analyst (line 8) | def create_market_analyst(llm):
FILE: tradingagents/agents/analysts/news_analyst.py
function create_news_analyst (line 8) | def create_news_analyst(llm):
FILE: tradingagents/agents/analysts/social_media_analyst.py
function create_social_media_analyst (line 8) | def create_social_media_analyst(llm):
FILE: tradingagents/agents/managers/research_manager.py
function create_research_manager (line 5) | def create_research_manager(llm, memory):
FILE: tradingagents/agents/managers/risk_manager.py
function create_risk_manager (line 5) | def create_risk_manager(llm, memory):
FILE: tradingagents/agents/researchers/bear_researcher.py
function create_bear_researcher (line 6) | def create_bear_researcher(llm, memory):
FILE: tradingagents/agents/researchers/bull_researcher.py
function create_bull_researcher (line 6) | def create_bull_researcher(llm, memory):
FILE: tradingagents/agents/risk_mgmt/aggressive_debator.py
function create_aggressive_debator (line 5) | def create_aggressive_debator(llm):
FILE: tradingagents/agents/risk_mgmt/conservative_debator.py
function create_conservative_debator (line 6) | def create_conservative_debator(llm):
FILE: tradingagents/agents/risk_mgmt/neutral_debator.py
function create_neutral_debator (line 5) | def create_neutral_debator(llm):
FILE: tradingagents/agents/trader/trader.py
function create_trader (line 6) | def create_trader(llm, memory):
FILE: tradingagents/agents/utils/agent_states.py
class InvestDebateState (line 11) | class InvestDebateState(TypedDict):
class RiskDebateState (line 25) | class RiskDebateState(TypedDict):
class AgentState (line 50) | class AgentState(MessagesState):
FILE: tradingagents/agents/utils/agent_utils.py
function create_msg_delete (line 22) | def create_msg_delete():
FILE: tradingagents/agents/utils/core_stock_tools.py
function get_stock_data (line 7) | def get_stock_data(
FILE: tradingagents/agents/utils/fundamental_data_tools.py
function get_fundamentals (line 7) | def get_fundamentals(
function get_balance_sheet (line 24) | def get_balance_sheet(
function get_cashflow (line 43) | def get_cashflow(
function get_income_statement (line 62) | def get_income_statement(
FILE: tradingagents/agents/utils/memory.py
class FinancialSituationMemory (line 12) | class FinancialSituationMemory:
method __init__ (line 15) | def __init__(self, name: str, config: dict = None):
method _tokenize (line 27) | def _tokenize(self, text: str) -> List[str]:
method _rebuild_index (line 36) | def _rebuild_index(self):
method add_situations (line 44) | def add_situations(self, situations_and_advice: List[Tuple[str, str]]):
method get_memories (line 57) | def get_memories(self, current_situation: str, n_matches: int = 1) -> ...
method clear (line 94) | def clear(self):
FILE: tradingagents/agents/utils/news_data_tools.py
function get_news (line 6) | def get_news(
function get_global_news (line 24) | def get_global_news(
function get_insider_transactions (line 42) | def get_insider_transactions(
FILE: tradingagents/agents/utils/technical_indicators_tools.py
function get_indicators (line 6) | def get_indicators(
FILE: tradingagents/dataflows/alpha_vantage_common.py
function get_api_key (line 10) | def get_api_key() -> str:
function format_datetime_for_api (line 17) | def format_datetime_for_api(date_input) -> str:
class AlphaVantageRateLimitError (line 38) | class AlphaVantageRateLimitError(Exception):
function _make_api_request (line 42) | def _make_api_request(function_name: str, params: dict) -> dict | str:
function _filter_csv_by_date_range (line 87) | def _filter_csv_by_date_range(csv_data: str, start_date: str, end_date: ...
FILE: tradingagents/dataflows/alpha_vantage_fundamentals.py
function get_fundamentals (line 4) | def get_fundamentals(ticker: str, curr_date: str = None) -> str:
function get_balance_sheet (line 22) | def get_balance_sheet(ticker: str, freq: str = "quarterly", curr_date: s...
function get_cashflow (line 41) | def get_cashflow(ticker: str, freq: str = "quarterly", curr_date: str = ...
function get_income_statement (line 60) | def get_income_statement(ticker: str, freq: str = "quarterly", curr_date...
FILE: tradingagents/dataflows/alpha_vantage_indicator.py
function get_indicator (line 3) | def get_indicator(
FILE: tradingagents/dataflows/alpha_vantage_news.py
function get_news (line 3) | def get_news(ticker, start_date, end_date) -> dict[str, str] | str:
function get_global_news (line 25) | def get_global_news(curr_date, look_back_days: int = 7, limit: int = 50)...
function get_insider_transactions (line 55) | def get_insider_transactions(symbol: str) -> dict[str, str] | str:
FILE: tradingagents/dataflows/alpha_vantage_stock.py
function get_stock (line 4) | def get_stock(
FILE: tradingagents/dataflows/config.py
function initialize_config (line 8) | def initialize_config():
function set_config (line 15) | def set_config(config: Dict):
function get_config (line 23) | def get_config() -> Dict:
FILE: tradingagents/dataflows/interface.py
function get_category_for_method (line 112) | def get_category_for_method(method: str) -> str:
function get_vendor (line 119) | def get_vendor(category: str, method: str = None) -> str:
function route_to_vendor (line 134) | def route_to_vendor(method: str, *args, **kwargs):
FILE: tradingagents/dataflows/stockstats_utils.py
function _clean_dataframe (line 9) | def _clean_dataframe(data: pd.DataFrame) -> pd.DataFrame:
class StockstatsUtils (line 22) | class StockstatsUtils:
method get_stock_stats (line 24) | def get_stock_stats(
FILE: tradingagents/dataflows/utils.py
function save_output (line 9) | def save_output(data: pd.DataFrame, tag: str, save_path: SavePathType = ...
function get_current_date (line 15) | def get_current_date():
function decorate_all_methods (line 19) | def decorate_all_methods(decorator):
function get_next_weekday (line 29) | def get_next_weekday(date):
FILE: tradingagents/dataflows/y_finance.py
function get_YFin_data_online (line 8) | def get_YFin_data_online(
function get_stock_stats_indicators_window (line 49) | def get_stock_stats_indicators_window(
function _get_stock_stats_bulk (line 187) | def _get_stock_stats_bulk(
function get_stockstats_indicator (line 270) | def get_stockstats_indicator(
function get_fundamentals (line 296) | def get_fundamentals(
function get_balance_sheet (line 353) | def get_balance_sheet(
function get_cashflow (line 383) | def get_cashflow(
function get_income_statement (line 413) | def get_income_statement(
function get_insider_transactions (line 443) | def get_insider_transactions(
FILE: tradingagents/dataflows/yfinance_news.py
function _extract_article_data (line 8) | def _extract_article_data(article: dict) -> dict:
function get_news_yfinance (line 49) | def get_news_yfinance(
function get_global_news_yfinance (line 105) | def get_global_news_yfinance(
FILE: tradingagents/graph/conditional_logic.py
class ConditionalLogic (line 6) | class ConditionalLogic:
method __init__ (line 9) | def __init__(self, max_debate_rounds=1, max_risk_discuss_rounds=1):
method should_continue_market (line 14) | def should_continue_market(self, state: AgentState):
method should_continue_social (line 22) | def should_continue_social(self, state: AgentState):
method should_continue_news (line 30) | def should_continue_news(self, state: AgentState):
method should_continue_fundamentals (line 38) | def should_continue_fundamentals(self, state: AgentState):
method should_continue_debate (line 46) | def should_continue_debate(self, state: AgentState) -> str:
method should_continue_risk_analysis (line 57) | def should_continue_risk_analysis(self, state: AgentState) -> str:
FILE: tradingagents/graph/propagation.py
class Propagator (line 11) | class Propagator:
method __init__ (line 14) | def __init__(self, max_recur_limit=100):
method create_initial_state (line 18) | def create_initial_state(
method get_graph_args (line 56) | def get_graph_args(self, callbacks: Optional[List] = None) -> Dict[str...
FILE: tradingagents/graph/reflection.py
class Reflector (line 7) | class Reflector:
method __init__ (line 10) | def __init__(self, quick_thinking_llm: ChatOpenAI):
method _get_reflection_prompt (line 15) | def _get_reflection_prompt(self) -> str:
method _extract_current_situation (line 49) | def _extract_current_situation(self, current_state: Dict[str, Any]) ->...
method _reflect_on_component (line 58) | def _reflect_on_component(
method reflect_bull_researcher (line 73) | def reflect_bull_researcher(self, current_state, returns_losses, bull_...
method reflect_bear_researcher (line 83) | def reflect_bear_researcher(self, current_state, returns_losses, bear_...
method reflect_trader (line 93) | def reflect_trader(self, current_state, returns_losses, trader_memory):
method reflect_invest_judge (line 103) | def reflect_invest_judge(self, current_state, returns_losses, invest_j...
method reflect_risk_manager (line 113) | def reflect_risk_manager(self, current_state, returns_losses, risk_man...
FILE: tradingagents/graph/setup.py
class GraphSetup (line 14) | class GraphSetup:
method __init__ (line 17) | def __init__(
method setup_graph (line 40) | def setup_graph(
FILE: tradingagents/graph/signal_processing.py
class SignalProcessor (line 6) | class SignalProcessor:
method __init__ (line 9) | def __init__(self, quick_thinking_llm: ChatOpenAI):
method process_signal (line 13) | def process_signal(self, full_signal: str) -> str:
FILE: tradingagents/graph/trading_graph.py
class TradingAgentsGraph (line 43) | class TradingAgentsGraph:
method __init__ (line 46) | def __init__(
method _get_provider_kwargs (line 136) | def _get_provider_kwargs(self) -> Dict[str, Any]:
method _create_tool_nodes (line 153) | def _create_tool_nodes(self) -> Dict[str, ToolNode]:
method propagate (line 189) | def propagate(self, company_name, trade_date):
method _log_state (line 224) | def _log_state(self, trade_date, final_state):
method reflect_and_remember (line 267) | def reflect_and_remember(self, returns_losses):
method process_signal (line 285) | def process_signal(self, full_signal):
FILE: tradingagents/llm_clients/anthropic_client.py
class AnthropicClient (line 9) | class AnthropicClient(BaseLLMClient):
method __init__ (line 12) | def __init__(self, model: str, base_url: Optional[str] = None, **kwargs):
method get_llm (line 15) | def get_llm(self) -> Any:
method validate_model (line 25) | def validate_model(self) -> bool:
FILE: tradingagents/llm_clients/base_client.py
class BaseLLMClient (line 5) | class BaseLLMClient(ABC):
method __init__ (line 8) | def __init__(self, model: str, base_url: Optional[str] = None, **kwargs):
method get_llm (line 14) | def get_llm(self) -> Any:
method validate_model (line 19) | def validate_model(self) -> bool:
FILE: tradingagents/llm_clients/factory.py
function create_llm_client (line 9) | def create_llm_client(
FILE: tradingagents/llm_clients/google_client.py
class NormalizedChatGoogleGenerativeAI (line 9) | class NormalizedChatGoogleGenerativeAI(ChatGoogleGenerativeAI):
method _normalize_content (line 16) | def _normalize_content(self, response):
method invoke (line 27) | def invoke(self, input, config=None, **kwargs):
class GoogleClient (line 31) | class GoogleClient(BaseLLMClient):
method __init__ (line 34) | def __init__(self, model: str, base_url: Optional[str] = None, **kwargs):
method get_llm (line 37) | def get_llm(self) -> Any:
method validate_model (line 63) | def validate_model(self) -> bool:
FILE: tradingagents/llm_clients/openai_client.py
class UnifiedChatOpenAI (line 10) | class UnifiedChatOpenAI(ChatOpenAI):
method __init__ (line 22) | def __init__(self, **kwargs):
class OpenAIClient (line 29) | class OpenAIClient(BaseLLMClient):
method __init__ (line 32) | def __init__(
method get_llm (line 42) | def get_llm(self) -> Any:
method validate_model (line 68) | def validate_model(self) -> bool:
FILE: tradingagents/llm_clients/validators.py
function validate_model (line 54) | def validate_model(provider: str, model: str) -> bool:
Condensed preview — 65 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (269K chars).
[
{
"path": ".gitignore",
"chars": 4586,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[codz]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packag"
},
{
"path": "LICENSE",
"chars": 11357,
"preview": " Apache License\n Version 2.0, January 2004\n "
},
{
"path": "README.md",
"chars": 10423,
"preview": "<p align=\"center\">\n <img src=\"assets/TauricResearch.png\" style=\"width: 60%; height: auto;\">\n</p>\n\n<div align=\"center\" s"
},
{
"path": "cli/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "cli/announcements.py",
"chars": 1507,
"preview": "import getpass\nimport requests\nfrom rich.console import Console\nfrom rich.panel import Panel\n\nfrom cli.config import CLI"
},
{
"path": "cli/config.py",
"chars": 300,
"preview": "CLI_CONFIG = {\n # Announcements\n \"announcements_url\": \"https://api.tauric.ai/v1/announcements\",\n \"announcements"
},
{
"path": "cli/main.py",
"chars": 48297,
"preview": "from typing import Optional\nimport datetime\nimport typer\nfrom pathlib import Path\nfrom functools import wraps\nfrom rich."
},
{
"path": "cli/models.py",
"chars": 221,
"preview": "from enum import Enum\nfrom typing import List, Optional, Dict\nfrom pydantic import BaseModel\n\n\nclass AnalystType(str, En"
},
{
"path": "cli/static/welcome.txt",
"chars": 427,
"preview": "\n ______ ___ ___ __ \n /_ __/________ _____/ (_)___ ____ _/ | ____"
},
{
"path": "cli/stats_handler.py",
"chars": 2373,
"preview": "import threading\nfrom typing import Any, Dict, List, Union\n\nfrom langchain_core.callbacks import BaseCallbackHandler\nfro"
},
{
"path": "cli/utils.py",
"chars": 12116,
"preview": "import questionary\nfrom typing import List, Optional, Tuple, Dict\n\nfrom rich.console import Console\n\nfrom cli.models imp"
},
{
"path": "main.py",
"chars": 1154,
"preview": "from tradingagents.graph.trading_graph import TradingAgentsGraph\nfrom tradingagents.default_config import DEFAULT_CONFIG"
},
{
"path": "pyproject.toml",
"chars": 953,
"preview": "[build-system]\nrequires = [\"setuptools>=61.0\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[project]\nname = \"tradingagents\""
},
{
"path": "requirements.txt",
"chars": 240,
"preview": "typing-extensions\nlangchain-core\nlangchain-openai\nlangchain-experimental\npandas\nyfinance\nstockstats\nlanggraph\nrank-bm25\n"
},
{
"path": "test.py",
"chars": 637,
"preview": "import time\nfrom tradingagents.dataflows.y_finance import get_YFin_data_online, get_stock_stats_indicators_window, get_b"
},
{
"path": "tradingagents/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "tradingagents/agents/__init__.py",
"chars": 1430,
"preview": "from .utils.agent_utils import create_msg_delete\nfrom .utils.agent_states import AgentState, InvestDebateState, RiskDeba"
},
{
"path": "tradingagents/agents/analysts/fundamentals_analyst.py",
"chars": 3272,
"preview": "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\nimport time\nimport json\nfrom tradingagents.ag"
},
{
"path": "tradingagents/agents/analysts/market_analyst.py",
"chars": 6158,
"preview": "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\nimport time\nimport json\nfrom tradingagents.ag"
},
{
"path": "tradingagents/agents/analysts/news_analyst.py",
"chars": 2844,
"preview": "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\nimport time\nimport json\nfrom tradingagents.ag"
},
{
"path": "tradingagents/agents/analysts/social_media_analyst.py",
"chars": 3260,
"preview": "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\nimport time\nimport json\nfrom tradingagents.ag"
},
{
"path": "tradingagents/agents/managers/research_manager.py",
"chars": 2721,
"preview": "import time\nimport json\n\n\ndef create_research_manager(llm, memory):\n def research_manager_node(state) -> dict:\n "
},
{
"path": "tradingagents/agents/managers/risk_manager.py",
"chars": 3240,
"preview": "import time\nimport json\n\n\ndef create_risk_manager(llm, memory):\n def risk_manager_node(state) -> dict:\n\n compa"
},
{
"path": "tradingagents/agents/researchers/bear_researcher.py",
"chars": 3235,
"preview": "from langchain_core.messages import AIMessage\nimport time\nimport json\n\n\ndef create_bear_researcher(llm, memory):\n def"
},
{
"path": "tradingagents/agents/researchers/bull_researcher.py",
"chars": 3168,
"preview": "from langchain_core.messages import AIMessage\nimport time\nimport json\n\n\ndef create_bull_researcher(llm, memory):\n def"
},
{
"path": "tradingagents/agents/risk_mgmt/aggressive_debator.py",
"chars": 3644,
"preview": "import time\nimport json\n\n\ndef create_aggressive_debator(llm):\n def aggressive_node(state) -> dict:\n risk_debat"
},
{
"path": "tradingagents/agents/risk_mgmt/conservative_debator.py",
"chars": 3597,
"preview": "from langchain_core.messages import AIMessage\nimport time\nimport json\n\n\ndef create_conservative_debator(llm):\n def co"
},
{
"path": "tradingagents/agents/risk_mgmt/neutral_debator.py",
"chars": 3403,
"preview": "import time\nimport json\n\n\ndef create_neutral_debator(llm):\n def neutral_node(state) -> dict:\n risk_debate_stat"
},
{
"path": "tradingagents/agents/trader/trader.py",
"chars": 2256,
"preview": "import functools\nimport time\nimport json\n\n\ndef create_trader(llm, memory):\n def trader_node(state, name):\n com"
},
{
"path": "tradingagents/agents/utils/agent_states.py",
"chars": 3136,
"preview": "from typing import Annotated, Sequence\nfrom datetime import date, timedelta, datetime\nfrom typing_extensions import Type"
},
{
"path": "tradingagents/agents/utils/agent_utils.py",
"chars": 1031,
"preview": "from langchain_core.messages import HumanMessage, RemoveMessage\n\n# Import tools from separate utility files\nfrom trading"
},
{
"path": "tradingagents/agents/utils/core_stock_tools.py",
"chars": 891,
"preview": "from langchain_core.tools import tool\nfrom typing import Annotated\nfrom tradingagents.dataflows.interface import route_t"
},
{
"path": "tradingagents/agents/utils/fundamental_data_tools.py",
"chars": 2920,
"preview": "from langchain_core.tools import tool\nfrom typing import Annotated\nfrom tradingagents.dataflows.interface import route_t"
},
{
"path": "tradingagents/agents/utils/memory.py",
"chars": 5303,
"preview": "\"\"\"Financial situation memory using BM25 for lexical similarity matching.\n\nUses BM25 (Best Matching 25) algorithm for re"
},
{
"path": "tradingagents/agents/utils/news_data_tools.py",
"chars": 1834,
"preview": "from langchain_core.tools import tool\nfrom typing import Annotated\nfrom tradingagents.dataflows.interface import route_t"
},
{
"path": "tradingagents/agents/utils/technical_indicators_tools.py",
"chars": 1595,
"preview": "from langchain_core.tools import tool\nfrom typing import Annotated\nfrom tradingagents.dataflows.interface import route_t"
},
{
"path": "tradingagents/dataflows/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "tradingagents/dataflows/alpha_vantage.py",
"chars": 333,
"preview": "# Import functions from specialized modules\nfrom .alpha_vantage_stock import get_stock\nfrom .alpha_vantage_indicator imp"
},
{
"path": "tradingagents/dataflows/alpha_vantage_common.py",
"chars": 4285,
"preview": "import os\nimport requests\nimport pandas as pd\nimport json\nfrom datetime import datetime\nfrom io import StringIO\n\nAPI_BAS"
},
{
"path": "tradingagents/dataflows/alpha_vantage_fundamentals.py",
"chars": 2469,
"preview": "from .alpha_vantage_common import _make_api_request\n\n\ndef get_fundamentals(ticker: str, curr_date: str = None) -> str:\n "
},
{
"path": "tradingagents/dataflows/alpha_vantage_indicator.py",
"chars": 10855,
"preview": "from .alpha_vantage_common import _make_api_request\n\ndef get_indicator(\n symbol: str,\n indicator: str,\n curr_da"
},
{
"path": "tradingagents/dataflows/alpha_vantage_news.py",
"chars": 2349,
"preview": "from .alpha_vantage_common import _make_api_request, format_datetime_for_api\n\ndef get_news(ticker, start_date, end_date)"
},
{
"path": "tradingagents/dataflows/alpha_vantage_stock.py",
"chars": 1309,
"preview": "from datetime import datetime\nfrom .alpha_vantage_common import _make_api_request, _filter_csv_by_date_range\n\ndef get_st"
},
{
"path": "tradingagents/dataflows/config.py",
"chars": 770,
"preview": "import tradingagents.default_config as default_config\nfrom typing import Dict, Optional\n\n# Use default config but allow "
},
{
"path": "tradingagents/dataflows/interface.py",
"chars": 5431,
"preview": "from typing import Annotated\n\n# Import from vendor-specific modules\nfrom .y_finance import (\n get_YFin_data_online,\n "
},
{
"path": "tradingagents/dataflows/stockstats_utils.py",
"chars": 2639,
"preview": "import pandas as pd\nimport yfinance as yf\nfrom stockstats import wrap\nfrom typing import Annotated\nimport os\nfrom .confi"
},
{
"path": "tradingagents/dataflows/utils.py",
"chars": 1039,
"preview": "import os\nimport json\nimport pandas as pd\nfrom datetime import date, timedelta, datetime\nfrom typing import Annotated\n\nS"
},
{
"path": "tradingagents/dataflows/y_finance.py",
"chars": 18096,
"preview": "from typing import Annotated\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\nimport yfina"
},
{
"path": "tradingagents/dataflows/yfinance_news.py",
"chars": 6099,
"preview": "\"\"\"yfinance-based news data fetching functions.\"\"\"\n\nimport yfinance as yf\nfrom datetime import datetime\nfrom dateutil.re"
},
{
"path": "tradingagents/default_config.py",
"chars": 1439,
"preview": "import os\n\nDEFAULT_CONFIG = {\n \"project_dir\": os.path.abspath(os.path.join(os.path.dirname(__file__), \".\")),\n \"res"
},
{
"path": "tradingagents/graph/__init__.py",
"chars": 417,
"preview": "# TradingAgents/graph/__init__.py\n\nfrom .trading_graph import TradingAgentsGraph\nfrom .conditional_logic import Conditio"
},
{
"path": "tradingagents/graph/conditional_logic.py",
"chars": 2704,
"preview": "# TradingAgents/graph/conditional_logic.py\n\nfrom tradingagents.agents.utils.agent_states import AgentState\n\n\nclass Condi"
},
{
"path": "tradingagents/graph/propagation.py",
"chars": 2365,
"preview": "# TradingAgents/graph/propagation.py\n\nfrom typing import Dict, Any, List, Optional\nfrom tradingagents.agents.utils.agent"
},
{
"path": "tradingagents/graph/reflection.py",
"chars": 5918,
"preview": "# TradingAgents/graph/reflection.py\n\nfrom typing import Dict, Any\nfrom langchain_openai import ChatOpenAI\n\n\nclass Reflec"
},
{
"path": "tradingagents/graph/setup.py",
"chars": 7783,
"preview": "# TradingAgents/graph/setup.py\n\nfrom typing import Dict, Any\nfrom langchain_openai import ChatOpenAI\nfrom langgraph.grap"
},
{
"path": "tradingagents/graph/signal_processing.py",
"chars": 1143,
"preview": "# TradingAgents/graph/signal_processing.py\n\nfrom langchain_openai import ChatOpenAI\n\n\nclass SignalProcessor:\n \"\"\"Proc"
},
{
"path": "tradingagents/graph/trading_graph.py",
"chars": 10568,
"preview": "# TradingAgents/graph/trading_graph.py\n\nimport os\nfrom pathlib import Path\nimport json\nfrom datetime import date\nfrom ty"
},
{
"path": "tradingagents/llm_clients/TODO.md",
"chars": 934,
"preview": "# LLM Clients - Consistency Improvements\n\n## Issues to Fix\n\n### 1. `validate_model()` is never called\n- Add validation c"
},
{
"path": "tradingagents/llm_clients/__init__.py",
"chars": 128,
"preview": "from .base_client import BaseLLMClient\nfrom .factory import create_llm_client\n\n__all__ = [\"BaseLLMClient\", \"create_llm_c"
},
{
"path": "tradingagents/llm_clients/anthropic_client.py",
"chars": 896,
"preview": "from typing import Any, Optional\n\nfrom langchain_anthropic import ChatAnthropic\n\nfrom .base_client import BaseLLMClient\n"
},
{
"path": "tradingagents/llm_clients/base_client.py",
"chars": 565,
"preview": "from abc import ABC, abstractmethod\nfrom typing import Any, Optional\n\n\nclass BaseLLMClient(ABC):\n \"\"\"Abstract base cl"
},
{
"path": "tradingagents/llm_clients/factory.py",
"chars": 1645,
"preview": "from typing import Optional\n\nfrom .base_client import BaseLLMClient\nfrom .openai_client import OpenAIClient\nfrom .anthro"
},
{
"path": "tradingagents/llm_clients/google_client.py",
"chars": 2565,
"preview": "from typing import Any, Optional\n\nfrom langchain_google_genai import ChatGoogleGenerativeAI\n\nfrom .base_client import Ba"
},
{
"path": "tradingagents/llm_clients/openai_client.py",
"chars": 2498,
"preview": "import os\nfrom typing import Any, Optional\n\nfrom langchain_openai import ChatOpenAI\n\nfrom .base_client import BaseLLMCli"
},
{
"path": "tradingagents/llm_clients/validators.py",
"chars": 1658,
"preview": "\"\"\"Model name validators for each provider.\n\nOnly validates model names - does NOT enforce limits.\nLet LLM providers use"
}
]
About this extraction
This page contains the full source code of the TauricResearch/TradingAgents GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 65 files (246.5 KB), approximately 56.9k tokens, and a symbol index with 174 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.