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TradingAgents
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TradingAgents/cli/utils.py

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import questionary
from typing import List, Optional, Tuple, Dict
from cli.models import AnalystType
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
# Centralized model definitions - single source of truth
SHALLOW_AGENT_OPTIONS = {
"openai": [
("GPT-4o-mini - Fast and efficient for quick tasks", "gpt-4o-mini"),
("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"),
("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"),
("GPT-4o - Standard model with solid capabilities", "gpt-4o"),
],
"anthropic": [
("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"),
("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"),
("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"),
("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"),
],
"google": [
("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
],
"openrouter": [
("Meta: Llama 4 Scout", "meta-llama/llama-4-scout:free"),
("Meta: Llama 3.3 8B Instruct - A lightweight and ultra-fast variant of Llama 3.3 70B", "meta-llama/llama-3.3-8b-instruct:free"),
("google/gemini-2.0-flash-exp:free - Gemini Flash 2.0 offers a significantly faster time to first token", "google/gemini-2.0-flash-exp:free"),
],
"ollama": [
("llama3.1 local", "llama3.1"),
("llama3.2 local", "llama3.2"),
]
}
DEEP_AGENT_OPTIONS = {
"openai": [
("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"),
("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"),
("GPT-4o - Standard model with solid capabilities", "gpt-4o"),
("o4-mini - Specialized reasoning model (compact)", "o4-mini"),
("o3-mini - Advanced reasoning model (lightweight)", "o3-mini"),
("o3 - Full advanced reasoning model", "o3"),
("o1 - Premier reasoning and problem-solving model", "o1"),
],
"anthropic": [
("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"),
("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"),
("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"),
("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"),
("Claude Opus 4 - Most powerful Anthropic model", "claude-opus-4-0"),
],
"google": [
("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
("Gemini 2.5 Pro", "gemini-2.5-pro-preview-06-05"),
],
"openrouter": [
("DeepSeek V3 - a 685B-parameter, mixture-of-experts model", "deepseek/deepseek-chat-v3-0324:free"),
("Deepseek - latest iteration of the flagship chat model family from the DeepSeek team.", "deepseek/deepseek-chat-v3-0324:free"),
],
"ollama": [
("llama3.1 local", "llama3.1"),
("qwen3", "qwen3"),
]
}
def _get_all_models_for_custom_provider(model_type: str) -> list:
"""Get unified model list for custom provider with all available models from all providers.
Args:
model_type: Either 'shallow' or 'deep' to get the appropriate model set
Returns:
List of (description, model_value) tuples
"""
# Use the centralized model definitions
if model_type == "shallow":
provider_models = SHALLOW_AGENT_OPTIONS
else: # deep
provider_models = DEEP_AGENT_OPTIONS
# Combine all models with provider labels
all_models = []
for provider_name, models in provider_models.items():
provider_display_name = provider_name.title()
for description, model_value in models:
labeled_description = f"{description} ({provider_display_name})"
all_models.append((labeled_description, model_value))
# Add custom model option at the end
all_models.append(("Custom Model - Enter your own model name", "__CUSTOM_MODEL__"))
return all_models
def _select_custom_provider_model(model_type: str, title: str, default_model: str) -> str:
"""Handle model selection for custom provider with unified model list.
Args:
model_type: Either 'shallow' or 'deep'
title: Title for the selection prompt
default_model: Default model name for custom input
Returns:
Selected model name
"""
all_models = _get_all_models_for_custom_provider(model_type)
choice = questionary.select(
title,
choices=[
questionary.Choice(display, value=value)
for display, value in all_models
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select\n- Your custom endpoint should support the selected model",
style=questionary.Style(
[
("selected", "fg:magenta noinherit"),
("highlighted", "fg:magenta noinherit"),
("pointer", "fg:magenta noinherit"),
]
),
).ask()
if choice is None:
from rich.console import Console
console = Console()
console.print(f"\n[red]No {model_type} thinking model selected. Exiting...[/red]")
exit(1)
# Handle custom model input
if choice == "__CUSTOM_MODEL__":
custom_model = questionary.text(
f"Enter your custom {model_type} thinking model name:",
default=default_model,
instruction="\n- Enter the exact model name as supported by your custom endpoint\n- Press Enter to confirm"
).ask()
if not custom_model:
from rich.console import Console
console = Console()
console.print(f"\n[red]No custom {model_type} model name entered. Exiting...[/red]")
exit(1)
return custom_model
return choice
def select_shallow_thinking_agent(provider) -> str:
"""Select shallow thinking llm engine using an interactive selection."""
# Handle custom provider - use unified model selection
if provider.lower().startswith("custom"):
return _select_custom_provider_model(
model_type="shallow",
title="Select Your [Quick-Thinking LLM Engine] (Custom Provider - All Models Available):",
default_model="gpt-4o-mini"
)
# Use centralized shallow thinking model definitions
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:
from rich.console import Console
console = Console()
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."""
# Handle custom provider - use unified model selection
if provider.lower().startswith("custom"):
return _select_custom_provider_model(
model_type="deep",
title="Select Your [Deep-Thinking LLM Engine] (Custom Provider - All Models Available):",
default_model="o4-mini"
)
# Use centralized deep thinking model definitions
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:
from rich.console import Console
console = Console()
console.print("\n[red]No deep thinking llm engine selected. Exiting...[/red]")
exit(1)
return choice
def validate_custom_url(url: str) -> str:
"""Validate that a custom URL is properly formatted and has a valid hostname."""
import re
from urllib.parse import urlparse
from rich.console import Console
if not url:
return ""
console = Console()
# Basic URL format validation
url_pattern = re.compile(
r'^https?://' # http:// or https://
r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+[A-Z]{2,6}\.?|' # domain...
r'localhost|' # localhost...
r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})' # ...or ip
r'(?::\d+)?' # optional port
r'(?:/?|[/?]\S+)$', re.IGNORECASE)
if not url_pattern.match(url):
console.print(f"[red]Error: Invalid CUSTOM_BASE_URL format: {url}[/red]")
console.print(f"[red]Please provide a valid URL (e.g., https://api.example.com/v1)[/red]")
exit(1)
# Additional validation using urlparse
try:
parsed = urlparse(url)
if not parsed.netloc:
raise ValueError("No hostname found")
return url
except Exception as e:
console.print(f"[red]Error: Invalid CUSTOM_BASE_URL: {url}[/red]")
console.print(f"[red]URL parsing error: {e}[/red]")
exit(1)
def get_custom_provider_info() -> tuple[str, str] | None:
"""Get custom provider info if both URL and API key are provided."""
import os
from urllib.parse import urlparse
custom_url = os.getenv("CUSTOM_BASE_URL")
custom_api_key = os.getenv("CUSTOM_API_KEY")
if custom_url and custom_api_key:
validated_url = validate_custom_url(custom_url)
parsed = urlparse(validated_url)
hostname = parsed.netloc
return f"Custom ({hostname})", validated_url
return None
def select_llm_provider() -> tuple[str, str]:
"""Select the LLM provider with support for a custom OpenAI-compatible endpoint."""
# Define default providers
BASE_URLS = [
("OpenAI", "https://api.openai.com/v1"),
("Anthropic", "https://api.anthropic.com/"),
("Google", "https://generativelanguage.googleapis.com/v1"),
("Openrouter", "https://openrouter.ai/api/v1"),
("Ollama", "http://localhost:11434/v1"),
]
# Add custom provider at the beginning if available
custom_info = get_custom_provider_info()
if custom_info:
BASE_URLS.insert(0, custom_info)
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:
from rich.console import Console
console = Console()
console.print("\n[red]No LLM provider selected. Exiting...[/red]")
exit(1)
display_name, url = choice
print(f"You selected: {display_name}\tURL: {url}")
return display_name, url
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