386 lines
14 KiB
Python
386 lines
14 KiB
Python
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 select_market():
|
|
"""Select stock market"""
|
|
markets = {
|
|
"1": {
|
|
"name": "US Stock",
|
|
"default": "SPY",
|
|
"examples": ["SPY", "AAPL", "TSLA", "NVDA", "MSFT"],
|
|
"format": "Stock symbol (e.g., AAPL)",
|
|
"pattern": r'^[A-Z]{1,5}$',
|
|
"data_source": "yahoo_finance"
|
|
},
|
|
"2": {
|
|
"name": "China A-Share",
|
|
"default": "600036",
|
|
"examples": ["000001", "600036", "000858", "300001", "688001"],
|
|
"format": "6-digit code (e.g., 600036, 000001)",
|
|
"pattern": r'^\d{6}$',
|
|
"data_source": "tongdaxin"
|
|
}
|
|
}
|
|
|
|
choices = []
|
|
for key, market in markets.items():
|
|
examples_str = ", ".join(market["examples"][:3])
|
|
display = f"{market['name']} - Examples: {examples_str}"
|
|
choices.append(questionary.Choice(display, value=key))
|
|
|
|
choice = questionary.select(
|
|
"Select Stock Market:",
|
|
choices=choices,
|
|
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
|
|
style=questionary.Style(
|
|
[
|
|
("selected", "fg:cyan noinherit"),
|
|
("highlighted", "fg:cyan noinherit"),
|
|
("pointer", "fg:cyan noinherit"),
|
|
]
|
|
),
|
|
).ask()
|
|
|
|
if choice is None:
|
|
from rich.console import Console
|
|
console = Console()
|
|
console.print("\n[red]No market selected. Exiting...[/red]")
|
|
exit(1)
|
|
|
|
selected_market = markets[choice]
|
|
from rich.console import Console
|
|
console = Console()
|
|
console.print(f"[green]✅ Selected: {selected_market['name']}[/green]")
|
|
return selected_market
|
|
|
|
|
|
def get_ticker(market=None) -> str:
|
|
"""Prompt the user to enter a ticker symbol with market-specific validation."""
|
|
if market is None:
|
|
# Fallback to original behavior for backward compatibility
|
|
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:
|
|
from rich.console import Console
|
|
console = Console()
|
|
console.print("\n[red]No ticker symbol provided. Exiting...[/red]")
|
|
exit(1)
|
|
|
|
return ticker.strip().upper()
|
|
|
|
# Market-specific ticker input with validation
|
|
from rich.console import Console
|
|
console = Console()
|
|
|
|
console.print(f"\n[dim]Format requirement: {market['format']}[/dim]")
|
|
console.print(f"[dim]Examples: {', '.join(market['examples'][:3])}[/dim]")
|
|
|
|
while True:
|
|
ticker = questionary.text(
|
|
f"Enter {market['name']} ticker symbol:",
|
|
default=market['default'],
|
|
style=questionary.Style(
|
|
[
|
|
("text", "fg:green"),
|
|
("highlighted", "noinherit"),
|
|
]
|
|
),
|
|
).ask()
|
|
|
|
if not ticker:
|
|
console.print("\n[red]No ticker symbol provided. Exiting...[/red]")
|
|
exit(1)
|
|
|
|
# Validate ticker format
|
|
import re
|
|
ticker_to_check = ticker.upper() if market['data_source'] != 'tongdaxin' else ticker
|
|
|
|
if re.match(market['pattern'], ticker_to_check):
|
|
# For A-shares, return pure numeric code
|
|
if market['data_source'] == 'tongdaxin':
|
|
console.print(f"[green]✅ Valid A-share code: {ticker} (will use TongDaXin data source)[/green]")
|
|
return ticker
|
|
else:
|
|
console.print(f"[green]✅ Valid ticker: {ticker.upper()}[/green]")
|
|
return ticker.upper()
|
|
else:
|
|
console.print(f"[red]❌ Invalid ticker format[/red]")
|
|
console.print(f"[yellow]Please use correct format: {market['format']}[/yellow]")
|
|
|
|
|
|
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
|
|
SHALLOW_AGENT_OPTIONS = {
|
|
"dashscope (alibaba cloud)": [
|
|
("Qwen-Turbo - Fast response, suitable for quick tasks", "qwen-turbo"),
|
|
("Qwen-Plus - Balanced performance and cost", "qwen-plus"),
|
|
("Qwen-Max - Best performance for complex analysis", "qwen-max"),
|
|
],
|
|
"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"),
|
|
]
|
|
}
|
|
|
|
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
|
|
DEEP_AGENT_OPTIONS = {
|
|
"dashscope (alibaba cloud)": [
|
|
("Qwen-Plus - Balanced performance and cost (Recommended)", "qwen-plus"),
|
|
("Qwen-Max - Best performance for complex analysis", "qwen-max"),
|
|
("Qwen-Max-LongContext - Ultra-long context support", "qwen-max-longcontext"),
|
|
("Qwen-Turbo - Fast response for lighter analysis", "qwen-turbo"),
|
|
],
|
|
"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"),
|
|
]
|
|
}
|
|
|
|
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 LLM provider using interactive selection."""
|
|
# Define LLM provider options with their corresponding endpoints
|
|
# DashScope (Alibaba Cloud) is recommended for Chinese users
|
|
BASE_URLS = [
|
|
("DashScope (Alibaba Cloud)", "https://dashscope.aliyuncs.com/api/v1"),
|
|
("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"),
|
|
]
|
|
|
|
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
|