mirror of
https://github.com/TauricResearch/TradingAgents.git
synced 2026-06-16 21:06:15 +03:00
merge upstream main into crypto-analysis-mvp
This commit is contained in:
CadeYu
275
cli/utils.py
275
cli/utils.py
@@ -4,9 +4,12 @@ from typing import List, Optional, Tuple, Dict
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from rich.console import Console
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from cli.models import AnalystType, AssetType
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from tradingagents.llm_clients.model_catalog import get_model_options
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console = Console()
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TICKER_INPUT_EXAMPLES = "Examples: SPY, CNC.TO, 7203.T, 0700.HK"
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ANALYST_ORDER = [
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("Market Analyst", AnalystType.MARKET),
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("Social Media Analyst", AnalystType.SOCIAL),
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@@ -20,7 +23,7 @@ CRYPTO_SUFFIXES = ("-USD", "-USDT", "-USDC", "-BTC", "-ETH")
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def get_ticker() -> str:
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"""Prompt the user to enter a ticker symbol."""
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ticker = questionary.text(
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"Enter the ticker symbol to analyze:",
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f"Enter the exact ticker symbol to analyze ({TICKER_INPUT_EXAMPLES}):",
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validate=lambda x: len(x.strip()) > 0 or "Please enter a valid ticker symbol.",
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style=questionary.Style(
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[
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@@ -34,6 +37,11 @@ def get_ticker() -> str:
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console.print("\n[red]No ticker symbol provided. Exiting...[/red]")
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exit(1)
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return normalize_ticker_symbol(ticker)
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def normalize_ticker_symbol(ticker: str) -> str:
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"""Normalize ticker input while preserving exchange suffixes."""
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return ticker.strip().upper()
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@@ -153,51 +161,70 @@ def select_research_depth() -> int:
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return choice
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def select_shallow_thinking_agent(provider) -> str:
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"""Select shallow thinking llm engine using an interactive selection."""
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def _fetch_openrouter_models() -> List[Tuple[str, str]]:
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"""Fetch available models from the OpenRouter API."""
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import requests
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try:
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resp = requests.get("https://openrouter.ai/api/v1/models", timeout=10)
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resp.raise_for_status()
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models = resp.json().get("data", [])
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return [(m.get("name") or m["id"], m["id"]) for m in models]
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except Exception as e:
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console.print(f"\n[yellow]Could not fetch OpenRouter models: {e}[/yellow]")
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return []
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# Define shallow thinking llm engine options with their corresponding model names
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# Ordering: medium → light → heavy (balanced first for quick tasks)
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# Within same tier, newer models first
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SHALLOW_AGENT_OPTIONS = {
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"openai": [
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("GPT-5 Mini - Balanced speed, cost, and capability", "gpt-5-mini"),
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("GPT-5 Nano - High-throughput, simple tasks", "gpt-5-nano"),
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("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
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("GPT-4.1 - Smartest non-reasoning model", "gpt-4.1"),
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],
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"anthropic": [
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("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
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("Claude Haiku 4.5 - Fast, near-instant responses", "claude-haiku-4-5"),
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("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
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],
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"google": [
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("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
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("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
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("Gemini 3.1 Flash Lite - Most cost-efficient", "gemini-3.1-flash-lite-preview"),
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("Gemini 2.5 Flash Lite - Fast, low-cost", "gemini-2.5-flash-lite"),
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],
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"xai": [
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("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
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("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"),
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("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
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],
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"openrouter": [
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("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
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("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),
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],
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"ollama": [
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("Qwen3:latest (8B, local)", "qwen3:latest"),
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("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
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("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
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],
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}
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def select_openrouter_model() -> str:
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"""Select an OpenRouter model from the newest available, or enter a custom ID."""
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models = _fetch_openrouter_models()
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choices = [questionary.Choice(name, value=mid) for name, mid in models[:5]]
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choices.append(questionary.Choice("Custom model ID", value="custom"))
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choice = questionary.select(
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"Select Your [Quick-Thinking LLM Engine]:",
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"Select OpenRouter Model (latest available):",
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choices=choices,
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instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
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style=questionary.Style([
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("selected", "fg:magenta noinherit"),
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("highlighted", "fg:magenta noinherit"),
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("pointer", "fg:magenta noinherit"),
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]),
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).ask()
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if choice is None or choice == "custom":
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return questionary.text(
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"Enter OpenRouter model ID (e.g. google/gemma-4-26b-a4b-it):",
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validate=lambda x: len(x.strip()) > 0 or "Please enter a model ID.",
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).ask().strip()
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return choice
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def _prompt_custom_model_id() -> str:
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"""Prompt user to type a custom model ID."""
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return questionary.text(
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"Enter model ID:",
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validate=lambda x: len(x.strip()) > 0 or "Please enter a model ID.",
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).ask().strip()
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def _select_model(provider: str, mode: str) -> str:
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"""Select a model for the given provider and mode (quick/deep)."""
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if provider.lower() == "openrouter":
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return select_openrouter_model()
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if provider.lower() == "azure":
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return questionary.text(
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f"Enter Azure deployment name ({mode}-thinking):",
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validate=lambda x: len(x.strip()) > 0 or "Please enter a deployment name.",
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).ask().strip()
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choice = questionary.select(
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f"Select Your [{mode.title()}-Thinking LLM Engine]:",
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choices=[
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questionary.Choice(display, value=value)
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for display, value in SHALLOW_AGENT_OPTIONS[provider.lower()]
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for display, value in get_model_options(provider, mode)
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],
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instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
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style=questionary.Style(
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@@ -210,95 +237,45 @@ def select_shallow_thinking_agent(provider) -> str:
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).ask()
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if choice is None:
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console.print(
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"\n[red]No shallow thinking llm engine selected. Exiting...[/red]"
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)
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console.print(f"\n[red]No {mode} thinking llm engine selected. Exiting...[/red]")
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exit(1)
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if choice == "custom":
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return _prompt_custom_model_id()
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return choice
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def select_shallow_thinking_agent(provider) -> str:
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"""Select shallow thinking llm engine using an interactive selection."""
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return _select_model(provider, "quick")
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def select_deep_thinking_agent(provider) -> str:
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"""Select deep thinking llm engine using an interactive selection."""
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return _select_model(provider, "deep")
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# Define deep thinking llm engine options with their corresponding model names
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# Ordering: heavy → medium → light (most capable first for deep tasks)
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# Within same tier, newer models first
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DEEP_AGENT_OPTIONS = {
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"openai": [
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("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
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("GPT-5.2 - Strong reasoning, cost-effective", "gpt-5.2"),
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("GPT-5 Mini - Balanced speed, cost, and capability", "gpt-5-mini"),
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("GPT-5.4 Pro - Most capable, expensive ($30/$180 per 1M tokens)", "gpt-5.4-pro"),
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],
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"anthropic": [
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("Claude Opus 4.6 - Most intelligent, agents and coding", "claude-opus-4-6"),
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("Claude Opus 4.5 - Premium, max intelligence", "claude-opus-4-5"),
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("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
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("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
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],
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"google": [
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("Gemini 3.1 Pro - Reasoning-first, complex workflows", "gemini-3.1-pro-preview"),
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("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
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("Gemini 2.5 Pro - Stable pro model", "gemini-2.5-pro"),
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("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
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],
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"xai": [
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("Grok 4 - Flagship model", "grok-4-0709"),
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("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
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("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
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("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
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],
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"openrouter": [
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("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),
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("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
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],
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"ollama": [
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("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
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("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
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("Qwen3:latest (8B, local)", "qwen3:latest"),
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],
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}
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choice = questionary.select(
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"Select Your [Deep-Thinking LLM Engine]:",
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choices=[
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questionary.Choice(display, value=value)
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for display, value in DEEP_AGENT_OPTIONS[provider.lower()]
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],
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instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
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style=questionary.Style(
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[
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("selected", "fg:magenta noinherit"),
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("highlighted", "fg:magenta noinherit"),
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("pointer", "fg:magenta noinherit"),
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]
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),
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).ask()
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if choice is None:
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console.print("\n[red]No deep thinking llm engine selected. Exiting...[/red]")
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exit(1)
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return choice
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def select_llm_provider() -> tuple[str, str]:
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"""Select the OpenAI api url using interactive selection."""
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# Define OpenAI api options with their corresponding endpoints
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BASE_URLS = [
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("OpenAI", "https://api.openai.com/v1"),
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("Google", "https://generativelanguage.googleapis.com/v1"),
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("Anthropic", "https://api.anthropic.com/"),
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("xAI", "https://api.x.ai/v1"),
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("Openrouter", "https://openrouter.ai/api/v1"),
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("Ollama", "http://localhost:11434/v1"),
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def select_llm_provider() -> tuple[str, str | None]:
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"""Select the LLM provider and its API endpoint."""
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# (display_name, provider_key, base_url)
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PROVIDERS = [
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("OpenAI", "openai", "https://api.openai.com/v1"),
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("Google", "google", None),
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("Anthropic", "anthropic", "https://api.anthropic.com/"),
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("xAI", "xai", "https://api.x.ai/v1"),
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("DeepSeek", "deepseek", "https://api.deepseek.com"),
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("Qwen", "qwen", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
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("GLM", "glm", "https://open.bigmodel.cn/api/paas/v4/"),
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("OpenRouter", "openrouter", "https://openrouter.ai/api/v1"),
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("Azure OpenAI", "azure", None),
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("Ollama", "ollama", "http://localhost:11434/v1"),
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]
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choice = questionary.select(
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"Select your LLM Provider:",
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choices=[
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questionary.Choice(display, value=(display, value))
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for display, value in BASE_URLS
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questionary.Choice(display, value=(provider_key, url))
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for display, provider_key, url in PROVIDERS
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],
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instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
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style=questionary.Style(
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@@ -311,13 +288,11 @@ def select_llm_provider() -> tuple[str, str]:
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).ask()
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if choice is None:
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console.print("\n[red]no OpenAI backend selected. Exiting...[/red]")
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console.print("\n[red]No LLM provider selected. Exiting...[/red]")
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exit(1)
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display_name, url = choice
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print(f"You selected: {display_name}\tURL: {url}")
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return display_name, url
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provider, url = choice
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return provider, url
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def ask_openai_reasoning_effort() -> str:
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@@ -338,6 +313,26 @@ def ask_openai_reasoning_effort() -> str:
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).ask()
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def ask_anthropic_effort() -> str | None:
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"""Ask for Anthropic effort level.
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Controls token usage and response thoroughness on Claude 4.5+ and 4.6 models.
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"""
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return questionary.select(
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"Select Effort Level:",
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choices=[
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questionary.Choice("High (recommended)", "high"),
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questionary.Choice("Medium (balanced)", "medium"),
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questionary.Choice("Low (faster, cheaper)", "low"),
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],
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style=questionary.Style([
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("selected", "fg:cyan noinherit"),
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("highlighted", "fg:cyan noinherit"),
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("pointer", "fg:cyan noinherit"),
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]),
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).ask()
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def ask_gemini_thinking_config() -> str | None:
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"""Ask for Gemini thinking configuration.
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@@ -356,3 +351,37 @@ def ask_gemini_thinking_config() -> str | None:
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("pointer", "fg:green noinherit"),
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]),
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).ask()
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def ask_output_language() -> str:
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"""Ask for report output language."""
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choice = questionary.select(
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"Select Output Language:",
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choices=[
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questionary.Choice("English (default)", "English"),
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questionary.Choice("Chinese (中文)", "Chinese"),
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questionary.Choice("Japanese (日本語)", "Japanese"),
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questionary.Choice("Korean (한국어)", "Korean"),
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questionary.Choice("Hindi (हिन्दी)", "Hindi"),
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questionary.Choice("Spanish (Español)", "Spanish"),
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questionary.Choice("Portuguese (Português)", "Portuguese"),
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questionary.Choice("French (Français)", "French"),
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questionary.Choice("German (Deutsch)", "German"),
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questionary.Choice("Arabic (العربية)", "Arabic"),
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questionary.Choice("Russian (Русский)", "Russian"),
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questionary.Choice("Custom language", "custom"),
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],
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style=questionary.Style([
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("selected", "fg:yellow noinherit"),
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("highlighted", "fg:yellow noinherit"),
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("pointer", "fg:yellow noinherit"),
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]),
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).ask()
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if choice == "custom":
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return questionary.text(
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"Enter language name (e.g. Turkish, Vietnamese, Thai, Indonesian):",
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validate=lambda x: len(x.strip()) > 0 or "Please enter a language name.",
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).ask().strip()
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return choice
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