mirror of
https://github.com/TauricResearch/TradingAgents.git
synced 2026-09-27 15:02:39 +03:00
refactor(cli): split the interactive choices and the run loop out of cli/main.py
- selections.py asks what to run; run.py builds the graph, streams it to the live view and saves the report - main.py keeps the Typer app and its two commands
This commit is contained in:
Yijia-Xiao
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"""The interactive choices for a run: ticker, date, analysts, depth, provider and models."""
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import datetime
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import os
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from pathlib import Path
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import typer
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from rich.align import Align
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from rich.panel import Panel
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from cli.announcements import display_announcements, fetch_announcements
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from cli.display import (
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console,
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)
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from cli.prefs import load_last_run, sanitize, save_last_run
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from cli.prompts import (
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ask_anthropic_effort,
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ask_gemini_thinking_config,
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ask_glm_region,
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ask_minimax_region,
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ask_openai_reasoning_effort,
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ask_output_language,
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ask_qwen_region,
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confirm_ollama_endpoint,
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detect_asset_type,
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ensure_api_key,
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get_ticker,
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prompt_openai_compatible_url,
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resolve_backend_url,
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select_analysts,
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select_deep_thinking_agent,
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select_llm_provider,
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select_research_depth,
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select_shallow_thinking_agent,
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)
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from tradingagents.default_config import DEFAULT_CONFIG
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def get_user_selections():
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"""Ask for the run's settings, offering the previous run's answers."""
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selections = _prompt_selections(load_last_run())
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save_last_run(selections)
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return selections
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def _prompt_selections(prefs):
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"""Walk the selection steps. ``prefs`` prefills, the environment skips."""
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# Display ASCII art welcome message
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with open(Path(__file__).parent / "static" / "welcome.txt", encoding="utf-8") as f:
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welcome_ascii = f.read()
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# Create welcome box content
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welcome_content = f"{welcome_ascii}\n"
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welcome_content += "[bold green]TradingAgents: Multi-Agents LLM Financial Trading Framework - CLI[/bold green]\n\n"
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welcome_content += "[bold]Workflow Steps:[/bold]\n"
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welcome_content += "I. Analyst Team → II. Research Team → III. Trader → IV. Risk Management → V. Portfolio Management\n\n"
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welcome_content += (
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"[dim]Built by [Tauric Research](https://github.com/TauricResearch)[/dim]"
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)
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# Create and center the welcome box
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welcome_box = Panel(
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welcome_content,
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border_style="green",
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padding=(1, 2),
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title="Welcome to TradingAgents",
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subtitle="Multi-Agents LLM Financial Trading Framework",
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)
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console.print(Align.center(welcome_box))
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console.print()
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console.print() # Add vertical space before announcements
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# Fetch and display announcements (silent on failure)
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announcements = fetch_announcements()
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display_announcements(console, announcements)
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# Create a boxed questionnaire for each step
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def create_question_box(title, prompt, default=None):
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box_content = f"[bold]{title}[/bold]\n"
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box_content += f"[dim]{prompt}[/dim]"
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if default:
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box_content += f"\n[dim]Default: {default}[/dim]"
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return Panel(box_content, border_style="blue", padding=(1, 2))
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def thinking_value_or_prompt(env_var, config_key, label, box_title, box_body, prompt_fn):
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"""Return the env-configured reasoning/thinking value, or prompt for it.
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When ``env_var`` is set the interactive choice is skipped and the value
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the env overlay placed on DEFAULT_CONFIG is used — mirroring the
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env-precedence rule applied to the other selection steps.
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"""
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if os.environ.get(env_var):
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value = DEFAULT_CONFIG[config_key]
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console.print(f"[green]✓ {label} from environment:[/green] {value}")
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return value
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console.print(create_question_box(box_title, box_body))
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return prompt_fn()
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# Step 1: Ticker symbol
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console.print(
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create_question_box(
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"Step 1: Ticker Symbol",
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"Enter the ticker, with exchange suffix when needed (e.g. SPY, 0700.HK, BTC-USD)",
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"SPY",
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)
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)
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selected_ticker = get_ticker()
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asset_type = detect_asset_type(selected_ticker)
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# Only announce when it's not the default stock path, to avoid printing
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# "stock" on every run.
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if asset_type.value != "stock":
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console.print(
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f"[green]Detected asset type:[/green] {asset_type.value}"
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)
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# Step 2: Analysis date
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default_date = datetime.datetime.now().strftime("%Y-%m-%d")
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console.print(
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create_question_box(
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"Step 2: Analysis Date",
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"Enter the analysis date (YYYY-MM-DD)",
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default_date,
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)
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)
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analysis_date = get_analysis_date()
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# Step 3: Output language (skipped when set via TRADINGAGENTS_OUTPUT_LANGUAGE)
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if os.environ.get("TRADINGAGENTS_OUTPUT_LANGUAGE"):
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output_language = DEFAULT_CONFIG["output_language"]
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console.print(
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f"[green]✓ Output language from environment:[/green] {output_language}"
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)
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else:
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console.print(
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create_question_box(
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"Step 3: Output Language",
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"Select the language for analyst reports and final decision"
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)
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)
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output_language = ask_output_language(prefs.get("output_language"))
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# Step 4: Select analysts
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console.print(
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create_question_box(
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"Step 4: Analysts Team", "Select your LLM analyst agents for the analysis"
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)
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)
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prefs = sanitize(prefs, asset_type.value)
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selected_analysts = select_analysts(asset_type, prefs.get("analysts"))
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console.print(
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f"[green]Selected analysts:[/green] {', '.join(analyst.value for analyst in selected_analysts)}"
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)
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# Step 5: Research depth (skipped when both round counts are set via env).
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# Research depth maps to the debate + risk round counts; when both are
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# supplied through TRADINGAGENTS_MAX_DEBATE_ROUNDS / _MAX_RISK_ROUNDS we keep
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# the run non-interactive and honor the env values (#977).
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depth_from_env = bool(os.environ.get("TRADINGAGENTS_MAX_DEBATE_ROUNDS")) and bool(
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os.environ.get("TRADINGAGENTS_MAX_RISK_ROUNDS")
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)
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if depth_from_env:
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selected_research_depth = DEFAULT_CONFIG["max_debate_rounds"]
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console.print(
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f"[green]✓ Research depth from environment:[/green] "
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f"{DEFAULT_CONFIG['max_debate_rounds']} debate / "
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f"{DEFAULT_CONFIG['max_risk_discuss_rounds']} risk rounds"
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)
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else:
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console.print(
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create_question_box(
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"Step 5: Research Depth", "Select your research depth level"
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)
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)
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selected_research_depth = select_research_depth(prefs.get("research_depth"))
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# Step 6: LLM Provider (skipped when set via TRADINGAGENTS_LLM_PROVIDER).
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# The backend URL comes from TRADINGAGENTS_LLM_BACKEND_URL when set,
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# otherwise the provider's default endpoint — the same value the menu
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# would have picked.
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provider_from_env = bool(os.environ.get("TRADINGAGENTS_LLM_PROVIDER"))
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if provider_from_env:
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selected_llm_provider = DEFAULT_CONFIG["llm_provider"].lower()
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backend_url = resolve_backend_url(
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selected_llm_provider, env_url=DEFAULT_CONFIG["backend_url"]
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)
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console.print(f"[green]✓ LLM provider from environment:[/green] {selected_llm_provider}")
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console.print(f"[green]✓ Backend URL:[/green] {backend_url}")
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# Still confirm/persist the API key so the run doesn't fail later.
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ensure_api_key(selected_llm_provider)
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else:
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console.print(
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create_question_box(
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"Step 6: LLM Provider", "Select your LLM provider"
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)
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)
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selected_llm_provider, backend_url = select_llm_provider(prefs.get("llm_provider"))
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# Providers with regional endpoints prompt for the region as a secondary
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# step so the main dropdown stays clean (mainland China and international
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# accounts cannot share API keys).
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if selected_llm_provider == "qwen":
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selected_llm_provider, backend_url = ask_qwen_region()
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elif selected_llm_provider == "minimax":
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selected_llm_provider, backend_url = ask_minimax_region()
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elif selected_llm_provider == "glm":
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selected_llm_provider, backend_url = ask_glm_region()
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# Honor an explicit env backend URL even when the provider was chosen
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# interactively, so it isn't overwritten by the menu default (#978).
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backend_url = resolve_backend_url(
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selected_llm_provider, backend_url, env_url=DEFAULT_CONFIG["backend_url"]
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)
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# The generic OpenAI-compatible endpoint has no default; ask for it if
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# neither the menu nor the environment supplied one.
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if selected_llm_provider == "openai_compatible" and not backend_url:
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remembered_url = (prefs.get("backend_url")
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if prefs.get("llm_provider") == selected_llm_provider else None)
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backend_url = prompt_openai_compatible_url(remembered_url)
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# For Ollama, surface the resolved endpoint (OLLAMA_BASE_URL vs default)
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# before model selection so it's obvious where we're connecting.
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if selected_llm_provider == "ollama":
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confirm_ollama_endpoint(backend_url)
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# Confirm the provider's API key is present; prompt the user to paste
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# one and persist it to .env if it's missing, so the analysis run
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# doesn't fail later at the first API call.
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ensure_api_key(selected_llm_provider)
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# Step 7: Thinking agents (skipped when either model is set via environment)
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if os.environ.get("TRADINGAGENTS_QUICK_THINK_LLM") or os.environ.get("TRADINGAGENTS_DEEP_THINK_LLM"):
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selected_shallow_thinker = DEFAULT_CONFIG["quick_think_llm"]
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selected_deep_thinker = DEFAULT_CONFIG["deep_think_llm"]
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console.print(
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f"[green]✓ Thinking agents from environment:[/green] "
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f"quick={selected_shallow_thinker}, deep={selected_deep_thinker}"
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)
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else:
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console.print(
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create_question_box(
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"Step 7: Thinking Agents", "Select your thinking agents for analysis"
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)
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)
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remembered = prefs if prefs.get("llm_provider") == selected_llm_provider else {}
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selected_shallow_thinker = select_shallow_thinking_agent(
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selected_llm_provider, remembered.get("quick_think_llm")
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)
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selected_deep_thinker = select_deep_thinking_agent(
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selected_llm_provider, remembered.get("deep_think_llm")
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)
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# Step 8: Provider-specific reasoning/thinking configuration. Each knob is
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# settable via its TRADINGAGENTS_* env var; when that var is set (or the
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# provider itself came from env) the prompt is skipped and the configured
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# value is used — same env-precedence rule as the steps above. None = each
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# provider's own default.
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thinking_level = None
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reasoning_effort = None
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anthropic_effort = None
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provider_lower = selected_llm_provider.lower()
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if provider_from_env:
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thinking_level = DEFAULT_CONFIG["google_thinking_level"]
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reasoning_effort = DEFAULT_CONFIG["openai_reasoning_effort"]
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anthropic_effort = DEFAULT_CONFIG["anthropic_effort"]
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elif provider_lower == "google":
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thinking_level = thinking_value_or_prompt(
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"TRADINGAGENTS_GOOGLE_THINKING_LEVEL", "google_thinking_level",
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"Gemini thinking mode", "Step 8: Thinking Mode",
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"Configure Gemini thinking mode", ask_gemini_thinking_config,
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)
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elif provider_lower == "openai":
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reasoning_effort = thinking_value_or_prompt(
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"TRADINGAGENTS_OPENAI_REASONING_EFFORT", "openai_reasoning_effort",
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"Reasoning effort", "Step 8: Reasoning Effort",
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"Configure OpenAI reasoning effort level", ask_openai_reasoning_effort,
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)
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elif provider_lower == "anthropic":
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anthropic_effort = thinking_value_or_prompt(
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"TRADINGAGENTS_ANTHROPIC_EFFORT", "anthropic_effort",
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"Claude effort", "Step 8: Effort Level",
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"Configure Claude effort level", ask_anthropic_effort,
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)
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return {
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"ticker": selected_ticker,
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"asset_type": asset_type.value,
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"analysis_date": analysis_date,
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"analysts": selected_analysts,
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"research_depth": selected_research_depth,
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"llm_provider": selected_llm_provider.lower(),
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"backend_url": backend_url,
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"quick_think_llm": selected_shallow_thinker,
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"deep_think_llm": selected_deep_thinker,
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"google_thinking_level": thinking_level,
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"openai_reasoning_effort": reasoning_effort,
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"anthropic_effort": anthropic_effort,
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"output_language": output_language,
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}
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def get_analysis_date():
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"""Get the analysis date from user input."""
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while True:
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date_str = typer.prompt(
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"", default=datetime.datetime.now().strftime("%Y-%m-%d")
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)
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try:
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# Validate date format and ensure it's not in the future
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analysis_date = datetime.datetime.strptime(date_str, "%Y-%m-%d")
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if analysis_date.date() > datetime.datetime.now().date():
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console.print("[red]Error: Analysis date cannot be in the future[/red]")
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continue
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return date_str
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except ValueError:
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console.print(
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"[red]Error: Invalid date format. Please use YYYY-MM-DD[/red]"
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)
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