github-mirrors/tradingagents
1
0
Fork 0
mirror of https://github.com/TauricResearch/TradingAgents.git synced 2026-09-26 06:22:49 +03:00
Code Issues Packages Projects Releases Wiki Activity

refactor(cli): split the interactive choices and the run loop out of cli/main.py

Browse Source 
- 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
2026-09-24 05:00:36 +00:00
parent 56bd98f690
commit 4a30cb1c0a
11 changed files with 846 additions and 830 deletions
Download Patch File Download Diff File Expand all files Collapse all files
cli/main.py
+2 -712
View File
@@ -1,60 +1,12 @@
import datetime
import os
import sys import sys
import time
from functools import wraps
from pathlib import Path
import typer import typer
from rich.align import Align
from rich.live import Live
from rich.panel import Panel
from cli.announcements import display_announcements, fetch_announcements from cli.display import console
from cli.display import ( from cli.run import run_analysis
ANALYST_ORDER,
AnalystWallTimeTracker,
classify_message_type,
console,
create_layout,
display_complete_report,
message_buffer,
update_analyst_statuses,
update_display,
update_research_team_status,
)
from cli.prefs import load_last_run, sanitize, save_last_run
from cli.prompts import (
ask_anthropic_effort,
ask_gemini_thinking_config,
ask_glm_region,
ask_minimax_region,
ask_openai_reasoning_effort,
ask_output_language,
ask_qwen_region,
confirm_ollama_endpoint,
detect_asset_type,
ensure_api_key,
get_ticker,
prompt_openai_compatible_url,
resolve_backend_url,
select_analysts,
select_deep_thinking_agent,
select_llm_provider,
select_research_depth,
select_shallow_thinking_agent,
)
from cli.stats_handler import StatsCallbackHandler
from tradingagents.agents.rating import is_review
from tradingagents.backtest import iter_grid, run_backtest, summarize from tradingagents.backtest import iter_grid, run_backtest, summarize
from tradingagents.dataflows.symbols import safe_ticker_component
from tradingagents.default_config import DEFAULT_CONFIG from tradingagents.default_config import DEFAULT_CONFIG
from tradingagents.graph.analyst_execution import (
build_analyst_execution_plan,
)
from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.portfolio import load_portfolio from tradingagents.portfolio import load_portfolio
from tradingagents.reporting import write_report_tree
# prompt_toolkit's win32 output module is importable only on Windows (it asserts # prompt_toolkit's win32 output module is importable only on Windows (it asserts
# the platform at import time), so gate on the platform rather than catching the # the platform at import time), so gate on the platform rather than catching the
@@ -75,667 +27,6 @@ app = typer.Typer(
) )
def get_user_selections():
"""Ask for the run's settings, offering the previous run's answers."""
selections = _prompt_selections(load_last_run())
save_last_run(selections)
return selections
def _prompt_selections(prefs):
"""Walk the selection steps. ``prefs`` prefills, the environment skips."""
# Display ASCII art welcome message
with open(Path(__file__).parent / "static" / "welcome.txt", 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))
def thinking_value_or_prompt(env_var, config_key, label, box_title, box_body, prompt_fn):
"""Return the env-configured reasoning/thinking value, or prompt for it.
When ``env_var`` is set the interactive choice is skipped and the value
the env overlay placed on DEFAULT_CONFIG is used — mirroring the
env-precedence rule applied to the other selection steps.
"""
if os.environ.get(env_var):
value = DEFAULT_CONFIG[config_key]
console.print(f"[green]✓ {label} from environment:[/green] {value}")
return value
console.print(create_question_box(box_title, box_body))
return prompt_fn()
# Step 1: Ticker symbol
console.print(
create_question_box(
"Step 1: Ticker Symbol",
"Enter the ticker, with exchange suffix when needed (e.g. SPY, 0700.HK, BTC-USD)",
"SPY",
)
)
selected_ticker = get_ticker()
asset_type = detect_asset_type(selected_ticker)
# Only announce when it's not the default stock path, to avoid printing
# "stock" on every run.
if asset_type.value != "stock":
console.print(
f"[green]Detected asset type:[/green] {asset_type.value}"
)
# 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: Output language (skipped when set via TRADINGAGENTS_OUTPUT_LANGUAGE)
if os.environ.get("TRADINGAGENTS_OUTPUT_LANGUAGE"):
output_language = DEFAULT_CONFIG["output_language"]
console.print(
f"[green]✓ Output language from environment:[/green] {output_language}"
)
else:
console.print(
create_question_box(
"Step 3: Output Language",
"Select the language for analyst reports and final decision"
)
)
output_language = ask_output_language(prefs.get("output_language"))
# Step 4: Select analysts
console.print(
create_question_box(
"Step 4: Analysts Team", "Select your LLM analyst agents for the analysis"
)
)
prefs = sanitize(prefs, asset_type.value)
selected_analysts = select_analysts(asset_type, prefs.get("analysts"))
console.print(
f"[green]Selected analysts:[/green] {', '.join(analyst.value for analyst in selected_analysts)}"
)
# Step 5: Research depth (skipped when both round counts are set via env).
# Research depth maps to the debate + risk round counts; when both are
# supplied through TRADINGAGENTS_MAX_DEBATE_ROUNDS / _MAX_RISK_ROUNDS we keep
# the run non-interactive and honor the env values (#977).
depth_from_env = bool(os.environ.get("TRADINGAGENTS_MAX_DEBATE_ROUNDS")) and bool(
os.environ.get("TRADINGAGENTS_MAX_RISK_ROUNDS")
)
if depth_from_env:
selected_research_depth = DEFAULT_CONFIG["max_debate_rounds"]
console.print(
f"[green]✓ Research depth from environment:[/green] "
f"{DEFAULT_CONFIG['max_debate_rounds']} debate / "
f"{DEFAULT_CONFIG['max_risk_discuss_rounds']} risk rounds"
)
else:
console.print(
create_question_box(
"Step 5: Research Depth", "Select your research depth level"
)
)
selected_research_depth = select_research_depth(prefs.get("research_depth"))
# Step 6: LLM Provider (skipped when set via TRADINGAGENTS_LLM_PROVIDER).
# The backend URL comes from TRADINGAGENTS_LLM_BACKEND_URL when set,
# otherwise the provider's default endpoint — the same value the menu
# would have picked.
provider_from_env = bool(os.environ.get("TRADINGAGENTS_LLM_PROVIDER"))
if provider_from_env:
selected_llm_provider = DEFAULT_CONFIG["llm_provider"].lower()
backend_url = resolve_backend_url(
selected_llm_provider, env_url=DEFAULT_CONFIG["backend_url"]
)
console.print(f"[green]✓ LLM provider from environment:[/green] {selected_llm_provider}")
console.print(f"[green]✓ Backend URL:[/green] {backend_url}")
# Still confirm/persist the API key so the run doesn't fail later.
ensure_api_key(selected_llm_provider)
else:
console.print(
create_question_box(
"Step 6: LLM Provider", "Select your LLM provider"
)
)
selected_llm_provider, backend_url = select_llm_provider(prefs.get("llm_provider"))
# Providers with regional endpoints prompt for the region as a secondary
# step so the main dropdown stays clean (mainland China and international
# accounts cannot share API keys).
if selected_llm_provider == "qwen":
selected_llm_provider, backend_url = ask_qwen_region()
elif selected_llm_provider == "minimax":
selected_llm_provider, backend_url = ask_minimax_region()
elif selected_llm_provider == "glm":
selected_llm_provider, backend_url = ask_glm_region()
# Honor an explicit env backend URL even when the provider was chosen
# interactively, so it isn't overwritten by the menu default (#978).
backend_url = resolve_backend_url(
selected_llm_provider, backend_url, env_url=DEFAULT_CONFIG["backend_url"]
)
# The generic OpenAI-compatible endpoint has no default; ask for it if
# neither the menu nor the environment supplied one.
if selected_llm_provider == "openai_compatible" and not backend_url:
remembered_url = (prefs.get("backend_url")
if prefs.get("llm_provider") == selected_llm_provider else None)
backend_url = prompt_openai_compatible_url(remembered_url)
# For Ollama, surface the resolved endpoint (OLLAMA_BASE_URL vs default)
# before model selection so it's obvious where we're connecting.
if selected_llm_provider == "ollama":
confirm_ollama_endpoint(backend_url)
# Confirm the provider's API key is present; prompt the user to paste
# one and persist it to .env if it's missing, so the analysis run
# doesn't fail later at the first API call.
ensure_api_key(selected_llm_provider)
# Step 7: Thinking agents (skipped when either model is set via environment)
if os.environ.get("TRADINGAGENTS_QUICK_THINK_LLM") or os.environ.get("TRADINGAGENTS_DEEP_THINK_LLM"):
selected_shallow_thinker = DEFAULT_CONFIG["quick_think_llm"]
selected_deep_thinker = DEFAULT_CONFIG["deep_think_llm"]
console.print(
f"[green]✓ Thinking agents from environment:[/green] "
f"quick={selected_shallow_thinker}, deep={selected_deep_thinker}"
)
else:
console.print(
create_question_box(
"Step 7: Thinking Agents", "Select your thinking agents for analysis"
)
)
remembered = prefs if prefs.get("llm_provider") == selected_llm_provider else {}
selected_shallow_thinker = select_shallow_thinking_agent(
selected_llm_provider, remembered.get("quick_think_llm")
)
selected_deep_thinker = select_deep_thinking_agent(
selected_llm_provider, remembered.get("deep_think_llm")
)
# Step 8: Provider-specific reasoning/thinking configuration. Each knob is
# settable via its TRADINGAGENTS_* env var; when that var is set (or the
# provider itself came from env) the prompt is skipped and the configured
# value is used — same env-precedence rule as the steps above. None = each
# provider's own default.
thinking_level = None
reasoning_effort = None
anthropic_effort = None
provider_lower = selected_llm_provider.lower()
if provider_from_env:
thinking_level = DEFAULT_CONFIG["google_thinking_level"]
reasoning_effort = DEFAULT_CONFIG["openai_reasoning_effort"]
anthropic_effort = DEFAULT_CONFIG["anthropic_effort"]
elif provider_lower == "google":
thinking_level = thinking_value_or_prompt(
"TRADINGAGENTS_GOOGLE_THINKING_LEVEL", "google_thinking_level",
"Gemini thinking mode", "Step 8: Thinking Mode",
"Configure Gemini thinking mode", ask_gemini_thinking_config,
)
elif provider_lower == "openai":
reasoning_effort = thinking_value_or_prompt(
"TRADINGAGENTS_OPENAI_REASONING_EFFORT", "openai_reasoning_effort",
"Reasoning effort", "Step 8: Reasoning Effort",
"Configure OpenAI reasoning effort level", ask_openai_reasoning_effort,
)
elif provider_lower == "anthropic":
anthropic_effort = thinking_value_or_prompt(
"TRADINGAGENTS_ANTHROPIC_EFFORT", "anthropic_effort",
"Claude effort", "Step 8: Effort Level",
"Configure Claude effort level", ask_anthropic_effort,
)
return {
"ticker": selected_ticker,
"asset_type": asset_type.value,
"analysis_date": analysis_date,
"analysts": selected_analysts,
"research_depth": selected_research_depth,
"llm_provider": selected_llm_provider.lower(),
"backend_url": backend_url,
"quick_think_llm": selected_shallow_thinker,
"deep_think_llm": selected_deep_thinker,
"google_thinking_level": thinking_level,
"openai_reasoning_effort": reasoning_effort,
"anthropic_effort": anthropic_effort,
"output_language": output_language,
}
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 _run_directory(config: dict, ticker: str, trade_date: str) -> Path:
"""Where this run writes, with the ticker validated as a path component.
Every other path that interpolates a ticker checks it first; a value of
".." here would place the run outside the results directory.
"""
return Path(config["results_dir"]) / safe_ticker_component(ticker) / trade_date
def _announce_checkpoint_state(graph, ticker: str, trade_date: str) -> None:
"""Say whether this run resumed a saved one, where the user can see it.
The graph logs this, but nothing in the CLI configures logging and the live
view owns the screen, so a resume was invisible.
"""
if getattr(graph, "_resuming", False):
message_buffer.add_message(
"System", f"Resuming the saved run for {ticker} on {trade_date}"
)
else:
message_buffer.add_message("System", f"Starting fresh for {ticker} on {trade_date}")
def _build_run_config(selections: dict, checkpoint: bool | None) -> dict:
"""Assemble the run config from interactive selections, honoring env precedence.
Round counts and checkpoint follow "explicit env/flag wins": an env-applied
value on DEFAULT_CONFIG is preserved unless the user overrode it on the CLI.
"""
config = DEFAULT_CONFIG.copy()
# Research depth sets both round counts, but an explicit env override
# (TRADINGAGENTS_MAX_DEBATE_ROUNDS / _MAX_RISK_ROUNDS) wins over the
# interactive selection — leave the env-applied value in place (#977).
for env_var, key in (("TRADINGAGENTS_MAX_DEBATE_ROUNDS", "max_debate_rounds"),
("TRADINGAGENTS_MAX_RISK_ROUNDS", "max_risk_discuss_rounds")):
if os.environ.get(env_var):
# The depth prompt still appeared (it is skipped only when both are
# set), so say which half of the answer the environment overrode.
console.print(
f"[green]✓ {key} from environment:[/green] {config[key]} "
f"(set by {env_var}, so the research depth you chose does not apply to it)"
)
else:
config[key] = selections["research_depth"]
config["quick_think_llm"] = selections["quick_think_llm"]
config["deep_think_llm"] = selections["deep_think_llm"]
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")
config["anthropic_effort"] = selections.get("anthropic_effort")
config["output_language"] = selections.get("output_language", "English")
# --checkpoint/--no-checkpoint overrides only when explicitly given; omitting
# the flag preserves TRADINGAGENTS_CHECKPOINT_ENABLED / the default (#976).
if checkpoint is not None:
config["checkpoint_enabled"] = checkpoint
return config
def run_analysis(checkpoint: bool | None = None, portfolio=None):
# First get all user selections
selections = get_user_selections()
config = _build_run_config(selections, checkpoint)
# 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]
analyst_execution_plan = build_analyst_execution_plan(selected_analyst_keys)
analyst_wall_time_tracker = AnalystWallTimeTracker(analyst_execution_plan)
# 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 = _run_directory(config, 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"
text = "\n".join(str(item) for item in content) if isinstance(content, list) else content
with open(report_dir / file_name, "w", encoding="utf-8") as f:
f.write(text)
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()
# The alternate screen keeps a layout taller than the window from redrawing
# by scrolling; the final report prints after this block, on the normal screen.
with Live(layout, refresh_per_second=4, screen=True):
# 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']}")
if selections["asset_type"] != "stock":
message_buffer.add_message("System", f"Detected asset type: {selections['asset_type']}")
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 = analyst_execution_plan.specs[0].agent_node
message_buffer.update_agent_status(first_analyst, "in_progress")
analyst_wall_time_tracker.mark_started(selected_analyst_keys[0])
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)
# The same initial state propagate() builds: settled decision log, past
# context and resolved instrument identity.
init_agent_state = graph.create_run_state(
selections["ticker"], selections["analysis_date"], selections["asset_type"], portfolio
)
# 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])
# Recompile with a checkpointer and inject the thread_id so --checkpoint
# actually saves and resumes on the CLI path (#1249); a no-op when
# checkpointing is disabled. Torn down in the finally below.
checkpoint_tid = graph.begin_checkpoint(
selections["ticker"], selections["analysis_date"], selections["asset_type"], portfolio
)
if checkpoint_tid is not None:
args.setdefault("config", {}).setdefault("configurable", {})["thread_id"] = checkpoint_tid
_announce_checkpoint_state(graph, selections["ticker"], selections["analysis_date"])
# Stream the analysis. On resume, feed None so LangGraph continues the
# interrupted run instead of re-appending the initial state (#1249); the
# try/finally tears the checkpointer down even if the stream raises.
trace = []
try:
for chunk in graph.graph.stream(graph.checkpoint_input(init_agent_state), **args):
# Process all messages in chunk, deduplicating by message ID
for message in chunk.get("messages", []):
msg_id = getattr(message, "id", None)
if msg_id is not None:
if msg_id in message_buffer._processed_message_ids:
continue
message_buffer._processed_message_ids.add(msg_id)
msg_type, content = classify_message_type(message)
if content and content.strip():
message_buffer.add_message(msg_type, content)
if hasattr(message, "tool_calls") and message.tool_calls:
for tool_call in 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,
wall_time_tracker=analyst_wall_time_tracker,
)
# 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 and 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)
# Streamed chunks are per-node deltas, not full state. Merge them
# so every report field populated across the run is present.
final_state = {}
for chunk in trace:
final_state.update(chunk)
# Clean run: log the decision, then drop this run's checkpoint so a
# later run starts fresh. A mid-stream failure skips both, keeping
# the checkpoint for resume.
graph.record_decision(selections["ticker"], selections["analysis_date"], final_state)
graph.clear_checkpoint_on_success(
selections["ticker"], selections["analysis_date"], selections["asset_type"], portfolio
)
finally:
# Always restore the plain uncheckpointed graph, even on failure.
graph.end_checkpoint()
# 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']}"
)
message_buffer.add_message("System", analyst_wall_time_tracker.format_summary())
# Update final report sections
for section in message_buffer.report_sections:
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")
# A decision nobody can read is not a position. Say so here rather than
# leaving the run to look like a normal result.
if is_review(graph.process_signal(final_state.get("final_trade_decision", ""))):
console.print(
"[yellow]No rating could be read from the final decision, so this run "
"is recorded for review rather than as a position. Re-run, or read the "
"decision text below and judge it yourself.[/yellow]\n"
)
console.print(f"[dim]{analyst_wall_time_tracker.format_summary()}[/dim]")
# 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")
# Under results_dir, not the working directory: in Docker the working
# directory is inside the container and the report goes with it, while
# results_dir is the mounted volume the rest of the run already writes to.
default_path = (Path(config["results_dir"]) / "reports"
/ f"{safe_ticker_component(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 = write_report_tree(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.callback(invoke_without_command=True) @app.callback(invoke_without_command=True)
def analyze( def analyze(
ctx: typer.Context, ctx: typer.Context,
@@ -766,7 +57,6 @@ def analyze(
console.print(f"[yellow]Cleared {n} checkpoint(s).[/yellow]") console.print(f"[yellow]Cleared {n} checkpoint(s).[/yellow]")
portfolio_context = None portfolio_context = None
if portfolio: if portfolio:
from tradingagents.portfolio import load_portfolio
try: try:
portfolio_context = load_portfolio(portfolio) portfolio_context = load_portfolio(portfolio)
except ValueError as exc: except ValueError as exc:
cli/run.py
+406
View File
@@ -0,0 +1,406 @@
"""Running one analysis from the CLI: build the graph, stream it into the live view, save the report."""
import datetime
import os
import time
from functools import wraps
from pathlib import Path
import typer
from rich.live import Live
from cli.display import (
ANALYST_ORDER,
AnalystWallTimeTracker,
classify_message_type,
console,
create_layout,
display_complete_report,
message_buffer,
update_analyst_statuses,
update_display,
update_research_team_status,
)
from cli.selections import get_user_selections
from cli.stats_handler import StatsCallbackHandler
from tradingagents.agents.rating import is_review
from tradingagents.dataflows.symbols import safe_ticker_component
from tradingagents.default_config import DEFAULT_CONFIG
from tradingagents.graph.analyst_execution import (
build_analyst_execution_plan,
)
from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.reporting import write_report_tree
def _run_directory(config: dict, ticker: str, trade_date: str) -> Path:
"""Where this run writes, with the ticker validated as a path component.
Every other path that interpolates a ticker checks it first; a value of
".." here would place the run outside the results directory.
"""
return Path(config["results_dir"]) / safe_ticker_component(ticker) / trade_date
def _announce_checkpoint_state(graph, ticker: str, trade_date: str) -> None:
"""Say whether this run resumed a saved one, where the user can see it.
The graph logs this, but nothing in the CLI configures logging and the live
view owns the screen, so a resume was invisible.
"""
if getattr(graph, "_resuming", False):
message_buffer.add_message(
"System", f"Resuming the saved run for {ticker} on {trade_date}"
)
else:
message_buffer.add_message("System", f"Starting fresh for {ticker} on {trade_date}")
def _build_run_config(selections: dict, checkpoint: bool | None) -> dict:
"""Assemble the run config from interactive selections, honoring env precedence.
Round counts and checkpoint follow "explicit env/flag wins": an env-applied
value on DEFAULT_CONFIG is preserved unless the user overrode it on the CLI.
"""
config = DEFAULT_CONFIG.copy()
# Research depth sets both round counts, but an explicit env override
# (TRADINGAGENTS_MAX_DEBATE_ROUNDS / _MAX_RISK_ROUNDS) wins over the
# interactive selection — leave the env-applied value in place (#977).
for env_var, key in (("TRADINGAGENTS_MAX_DEBATE_ROUNDS", "max_debate_rounds"),
("TRADINGAGENTS_MAX_RISK_ROUNDS", "max_risk_discuss_rounds")):
if os.environ.get(env_var):
# The depth prompt still appeared (it is skipped only when both are
# set), so say which half of the answer the environment overrode.
console.print(
f"[green]✓ {key} from environment:[/green] {config[key]} "
f"(set by {env_var}, so the research depth you chose does not apply to it)"
)
else:
config[key] = selections["research_depth"]
config["quick_think_llm"] = selections["quick_think_llm"]
config["deep_think_llm"] = selections["deep_think_llm"]
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")
config["anthropic_effort"] = selections.get("anthropic_effort")
config["output_language"] = selections.get("output_language", "English")
# --checkpoint/--no-checkpoint overrides only when explicitly given; omitting
# the flag preserves TRADINGAGENTS_CHECKPOINT_ENABLED / the default (#976).
if checkpoint is not None:
config["checkpoint_enabled"] = checkpoint
return config
def run_analysis(checkpoint: bool | None = None, portfolio=None):
# First get all user selections
selections = get_user_selections()
config = _build_run_config(selections, checkpoint)
# 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]
analyst_execution_plan = build_analyst_execution_plan(selected_analyst_keys)
analyst_wall_time_tracker = AnalystWallTimeTracker(analyst_execution_plan)
# 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 = _run_directory(config, 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"
text = "\n".join(str(item) for item in content) if isinstance(content, list) else content
with open(report_dir / file_name, "w", encoding="utf-8") as f:
f.write(text)
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()
# The alternate screen keeps a layout taller than the window from redrawing
# by scrolling; the final report prints after this block, on the normal screen.
with Live(layout, refresh_per_second=4, screen=True):
# 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']}")
if selections["asset_type"] != "stock":
message_buffer.add_message("System", f"Detected asset type: {selections['asset_type']}")
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 = analyst_execution_plan.specs[0].agent_node
message_buffer.update_agent_status(first_analyst, "in_progress")
analyst_wall_time_tracker.mark_started(selected_analyst_keys[0])
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)
# The same initial state propagate() builds: settled decision log, past
# context and resolved instrument identity.
init_agent_state = graph.create_run_state(
selections["ticker"], selections["analysis_date"], selections["asset_type"], portfolio
)
# 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])
# Recompile with a checkpointer and inject the thread_id so --checkpoint
# actually saves and resumes on the CLI path (#1249); a no-op when
# checkpointing is disabled. Torn down in the finally below.
checkpoint_tid = graph.begin_checkpoint(
selections["ticker"], selections["analysis_date"], selections["asset_type"], portfolio
)
if checkpoint_tid is not None:
args.setdefault("config", {}).setdefault("configurable", {})["thread_id"] = checkpoint_tid
_announce_checkpoint_state(graph, selections["ticker"], selections["analysis_date"])
# Stream the analysis. On resume, feed None so LangGraph continues the
# interrupted run instead of re-appending the initial state (#1249); the
# try/finally tears the checkpointer down even if the stream raises.
trace = []
try:
for chunk in graph.graph.stream(graph.checkpoint_input(init_agent_state), **args):
# Process all messages in chunk, deduplicating by message ID
for message in chunk.get("messages", []):
msg_id = getattr(message, "id", None)
if msg_id is not None:
if msg_id in message_buffer._processed_message_ids:
continue
message_buffer._processed_message_ids.add(msg_id)
msg_type, content = classify_message_type(message)
if content and content.strip():
message_buffer.add_message(msg_type, content)
if hasattr(message, "tool_calls") and message.tool_calls:
for tool_call in 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,
wall_time_tracker=analyst_wall_time_tracker,
)
# 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 and 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)
# Streamed chunks are per-node deltas, not full state. Merge them
# so every report field populated across the run is present.
final_state = {}
for chunk in trace:
final_state.update(chunk)
# Clean run: log the decision, then drop this run's checkpoint so a
# later run starts fresh. A mid-stream failure skips both, keeping
# the checkpoint for resume.
graph.record_decision(selections["ticker"], selections["analysis_date"], final_state)
graph.clear_checkpoint_on_success(
selections["ticker"], selections["analysis_date"], selections["asset_type"], portfolio
)
finally:
# Always restore the plain uncheckpointed graph, even on failure.
graph.end_checkpoint()
# 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']}"
)
message_buffer.add_message("System", analyst_wall_time_tracker.format_summary())
# Update final report sections
for section in message_buffer.report_sections:
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")
# A decision nobody can read is not a position. Say so here rather than
# leaving the run to look like a normal result.
if is_review(graph.process_signal(final_state.get("final_trade_decision", ""))):
console.print(
"[yellow]No rating could be read from the final decision, so this run "
"is recorded for review rather than as a position. Re-run, or read the "
"decision text below and judge it yourself.[/yellow]\n"
)
console.print(f"[dim]{analyst_wall_time_tracker.format_summary()}[/dim]")
# 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")
# Under results_dir, not the working directory: in Docker the working
# directory is inside the container and the report goes with it, while
# results_dir is the mounted volume the rest of the run already writes to.
default_path = (Path(config["results_dir"]) / "reports"
/ f"{safe_ticker_component(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 = write_report_tree(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)
cli/selections.py
+319
View File
@@ -0,0 +1,319 @@
"""The interactive choices for a run: ticker, date, analysts, depth, provider and models."""
import datetime
import os
from pathlib import Path
import typer
from rich.align import Align
from rich.panel import Panel
from cli.announcements import display_announcements, fetch_announcements
from cli.display import (
console,
)
from cli.prefs import load_last_run, sanitize, save_last_run
from cli.prompts import (
ask_anthropic_effort,
ask_gemini_thinking_config,
ask_glm_region,
ask_minimax_region,
ask_openai_reasoning_effort,
ask_output_language,
ask_qwen_region,
confirm_ollama_endpoint,
detect_asset_type,
ensure_api_key,
get_ticker,
prompt_openai_compatible_url,
resolve_backend_url,
select_analysts,
select_deep_thinking_agent,
select_llm_provider,
select_research_depth,
select_shallow_thinking_agent,
)
from tradingagents.default_config import DEFAULT_CONFIG
def get_user_selections():
"""Ask for the run's settings, offering the previous run's answers."""
selections = _prompt_selections(load_last_run())
save_last_run(selections)
return selections
def _prompt_selections(prefs):
"""Walk the selection steps. ``prefs`` prefills, the environment skips."""
# Display ASCII art welcome message
with open(Path(__file__).parent / "static" / "welcome.txt", 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))
def thinking_value_or_prompt(env_var, config_key, label, box_title, box_body, prompt_fn):
"""Return the env-configured reasoning/thinking value, or prompt for it.
When ``env_var`` is set the interactive choice is skipped and the value
the env overlay placed on DEFAULT_CONFIG is used — mirroring the
env-precedence rule applied to the other selection steps.
"""
if os.environ.get(env_var):
value = DEFAULT_CONFIG[config_key]
console.print(f"[green]✓ {label} from environment:[/green] {value}")
return value
console.print(create_question_box(box_title, box_body))
return prompt_fn()
# Step 1: Ticker symbol
console.print(
create_question_box(
"Step 1: Ticker Symbol",
"Enter the ticker, with exchange suffix when needed (e.g. SPY, 0700.HK, BTC-USD)",
"SPY",
)
)
selected_ticker = get_ticker()
asset_type = detect_asset_type(selected_ticker)
# Only announce when it's not the default stock path, to avoid printing
# "stock" on every run.
if asset_type.value != "stock":
console.print(
f"[green]Detected asset type:[/green] {asset_type.value}"
)
# 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: Output language (skipped when set via TRADINGAGENTS_OUTPUT_LANGUAGE)
if os.environ.get("TRADINGAGENTS_OUTPUT_LANGUAGE"):
output_language = DEFAULT_CONFIG["output_language"]
console.print(
f"[green]✓ Output language from environment:[/green] {output_language}"
)
else:
console.print(
create_question_box(
"Step 3: Output Language",
"Select the language for analyst reports and final decision"
)
)
output_language = ask_output_language(prefs.get("output_language"))
# Step 4: Select analysts
console.print(
create_question_box(
"Step 4: Analysts Team", "Select your LLM analyst agents for the analysis"
)
)
prefs = sanitize(prefs, asset_type.value)
selected_analysts = select_analysts(asset_type, prefs.get("analysts"))
console.print(
f"[green]Selected analysts:[/green] {', '.join(analyst.value for analyst in selected_analysts)}"
)
# Step 5: Research depth (skipped when both round counts are set via env).
# Research depth maps to the debate + risk round counts; when both are
# supplied through TRADINGAGENTS_MAX_DEBATE_ROUNDS / _MAX_RISK_ROUNDS we keep
# the run non-interactive and honor the env values (#977).
depth_from_env = bool(os.environ.get("TRADINGAGENTS_MAX_DEBATE_ROUNDS")) and bool(
os.environ.get("TRADINGAGENTS_MAX_RISK_ROUNDS")
)
if depth_from_env:
selected_research_depth = DEFAULT_CONFIG["max_debate_rounds"]
console.print(
f"[green]✓ Research depth from environment:[/green] "
f"{DEFAULT_CONFIG['max_debate_rounds']} debate / "
f"{DEFAULT_CONFIG['max_risk_discuss_rounds']} risk rounds"
)
else:
console.print(
create_question_box(
"Step 5: Research Depth", "Select your research depth level"
)
)
selected_research_depth = select_research_depth(prefs.get("research_depth"))
# Step 6: LLM Provider (skipped when set via TRADINGAGENTS_LLM_PROVIDER).
# The backend URL comes from TRADINGAGENTS_LLM_BACKEND_URL when set,
# otherwise the provider's default endpoint — the same value the menu
# would have picked.
provider_from_env = bool(os.environ.get("TRADINGAGENTS_LLM_PROVIDER"))
if provider_from_env:
selected_llm_provider = DEFAULT_CONFIG["llm_provider"].lower()
backend_url = resolve_backend_url(
selected_llm_provider, env_url=DEFAULT_CONFIG["backend_url"]
)
console.print(f"[green]✓ LLM provider from environment:[/green] {selected_llm_provider}")
console.print(f"[green]✓ Backend URL:[/green] {backend_url}")
# Still confirm/persist the API key so the run doesn't fail later.
ensure_api_key(selected_llm_provider)
else:
console.print(
create_question_box(
"Step 6: LLM Provider", "Select your LLM provider"
)
)
selected_llm_provider, backend_url = select_llm_provider(prefs.get("llm_provider"))
# Providers with regional endpoints prompt for the region as a secondary
# step so the main dropdown stays clean (mainland China and international
# accounts cannot share API keys).
if selected_llm_provider == "qwen":
selected_llm_provider, backend_url = ask_qwen_region()
elif selected_llm_provider == "minimax":
selected_llm_provider, backend_url = ask_minimax_region()
elif selected_llm_provider == "glm":
selected_llm_provider, backend_url = ask_glm_region()
# Honor an explicit env backend URL even when the provider was chosen
# interactively, so it isn't overwritten by the menu default (#978).
backend_url = resolve_backend_url(
selected_llm_provider, backend_url, env_url=DEFAULT_CONFIG["backend_url"]
)
# The generic OpenAI-compatible endpoint has no default; ask for it if
# neither the menu nor the environment supplied one.
if selected_llm_provider == "openai_compatible" and not backend_url:
remembered_url = (prefs.get("backend_url")
if prefs.get("llm_provider") == selected_llm_provider else None)
backend_url = prompt_openai_compatible_url(remembered_url)
# For Ollama, surface the resolved endpoint (OLLAMA_BASE_URL vs default)
# before model selection so it's obvious where we're connecting.
if selected_llm_provider == "ollama":
confirm_ollama_endpoint(backend_url)
# Confirm the provider's API key is present; prompt the user to paste
# one and persist it to .env if it's missing, so the analysis run
# doesn't fail later at the first API call.
ensure_api_key(selected_llm_provider)
# Step 7: Thinking agents (skipped when either model is set via environment)
if os.environ.get("TRADINGAGENTS_QUICK_THINK_LLM") or os.environ.get("TRADINGAGENTS_DEEP_THINK_LLM"):
selected_shallow_thinker = DEFAULT_CONFIG["quick_think_llm"]
selected_deep_thinker = DEFAULT_CONFIG["deep_think_llm"]
console.print(
f"[green]✓ Thinking agents from environment:[/green] "
f"quick={selected_shallow_thinker}, deep={selected_deep_thinker}"
)
else:
console.print(
create_question_box(
"Step 7: Thinking Agents", "Select your thinking agents for analysis"
)
)
remembered = prefs if prefs.get("llm_provider") == selected_llm_provider else {}
selected_shallow_thinker = select_shallow_thinking_agent(
selected_llm_provider, remembered.get("quick_think_llm")
)
selected_deep_thinker = select_deep_thinking_agent(
selected_llm_provider, remembered.get("deep_think_llm")
)
# Step 8: Provider-specific reasoning/thinking configuration. Each knob is
# settable via its TRADINGAGENTS_* env var; when that var is set (or the
# provider itself came from env) the prompt is skipped and the configured
# value is used — same env-precedence rule as the steps above. None = each
# provider's own default.
thinking_level = None
reasoning_effort = None
anthropic_effort = None
provider_lower = selected_llm_provider.lower()
if provider_from_env:
thinking_level = DEFAULT_CONFIG["google_thinking_level"]
reasoning_effort = DEFAULT_CONFIG["openai_reasoning_effort"]
anthropic_effort = DEFAULT_CONFIG["anthropic_effort"]
elif provider_lower == "google":
thinking_level = thinking_value_or_prompt(
"TRADINGAGENTS_GOOGLE_THINKING_LEVEL", "google_thinking_level",
"Gemini thinking mode", "Step 8: Thinking Mode",
"Configure Gemini thinking mode", ask_gemini_thinking_config,
)
elif provider_lower == "openai":
reasoning_effort = thinking_value_or_prompt(
"TRADINGAGENTS_OPENAI_REASONING_EFFORT", "openai_reasoning_effort",
"Reasoning effort", "Step 8: Reasoning Effort",
"Configure OpenAI reasoning effort level", ask_openai_reasoning_effort,
)
elif provider_lower == "anthropic":
anthropic_effort = thinking_value_or_prompt(
"TRADINGAGENTS_ANTHROPIC_EFFORT", "anthropic_effort",
"Claude effort", "Step 8: Effort Level",
"Configure Claude effort level", ask_anthropic_effort,
)
return {
"ticker": selected_ticker,
"asset_type": asset_type.value,
"analysis_date": analysis_date,
"analysts": selected_analysts,
"research_depth": selected_research_depth,
"llm_provider": selected_llm_provider.lower(),
"backend_url": backend_url,
"quick_think_llm": selected_shallow_thinker,
"deep_think_llm": selected_deep_thinker,
"google_thinking_level": thinking_level,
"openai_reasoning_effort": reasoning_effort,
"anthropic_effort": anthropic_effort,
"output_language": output_language,
}
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]"
)
tests/test_cli_config_precedence.py
+15 -15
View File
@@ -10,6 +10,7 @@ from unittest import mock
import pytest import pytest
import cli.main as m import cli.main as m
import cli.run as cli_run
# Minimal selections dict shaped like get_user_selections()'s return value. # Minimal selections dict shaped like get_user_selections()'s return value.
SELECTIONS = { SELECTIONS = {
@@ -28,7 +29,7 @@ SELECTIONS = {
def test_research_depth_sets_both_rounds_without_env(monkeypatch): def test_research_depth_sets_both_rounds_without_env(monkeypatch):
for var in ("TRADINGAGENTS_MAX_DEBATE_ROUNDS", "TRADINGAGENTS_MAX_RISK_ROUNDS"): for var in ("TRADINGAGENTS_MAX_DEBATE_ROUNDS", "TRADINGAGENTS_MAX_RISK_ROUNDS"):
monkeypatch.delenv(var, raising=False) monkeypatch.delenv(var, raising=False)
cfg = m._build_run_config(SELECTIONS, checkpoint=None) cfg = cli_run._build_run_config(SELECTIONS, checkpoint=None)
assert cfg["max_debate_rounds"] == 5 assert cfg["max_debate_rounds"] == 5
assert cfg["max_risk_discuss_rounds"] == 5 assert cfg["max_risk_discuss_rounds"] == 5
@@ -37,9 +38,9 @@ def test_env_round_counts_win_over_selection(monkeypatch):
monkeypatch.setenv("TRADINGAGENTS_MAX_DEBATE_ROUNDS", "2") monkeypatch.setenv("TRADINGAGENTS_MAX_DEBATE_ROUNDS", "2")
monkeypatch.setenv("TRADINGAGENTS_MAX_RISK_ROUNDS", "4") monkeypatch.setenv("TRADINGAGENTS_MAX_RISK_ROUNDS", "4")
# DEFAULT_CONFIG already reflects the env (applied at import); emulate that. # DEFAULT_CONFIG already reflects the env (applied at import); emulate that.
patched = dict(m.DEFAULT_CONFIG, max_debate_rounds=2, max_risk_discuss_rounds=4) patched = dict(cli_run.DEFAULT_CONFIG, max_debate_rounds=2, max_risk_discuss_rounds=4)
with mock.patch.object(m, "DEFAULT_CONFIG", patched): with mock.patch.object(cli_run, "DEFAULT_CONFIG", patched):
cfg = m._build_run_config(SELECTIONS, checkpoint=None) cfg = cli_run._build_run_config(SELECTIONS, checkpoint=None)
assert cfg["max_debate_rounds"] == 2 # env value, not research_depth=5 assert cfg["max_debate_rounds"] == 2 # env value, not research_depth=5
assert cfg["max_risk_discuss_rounds"] == 4 assert cfg["max_risk_discuss_rounds"] == 4
@@ -47,25 +48,25 @@ def test_env_round_counts_win_over_selection(monkeypatch):
def test_partial_env_only_overrides_that_count(monkeypatch): def test_partial_env_only_overrides_that_count(monkeypatch):
monkeypatch.setenv("TRADINGAGENTS_MAX_DEBATE_ROUNDS", "2") monkeypatch.setenv("TRADINGAGENTS_MAX_DEBATE_ROUNDS", "2")
monkeypatch.delenv("TRADINGAGENTS_MAX_RISK_ROUNDS", raising=False) monkeypatch.delenv("TRADINGAGENTS_MAX_RISK_ROUNDS", raising=False)
patched = dict(m.DEFAULT_CONFIG, max_debate_rounds=2) patched = dict(cli_run.DEFAULT_CONFIG, max_debate_rounds=2)
with mock.patch.object(m, "DEFAULT_CONFIG", patched): with mock.patch.object(cli_run, "DEFAULT_CONFIG", patched):
cfg = m._build_run_config(SELECTIONS, checkpoint=None) cfg = cli_run._build_run_config(SELECTIONS, checkpoint=None)
assert cfg["max_debate_rounds"] == 2 # env wins assert cfg["max_debate_rounds"] == 2 # env wins
assert cfg["max_risk_discuss_rounds"] == 5 # falls through to research_depth assert cfg["max_risk_discuss_rounds"] == 5 # falls through to research_depth
def test_checkpoint_none_preserves_env_default(): def test_checkpoint_none_preserves_env_default():
patched = dict(m.DEFAULT_CONFIG, checkpoint_enabled=True) # e.g. env-enabled patched = dict(cli_run.DEFAULT_CONFIG, checkpoint_enabled=True) # e.g. env-enabled
with mock.patch.object(m, "DEFAULT_CONFIG", patched): with mock.patch.object(cli_run, "DEFAULT_CONFIG", patched):
cfg = m._build_run_config(SELECTIONS, checkpoint=None) cfg = cli_run._build_run_config(SELECTIONS, checkpoint=None)
assert cfg["checkpoint_enabled"] is True # not clobbered back to False assert cfg["checkpoint_enabled"] is True # not clobbered back to False
@pytest.mark.parametrize("flag", [True, False]) @pytest.mark.parametrize("flag", [True, False])
def test_checkpoint_flag_overrides_env(flag): def test_checkpoint_flag_overrides_env(flag):
patched = dict(m.DEFAULT_CONFIG, checkpoint_enabled=not flag) patched = dict(cli_run.DEFAULT_CONFIG, checkpoint_enabled=not flag)
with mock.patch.object(m, "DEFAULT_CONFIG", patched): with mock.patch.object(cli_run, "DEFAULT_CONFIG", patched):
cfg = m._build_run_config(SELECTIONS, checkpoint=flag) cfg = cli_run._build_run_config(SELECTIONS, checkpoint=flag)
assert cfg["checkpoint_enabled"] is flag assert cfg["checkpoint_enabled"] is flag
@@ -89,14 +90,13 @@ def test_glm_resolves_to_the_endpoint_its_key_belongs_to():
def test_a_half_set_round_count_says_which_value_won(capsys, monkeypatch): def test_a_half_set_round_count_says_which_value_won(capsys, monkeypatch):
"""With only one of the two round-count variables set, the depth prompt is """With only one of the two round-count variables set, the depth prompt is
still shown but half the answer is discarded; the user was never told.""" still shown but half the answer is discarded; the user was never told."""
import cli.main as m
monkeypatch.setenv("TRADINGAGENTS_MAX_DEBATE_ROUNDS", "1") monkeypatch.setenv("TRADINGAGENTS_MAX_DEBATE_ROUNDS", "1")
monkeypatch.delenv("TRADINGAGENTS_MAX_RISK_ROUNDS", raising=False) monkeypatch.delenv("TRADINGAGENTS_MAX_RISK_ROUNDS", raising=False)
printed = [] printed = []
monkeypatch.setattr(m.console, "print", lambda *a, **k: printed.append(str(a[0]) if a else "")) monkeypatch.setattr(m.console, "print", lambda *a, **k: printed.append(str(a[0]) if a else ""))
config = m._build_run_config({ config = cli_run._build_run_config({
"ticker": "NVDA", "analysis_date": "2026-09-01", "asset_type": "stock", "ticker": "NVDA", "analysis_date": "2026-09-01", "asset_type": "stock",
"analysts": [], "research_depth": 5, "llm_provider": "openai", "analysts": [], "research_depth": 5, "llm_provider": "openai",
"quick_think_llm": "gpt-5.6-luna", "deep_think_llm": "gpt-5.6", "quick_think_llm": "gpt-5.6-luna", "deep_think_llm": "gpt-5.6",
tests/test_cli_decision_log.py
+9 -8
View File
@@ -11,6 +11,7 @@ from __future__ import annotations
import pytest import pytest
import cli.run as cli_run
from tradingagents.decision_log import TradingMemoryLog from tradingagents.decision_log import TradingMemoryLog
from tradingagents.graph.trading_graph import TradingAgentsGraph from tradingagents.graph.trading_graph import TradingAgentsGraph
@@ -145,20 +146,20 @@ def _run_cli(monkeypatch, tmp_path, fake):
from cli.models import AnalystType from cli.models import AnalystType
buffer = _FakeBuffer() buffer = _FakeBuffer()
monkeypatch.setattr(m, "TradingAgentsGraph", lambda *a, **k: fake) monkeypatch.setattr(cli_run, "TradingAgentsGraph", lambda *a, **k: fake)
monkeypatch.setattr(m, "message_buffer", buffer) monkeypatch.setattr(cli_run, "message_buffer", buffer)
monkeypatch.setattr(m, "create_layout", lambda: None) monkeypatch.setattr(cli_run, "create_layout", lambda: None)
monkeypatch.setattr(m, "update_display", lambda *a, **k: None) monkeypatch.setattr(cli_run, "update_display", lambda *a, **k: None)
monkeypatch.setattr(m, "Live", _NullLive) monkeypatch.setattr(cli_run, "Live", _NullLive)
monkeypatch.setattr(m, "get_user_selections", lambda: { monkeypatch.setattr(cli_run, "get_user_selections", lambda: {
"ticker": "NVDA", "analysis_date": "2026-01-10", "ticker": "NVDA", "analysis_date": "2026-01-10",
"analysts": [AnalystType.MARKET], "asset_type": "stock", "analysts": [AnalystType.MARKET], "asset_type": "stock",
}) })
monkeypatch.setattr(m, "_build_run_config", lambda selections, checkpoint: { monkeypatch.setattr(cli_run, "_build_run_config", lambda selections, checkpoint: {
"data_cache_dir": str(tmp_path / "cache"), "results_dir": str(tmp_path / "results"), "data_cache_dir": str(tmp_path / "cache"), "results_dir": str(tmp_path / "results"),
}) })
monkeypatch.setattr(m.typer, "prompt", lambda *a, **k: "N") monkeypatch.setattr(m.typer, "prompt", lambda *a, **k: "N")
m.run_analysis() cli_run.run_analysis()
return buffer return buffer
tests/test_cli_env_skip.py
+46 -47
View File
@@ -11,6 +11,8 @@ from unittest import mock
import pytest import pytest
import cli.selections as cli_selections
@pytest.mark.unit @pytest.mark.unit
class TestProviderDefaultUrl(unittest.TestCase): class TestProviderDefaultUrl(unittest.TestCase):
@@ -33,7 +35,6 @@ class TestProviderDefaultUrl(unittest.TestCase):
@pytest.mark.unit @pytest.mark.unit
class TestCliSkipsPromptsFromEnv(unittest.TestCase): class TestCliSkipsPromptsFromEnv(unittest.TestCase):
def test_env_config_skips_llm_prompts(self): def test_env_config_skips_llm_prompts(self):
import cli.main as m
env = { env = {
"TRADINGAGENTS_LLM_PROVIDER": "openai", "TRADINGAGENTS_LLM_PROVIDER": "openai",
@@ -42,7 +43,7 @@ class TestCliSkipsPromptsFromEnv(unittest.TestCase):
"TRADINGAGENTS_LLM_BACKEND_URL": "https://opencode.ai/zen/go/v1", "TRADINGAGENTS_LLM_BACKEND_URL": "https://opencode.ai/zen/go/v1",
"TRADINGAGENTS_OUTPUT_LANGUAGE": "Japanese", "TRADINGAGENTS_OUTPUT_LANGUAGE": "Japanese",
} }
fake_cfg = dict(m.DEFAULT_CONFIG) fake_cfg = dict(cli_selections.DEFAULT_CONFIG)
fake_cfg.update({ fake_cfg.update({
"llm_provider": "openai", "llm_provider": "openai",
"backend_url": "https://opencode.ai/zen/go/v1", "backend_url": "https://opencode.ai/zen/go/v1",
@@ -52,19 +53,19 @@ class TestCliSkipsPromptsFromEnv(unittest.TestCase):
}) })
with mock.patch.dict(os.environ, env, clear=False), \ with mock.patch.dict(os.environ, env, clear=False), \
mock.patch.object(m, "DEFAULT_CONFIG", fake_cfg), \ mock.patch.object(cli_selections, "DEFAULT_CONFIG", fake_cfg), \
mock.patch.object(m, "fetch_announcements", return_value=None), \ mock.patch.object(cli_selections, "fetch_announcements", return_value=None), \
mock.patch.object(m, "display_announcements"), \ mock.patch.object(cli_selections, "display_announcements"), \
mock.patch.object(m, "get_ticker", return_value="AAPL"), \ mock.patch.object(cli_selections, "get_ticker", return_value="AAPL"), \
mock.patch.object(m, "get_analysis_date", return_value="2026-05-29"), \ mock.patch.object(cli_selections, "get_analysis_date", return_value="2026-05-29"), \
mock.patch.object(m, "select_analysts", return_value=[]), \ mock.patch.object(cli_selections, "select_analysts", return_value=[]), \
mock.patch.object(m, "select_research_depth", return_value=1), \ mock.patch.object(cli_selections, "select_research_depth", return_value=1), \
mock.patch.object(m, "ensure_api_key") as ensure_key, \ mock.patch.object(cli_selections, "ensure_api_key") as ensure_key, \
mock.patch.object(m, "select_llm_provider") as prompt_provider, \ mock.patch.object(cli_selections, "select_llm_provider") as prompt_provider, \
mock.patch.object(m, "ask_output_language") as prompt_lang, \ mock.patch.object(cli_selections, "ask_output_language") as prompt_lang, \
mock.patch.object(m, "select_shallow_thinking_agent") as prompt_quick, \ mock.patch.object(cli_selections, "select_shallow_thinking_agent") as prompt_quick, \
mock.patch.object(m, "select_deep_thinking_agent") as prompt_deep: mock.patch.object(cli_selections, "select_deep_thinking_agent") as prompt_deep:
sel = m.get_user_selections() sel = cli_selections.get_user_selections()
# None of the LLM selection prompts should have been shown. # None of the LLM selection prompts should have been shown.
prompt_provider.assert_not_called() prompt_provider.assert_not_called()
@@ -85,30 +86,29 @@ class TestCliSkipsPromptsFromEnv(unittest.TestCase):
@pytest.mark.unit @pytest.mark.unit
class TestResearchDepthSkippedFromEnv(unittest.TestCase): class TestResearchDepthSkippedFromEnv(unittest.TestCase):
def test_both_round_envs_skip_depth_prompt(self): def test_both_round_envs_skip_depth_prompt(self):
import cli.main as m
env = { env = {
"TRADINGAGENTS_MAX_DEBATE_ROUNDS": "2", "TRADINGAGENTS_MAX_DEBATE_ROUNDS": "2",
"TRADINGAGENTS_MAX_RISK_ROUNDS": "4", "TRADINGAGENTS_MAX_RISK_ROUNDS": "4",
} }
fake_cfg = dict(m.DEFAULT_CONFIG) fake_cfg = dict(cli_selections.DEFAULT_CONFIG)
fake_cfg.update({"max_debate_rounds": 2, "max_risk_discuss_rounds": 4}) fake_cfg.update({"max_debate_rounds": 2, "max_risk_discuss_rounds": 4})
with mock.patch.dict(os.environ, env, clear=False), \ with mock.patch.dict(os.environ, env, clear=False), \
mock.patch.object(m, "DEFAULT_CONFIG", fake_cfg), \ mock.patch.object(cli_selections, "DEFAULT_CONFIG", fake_cfg), \
mock.patch.object(m, "fetch_announcements", return_value=None), \ mock.patch.object(cli_selections, "fetch_announcements", return_value=None), \
mock.patch.object(m, "display_announcements"), \ mock.patch.object(cli_selections, "display_announcements"), \
mock.patch.object(m, "get_ticker", return_value="AAPL"), \ mock.patch.object(cli_selections, "get_ticker", return_value="AAPL"), \
mock.patch.object(m, "get_analysis_date", return_value="2026-05-29"), \ mock.patch.object(cli_selections, "get_analysis_date", return_value="2026-05-29"), \
mock.patch.object(m, "select_analysts", return_value=[]), \ mock.patch.object(cli_selections, "select_analysts", return_value=[]), \
mock.patch.object(m, "select_research_depth") as prompt_depth, \ mock.patch.object(cli_selections, "select_research_depth") as prompt_depth, \
mock.patch.object(m, "ensure_api_key"), \ mock.patch.object(cli_selections, "ensure_api_key"), \
mock.patch.object(m, "select_llm_provider", return_value=("openai", None)), \ mock.patch.object(cli_selections, "select_llm_provider", return_value=("openai", None)), \
mock.patch.object(m, "ask_output_language", return_value="English"), \ mock.patch.object(cli_selections, "ask_output_language", return_value="English"), \
mock.patch.object(m, "select_shallow_thinking_agent", return_value="gpt-5.4-mini"), \ mock.patch.object(cli_selections, "select_shallow_thinking_agent", return_value="gpt-5.4-mini"), \
mock.patch.object(m, "select_deep_thinking_agent", return_value="gpt-5.5"), \ mock.patch.object(cli_selections, "select_deep_thinking_agent", return_value="gpt-5.5"), \
mock.patch.object(m, "ask_openai_reasoning_effort", return_value=None): mock.patch.object(cli_selections, "ask_openai_reasoning_effort", return_value=None):
sel = m.get_user_selections() sel = cli_selections.get_user_selections()
# The research-depth prompt is skipped; the value comes from the env config. # The research-depth prompt is skipped; the value comes from the env config.
prompt_depth.assert_not_called() prompt_depth.assert_not_called()
@@ -118,27 +118,26 @@ class TestResearchDepthSkippedFromEnv(unittest.TestCase):
@pytest.mark.unit @pytest.mark.unit
class TestReasoningEffortSkippedFromEnv(unittest.TestCase): class TestReasoningEffortSkippedFromEnv(unittest.TestCase):
def test_effort_env_skips_step8_prompt(self): def test_effort_env_skips_step8_prompt(self):
import cli.main as m
env = {"TRADINGAGENTS_OPENAI_REASONING_EFFORT": "high"} env = {"TRADINGAGENTS_OPENAI_REASONING_EFFORT": "high"}
fake_cfg = dict(m.DEFAULT_CONFIG) fake_cfg = dict(cli_selections.DEFAULT_CONFIG)
fake_cfg.update({"openai_reasoning_effort": "high"}) fake_cfg.update({"openai_reasoning_effort": "high"})
with mock.patch.dict(os.environ, env, clear=False), \ with mock.patch.dict(os.environ, env, clear=False), \
mock.patch.object(m, "DEFAULT_CONFIG", fake_cfg), \ mock.patch.object(cli_selections, "DEFAULT_CONFIG", fake_cfg), \
mock.patch.object(m, "fetch_announcements", return_value=None), \ mock.patch.object(cli_selections, "fetch_announcements", return_value=None), \
mock.patch.object(m, "display_announcements"), \ mock.patch.object(cli_selections, "display_announcements"), \
mock.patch.object(m, "get_ticker", return_value="AAPL"), \ mock.patch.object(cli_selections, "get_ticker", return_value="AAPL"), \
mock.patch.object(m, "get_analysis_date", return_value="2026-05-29"), \ mock.patch.object(cli_selections, "get_analysis_date", return_value="2026-05-29"), \
mock.patch.object(m, "select_analysts", return_value=[]), \ mock.patch.object(cli_selections, "select_analysts", return_value=[]), \
mock.patch.object(m, "select_research_depth", return_value=1), \ mock.patch.object(cli_selections, "select_research_depth", return_value=1), \
mock.patch.object(m, "ensure_api_key"), \ mock.patch.object(cli_selections, "ensure_api_key"), \
mock.patch.object(m, "select_llm_provider", return_value=("openai", None)), \ mock.patch.object(cli_selections, "select_llm_provider", return_value=("openai", None)), \
mock.patch.object(m, "ask_output_language", return_value="English"), \ mock.patch.object(cli_selections, "ask_output_language", return_value="English"), \
mock.patch.object(m, "select_shallow_thinking_agent", return_value="gpt-5.4-mini"), \ mock.patch.object(cli_selections, "select_shallow_thinking_agent", return_value="gpt-5.4-mini"), \
mock.patch.object(m, "select_deep_thinking_agent", return_value="gpt-5.5"), \ mock.patch.object(cli_selections, "select_deep_thinking_agent", return_value="gpt-5.5"), \
mock.patch.object(m, "ask_openai_reasoning_effort") as prompt_effort: mock.patch.object(cli_selections, "ask_openai_reasoning_effort") as prompt_effort:
sel = m.get_user_selections() sel = cli_selections.get_user_selections()
# The reasoning-effort prompt is skipped; the value comes from env config. # The reasoning-effort prompt is skipped; the value comes from env config.
prompt_effort.assert_not_called() prompt_effort.assert_not_called()
tests/test_cli_prefs.py
+26 -26
View File
@@ -13,6 +13,7 @@ from unittest import mock
import pytest import pytest
import cli.selections as cli_selections
from cli.models import AnalystType from cli.models import AnalystType
from cli.prefs import load_last_run, sanitize, save_last_run from cli.prefs import load_last_run, sanitize, save_last_run
@@ -100,26 +101,26 @@ def _answer_every_prompt(monkeypatch):
"""Drive the real selection flow, answering each prompt with a fixed value.""" """Drive the real selection flow, answering each prompt with a fixed value."""
import cli.main as m import cli.main as m
monkeypatch.setattr(m, "fetch_announcements", lambda: []) monkeypatch.setattr(cli_selections, "fetch_announcements", lambda: [])
monkeypatch.setattr(m, "display_announcements", lambda *a: None) monkeypatch.setattr(cli_selections, "display_announcements", lambda *a: None)
monkeypatch.setattr(m, "get_ticker", lambda: "NVDA") monkeypatch.setattr(cli_selections, "get_ticker", lambda: "NVDA")
monkeypatch.setattr(m, "get_analysis_date", lambda: "2026-09-01") monkeypatch.setattr(cli_selections, "get_analysis_date", lambda: "2026-09-01")
monkeypatch.setattr(m, "ask_output_language", lambda default=None: "English") monkeypatch.setattr(cli_selections, "ask_output_language", lambda default=None: "English")
monkeypatch.setattr(m, "select_analysts", lambda asset_type, default=None: [AnalystType.MARKET]) monkeypatch.setattr(cli_selections, "select_analysts", lambda asset_type, default=None: [AnalystType.MARKET])
monkeypatch.setattr(m, "select_research_depth", lambda default=None: 3) monkeypatch.setattr(cli_selections, "select_research_depth", lambda default=None: 3)
monkeypatch.setattr(m, "select_llm_provider", lambda default=None: ("openai", None)) monkeypatch.setattr(cli_selections, "select_llm_provider", lambda default=None: ("openai", None))
monkeypatch.setattr(m, "select_shallow_thinking_agent", lambda p, default=None: "gpt-5.6-mini") monkeypatch.setattr(cli_selections, "select_shallow_thinking_agent", lambda p, default=None: "gpt-5.6-mini")
monkeypatch.setattr(m, "select_deep_thinking_agent", lambda p, default=None: "gpt-5.6") monkeypatch.setattr(cli_selections, "select_deep_thinking_agent", lambda p, default=None: "gpt-5.6")
monkeypatch.setattr(m, "ask_openai_reasoning_effort", lambda: "medium") monkeypatch.setattr(cli_selections, "ask_openai_reasoning_effort", lambda: "medium")
return m return m
@pytest.mark.unit @pytest.mark.unit
def test_selections_are_remembered_after_a_run(monkeypatch): def test_selections_are_remembered_after_a_run(monkeypatch):
"""Drives the real flow: a stubbed selections dict would hide a key mismatch.""" """Drives the real flow: a stubbed selections dict would hide a key mismatch."""
m = _answer_every_prompt(monkeypatch) _answer_every_prompt(monkeypatch)
m.get_user_selections() cli_selections.get_user_selections()
remembered = load_last_run() remembered = load_last_run()
assert remembered["analysts"] == ["market"] assert remembered["analysts"] == ["market"]
@@ -147,23 +148,22 @@ def test_a_custom_language_is_remembered_without_breaking_the_next_run():
def test_a_remembered_endpoint_is_offered_back(monkeypatch): def test_a_remembered_endpoint_is_offered_back(monkeypatch):
"""Users of a local or custom endpoint retyped the URL every run: it was """Users of a local or custom endpoint retyped the URL every run: it was
remembered and validated, then never read.""" remembered and validated, then never read."""
import cli.main as m
save_last_run({"llm_provider": "openai_compatible", "backend_url": "http://localhost:1234/v1"}) save_last_run({"llm_provider": "openai_compatible", "backend_url": "http://localhost:1234/v1"})
offered = {} offered = {}
monkeypatch.setattr(m, "select_llm_provider", lambda default=None: ("openai_compatible", None)) monkeypatch.setattr(cli_selections, "select_llm_provider", lambda default=None: ("openai_compatible", None))
monkeypatch.setattr(m, "prompt_openai_compatible_url", monkeypatch.setattr(cli_selections, "prompt_openai_compatible_url",
lambda default=None: offered.setdefault("default", default) or "http://x/v1") lambda default=None: offered.setdefault("default", default) or "http://x/v1")
monkeypatch.setattr(m, "fetch_announcements", lambda: []) monkeypatch.setattr(cli_selections, "fetch_announcements", lambda: [])
monkeypatch.setattr(m, "display_announcements", lambda *a: None) monkeypatch.setattr(cli_selections, "display_announcements", lambda *a: None)
monkeypatch.setattr(m, "get_ticker", lambda: "NVDA") monkeypatch.setattr(cli_selections, "get_ticker", lambda: "NVDA")
monkeypatch.setattr(m, "get_analysis_date", lambda: "2026-09-01") monkeypatch.setattr(cli_selections, "get_analysis_date", lambda: "2026-09-01")
monkeypatch.setattr(m, "ask_output_language", lambda default=None: "English") monkeypatch.setattr(cli_selections, "ask_output_language", lambda default=None: "English")
monkeypatch.setattr(m, "select_analysts", lambda asset_type, default=None: [AnalystType.MARKET]) monkeypatch.setattr(cli_selections, "select_analysts", lambda asset_type, default=None: [AnalystType.MARKET])
monkeypatch.setattr(m, "select_research_depth", lambda default=None: 1) monkeypatch.setattr(cli_selections, "select_research_depth", lambda default=None: 1)
monkeypatch.setattr(m, "select_shallow_thinking_agent", lambda p, default=None: "local-model") monkeypatch.setattr(cli_selections, "select_shallow_thinking_agent", lambda p, default=None: "local-model")
monkeypatch.setattr(m, "select_deep_thinking_agent", lambda p, default=None: "local-model") monkeypatch.setattr(cli_selections, "select_deep_thinking_agent", lambda p, default=None: "local-model")
m.get_user_selections() cli_selections.get_user_selections()
assert offered["default"] == "http://localhost:1234/v1" assert offered["default"] == "http://localhost:1234/v1"
tests/test_cli_symbol_handling.py
+3 -3
View File
@@ -5,6 +5,7 @@ stock), #982 (BTC-USDT accepted but unpriceable on Yahoo).
""" """
import pytest import pytest
import cli.run as cli_run
from cli.models import AssetType from cli.models import AssetType
from cli.prompts import detect_asset_type, is_valid_ticker_input, normalize_ticker_symbol from cli.prompts import detect_asset_type, is_valid_ticker_input, normalize_ticker_symbol
from tradingagents.dataflows.symbols import normalize_symbol from tradingagents.dataflows.symbols import normalize_symbol
@@ -66,10 +67,9 @@ def test_cli_normalize_delegates_to_data_layer():
def test_the_run_directory_cannot_escape_the_results_directory(tmp_path, monkeypatch): def test_the_run_directory_cannot_escape_the_results_directory(tmp_path, monkeypatch):
"""Every other path that interpolates a ticker validates it first; the CLI's """Every other path that interpolates a ticker validates it first; the CLI's
own results tree did not, so a ticker of '..' wrote a level up.""" own results tree did not, so a ticker of '..' wrote a level up."""
import cli.main as m
with pytest.raises(ValueError): with pytest.raises(ValueError):
m._run_directory({"results_dir": str(tmp_path)}, "..", "2026-09-01") cli_run._run_directory({"results_dir": str(tmp_path)}, "..", "2026-09-01")
ok = m._run_directory({"results_dir": str(tmp_path)}, "NVDA", "2026-09-01") ok = cli_run._run_directory({"results_dir": str(tmp_path)}, "NVDA", "2026-09-01")
assert str(ok).startswith(str(tmp_path)) assert str(ok).startswith(str(tmp_path))
tests/test_ollama_base_url.py
+7 -6
View File
@@ -23,16 +23,17 @@ def _resync_reloaded_modules():
"""Restore module state after this file's importlib.reload() calls. """Restore module state after this file's importlib.reload() calls.
Several tests below reload ``cli.prompts`` to re-evaluate OLLAMA_BASE_URL. Several tests below reload ``cli.prompts`` to re-evaluate OLLAMA_BASE_URL.
That leaves ``cli.main``'s star-imported names (e.g. get_ticker) bound to That leaves the modules importing from it (cli.selections, then cli.run and
the pre-reload module objects, which breaks identity checks in unrelated cli.main) bound to the pre-reload functions, which breaks identity checks in
tests that happen to run afterward. Re-sync once on teardown so the reload unrelated tests that run afterward. Re-sync them in import order on teardown.
doesn't leak across test modules.
""" """
yield yield
import cli.main import cli.main
import cli.prompts import cli.prompts
importlib.reload(cli.prompts) import cli.run
importlib.reload(cli.main) import cli.selections
for module in (cli.prompts, cli.selections, cli.run, cli.main):
importlib.reload(module)
# ---- openai_client side: registry-driven base_url resolution -------------- # ---- openai_client side: registry-driven base_url resolution --------------
tests/test_rating_integrity.py
+9 -8
View File
@@ -11,6 +11,7 @@ from __future__ import annotations
import pytest import pytest
import cli.run as cli_run
from tradingagents.agents.rating import RATING_REVIEW, extract_rating, parse_rating from tradingagents.agents.rating import RATING_REVIEW, extract_rating, parse_rating
INVERTED = ("The aggressive analyst pushed hard for a Buy on the AI backlog, but the " INVERTED = ("The aggressive analyst pushed hard for a Buy on the AI backlog, but the "
@@ -145,23 +146,23 @@ def test_the_cli_says_when_a_run_produced_no_usable_rating(monkeypatch, tmp_path
fake = _Graph() fake = _Graph()
fake.graph = fake fake.graph = fake
fake.propagator = fake fake.propagator = fake
monkeypatch.setattr(m, "TradingAgentsGraph", lambda *a, **k: fake) monkeypatch.setattr(cli_run, "TradingAgentsGraph", lambda *a, **k: fake)
monkeypatch.setattr(m, "create_layout", lambda: None) monkeypatch.setattr(cli_run, "create_layout", lambda: None)
monkeypatch.setattr(m, "update_display", lambda *a, **k: None) monkeypatch.setattr(cli_run, "update_display", lambda *a, **k: None)
monkeypatch.setattr(m, "Live", type("L", (), {"__init__": lambda s, *a, **k: None, monkeypatch.setattr(cli_run, "Live", type("L", (), {"__init__": lambda s, *a, **k: None,
"__enter__": lambda s: s, "__enter__": lambda s: s,
"__exit__": lambda s, *a: False})) "__exit__": lambda s, *a: False}))
monkeypatch.setattr(m.console, "print", lambda *a, **k: printed.append(" ".join(str(x) for x in a))) monkeypatch.setattr(m.console, "print", lambda *a, **k: printed.append(" ".join(str(x) for x in a)))
monkeypatch.setattr(m, "display_complete_report", lambda *a, **k: None) monkeypatch.setattr(cli_run, "display_complete_report", lambda *a, **k: None)
monkeypatch.setattr(m.typer, "prompt", lambda *a, **k: "N") monkeypatch.setattr(m.typer, "prompt", lambda *a, **k: "N")
monkeypatch.setattr(m, "get_user_selections", lambda: { monkeypatch.setattr(cli_run, "get_user_selections", lambda: {
"ticker": "NVDA", "analysis_date": "2026-01-10", "ticker": "NVDA", "analysis_date": "2026-01-10",
"analysts": [AnalystType.MARKET], "asset_type": "stock", "analysts": [AnalystType.MARKET], "asset_type": "stock",
}) })
monkeypatch.setattr(m, "_build_run_config", lambda s, c: { monkeypatch.setattr(cli_run, "_build_run_config", lambda s, c: {
"data_cache_dir": str(tmp_path / "c"), "results_dir": str(tmp_path / "r")}) "data_cache_dir": str(tmp_path / "c"), "results_dir": str(tmp_path / "r")})
m.run_analysis() cli_run.run_analysis()
assert any("review" in line.lower() for line in printed), printed[-5:] assert any("review" in line.lower() for line in printed), printed[-5:]
tests/test_ticker_symbol_handling.py
+4 -5
View File
@@ -17,12 +17,11 @@ class TickerSymbolHandlingTests(unittest.TestCase):
self.assertIn("exchange suffix", context) self.assertIn("exchange suffix", context)
def test_single_get_ticker_no_shadow(self): def test_single_get_ticker_no_shadow(self):
# Regression: cli/main.py had a duplicate get_ticker with an empty # A second get_ticker with an empty prompt (a bare "?") once shadowed
# questionary prompt (rendered as a bare "?") that shadowed the # the descriptive one; the selection flow must use the one in prompts.
# descriptive one in cli/prompts. Keep a single canonical definition.
import cli.main
import cli.prompts import cli.prompts
self.assertIs(cli.main.get_ticker, cli.prompts.get_ticker) import cli.selections
self.assertIs(cli.selections.get_ticker, cli.prompts.get_ticker)
if __name__ == "__main__": if __name__ == "__main__":