Resolve merge conflicts with main: preserve llamacpp provider, env-based config, and main.py cleanup

.dockerignore

@@ -0,0 +1,15 @@
+.git
+.venv
+.env
+.claude
+.idea
+.vscode
+.DS_Store
+__pycache__
+*.egg-info
+build
+dist
+results
+eval_results
+Dockerfile
+docker-compose.yml

Dockerfile

@@ -0,0 +1,27 @@
+FROM python:3.12-slim AS builder
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1
+
+RUN python -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+WORKDIR /build
+COPY . .
+RUN pip install --no-cache-dir .
+
+FROM python:3.12-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1
+
+COPY --from=builder /opt/venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+RUN useradd --create-home appuser
+USER appuser
+WORKDIR /home/appuser/app
+
+COPY --from=builder --chown=appuser:appuser /build .
+
+ENTRYPOINT ["tradingagents"]

README.md

@@ -28,7 +28,8 @@
 # TradingAgents: Multi-Agents LLM Financial Trading Framework
 
 ## News
-- [2026-03] **TradingAgents v0.2.1** released with GPT-5.4, Gemini 3.1, Claude 4.6 model coverage and improved system stability.
+- [2026-03] **TradingAgents v0.2.3** released with multi-language support, GPT-5.4 family models, unified model catalog, backtesting date fidelity, and proxy support.
+- [2026-03] **TradingAgents v0.2.2** released with GPT-5.4/Gemini 3.1/Claude 4.6 model coverage, five-tier rating scale, OpenAI Responses API, Anthropic effort control, and cross-platform stability.
 - [2026-02] **TradingAgents v0.2.0** released with multi-provider LLM support (GPT-5.x, Gemini 3.x, Claude 4.x, Grok 4.x) and improved system architecture.
 - [2026-01] **Trading-R1** [Technical Report](https://arxiv.org/abs/2509.11420) released, with [Terminal](https://github.com/TauricResearch/Trading-R1) expected to land soon.
 
@@ -112,9 +113,22 @@ conda create -n tradingagents python=3.13
 conda activate tradingagents
 ```
 
-Install dependencies:
+Install the package and its dependencies:
 ```bash
-pip install -r requirements.txt
+pip install .
+```
+
+### Docker
+
+Alternatively, run with Docker:
+```bash
+cp .env.example .env  # add your API keys
+docker compose run --rm tradingagents
+```
+
+For local models with Ollama:
+```bash
+docker compose --profile ollama run --rm tradingagents-ollama
 ```
 
 ### Required APIs
@@ -139,11 +153,12 @@ cp .env.example .env
 
 ### CLI Usage
 
-You can also try out the CLI directly by running:
+Launch the interactive CLI:
 ```bash
-python -m cli.main
+tradingagents          # installed command
+python -m cli.main     # alternative: run directly from source
 ```
-You will see a screen where you can select your desired tickers, date, LLMs, research depth, etc.
+You will see a screen where you can select your desired tickers, analysis date, LLM provider, research depth, and more.
 
 <p align="center">
   <img src="assets/cli/cli_init.png" width="100%" style="display: inline-block; margin: 0 2%;">
@@ -188,8 +203,8 @@ from tradingagents.default_config import DEFAULT_CONFIG
 
 config = DEFAULT_CONFIG.copy()
 config["llm_provider"] = "openai"        # openai, google, anthropic, xai, openrouter, ollama
-config["deep_think_llm"] = "gpt-5.2"     # Model for complex reasoning
-config["quick_think_llm"] = "gpt-5-mini" # Model for quick tasks
+config["deep_think_llm"] = "gpt-5.4"     # Model for complex reasoning
+config["quick_think_llm"] = "gpt-5.4-mini" # Model for quick tasks
 config["max_debate_rounds"] = 2
 
 ta = TradingAgentsGraph(debug=True, config=config)

cli/main.py

@@ -462,7 +462,7 @@ def update_display(layout, spinner_text=None, stats_handler=None, start_time=Non
 def get_user_selections():
     """Get all user selections before starting the analysis display."""
     # Display ASCII art welcome message
-    with open("./cli/static/welcome.txt", "r", encoding="utf-8") as f:
+    with open(Path(__file__).parent / "static" / "welcome.txt", "r") as f:
         welcome_ascii = f.read()
 
     # Create welcome box content
@@ -501,7 +501,9 @@ def get_user_selections():
     # Step 1: Ticker symbol
     console.print(
         create_question_box(
-            "Step 1: Ticker Symbol", "Enter the ticker symbol to analyze", "SPY"
+            "Step 1: Ticker Symbol",
+            "Enter the exact ticker symbol to analyze, including exchange suffix when needed (examples: SPY, CNC.TO, 7203.T, 0700.HK)",
+            "SPY",
         )
     )
     selected_ticker = get_ticker()
@@ -517,10 +519,19 @@ def get_user_selections():
     )
     analysis_date = get_analysis_date()
 
-    # Step 3: Select analysts
+    # Step 3: Output language
     console.print(
         create_question_box(
-            "Step 3: Analysts Team", "Select your LLM analyst agents for the analysis"
+            "Step 3: Output Language",
+            "Select the language for analyst reports and final decision"
+        )
+    )
+    output_language = ask_output_language()
+
+    # Step 4: Select analysts
+    console.print(
+        create_question_box(
+            "Step 4: Analysts Team", "Select your LLM analyst agents for the analysis"
         )
     )
     selected_analysts = select_analysts()
@@ -528,40 +539,41 @@ def get_user_selections():
         f"[green]Selected analysts:[/green] {', '.join(analyst.value for analyst in selected_analysts)}"
     )
 
-    # Step 4: Research depth
+    # Step 5: Research depth
     console.print(
         create_question_box(
-            "Step 4: Research Depth", "Select your research depth level"
+            "Step 5: Research Depth", "Select your research depth level"
         )
     )
     selected_research_depth = select_research_depth()
 
-    # Step 5: OpenAI backend
+    # Step 6: LLM Provider
     console.print(
         create_question_box(
-            "Step 5: OpenAI backend", "Select which service to talk to"
+            "Step 6: LLM Provider", "Select your LLM provider"
         )
     )
     selected_llm_provider, backend_url = select_llm_provider()
-    
-    # Step 6: Thinking agents
+
+    # Step 7: Thinking agents
     console.print(
         create_question_box(
-            "Step 6: Thinking Agents", "Select your thinking agents for analysis"
+            "Step 7: Thinking Agents", "Select your thinking agents for analysis"
         )
     )
     selected_shallow_thinker = select_shallow_thinking_agent(selected_llm_provider)
     selected_deep_thinker = select_deep_thinking_agent(selected_llm_provider)
 
-    # Step 7: Provider-specific thinking configuration
+    # Step 8: Provider-specific thinking configuration
     thinking_level = None
     reasoning_effort = None
+    anthropic_effort = None
 
     provider_lower = selected_llm_provider.lower()
     if provider_lower == "google":
         console.print(
             create_question_box(
-                "Step 7: Thinking Mode",
+                "Step 8: Thinking Mode",
                 "Configure Gemini thinking mode"
             )
         )
@@ -569,11 +581,19 @@ def get_user_selections():
     elif provider_lower == "openai":
         console.print(
             create_question_box(
-                "Step 7: Reasoning Effort",
+                "Step 8: Reasoning Effort",
                 "Configure OpenAI reasoning effort level"
             )
         )
         reasoning_effort = ask_openai_reasoning_effort()
+    elif provider_lower == "anthropic":
+        console.print(
+            create_question_box(
+                "Step 8: Effort Level",
+                "Configure Claude effort level"
+            )
+        )
+        anthropic_effort = ask_anthropic_effort()
 
     return {
         "ticker": selected_ticker,
@@ -586,6 +606,8 @@ def get_user_selections():
         "deep_thinker": selected_deep_thinker,
         "google_thinking_level": thinking_level,
         "openai_reasoning_effort": reasoning_effort,
+        "anthropic_effort": anthropic_effort,
+        "output_language": output_language,
     }
 
 
@@ -788,9 +810,11 @@ ANALYST_REPORT_MAP = {
 
 
 def update_analyst_statuses(message_buffer, chunk):
-    """Update all analyst statuses based on current report state.
+    """Update analyst statuses based on accumulated report state.
 
     Logic:
+    - Store new report content from the current chunk if present
+    - Check accumulated report_sections (not just current chunk) for status
     - Analysts with reports = completed
     - First analyst without report = in_progress
     - Remaining analysts without reports = pending
@@ -805,11 +829,16 @@ def update_analyst_statuses(message_buffer, chunk):
 
         agent_name = ANALYST_AGENT_NAMES[analyst_key]
         report_key = ANALYST_REPORT_MAP[analyst_key]
-        has_report = bool(chunk.get(report_key))
+
+        # Capture new report content from current chunk
+        if chunk.get(report_key):
+            message_buffer.update_report_section(report_key, chunk[report_key])
+
+        # Determine status from accumulated sections, not just current chunk
+        has_report = bool(message_buffer.report_sections.get(report_key))
 
         if has_report:
             message_buffer.update_agent_status(agent_name, "completed")
-            message_buffer.update_report_section(report_key, chunk[report_key])
         elif not found_active:
             message_buffer.update_agent_status(agent_name, "in_progress")
             found_active = True
@@ -911,6 +940,8 @@ def run_analysis():
     # 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")
 
     # Create stats callback handler for tracking LLM/tool calls
     stats_handler = StatsCallbackHandler()
@@ -948,7 +979,7 @@ def run_analysis():
             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:
+            with open(log_file, "a") as f:
                 f.write(f"{timestamp} [{message_type}] {content}\n")
         return wrapper
     
@@ -959,7 +990,7 @@ def run_analysis():
             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:
+            with open(log_file, "a") as f:
                 f.write(f"{timestamp} [Tool Call] {tool_name}({args_str})\n")
         return wrapper
 
@@ -972,8 +1003,9 @@ def run_analysis():
                 content = obj.report_sections[section_name]
                 if content:
                     file_name = f"{section_name}.md"
-                    with open(report_dir / file_name, "w", encoding="utf-8") as f:
-                        f.write(content)
+                    text = "\n".join(str(item) for item in content) if isinstance(content, list) else content
+                    with open(report_dir / file_name, "w") as f:
+                        f.write(text)
         return wrapper
 
     message_buffer.add_message = save_message_decorator(message_buffer, "add_message")

cli/utils.py

@@ -4,9 +4,12 @@ from typing import List, Optional, Tuple, Dict
 from rich.console import Console
 
 from cli.models import AnalystType
+from tradingagents.llm_clients.model_catalog import get_model_options
 
 console = Console()
 
+TICKER_INPUT_EXAMPLES = "Examples: SPY, CNC.TO, 7203.T, 0700.HK"
+
 ANALYST_ORDER = [
     ("Market Analyst", AnalystType.MARKET),
     ("Social Media Analyst", AnalystType.SOCIAL),
@@ -18,7 +21,7 @@ ANALYST_ORDER = [
 def get_ticker() -> str:
     """Prompt the user to enter a ticker symbol."""
     ticker = questionary.text(
-        "Enter the ticker symbol to analyze:",
+        f"Enter the exact ticker symbol to analyze ({TICKER_INPUT_EXAMPLES}):",
         validate=lambda x: len(x.strip()) > 0 or "Please enter a valid ticker symbol.",
         style=questionary.Style(
             [
@@ -32,6 +35,11 @@ def get_ticker() -> str:
         console.print("\n[red]No ticker symbol provided. Exiting...[/red]")
         exit(1)
 
+    return normalize_ticker_symbol(ticker)
+
+
+def normalize_ticker_symbol(ticker: str) -> str:
+    """Normalize ticker input while preserving exchange suffixes."""
     return ticker.strip().upper()
 
 
@@ -126,51 +134,57 @@ def select_research_depth() -> int:
     return choice
 
 
+def _fetch_openrouter_models() -> List[Tuple[str, str]]:
+    """Fetch available models from the OpenRouter API."""
+    import requests
+    try:
+        resp = requests.get("https://openrouter.ai/api/v1/models", timeout=10)
+        resp.raise_for_status()
+        models = resp.json().get("data", [])
+        return [(m.get("name") or m["id"], m["id"]) for m in models]
+    except Exception as e:
+        console.print(f"\n[yellow]Could not fetch OpenRouter models: {e}[/yellow]")
+        return []
+
+
+def select_openrouter_model() -> str:
+    """Select an OpenRouter model from the newest available, or enter a custom ID."""
+    models = _fetch_openrouter_models()
+
+    choices = [questionary.Choice(name, value=mid) for name, mid in models[:5]]
+    choices.append(questionary.Choice("Custom model ID", value="custom"))
+
+    choice = questionary.select(
+        "Select OpenRouter Model (latest available):",
+        choices=choices,
+        instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
+        style=questionary.Style([
+            ("selected", "fg:magenta noinherit"),
+            ("highlighted", "fg:magenta noinherit"),
+            ("pointer", "fg:magenta noinherit"),
+        ]),
+    ).ask()
+
+    if choice is None or choice == "custom":
+        return questionary.text(
+            "Enter OpenRouter model ID (e.g. google/gemma-4-26b-a4b-it):",
+            validate=lambda x: len(x.strip()) > 0 or "Please enter a model ID.",
+        ).ask().strip()
+
+    return choice
+
+
 def select_shallow_thinking_agent(provider) -> str:
     """Select shallow thinking llm engine using an interactive selection."""
 
-    # Define shallow thinking llm engine options with their corresponding model names
-    # Ordering: medium → light → heavy (balanced first for quick tasks)
-    # Within same tier, newer models first
-    SHALLOW_AGENT_OPTIONS = {
-        "openai": [
-            ("GPT-5 Mini - Balanced speed, cost, and capability", "gpt-5-mini"),
-            ("GPT-5 Nano - High-throughput, simple tasks", "gpt-5-nano"),
-            ("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
-            ("GPT-4.1 - Smartest non-reasoning model", "gpt-4.1"),
-        ],
-        "anthropic": [
-            ("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
-            ("Claude Haiku 4.5 - Fast, near-instant responses", "claude-haiku-4-5"),
-            ("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
-        ],
-        "google": [
-            ("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
-            ("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
-            ("Gemini 3.1 Flash Lite - Most cost-efficient", "gemini-3.1-flash-lite-preview"),
-            ("Gemini 2.5 Flash Lite - Fast, low-cost", "gemini-2.5-flash-lite"),
-        ],
-        "xai": [
-            ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
-            ("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"),
-            ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
-        ],
-        "openrouter": [
-            ("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
-            ("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),
-        ],
-        "ollama": [
-            ("Qwen3:latest (8B, local)", "qwen3:latest"),
-            ("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
-            ("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
-        ],
-    }
+    if provider.lower() == "openrouter":
+        return select_openrouter_model()
 
     choice = questionary.select(
         "Select Your [Quick-Thinking LLM Engine]:",
         choices=[
             questionary.Choice(display, value=value)
-            for display, value in SHALLOW_AGENT_OPTIONS[provider.lower()]
+            for display, value in get_model_options(provider, "quick")
         ],
         instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
         style=questionary.Style(
@@ -194,50 +208,14 @@ def select_shallow_thinking_agent(provider) -> str:
 def select_deep_thinking_agent(provider) -> str:
     """Select deep thinking llm engine using an interactive selection."""
 
-    # Define deep thinking llm engine options with their corresponding model names
-    # Ordering: heavy → medium → light (most capable first for deep tasks)
-    # Within same tier, newer models first
-    DEEP_AGENT_OPTIONS = {
-        "openai": [
-            ("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
-            ("GPT-5.2 - Strong reasoning, cost-effective", "gpt-5.2"),
-            ("GPT-5 Mini - Balanced speed, cost, and capability", "gpt-5-mini"),
-            ("GPT-5.4 Pro - Most capable, expensive ($30/$180 per 1M tokens)", "gpt-5.4-pro"),
-        ],
-        "anthropic": [
-            ("Claude Opus 4.6 - Most intelligent, agents and coding", "claude-opus-4-6"),
-            ("Claude Opus 4.5 - Premium, max intelligence", "claude-opus-4-5"),
-            ("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
-            ("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
-        ],
-        "google": [
-            ("Gemini 3.1 Pro - Reasoning-first, complex workflows", "gemini-3.1-pro-preview"),
-            ("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
-            ("Gemini 2.5 Pro - Stable pro model", "gemini-2.5-pro"),
-            ("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
-        ],
-        "xai": [
-            ("Grok 4 - Flagship model", "grok-4-0709"),
-            ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
-            ("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
-            ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
-        ],
-        "openrouter": [
-            ("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),
-            ("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
-        ],
-        "ollama": [
-            ("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
-            ("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
-            ("Qwen3:latest (8B, local)", "qwen3:latest"),
-        ],
-    }
+    if provider.lower() == "openrouter":
+        return select_openrouter_model()
 
     choice = questionary.select(
         "Select Your [Deep-Thinking LLM Engine]:",
         choices=[
             questionary.Choice(display, value=value)
-            for display, value in DEEP_AGENT_OPTIONS[provider.lower()]
+            for display, value in get_model_options(provider, "deep")
         ],
         instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
         style=questionary.Style(
@@ -255,12 +233,11 @@ def select_deep_thinking_agent(provider) -> str:
 
     return choice
 
-def select_llm_provider() -> tuple[str, str]:
-    """Select the OpenAI api url using interactive selection."""
-    # Define OpenAI api options with their corresponding endpoints
+def select_llm_provider() -> tuple[str, str | None]:
+    """Select the LLM provider and its API endpoint."""
     BASE_URLS = [
         ("OpenAI", "https://api.openai.com/v1"),
-        ("Google", "https://generativelanguage.googleapis.com/v1"),
+        ("Google", None),  # google-genai SDK manages its own endpoint
         ("Anthropic", "https://api.anthropic.com/"),
         ("xAI", "https://api.x.ai/v1"),
         ("Openrouter", "https://openrouter.ai/api/v1"),
@@ -311,6 +288,26 @@ def ask_openai_reasoning_effort() -> str:
     ).ask()
 
 
+def ask_anthropic_effort() -> str | None:
+    """Ask for Anthropic effort level.
+
+    Controls token usage and response thoroughness on Claude 4.5+ and 4.6 models.
+    """
+    return questionary.select(
+        "Select Effort Level:",
+        choices=[
+            questionary.Choice("High (recommended)", "high"),
+            questionary.Choice("Medium (balanced)", "medium"),
+            questionary.Choice("Low (faster, cheaper)", "low"),
+        ],
+        style=questionary.Style([
+            ("selected", "fg:cyan noinherit"),
+            ("highlighted", "fg:cyan noinherit"),
+            ("pointer", "fg:cyan noinherit"),
+        ]),
+    ).ask()
+
+
 def ask_gemini_thinking_config() -> str | None:
     """Ask for Gemini thinking configuration.
 
@@ -329,3 +326,37 @@ def ask_gemini_thinking_config() -> str | None:
             ("pointer", "fg:green noinherit"),
         ]),
     ).ask()
+
+
+def ask_output_language() -> str:
+    """Ask for report output language."""
+    choice = questionary.select(
+        "Select Output Language:",
+        choices=[
+            questionary.Choice("English (default)", "English"),
+            questionary.Choice("Chinese (中文)", "Chinese"),
+            questionary.Choice("Japanese (日本語)", "Japanese"),
+            questionary.Choice("Korean (한국어)", "Korean"),
+            questionary.Choice("Hindi (हिन्दी)", "Hindi"),
+            questionary.Choice("Spanish (Español)", "Spanish"),
+            questionary.Choice("Portuguese (Português)", "Portuguese"),
+            questionary.Choice("French (Français)", "French"),
+            questionary.Choice("German (Deutsch)", "German"),
+            questionary.Choice("Arabic (العربية)", "Arabic"),
+            questionary.Choice("Russian (Русский)", "Russian"),
+            questionary.Choice("Custom language", "custom"),
+        ],
+        style=questionary.Style([
+            ("selected", "fg:yellow noinherit"),
+            ("highlighted", "fg:yellow noinherit"),
+            ("pointer", "fg:yellow noinherit"),
+        ]),
+    ).ask()
+
+    if choice == "custom":
+        return questionary.text(
+            "Enter language name (e.g. Turkish, Vietnamese, Thai, Indonesian):",
+            validate=lambda x: len(x.strip()) > 0 or "Please enter a language name.",
+        ).ask().strip()
+
+    return choice

docker-compose.yml

@@ -0,0 +1,34 @@
+services:
+  tradingagents:
+    build: .
+    env_file:
+      - .env
+    volumes:
+      - ./results:/home/appuser/app/results
+    tty: true
+    stdin_open: true
+
+  ollama:
+    image: ollama/ollama:latest
+    volumes:
+      - ollama_data:/root/.ollama
+    profiles:
+      - ollama
+
+  tradingagents-ollama:
+    build: .
+    env_file:
+      - .env
+    environment:
+      - LLM_PROVIDER=ollama
+    volumes:
+      - ./results:/home/appuser/app/results
+    depends_on:
+      - ollama
+    tty: true
+    stdin_open: true
+    profiles:
+      - ollama
+
+volumes:
+  ollama_data:

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "tradingagents"
-version = "0.2.1"
+version = "0.2.3"
 description = "TradingAgents: Multi-Agents LLM Financial Trading Framework"
 readme = "README.md"
 requires-python = ">=3.10"
@@ -13,7 +13,7 @@ dependencies = [
     "backtrader>=1.9.78.123",
     "langchain-anthropic>=0.3.15",
     "langchain-experimental>=0.3.4",
-    "langchain-google-genai>=2.1.5",
+    "langchain-google-genai>=4.0.0",
     "langchain-openai>=0.3.23",
     "langgraph>=0.4.8",
     "pandas>=2.3.0",
@@ -37,3 +37,6 @@ tradingagents = "cli.main:app"
 
 [tool.setuptools.packages.find]
 include = ["tradingagents*", "cli*"]
+
+[tool.setuptools.package-data]
+cli = ["static/*"]

requirements.txt

@@ -1,21 +1 @@
-typing-extensions
-langchain-core
-langchain-openai
-langchain-experimental
-pandas
-yfinance
-stockstats
-langgraph
-rank-bm25
-setuptools
-backtrader
-parsel
-requests
-tqdm
-pytz
-redis
-rich
-typer
-questionary
-langchain_anthropic
-langchain-google-genai
+.

tests/test_google_api_key.py

@@ -0,0 +1,28 @@
+import unittest
+from unittest.mock import patch
+
+from tradingagents.llm_clients.google_client import GoogleClient
+
+
+class TestGoogleApiKeyStandardization(unittest.TestCase):
+    """Verify GoogleClient accepts unified api_key parameter."""
+
+    @patch("tradingagents.llm_clients.google_client.NormalizedChatGoogleGenerativeAI")
+    def test_api_key_handling(self, mock_chat):
+        test_cases = [
+            ("unified api_key is mapped", {"api_key": "test-key-123"}, "test-key-123"),
+            ("legacy google_api_key still works", {"google_api_key": "legacy-key-456"}, "legacy-key-456"),
+            ("unified api_key takes precedence", {"api_key": "unified", "google_api_key": "legacy"}, "unified"),
+        ]
+
+        for msg, kwargs, expected_key in test_cases:
+            with self.subTest(msg=msg):
+                mock_chat.reset_mock()
+                client = GoogleClient("gemini-2.5-flash", **kwargs)
+                client.get_llm()
+                call_kwargs = mock_chat.call_args[1]
+                self.assertEqual(call_kwargs.get("google_api_key"), expected_key)
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_model_validation.py

@@ -0,0 +1,52 @@
+import unittest
+import warnings
+
+from tradingagents.llm_clients.base_client import BaseLLMClient
+from tradingagents.llm_clients.model_catalog import get_known_models
+from tradingagents.llm_clients.validators import validate_model
+
+
+class DummyLLMClient(BaseLLMClient):
+    def __init__(self, provider: str, model: str):
+        self.provider = provider
+        super().__init__(model)
+
+    def get_llm(self):
+        self.warn_if_unknown_model()
+        return object()
+
+    def validate_model(self) -> bool:
+        return validate_model(self.provider, self.model)
+
+
+class ModelValidationTests(unittest.TestCase):
+    def test_cli_catalog_models_are_all_validator_approved(self):
+        for provider, models in get_known_models().items():
+            if provider in ("ollama", "openrouter"):
+                continue
+
+            for model in models:
+                with self.subTest(provider=provider, model=model):
+                    self.assertTrue(validate_model(provider, model))
+
+    def test_unknown_model_emits_warning_for_strict_provider(self):
+        client = DummyLLMClient("openai", "not-a-real-openai-model")
+
+        with warnings.catch_warnings(record=True) as caught:
+            warnings.simplefilter("always")
+            client.get_llm()
+
+        self.assertEqual(len(caught), 1)
+        self.assertIn("not-a-real-openai-model", str(caught[0].message))
+        self.assertIn("openai", str(caught[0].message))
+
+    def test_openrouter_and_ollama_accept_custom_models_without_warning(self):
+        for provider in ("openrouter", "ollama"):
+            client = DummyLLMClient(provider, "custom-model-name")
+
+            with self.subTest(provider=provider):
+                with warnings.catch_warnings(record=True) as caught:
+                    warnings.simplefilter("always")
+                    client.get_llm()
+
+                self.assertEqual(caught, [])

tests/test_ticker_symbol_handling.py

@@ -0,0 +1,18 @@
+import unittest
+
+from cli.utils import normalize_ticker_symbol
+from tradingagents.agents.utils.agent_utils import build_instrument_context
+
+
+class TickerSymbolHandlingTests(unittest.TestCase):
+    def test_normalize_ticker_symbol_preserves_exchange_suffix(self):
+        self.assertEqual(normalize_ticker_symbol(" cnc.to "), "CNC.TO")
+
+    def test_build_instrument_context_mentions_exact_symbol(self):
+        context = build_instrument_context("7203.T")
+        self.assertIn("7203.T", context)
+        self.assertIn("exchange suffix", context)
+
+
+if __name__ == "__main__":
+    unittest.main()

tradingagents/__init__.py

@@ -0,0 +1,2 @@
+import os
+os.environ.setdefault("PYTHONUTF8", "1")

tradingagents/agents/__init__.py

@@ -15,7 +15,7 @@ from .risk_mgmt.conservative_debator import create_conservative_debator
 from .risk_mgmt.neutral_debator import create_neutral_debator
 
 from .managers.research_manager import create_research_manager
-from .managers.risk_manager import create_risk_manager
+from .managers.portfolio_manager import create_portfolio_manager
 
 from .trader.trader import create_trader
 
@@ -33,7 +33,7 @@ __all__ = [
     "create_neutral_debator",
     "create_news_analyst",
     "create_aggressive_debator",
-    "create_risk_manager",
+    "create_portfolio_manager",
     "create_conservative_debator",
     "create_social_media_analyst",
     "create_trader",

tradingagents/agents/analysts/fundamentals_analyst.py

@@ -1,15 +1,20 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
-import json
-from tradingagents.agents.utils.agent_utils import get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement, get_insider_transactions
+from tradingagents.agents.utils.agent_utils import (
+    build_instrument_context,
+    get_balance_sheet,
+    get_cashflow,
+    get_fundamentals,
+    get_income_statement,
+    get_insider_transactions,
+    get_language_instruction,
+)
 from tradingagents.dataflows.config import get_config
 
 
 def create_fundamentals_analyst(llm):
     def fundamentals_analyst_node(state):
         current_date = state["trade_date"]
-        ticker = state["company_of_interest"]
-        company_name = state["company_of_interest"]
+        instrument_context = build_instrument_context(state["company_of_interest"])
 
         tools = [
             get_fundamentals,
@@ -19,9 +24,10 @@ def create_fundamentals_analyst(llm):
         ]
 
         system_message = (
-            "You are a researcher tasked with analyzing fundamental information over the past week about a company. Please write a comprehensive report of the company's fundamental information such as financial documents, company profile, basic company financials, and company financial history to gain a full view of the company's fundamental information to inform traders. Make sure to include as much detail as possible. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
+            "You are a researcher tasked with analyzing fundamental information over the past week about a company. Please write a comprehensive report of the company's fundamental information such as financial documents, company profile, basic company financials, and company financial history to gain a full view of the company's fundamental information to inform traders. Make sure to include as much detail as possible. Provide specific, actionable insights with supporting evidence to help traders make informed decisions."
             + " Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."
-            + " Use the available tools: `get_fundamentals` for comprehensive company analysis, `get_balance_sheet`, `get_cashflow`, and `get_income_statement` for specific financial statements.",
+            + " Use the available tools: `get_fundamentals` for comprehensive company analysis, `get_balance_sheet`, `get_cashflow`, and `get_income_statement` for specific financial statements."
+            + get_language_instruction(),
         )
 
         prompt = ChatPromptTemplate.from_messages(
@@ -35,7 +41,7 @@ def create_fundamentals_analyst(llm):
                     " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
                     " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
                     " You have access to the following tools: {tool_names}.\n{system_message}"
-                    "For your reference, the current date is {current_date}. The company we want to look at is {ticker}",
+                    "For your reference, the current date is {current_date}. {instrument_context}",
                 ),
                 MessagesPlaceholder(variable_name="messages"),
             ]
@@ -44,7 +50,7 @@ def create_fundamentals_analyst(llm):
         prompt = prompt.partial(system_message=system_message)
         prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
         prompt = prompt.partial(current_date=current_date)
-        prompt = prompt.partial(ticker=ticker)
+        prompt = prompt.partial(instrument_context=instrument_context)
 
         chain = prompt | llm.bind_tools(tools)
 

tradingagents/agents/analysts/market_analyst.py

@@ -1,7 +1,10 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
-import json
-from tradingagents.agents.utils.agent_utils import get_stock_data, get_indicators
+from tradingagents.agents.utils.agent_utils import (
+    build_instrument_context,
+    get_indicators,
+    get_language_instruction,
+    get_stock_data,
+)
 from tradingagents.dataflows.config import get_config
 
 
@@ -9,8 +12,7 @@ def create_market_analyst(llm):
 
     def market_analyst_node(state):
         current_date = state["trade_date"]
-        ticker = state["company_of_interest"]
-        company_name = state["company_of_interest"]
+        instrument_context = build_instrument_context(state["company_of_interest"])
 
         tools = [
             get_stock_data,
@@ -42,8 +44,9 @@ Volatility Indicators:
 Volume-Based Indicators:
 - vwma: VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses.
 
-- Select indicators that provide diverse and complementary information. Avoid redundancy (e.g., do not select both rsi and stochrsi). Also briefly explain why they are suitable for the given market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_stock_data first to retrieve the CSV that is needed to generate indicators. Then use get_indicators with the specific indicator names. Write a very detailed and nuanced report of the trends you observe. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."""
+- Select indicators that provide diverse and complementary information. Avoid redundancy (e.g., do not select both rsi and stochrsi). Also briefly explain why they are suitable for the given market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_stock_data first to retrieve the CSV that is needed to generate indicators. Then use get_indicators with the specific indicator names. Write a very detailed and nuanced report of the trends you observe. Provide specific, actionable insights with supporting evidence to help traders make informed decisions."""
             + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
+            + get_language_instruction()
         )
 
         prompt = ChatPromptTemplate.from_messages(
@@ -57,7 +60,7 @@ Volume-Based Indicators:
                     " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
                     " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
                     " You have access to the following tools: {tool_names}.\n{system_message}"
-                    "For your reference, the current date is {current_date}. The company we want to look at is {ticker}",
+                    "For your reference, the current date is {current_date}. {instrument_context}",
                 ),
                 MessagesPlaceholder(variable_name="messages"),
             ]
@@ -66,7 +69,7 @@ Volume-Based Indicators:
         prompt = prompt.partial(system_message=system_message)
         prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
         prompt = prompt.partial(current_date=current_date)
-        prompt = prompt.partial(ticker=ticker)
+        prompt = prompt.partial(instrument_context=instrument_context)
 
         chain = prompt | llm.bind_tools(tools)
 

tradingagents/agents/analysts/news_analyst.py

@@ -1,14 +1,17 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
-import json
-from tradingagents.agents.utils.agent_utils import get_news, get_global_news
+from tradingagents.agents.utils.agent_utils import (
+    build_instrument_context,
+    get_global_news,
+    get_language_instruction,
+    get_news,
+)
 from tradingagents.dataflows.config import get_config
 
 
 def create_news_analyst(llm):
     def news_analyst_node(state):
         current_date = state["trade_date"]
-        ticker = state["company_of_interest"]
+        instrument_context = build_instrument_context(state["company_of_interest"])
 
         tools = [
             get_news,
@@ -16,8 +19,9 @@ def create_news_analyst(llm):
         ]
 
         system_message = (
-            "You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the available tools: get_news(query, start_date, end_date) for company-specific or targeted news searches, and get_global_news(curr_date, look_back_days, limit) for broader macroeconomic news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
+            "You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the available tools: get_news(query, start_date, end_date) for company-specific or targeted news searches, and get_global_news(curr_date, look_back_days, limit) for broader macroeconomic news. Provide specific, actionable insights with supporting evidence to help traders make informed decisions."
             + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
+            + get_language_instruction()
         )
 
         prompt = ChatPromptTemplate.from_messages(
@@ -31,7 +35,7 @@ def create_news_analyst(llm):
                     " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
                     " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
                     " You have access to the following tools: {tool_names}.\n{system_message}"
-                    "For your reference, the current date is {current_date}. We are looking at the company {ticker}",
+                    "For your reference, the current date is {current_date}. {instrument_context}",
                 ),
                 MessagesPlaceholder(variable_name="messages"),
             ]
@@ -40,7 +44,7 @@ def create_news_analyst(llm):
         prompt = prompt.partial(system_message=system_message)
         prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
         prompt = prompt.partial(current_date=current_date)
-        prompt = prompt.partial(ticker=ticker)
+        prompt = prompt.partial(instrument_context=instrument_context)
 
         chain = prompt | llm.bind_tools(tools)
         result = chain.invoke(state["messages"])

tradingagents/agents/analysts/social_media_analyst.py

@@ -1,23 +1,21 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
-import json
-from tradingagents.agents.utils.agent_utils import get_news
+from tradingagents.agents.utils.agent_utils import build_instrument_context, get_language_instruction, get_news
 from tradingagents.dataflows.config import get_config
 
 
 def create_social_media_analyst(llm):
     def social_media_analyst_node(state):
         current_date = state["trade_date"]
-        ticker = state["company_of_interest"]
-        company_name = state["company_of_interest"]
+        instrument_context = build_instrument_context(state["company_of_interest"])
 
         tools = [
             get_news,
         ]
 
         system_message = (
-            "You are a social media and company specific news researcher/analyst tasked with analyzing social media posts, recent company news, and public sentiment for a specific company over the past week. You will be given a company's name your objective is to write a comprehensive long report detailing your analysis, insights, and implications for traders and investors on this company's current state after looking at social media and what people are saying about that company, analyzing sentiment data of what people feel each day about the company, and looking at recent company news. Use the get_news(query, start_date, end_date) tool to search for company-specific news and social media discussions. Try to look at all sources possible from social media to sentiment to news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
-            + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.""",
+            "You are a social media and company specific news researcher/analyst tasked with analyzing social media posts, recent company news, and public sentiment for a specific company over the past week. You will be given a company's name your objective is to write a comprehensive long report detailing your analysis, insights, and implications for traders and investors on this company's current state after looking at social media and what people are saying about that company, analyzing sentiment data of what people feel each day about the company, and looking at recent company news. Use the get_news(query, start_date, end_date) tool to search for company-specific news and social media discussions. Try to look at all sources possible from social media to sentiment to news. Provide specific, actionable insights with supporting evidence to help traders make informed decisions."
+            + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
+            + get_language_instruction()
         )
 
         prompt = ChatPromptTemplate.from_messages(
@@ -31,7 +29,7 @@ def create_social_media_analyst(llm):
                     " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
                     " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
                     " You have access to the following tools: {tool_names}.\n{system_message}"
-                    "For your reference, the current date is {current_date}. The current company we want to analyze is {ticker}",
+                    "For your reference, the current date is {current_date}. {instrument_context}",
                 ),
                 MessagesPlaceholder(variable_name="messages"),
             ]
@@ -40,7 +38,7 @@ def create_social_media_analyst(llm):
         prompt = prompt.partial(system_message=system_message)
         prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
         prompt = prompt.partial(current_date=current_date)
-        prompt = prompt.partial(ticker=ticker)
+        prompt = prompt.partial(instrument_context=instrument_context)
 
         chain = prompt | llm.bind_tools(tools)
 

tradingagents/agents/managers/portfolio_manager.py

@@ -0,0 +1,77 @@
+from tradingagents.agents.utils.agent_utils import build_instrument_context, get_language_instruction
+
+
+def create_portfolio_manager(llm, memory):
+    def portfolio_manager_node(state) -> dict:
+
+        instrument_context = build_instrument_context(state["company_of_interest"])
+
+        history = state["risk_debate_state"]["history"]
+        risk_debate_state = state["risk_debate_state"]
+        market_research_report = state["market_report"]
+        news_report = state["news_report"]
+        fundamentals_report = state["fundamentals_report"]
+        sentiment_report = state["sentiment_report"]
+        research_plan = state["investment_plan"]
+        trader_plan = state["trader_investment_plan"]
+
+        curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
+        past_memories = memory.get_memories(curr_situation, n_matches=2)
+
+        past_memory_str = ""
+        for i, rec in enumerate(past_memories, 1):
+            past_memory_str += rec["recommendation"] + "\n\n"
+
+        prompt = f"""As the Portfolio Manager, synthesize the risk analysts' debate and deliver the final trading decision.
+
+{instrument_context}
+
+---
+
+**Rating Scale** (use exactly one):
+- **Buy**: Strong conviction to enter or add to position
+- **Overweight**: Favorable outlook, gradually increase exposure
+- **Hold**: Maintain current position, no action needed
+- **Underweight**: Reduce exposure, take partial profits
+- **Sell**: Exit position or avoid entry
+
+**Context:**
+- Research Manager's investment plan: **{research_plan}**
+- Trader's transaction proposal: **{trader_plan}**
+- Lessons from past decisions: **{past_memory_str}**
+
+**Required Output Structure:**
+1. **Rating**: State one of Buy / Overweight / Hold / Underweight / Sell.
+2. **Executive Summary**: A concise action plan covering entry strategy, position sizing, key risk levels, and time horizon.
+3. **Investment Thesis**: Detailed reasoning anchored in the analysts' debate and past reflections.
+
+---
+
+**Risk Analysts Debate History:**
+{history}
+
+---
+
+Be decisive and ground every conclusion in specific evidence from the analysts.{get_language_instruction()}"""
+
+        response = llm.invoke(prompt)
+
+        new_risk_debate_state = {
+            "judge_decision": response.content,
+            "history": risk_debate_state["history"],
+            "aggressive_history": risk_debate_state["aggressive_history"],
+            "conservative_history": risk_debate_state["conservative_history"],
+            "neutral_history": risk_debate_state["neutral_history"],
+            "latest_speaker": "Judge",
+            "current_aggressive_response": risk_debate_state["current_aggressive_response"],
+            "current_conservative_response": risk_debate_state["current_conservative_response"],
+            "current_neutral_response": risk_debate_state["current_neutral_response"],
+            "count": risk_debate_state["count"],
+        }
+
+        return {
+            "risk_debate_state": new_risk_debate_state,
+            "final_trade_decision": response.content,
+        }
+
+    return portfolio_manager_node

tradingagents/agents/managers/research_manager.py

@@ -1,9 +1,10 @@
-import time
-import json
+
+from tradingagents.agents.utils.agent_utils import build_instrument_context
 
 
 def create_research_manager(llm, memory):
     def research_manager_node(state) -> dict:
+        instrument_context = build_instrument_context(state["company_of_interest"])
         history = state["investment_debate_state"].get("history", "")
         market_research_report = state["market_report"]
         sentiment_report = state["sentiment_report"]
@@ -33,6 +34,8 @@ Take into account your past mistakes on similar situations. Use these insights t
 Here are your past reflections on mistakes:
 \"{past_memory_str}\"
 
+{instrument_context}
+
 Here is the debate:
 Debate History:
 {history}"""

tradingagents/agents/managers/risk_manager.py

@@ -1,66 +0,0 @@
-import time
-import json
-
-
-def create_risk_manager(llm, memory):
-    def risk_manager_node(state) -> dict:
-
-        company_name = state["company_of_interest"]
-
-        history = state["risk_debate_state"]["history"]
-        risk_debate_state = state["risk_debate_state"]
-        market_research_report = state["market_report"]
-        news_report = state["news_report"]
-        fundamentals_report = state["fundamentals_report"]
-        sentiment_report = state["sentiment_report"]
-        trader_plan = state["investment_plan"]
-
-        curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
-        past_memories = memory.get_memories(curr_situation, n_matches=2)
-
-        past_memory_str = ""
-        for i, rec in enumerate(past_memories, 1):
-            past_memory_str += rec["recommendation"] + "\n\n"
-
-        prompt = f"""As the Risk Management Judge and Debate Facilitator, your goal is to evaluate the debate between three risk analysts—Aggressive, Neutral, and Conservative—and determine the best course of action for the trader. Your decision must result in a clear recommendation: Buy, Sell, or Hold. Choose Hold only if strongly justified by specific arguments, not as a fallback when all sides seem valid. Strive for clarity and decisiveness.
-
-Guidelines for Decision-Making:
-1. **Summarize Key Arguments**: Extract the strongest points from each analyst, focusing on relevance to the context.
-2. **Provide Rationale**: Support your recommendation with direct quotes and counterarguments from the debate.
-3. **Refine the Trader's Plan**: Start with the trader's original plan, **{trader_plan}**, and adjust it based on the analysts' insights.
-4. **Learn from Past Mistakes**: Use lessons from **{past_memory_str}** to address prior misjudgments and improve the decision you are making now to make sure you don't make a wrong BUY/SELL/HOLD call that loses money.
-
-Deliverables:
-- A clear and actionable recommendation: Buy, Sell, or Hold.
-- Detailed reasoning anchored in the debate and past reflections.
-
----
-
-**Analysts Debate History:**  
-{history}
-
----
-
-Focus on actionable insights and continuous improvement. Build on past lessons, critically evaluate all perspectives, and ensure each decision advances better outcomes."""
-
-        response = llm.invoke(prompt)
-
-        new_risk_debate_state = {
-            "judge_decision": response.content,
-            "history": risk_debate_state["history"],
-            "aggressive_history": risk_debate_state["aggressive_history"],
-            "conservative_history": risk_debate_state["conservative_history"],
-            "neutral_history": risk_debate_state["neutral_history"],
-            "latest_speaker": "Judge",
-            "current_aggressive_response": risk_debate_state["current_aggressive_response"],
-            "current_conservative_response": risk_debate_state["current_conservative_response"],
-            "current_neutral_response": risk_debate_state["current_neutral_response"],
-            "count": risk_debate_state["count"],
-        }
-
-        return {
-            "risk_debate_state": new_risk_debate_state,
-            "final_trade_decision": response.content,
-        }
-
-    return risk_manager_node

tradingagents/agents/researchers/bear_researcher.py

@@ -1,6 +1,3 @@
-from langchain_core.messages import AIMessage
-import time
-import json
 
 
 def create_bear_researcher(llm, memory):

tradingagents/agents/researchers/bull_researcher.py

@@ -1,6 +1,3 @@
-from langchain_core.messages import AIMessage
-import time
-import json
 
 
 def create_bull_researcher(llm, memory):

tradingagents/agents/risk_mgmt/aggressive_debator.py

@@ -1,5 +1,3 @@
-import time
-import json
 
 
 def create_aggressive_debator(llm):
@@ -28,7 +26,7 @@ Market Research Report: {market_research_report}
 Social Media Sentiment Report: {sentiment_report}
 Latest World Affairs Report: {news_report}
 Company Fundamentals Report: {fundamentals_report}
-Here is the current conversation history: {history} Here are the last arguments from the conservative analyst: {current_conservative_response} Here are the last arguments from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not hallucinate and just present your point.
+Here is the current conversation history: {history} Here are the last arguments from the conservative analyst: {current_conservative_response} Here are the last arguments from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints yet, present your own argument based on the available data.
 
 Engage actively by addressing any specific concerns raised, refuting the weaknesses in their logic, and asserting the benefits of risk-taking to outpace market norms. Maintain a focus on debating and persuading, not just presenting data. Challenge each counterpoint to underscore why a high-risk approach is optimal. Output conversationally as if you are speaking without any special formatting."""
 

tradingagents/agents/risk_mgmt/conservative_debator.py

@@ -1,6 +1,3 @@
-from langchain_core.messages import AIMessage
-import time
-import json
 
 
 def create_conservative_debator(llm):
@@ -29,7 +26,7 @@ Market Research Report: {market_research_report}
 Social Media Sentiment Report: {sentiment_report}
 Latest World Affairs Report: {news_report}
 Company Fundamentals Report: {fundamentals_report}
-Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not hallucinate and just present your point.
+Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints yet, present your own argument based on the available data.
 
 Engage by questioning their optimism and emphasizing the potential downsides they may have overlooked. Address each of their counterpoints to showcase why a conservative stance is ultimately the safest path for the firm's assets. Focus on debating and critiquing their arguments to demonstrate the strength of a low-risk strategy over their approaches. Output conversationally as if you are speaking without any special formatting."""
 

tradingagents/agents/risk_mgmt/neutral_debator.py

@@ -1,5 +1,3 @@
-import time
-import json
 
 
 def create_neutral_debator(llm):
@@ -28,7 +26,7 @@ Market Research Report: {market_research_report}
 Social Media Sentiment Report: {sentiment_report}
 Latest World Affairs Report: {news_report}
 Company Fundamentals Report: {fundamentals_report}
-Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the conservative analyst: {current_conservative_response}. If there are no responses from the other viewpoints, do not hallucinate and just present your point.
+Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the conservative analyst: {current_conservative_response}. If there are no responses from the other viewpoints yet, present your own argument based on the available data.
 
 Engage actively by analyzing both sides critically, addressing weaknesses in the aggressive and conservative arguments to advocate for a more balanced approach. Challenge each of their points to illustrate why a moderate risk strategy might offer the best of both worlds, providing growth potential while safeguarding against extreme volatility. Focus on debating rather than simply presenting data, aiming to show that a balanced view can lead to the most reliable outcomes. Output conversationally as if you are speaking without any special formatting."""
 

tradingagents/agents/trader/trader.py

@@ -1,11 +1,12 @@
 import functools
-import time
-import json
+
+from tradingagents.agents.utils.agent_utils import build_instrument_context
 
 
 def create_trader(llm, memory):
     def trader_node(state, name):
         company_name = state["company_of_interest"]
+        instrument_context = build_instrument_context(company_name)
         investment_plan = state["investment_plan"]
         market_research_report = state["market_report"]
         sentiment_report = state["sentiment_report"]
@@ -24,13 +25,13 @@ def create_trader(llm, memory):
 
         context = {
             "role": "user",
-            "content": f"Based on a comprehensive analysis by a team of analysts, here is an investment plan tailored for {company_name}. This plan incorporates insights from current technical market trends, macroeconomic indicators, and social media sentiment. Use this plan as a foundation for evaluating your next trading decision.\n\nProposed Investment Plan: {investment_plan}\n\nLeverage these insights to make an informed and strategic decision.",
+            "content": f"Based on a comprehensive analysis by a team of analysts, here is an investment plan tailored for {company_name}. {instrument_context} This plan incorporates insights from current technical market trends, macroeconomic indicators, and social media sentiment. Use this plan as a foundation for evaluating your next trading decision.\n\nProposed Investment Plan: {investment_plan}\n\nLeverage these insights to make an informed and strategic decision.",
         }
 
         messages = [
             {
                 "role": "system",
-                "content": f"""You are a trading agent analyzing market data to make investment decisions. Based on your analysis, provide a specific recommendation to buy, sell, or hold. End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**' to confirm your recommendation. Do not forget to utilize lessons from past decisions to learn from your mistakes. Here is some reflections from similar situatiosn you traded in and the lessons learned: {past_memory_str}""",
+                "content": f"""You are a trading agent analyzing market data to make investment decisions. Based on your analysis, provide a specific recommendation to buy, sell, or hold. End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**' to confirm your recommendation. Apply lessons from past decisions to strengthen your analysis. Here are reflections from similar situations you traded in and the lessons learned: {past_memory_str}""",
             },
             context,
         ]

tradingagents/agents/utils/agent_states.py

@@ -1,10 +1,6 @@
-from typing import Annotated, Sequence
-from datetime import date, timedelta, datetime
-from typing_extensions import TypedDict, Optional
-from langchain_openai import ChatOpenAI
-from tradingagents.agents import *
-from langgraph.prebuilt import ToolNode
-from langgraph.graph import END, StateGraph, START, MessagesState
+from typing import Annotated
+from typing_extensions import TypedDict
+from langgraph.graph import MessagesState
 
 
 # Researcher team state

tradingagents/agents/utils/agent_utils.py

@@ -19,6 +19,29 @@ from tradingagents.agents.utils.news_data_tools import (
     get_global_news
 )
 
+
+def get_language_instruction() -> str:
+    """Return a prompt instruction for the configured output language.
+
+    Returns empty string when English (default), so no extra tokens are used.
+    Only applied to user-facing agents (analysts, portfolio manager).
+    Internal debate agents stay in English for reasoning quality.
+    """
+    from tradingagents.dataflows.config import get_config
+    lang = get_config().get("output_language", "English")
+    if lang.strip().lower() == "english":
+        return ""
+    return f" Write your entire response in {lang}."
+
+
+def build_instrument_context(ticker: str) -> str:
+    """Describe the exact instrument so agents preserve exchange-qualified tickers."""
+    return (
+        f"The instrument to analyze is `{ticker}`. "
+        "Use this exact ticker in every tool call, report, and recommendation, "
+        "preserving any exchange suffix (e.g. `.TO`, `.L`, `.HK`, `.T`)."
+    )
+
 def create_msg_delete():
     def delete_messages(state):
         """Clear messages and add placeholder for Anthropic compatibility"""
@@ -35,4 +58,4 @@ def create_msg_delete():
     return delete_messages
 
 
-        
+        

tradingagents/agents/utils/technical_indicators_tools.py

@@ -22,10 +22,11 @@ def get_indicators(
     """
     # LLMs sometimes pass multiple indicators as a comma-separated string;
     # split and process each individually.
-    indicators = [i.strip() for i in indicator.split(",") if i.strip()]
-    if len(indicators) > 1:
-        results = []
-        for ind in indicators:
+    indicators = [i.strip().lower() for i in indicator.split(",") if i.strip()]
+    results = []
+    for ind in indicators:
+        try:
             results.append(route_to_vendor("get_indicators", symbol, ind, curr_date, look_back_days))
-        return "\n\n".join(results)
-    return route_to_vendor("get_indicators", symbol, indicator.strip(), curr_date, look_back_days)
+        except ValueError as e:
+            results.append(str(e))
+    return "\n\n".join(results)

tradingagents/dataflows/alpha_vantage_fundamentals.py

@@ -1,6 +1,23 @@
 from .alpha_vantage_common import _make_api_request
 
 
+def _filter_reports_by_date(result, curr_date: str):
+    """Filter annualReports/quarterlyReports to exclude entries after curr_date.
+
+    Prevents look-ahead bias by removing fiscal periods that end after
+    the simulation's current date.
+    """
+    if not curr_date or not isinstance(result, dict):
+        return result
+    for key in ("annualReports", "quarterlyReports"):
+        if key in result:
+            result[key] = [
+                r for r in result[key]
+                if r.get("fiscalDateEnding", "") <= curr_date
+            ]
+    return result
+
+
 def get_fundamentals(ticker: str, curr_date: str = None) -> str:
     """
     Retrieve comprehensive fundamental data for a given ticker symbol using Alpha Vantage.
@@ -19,59 +36,20 @@ def get_fundamentals(ticker: str, curr_date: str = None) -> str:
     return _make_api_request("OVERVIEW", params)
 
 
-def get_balance_sheet(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str:
-    """
-    Retrieve balance sheet data for a given ticker symbol using Alpha Vantage.
-
-    Args:
-        ticker (str): Ticker symbol of the company
-        freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage
-        curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage)
-
-    Returns:
-        str: Balance sheet data with normalized fields
-    """
-    params = {
-        "symbol": ticker,
-    }
-
-    return _make_api_request("BALANCE_SHEET", params)
+def get_balance_sheet(ticker: str, freq: str = "quarterly", curr_date: str = None):
+    """Retrieve balance sheet data for a given ticker symbol using Alpha Vantage."""
+    result = _make_api_request("BALANCE_SHEET", {"symbol": ticker})
+    return _filter_reports_by_date(result, curr_date)
 
 
-def get_cashflow(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str:
-    """
-    Retrieve cash flow statement data for a given ticker symbol using Alpha Vantage.
-
-    Args:
-        ticker (str): Ticker symbol of the company
-        freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage
-        curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage)
-
-    Returns:
-        str: Cash flow statement data with normalized fields
-    """
-    params = {
-        "symbol": ticker,
-    }
-
-    return _make_api_request("CASH_FLOW", params)
+def get_cashflow(ticker: str, freq: str = "quarterly", curr_date: str = None):
+    """Retrieve cash flow statement data for a given ticker symbol using Alpha Vantage."""
+    result = _make_api_request("CASH_FLOW", {"symbol": ticker})
+    return _filter_reports_by_date(result, curr_date)
 
 
-def get_income_statement(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str:
-    """
-    Retrieve income statement data for a given ticker symbol using Alpha Vantage.
-
-    Args:
-        ticker (str): Ticker symbol of the company
-        freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage
-        curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage)
-
-    Returns:
-        str: Income statement data with normalized fields
-    """
-    params = {
-        "symbol": ticker,
-    }
-
-    return _make_api_request("INCOME_STATEMENT", params)
+def get_income_statement(ticker: str, freq: str = "quarterly", curr_date: str = None):
+    """Retrieve income statement data for a given ticker symbol using Alpha Vantage."""
+    result = _make_api_request("INCOME_STATEMENT", {"symbol": ticker})
+    return _filter_reports_by_date(result, curr_date)
 

tradingagents/dataflows/stockstats_utils.py

@@ -1,10 +1,35 @@
+import time
+import logging
+
 import pandas as pd
 import yfinance as yf
+from yfinance.exceptions import YFRateLimitError
 from stockstats import wrap
 from typing import Annotated
 import os
 from .config import get_config
 
+logger = logging.getLogger(__name__)
+
+
+def yf_retry(func, max_retries=3, base_delay=2.0):
+    """Execute a yfinance call with exponential backoff on rate limits.
+
+    yfinance raises YFRateLimitError on HTTP 429 responses but does not
+    retry them internally. This wrapper adds retry logic specifically
+    for rate limits. Other exceptions propagate immediately.
+    """
+    for attempt in range(max_retries + 1):
+        try:
+            return func()
+        except YFRateLimitError:
+            if attempt < max_retries:
+                delay = base_delay * (2 ** attempt)
+                logger.warning(f"Yahoo Finance rate limited, retrying in {delay:.0f}s (attempt {attempt + 1}/{max_retries})")
+                time.sleep(delay)
+            else:
+                raise
+
 
 def _clean_dataframe(data: pd.DataFrame) -> pd.DataFrame:
     """Normalize a stock DataFrame for stockstats: parse dates, drop invalid rows, fill price gaps."""
@@ -19,6 +44,64 @@ def _clean_dataframe(data: pd.DataFrame) -> pd.DataFrame:
     return data
 
 
+def load_ohlcv(symbol: str, curr_date: str) -> pd.DataFrame:
+    """Fetch OHLCV data with caching, filtered to prevent look-ahead bias.
+
+    Downloads 15 years of data up to today and caches per symbol. On
+    subsequent calls the cache is reused. Rows after curr_date are
+    filtered out so backtests never see future prices.
+    """
+    config = get_config()
+    curr_date_dt = pd.to_datetime(curr_date)
+
+    # Cache uses a fixed window (15y to today) so one file per symbol
+    today_date = pd.Timestamp.today()
+    start_date = today_date - pd.DateOffset(years=5)
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = today_date.strftime("%Y-%m-%d")
+
+    os.makedirs(config["data_cache_dir"], exist_ok=True)
+    data_file = os.path.join(
+        config["data_cache_dir"],
+        f"{symbol}-YFin-data-{start_str}-{end_str}.csv",
+    )
+
+    if os.path.exists(data_file):
+        data = pd.read_csv(data_file, on_bad_lines="skip")
+    else:
+        data = yf_retry(lambda: yf.download(
+            symbol,
+            start=start_str,
+            end=end_str,
+            multi_level_index=False,
+            progress=False,
+            auto_adjust=True,
+        ))
+        data = data.reset_index()
+        data.to_csv(data_file, index=False)
+
+    data = _clean_dataframe(data)
+
+    # Filter to curr_date to prevent look-ahead bias in backtesting
+    data = data[data["Date"] <= curr_date_dt]
+
+    return data
+
+
+def filter_financials_by_date(data: pd.DataFrame, curr_date: str) -> pd.DataFrame:
+    """Drop financial statement columns (fiscal period timestamps) after curr_date.
+
+    yfinance financial statements use fiscal period end dates as columns.
+    Columns after curr_date represent future data and are removed to
+    prevent look-ahead bias.
+    """
+    if not curr_date or data.empty:
+        return data
+    cutoff = pd.Timestamp(curr_date)
+    mask = pd.to_datetime(data.columns, errors="coerce") <= cutoff
+    return data.loc[:, mask]
+
+
 class StockstatsUtils:
     @staticmethod
     def get_stock_stats(
@@ -30,42 +113,10 @@ class StockstatsUtils:
             str, "curr date for retrieving stock price data, YYYY-mm-dd"
         ],
     ):
-        config = get_config()
-
-        today_date = pd.Timestamp.today()
-        curr_date_dt = pd.to_datetime(curr_date)
-
-        end_date = today_date
-        start_date = today_date - pd.DateOffset(years=15)
-        start_date_str = start_date.strftime("%Y-%m-%d")
-        end_date_str = end_date.strftime("%Y-%m-%d")
-
-        # Ensure cache directory exists
-        os.makedirs(config["data_cache_dir"], exist_ok=True)
-
-        data_file = os.path.join(
-            config["data_cache_dir"],
-            f"{symbol}-YFin-data-{start_date_str}-{end_date_str}.csv",
-        )
-
-        if os.path.exists(data_file):
-            data = pd.read_csv(data_file, on_bad_lines="skip")
-        else:
-            data = yf.download(
-                symbol,
-                start=start_date_str,
-                end=end_date_str,
-                multi_level_index=False,
-                progress=False,
-                auto_adjust=True,
-            )
-            data = data.reset_index()
-            data.to_csv(data_file, index=False)
-
-        data = _clean_dataframe(data)
+        data = load_ohlcv(symbol, curr_date)
         df = wrap(data)
         df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
-        curr_date_str = curr_date_dt.strftime("%Y-%m-%d")
+        curr_date_str = pd.to_datetime(curr_date).strftime("%Y-%m-%d")
 
         df[indicator]  # trigger stockstats to calculate the indicator
         matching_rows = df[df["Date"].str.startswith(curr_date_str)]

tradingagents/dataflows/y_finance.py

@@ -1,9 +1,10 @@
 from typing import Annotated
 from datetime import datetime
 from dateutil.relativedelta import relativedelta
+import pandas as pd
 import yfinance as yf
 import os
-from .stockstats_utils import StockstatsUtils, _clean_dataframe
+from .stockstats_utils import StockstatsUtils, _clean_dataframe, yf_retry, load_ohlcv, filter_financials_by_date
 
 def get_YFin_data_online(
     symbol: Annotated[str, "ticker symbol of the company"],
@@ -18,7 +19,7 @@ def get_YFin_data_online(
     ticker = yf.Ticker(symbol.upper())
 
     # Fetch historical data for the specified date range
-    data = ticker.history(start=start_date, end=end_date)
+    data = yf_retry(lambda: ticker.history(start=start_date, end=end_date))
 
     # Check if data is empty
     if data.empty:
@@ -194,58 +195,9 @@ def _get_stock_stats_bulk(
     Fetches data once and calculates indicator for all available dates.
     Returns dict mapping date strings to indicator values.
     """
-    from .config import get_config
-    import pandas as pd
     from stockstats import wrap
-    import os
-    
-    config = get_config()
-    online = config["data_vendors"]["technical_indicators"] != "local"
-    
-    if not online:
-        # Local data path
-        try:
-            data = pd.read_csv(
-                os.path.join(
-                    config.get("data_cache_dir", "data"),
-                    f"{symbol}-YFin-data-2015-01-01-2025-03-25.csv",
-                ),
-                on_bad_lines="skip",
-            )
-        except FileNotFoundError:
-            raise Exception("Stockstats fail: Yahoo Finance data not fetched yet!")
-    else:
-        # Online data fetching with caching
-        today_date = pd.Timestamp.today()
-        curr_date_dt = pd.to_datetime(curr_date)
 
-        end_date = today_date
-        start_date = today_date - pd.DateOffset(years=15)
-        start_date_str = start_date.strftime("%Y-%m-%d")
-        end_date_str = end_date.strftime("%Y-%m-%d")
-
-        os.makedirs(config["data_cache_dir"], exist_ok=True)
-
-        data_file = os.path.join(
-            config["data_cache_dir"],
-            f"{symbol}-YFin-data-{start_date_str}-{end_date_str}.csv",
-        )
-
-        if os.path.exists(data_file):
-            data = pd.read_csv(data_file, on_bad_lines="skip")
-        else:
-            data = yf.download(
-                symbol,
-                start=start_date_str,
-                end=end_date_str,
-                multi_level_index=False,
-                progress=False,
-                auto_adjust=True,
-            )
-            data = data.reset_index()
-            data.to_csv(data_file, index=False)
-
-    data = _clean_dataframe(data)
+    data = load_ohlcv(symbol, curr_date)
     df = wrap(data)
     df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
     
@@ -300,7 +252,7 @@ def get_fundamentals(
     """Get company fundamentals overview from yfinance."""
     try:
         ticker_obj = yf.Ticker(ticker.upper())
-        info = ticker_obj.info
+        info = yf_retry(lambda: ticker_obj.info)
 
         if not info:
             return f"No fundamentals data found for symbol '{ticker}'"
@@ -353,17 +305,19 @@ def get_fundamentals(
 def get_balance_sheet(
     ticker: Annotated[str, "ticker symbol of the company"],
     freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
-    curr_date: Annotated[str, "current date (not used for yfinance)"] = None
+    curr_date: Annotated[str, "current date in YYYY-MM-DD format"] = None
 ):
     """Get balance sheet data from yfinance."""
     try:
         ticker_obj = yf.Ticker(ticker.upper())
-        
+
         if freq.lower() == "quarterly":
-            data = ticker_obj.quarterly_balance_sheet
+            data = yf_retry(lambda: ticker_obj.quarterly_balance_sheet)
         else:
-            data = ticker_obj.balance_sheet
-            
+            data = yf_retry(lambda: ticker_obj.balance_sheet)
+
+        data = filter_financials_by_date(data, curr_date)
+
         if data.empty:
             return f"No balance sheet data found for symbol '{ticker}'"
             
@@ -383,17 +337,19 @@ def get_balance_sheet(
 def get_cashflow(
     ticker: Annotated[str, "ticker symbol of the company"],
     freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
-    curr_date: Annotated[str, "current date (not used for yfinance)"] = None
+    curr_date: Annotated[str, "current date in YYYY-MM-DD format"] = None
 ):
     """Get cash flow data from yfinance."""
     try:
         ticker_obj = yf.Ticker(ticker.upper())
-        
+
         if freq.lower() == "quarterly":
-            data = ticker_obj.quarterly_cashflow
+            data = yf_retry(lambda: ticker_obj.quarterly_cashflow)
         else:
-            data = ticker_obj.cashflow
-            
+            data = yf_retry(lambda: ticker_obj.cashflow)
+
+        data = filter_financials_by_date(data, curr_date)
+
         if data.empty:
             return f"No cash flow data found for symbol '{ticker}'"
             
@@ -413,17 +369,19 @@ def get_cashflow(
 def get_income_statement(
     ticker: Annotated[str, "ticker symbol of the company"],
     freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
-    curr_date: Annotated[str, "current date (not used for yfinance)"] = None
+    curr_date: Annotated[str, "current date in YYYY-MM-DD format"] = None
 ):
     """Get income statement data from yfinance."""
     try:
         ticker_obj = yf.Ticker(ticker.upper())
-        
+
         if freq.lower() == "quarterly":
-            data = ticker_obj.quarterly_income_stmt
+            data = yf_retry(lambda: ticker_obj.quarterly_income_stmt)
         else:
-            data = ticker_obj.income_stmt
-            
+            data = yf_retry(lambda: ticker_obj.income_stmt)
+
+        data = filter_financials_by_date(data, curr_date)
+
         if data.empty:
             return f"No income statement data found for symbol '{ticker}'"
             
@@ -446,7 +404,7 @@ def get_insider_transactions(
     """Get insider transactions data from yfinance."""
     try:
         ticker_obj = yf.Ticker(ticker.upper())
-        data = ticker_obj.insider_transactions
+        data = yf_retry(lambda: ticker_obj.insider_transactions)
         
         if data is None or data.empty:
             return f"No insider transactions data found for symbol '{ticker}'"

tradingagents/dataflows/yfinance_news.py

@@ -4,6 +4,8 @@ import yfinance as yf
 from datetime import datetime
 from dateutil.relativedelta import relativedelta
 
+from .stockstats_utils import yf_retry
+
 
 def _extract_article_data(article: dict) -> dict:
     """Extract article data from yfinance news format (handles nested 'content' structure)."""
@@ -64,7 +66,7 @@ def get_news_yfinance(
     """
     try:
         stock = yf.Ticker(ticker)
-        news = stock.get_news(count=20)
+        news = yf_retry(lambda: stock.get_news(count=20))
 
         if not news:
             return f"No news found for {ticker}"
@@ -131,11 +133,11 @@ def get_global_news_yfinance(
 
     try:
         for query in search_queries:
-            search = yf.Search(
-                query=query,
+            search = yf_retry(lambda q=query: yf.Search(
+                query=q,
                 news_count=limit,
                 enable_fuzzy_query=True,
-            )
+            ))
 
             if search.news:
                 for article in search.news:
@@ -167,6 +169,11 @@ def get_global_news_yfinance(
             # Handle both flat and nested structures
             if "content" in article:
                 data = _extract_article_data(article)
+                # Skip articles published after curr_date (look-ahead guard)
+                if data.get("pub_date"):
+                    pub_naive = data["pub_date"].replace(tzinfo=None) if hasattr(data["pub_date"], "replace") else data["pub_date"]
+                    if pub_naive > curr_dt + relativedelta(days=1):
+                        continue
                 title = data["title"]
                 publisher = data["publisher"]
                 link = data["link"]

tradingagents/default_config.py

@@ -15,12 +15,16 @@ DEFAULT_CONFIG = {
     ),
     # LLM settings
     "llm_provider": os.environ.get("LLM_PROVIDER", "openai"),
-    "deep_think_llm": os.environ.get("DEEP_THINK_LLM", "gpt-5.2"),
-    "quick_think_llm": os.environ.get("QUICK_THINK_LLM", "gpt-5-mini"),
+    "deep_think_llm": os.environ.get("DEEP_THINK_LLM", "gpt-5.4"),
+    "quick_think_llm": os.environ.get("QUICK_THINK_LLM", "gpt-5.4-mini"),
     "backend_url": os.environ.get("BACKEND_URL", "https://api.openai.com/v1"),
     # Provider-specific thinking configuration
     "google_thinking_level": None,      # "high", "minimal", etc.
     "openai_reasoning_effort": None,    # "medium", "high", "low"
+    "anthropic_effort": None,           # "high", "medium", "low"
+    # Output language for analyst reports and final decision
+    # Internal agent debate stays in English for reasoning quality
+    "output_language": "English",
     # Debate and discussion settings
     "max_debate_rounds": 1,
     "max_risk_discuss_rounds": 1,

tradingagents/graph/conditional_logic.py

@@ -59,7 +59,7 @@ class ConditionalLogic:
         if (
             state["risk_debate_state"]["count"] >= 3 * self.max_risk_discuss_rounds
         ):  # 3 rounds of back-and-forth between 3 agents
-            return "Risk Judge"
+            return "Portfolio Manager"
         if state["risk_debate_state"]["latest_speaker"].startswith("Aggressive"):
             return "Conservative Analyst"
         if state["risk_debate_state"]["latest_speaker"].startswith("Conservative"):

tradingagents/graph/reflection.py

@@ -1,13 +1,12 @@
 # TradingAgents/graph/reflection.py
 
-from typing import Dict, Any
-from langchain_openai import ChatOpenAI
+from typing import Any, Dict
 
 
 class Reflector:
     """Handles reflection on decisions and updating memory."""
 
-    def __init__(self, quick_thinking_llm: ChatOpenAI):
+    def __init__(self, quick_thinking_llm: Any):
         """Initialize the reflector with an LLM."""
         self.quick_thinking_llm = quick_thinking_llm
         self.reflection_system_prompt = self._get_reflection_prompt()
@@ -110,12 +109,12 @@ Adhere strictly to these instructions, and ensure your output is detailed, accur
         )
         invest_judge_memory.add_situations([(situation, result)])
 
-    def reflect_risk_manager(self, current_state, returns_losses, risk_manager_memory):
-        """Reflect on risk manager's decision and update memory."""
+    def reflect_portfolio_manager(self, current_state, returns_losses, portfolio_manager_memory):
+        """Reflect on portfolio manager's decision and update memory."""
         situation = self._extract_current_situation(current_state)
         judge_decision = current_state["risk_debate_state"]["judge_decision"]
 
         result = self._reflect_on_component(
-            "RISK JUDGE", judge_decision, situation, returns_losses
+            "PORTFOLIO MANAGER", judge_decision, situation, returns_losses
         )
-        risk_manager_memory.add_situations([(situation, result)])
+        portfolio_manager_memory.add_situations([(situation, result)])

tradingagents/graph/setup.py

@@ -1,8 +1,7 @@
 # TradingAgents/graph/setup.py
 
-from typing import Dict, Any
-from langchain_openai import ChatOpenAI
-from langgraph.graph import END, StateGraph, START
+from typing import Any, Dict
+from langgraph.graph import END, START, StateGraph
 from langgraph.prebuilt import ToolNode
 
 from tradingagents.agents import *
@@ -16,14 +15,14 @@ class GraphSetup:
 
     def __init__(
         self,
-        quick_thinking_llm: ChatOpenAI,
-        deep_thinking_llm: ChatOpenAI,
+        quick_thinking_llm: Any,
+        deep_thinking_llm: Any,
         tool_nodes: Dict[str, ToolNode],
         bull_memory,
         bear_memory,
         trader_memory,
         invest_judge_memory,
-        risk_manager_memory,
+        portfolio_manager_memory,
         conditional_logic: ConditionalLogic,
     ):
         """Initialize with required components."""
@@ -34,7 +33,7 @@ class GraphSetup:
         self.bear_memory = bear_memory
         self.trader_memory = trader_memory
         self.invest_judge_memory = invest_judge_memory
-        self.risk_manager_memory = risk_manager_memory
+        self.portfolio_manager_memory = portfolio_manager_memory
         self.conditional_logic = conditional_logic
 
     def setup_graph(
@@ -101,8 +100,8 @@ class GraphSetup:
         aggressive_analyst = create_aggressive_debator(self.quick_thinking_llm)
         neutral_analyst = create_neutral_debator(self.quick_thinking_llm)
         conservative_analyst = create_conservative_debator(self.quick_thinking_llm)
-        risk_manager_node = create_risk_manager(
-            self.deep_thinking_llm, self.risk_manager_memory
+        portfolio_manager_node = create_portfolio_manager(
+            self.deep_thinking_llm, self.portfolio_manager_memory
         )
 
         # Create workflow
@@ -124,7 +123,7 @@ class GraphSetup:
         workflow.add_node("Aggressive Analyst", aggressive_analyst)
         workflow.add_node("Neutral Analyst", neutral_analyst)
         workflow.add_node("Conservative Analyst", conservative_analyst)
-        workflow.add_node("Risk Judge", risk_manager_node)
+        workflow.add_node("Portfolio Manager", portfolio_manager_node)
 
         # Define edges
         # Start with the first analyst
@@ -176,7 +175,7 @@ class GraphSetup:
             self.conditional_logic.should_continue_risk_analysis,
             {
                 "Conservative Analyst": "Conservative Analyst",
-                "Risk Judge": "Risk Judge",
+                "Portfolio Manager": "Portfolio Manager",
             },
         )
         workflow.add_conditional_edges(
@@ -184,7 +183,7 @@ class GraphSetup:
             self.conditional_logic.should_continue_risk_analysis,
             {
                 "Neutral Analyst": "Neutral Analyst",
-                "Risk Judge": "Risk Judge",
+                "Portfolio Manager": "Portfolio Manager",
             },
         )
         workflow.add_conditional_edges(
@@ -192,11 +191,11 @@ class GraphSetup:
             self.conditional_logic.should_continue_risk_analysis,
             {
                 "Aggressive Analyst": "Aggressive Analyst",
-                "Risk Judge": "Risk Judge",
+                "Portfolio Manager": "Portfolio Manager",
             },
         )
 
-        workflow.add_edge("Risk Judge", END)
+        workflow.add_edge("Portfolio Manager", END)
 
         # Compile and return
         return workflow.compile()

tradingagents/graph/signal_processing.py

@@ -1,12 +1,12 @@
 # TradingAgents/graph/signal_processing.py
 
-from langchain_openai import ChatOpenAI
+from typing import Any
 
 
 class SignalProcessor:
     """Processes trading signals to extract actionable decisions."""
 
-    def __init__(self, quick_thinking_llm: ChatOpenAI):
+    def __init__(self, quick_thinking_llm: Any):
         """Initialize with an LLM for processing."""
         self.quick_thinking_llm = quick_thinking_llm
 
@@ -18,12 +18,14 @@ class SignalProcessor:
             full_signal: Complete trading signal text
 
         Returns:
-            Extracted decision (BUY, SELL, or HOLD)
+            Extracted rating (BUY, OVERWEIGHT, HOLD, UNDERWEIGHT, or SELL)
         """
         messages = [
             (
                 "system",
-                "You are an efficient assistant designed to analyze paragraphs or financial reports provided by a group of analysts. Your task is to extract the investment decision: SELL, BUY, or HOLD. Provide only the extracted decision (SELL, BUY, or HOLD) as your output, without adding any additional text or information.",
+                "You are an efficient assistant that extracts the trading decision from analyst reports. "
+                "Extract the rating as exactly one of: BUY, OVERWEIGHT, HOLD, UNDERWEIGHT, SELL. "
+                "Output only the single rating word, nothing else.",
             ),
             ("human", full_signal),
         ]

tradingagents/graph/trading_graph.py

@@ -99,7 +99,7 @@ class TradingAgentsGraph:
         self.bear_memory = FinancialSituationMemory("bear_memory", self.config)
         self.trader_memory = FinancialSituationMemory("trader_memory", self.config)
         self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory", self.config)
-        self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory", self.config)
+        self.portfolio_manager_memory = FinancialSituationMemory("portfolio_manager_memory", self.config)
 
         # Create tool nodes
         self.tool_nodes = self._create_tool_nodes()
@@ -117,7 +117,7 @@ class TradingAgentsGraph:
             self.bear_memory,
             self.trader_memory,
             self.invest_judge_memory,
-            self.risk_manager_memory,
+            self.portfolio_manager_memory,
             self.conditional_logic,
         )
 
@@ -148,6 +148,11 @@ class TradingAgentsGraph:
             if reasoning_effort:
                 kwargs["reasoning_effort"] = reasoning_effort
 
+        elif provider == "anthropic":
+            effort = self.config.get("anthropic_effort")
+            if effort:
+                kwargs["effort"] = effort
+
         return kwargs
 
     def _create_tool_nodes(self) -> Dict[str, ToolNode]:
@@ -254,15 +259,12 @@ class TradingAgentsGraph:
         }
 
         # Save to file
-        directory = Path(f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/")
+        directory = Path(self.config["results_dir"]) / self.ticker / "TradingAgentsStrategy_logs"
         directory.mkdir(parents=True, exist_ok=True)
 
-        with open(
-            f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/full_states_log_{trade_date}.json",
-            "w",
-            encoding="utf-8",
-        ) as f:
-            json.dump(self.log_states_dict, f, indent=4)
+        log_path = directory / f"full_states_log_{trade_date}.json"
+        with open(log_path, "w", encoding="utf-8") as f:
+            json.dump(self.log_states_dict[str(trade_date)], f, indent=4)
 
     def reflect_and_remember(self, returns_losses):
         """Reflect on decisions and update memory based on returns."""
@@ -278,8 +280,8 @@ class TradingAgentsGraph:
         self.reflector.reflect_invest_judge(
             self.curr_state, returns_losses, self.invest_judge_memory
         )
-        self.reflector.reflect_risk_manager(
-            self.curr_state, returns_losses, self.risk_manager_memory
+        self.reflector.reflect_portfolio_manager(
+            self.curr_state, returns_losses, self.portfolio_manager_memory
         )
 
     def process_signal(self, full_signal):

tradingagents/llm_clients/TODO.md

@@ -5,20 +5,11 @@
 ### 1. `validate_model()` is never called
 - Add validation call in `get_llm()` with warning (not error) for unknown models
 
-### 2. Inconsistent parameter handling
-| Client | API Key Param | Special Params |
-|--------|---------------|----------------|
-| OpenAI | `api_key` | `reasoning_effort` |
-| Anthropic | `api_key` | `thinking_config` → `thinking` |
-| Google | `google_api_key` | `thinking_budget` |
+### 2. ~~Inconsistent parameter handling~~ (Fixed)
+- GoogleClient now accepts unified `api_key` and maps it to `google_api_key`
 
-**Fix:** Standardize with unified `api_key` that maps to provider-specific keys
+### 3. ~~`base_url` accepted but ignored~~ (Fixed)
+- All clients now pass `base_url` to their respective LLM constructors
 
-### 3. `base_url` accepted but ignored
-- `AnthropicClient`: accepts `base_url` but never uses it
-- `GoogleClient`: accepts `base_url` but never uses it (correct - Google doesn't support it)
-
-**Fix:** Remove unused `base_url` from clients that don't support it
-
-### 4. Update validators.py with models from CLI
-- Sync `VALID_MODELS` dict with CLI model options after Feature 2 is complete
+### 4. ~~Update validators.py with models from CLI~~ (Fixed)
+- Synced in v0.2.2

tradingagents/llm_clients/anthropic_client.py

@@ -2,9 +2,26 @@ from typing import Any, Optional
 
 from langchain_anthropic import ChatAnthropic
 
-from .base_client import BaseLLMClient
+from .base_client import BaseLLMClient, normalize_content
 from .validators import validate_model
 
+_PASSTHROUGH_KWARGS = (
+    "timeout", "max_retries", "api_key", "max_tokens",
+    "callbacks", "http_client", "http_async_client", "effort",
+)
+
+
+class NormalizedChatAnthropic(ChatAnthropic):
+    """ChatAnthropic with normalized content output.
+
+    Claude models with extended thinking or tool use return content as a
+    list of typed blocks. This normalizes to string for consistent
+    downstream handling.
+    """
+
+    def invoke(self, input, config=None, **kwargs):
+        return normalize_content(super().invoke(input, config, **kwargs))
+
 
 class AnthropicClient(BaseLLMClient):
     """Client for Anthropic Claude models."""
@@ -14,13 +31,17 @@ class AnthropicClient(BaseLLMClient):
 
     def get_llm(self) -> Any:
         """Return configured ChatAnthropic instance."""
+        self.warn_if_unknown_model()
         llm_kwargs = {"model": self.model}
 
-        for key in ("timeout", "max_retries", "api_key", "max_tokens", "callbacks", "http_client", "http_async_client"):
+        if self.base_url:
+            llm_kwargs["base_url"] = self.base_url
+
+        for key in _PASSTHROUGH_KWARGS:
             if key in self.kwargs:
                 llm_kwargs[key] = self.kwargs[key]
 
-        return ChatAnthropic(**llm_kwargs)
+        return NormalizedChatAnthropic(**llm_kwargs)
 
     def validate_model(self) -> bool:
         """Validate model for Anthropic."""

tradingagents/llm_clients/base_client.py

@@ -1,5 +1,25 @@
 from abc import ABC, abstractmethod
 from typing import Any, Optional
+import warnings
+
+
+def normalize_content(response):
+    """Normalize LLM response content to a plain string.
+
+    Multiple providers (OpenAI Responses API, Google Gemini 3) return content
+    as a list of typed blocks, e.g. [{'type': 'reasoning', ...}, {'type': 'text', 'text': '...'}].
+    Downstream agents expect response.content to be a string. This extracts
+    and joins the text blocks, discarding reasoning/metadata blocks.
+    """
+    content = response.content
+    if isinstance(content, list):
+        texts = [
+            item.get("text", "") if isinstance(item, dict) and item.get("type") == "text"
+            else item if isinstance(item, str) else ""
+            for item in content
+        ]
+        response.content = "\n".join(t for t in texts if t)
+    return response
 
 
 class BaseLLMClient(ABC):
@@ -10,6 +30,27 @@ class BaseLLMClient(ABC):
         self.base_url = base_url
         self.kwargs = kwargs
 
+    def get_provider_name(self) -> str:
+        """Return the provider name used in warning messages."""
+        provider = getattr(self, "provider", None)
+        if provider:
+            return str(provider)
+        return self.__class__.__name__.removesuffix("Client").lower()
+
+    def warn_if_unknown_model(self) -> None:
+        """Warn when the model is outside the known list for the provider."""
+        if self.validate_model():
+            return
+
+        warnings.warn(
+            (
+                f"Model '{self.model}' is not in the known model list for "
+                f"provider '{self.get_provider_name()}'. Continuing anyway."
+            ),
+            RuntimeWarning,
+            stacklevel=2,
+        )
+
     @abstractmethod
     def get_llm(self) -> Any:
         """Return the configured LLM instance."""

tradingagents/llm_clients/google_client.py

@@ -2,30 +2,19 @@ from typing import Any, Optional
 
 from langchain_google_genai import ChatGoogleGenerativeAI
 
-from .base_client import BaseLLMClient
+from .base_client import BaseLLMClient, normalize_content
 from .validators import validate_model
 
 
 class NormalizedChatGoogleGenerativeAI(ChatGoogleGenerativeAI):
     """ChatGoogleGenerativeAI with normalized content output.
 
-    Gemini 3 models return content as list: [{'type': 'text', 'text': '...'}]
+    Gemini 3 models return content as list of typed blocks.
     This normalizes to string for consistent downstream handling.
     """
 
-    def _normalize_content(self, response):
-        content = response.content
-        if isinstance(content, list):
-            texts = [
-                item.get("text", "") if isinstance(item, dict) and item.get("type") == "text"
-                else item if isinstance(item, str) else ""
-                for item in content
-            ]
-            response.content = "\n".join(t for t in texts if t)
-        return response
-
     def invoke(self, input, config=None, **kwargs):
-        return self._normalize_content(super().invoke(input, config, **kwargs))
+        return normalize_content(super().invoke(input, config, **kwargs))
 
 
 class GoogleClient(BaseLLMClient):
@@ -36,12 +25,21 @@ class GoogleClient(BaseLLMClient):
 
     def get_llm(self) -> Any:
         """Return configured ChatGoogleGenerativeAI instance."""
+        self.warn_if_unknown_model()
         llm_kwargs = {"model": self.model}
 
-        for key in ("timeout", "max_retries", "google_api_key", "callbacks", "http_client", "http_async_client"):
+        if self.base_url:
+            llm_kwargs["base_url"] = self.base_url
+
+        for key in ("timeout", "max_retries", "callbacks", "http_client", "http_async_client"):
             if key in self.kwargs:
                 llm_kwargs[key] = self.kwargs[key]
 
+        # Unified api_key maps to provider-specific google_api_key
+        google_api_key = self.kwargs.get("api_key") or self.kwargs.get("google_api_key")
+        if google_api_key:
+            llm_kwargs["google_api_key"] = google_api_key
+
         # Map thinking_level to appropriate API param based on model
         # Gemini 3 Pro: low, high
         # Gemini 3 Flash: minimal, low, medium, high

tradingagents/llm_clients/model_catalog.py

@@ -0,0 +1,99 @@
+"""Shared model catalog for CLI selections and validation."""
+
+from __future__ import annotations
+
+from typing import Dict, List, Tuple
+
+ModelOption = Tuple[str, str]
+ProviderModeOptions = Dict[str, Dict[str, List[ModelOption]]]
+
+
+MODEL_OPTIONS: ProviderModeOptions = {
+    "openai": {
+        "quick": [
+            ("GPT-5.4 Mini - Fast, strong coding and tool use", "gpt-5.4-mini"),
+            ("GPT-5.4 Nano - Cheapest, high-volume tasks", "gpt-5.4-nano"),
+            ("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
+            ("GPT-4.1 - Smartest non-reasoning model", "gpt-4.1"),
+        ],
+        "deep": [
+            ("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
+            ("GPT-5.2 - Strong reasoning, cost-effective", "gpt-5.2"),
+            ("GPT-5.4 Mini - Fast, strong coding and tool use", "gpt-5.4-mini"),
+            ("GPT-5.4 Pro - Most capable, expensive ($30/$180 per 1M tokens)", "gpt-5.4-pro"),
+        ],
+    },
+    "anthropic": {
+        "quick": [
+            ("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
+            ("Claude Haiku 4.5 - Fast, near-instant responses", "claude-haiku-4-5"),
+            ("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
+        ],
+        "deep": [
+            ("Claude Opus 4.6 - Most intelligent, agents and coding", "claude-opus-4-6"),
+            ("Claude Opus 4.5 - Premium, max intelligence", "claude-opus-4-5"),
+            ("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
+            ("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
+        ],
+    },
+    "google": {
+        "quick": [
+            ("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
+            ("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
+            ("Gemini 3.1 Flash Lite - Most cost-efficient", "gemini-3.1-flash-lite-preview"),
+            ("Gemini 2.5 Flash Lite - Fast, low-cost", "gemini-2.5-flash-lite"),
+        ],
+        "deep": [
+            ("Gemini 3.1 Pro - Reasoning-first, complex workflows", "gemini-3.1-pro-preview"),
+            ("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
+            ("Gemini 2.5 Pro - Stable pro model", "gemini-2.5-pro"),
+            ("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
+        ],
+    },
+    "xai": {
+        "quick": [
+            ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
+            ("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"),
+            ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
+        ],
+        "deep": [
+            ("Grok 4 - Flagship model", "grok-4-0709"),
+            ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
+            ("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
+            ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
+        ],
+    },
+    # OpenRouter models are fetched dynamically at CLI runtime.
+    # No static entries needed; any model ID is accepted by the validator.
+    "ollama": {
+        "quick": [
+            ("Qwen3:latest (8B, local)", "qwen3:latest"),
+            ("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
+            ("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
+        ],
+        "deep": [
+            ("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
+            ("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
+            ("Qwen3:latest (8B, local)", "qwen3:latest"),
+        ],
+    },
+}
+
+
+def get_model_options(provider: str, mode: str) -> List[ModelOption]:
+    """Return shared model options for a provider and selection mode."""
+    return MODEL_OPTIONS[provider.lower()][mode]
+
+
+def get_known_models() -> Dict[str, List[str]]:
+    """Build known model names from the shared CLI catalog."""
+    return {
+        provider: sorted(
+            {
+                value
+                for options in mode_options.values()
+                for _, value in options
+            }
+        )
+        for provider, mode_options in MODEL_OPTIONS.items()
+    }

tradingagents/llm_clients/openai_client.py

@@ -3,31 +3,43 @@ from typing import Any, Optional
 
 from langchain_openai import ChatOpenAI
 
-from .base_client import BaseLLMClient
+from .base_client import BaseLLMClient, normalize_content
 from .validators import validate_model
 
 
-class UnifiedChatOpenAI(ChatOpenAI):
-    """ChatOpenAI subclass that strips temperature/top_p for GPT-5 family models.
+class NormalizedChatOpenAI(ChatOpenAI):
+    """ChatOpenAI with normalized content output.
 
-    GPT-5 family models use reasoning natively. temperature/top_p are only
-    accepted when reasoning.effort is 'none'; with any other effort level
-    (or for older GPT-5/GPT-5-mini/GPT-5-nano which always reason) the API
-    rejects these params. Langchain defaults temperature=0.7, so we must
-    strip it to avoid errors.
-
-    Non-GPT-5 models (GPT-4.1, xAI, Ollama, etc.) are unaffected.
+    The Responses API returns content as a list of typed blocks
+    (reasoning, text, etc.). This normalizes to string for consistent
+    downstream handling.
     """
 
-    def __init__(self, **kwargs):
-        if "gpt-5" in kwargs.get("model", "").lower():
-            kwargs.pop("temperature", None)
-            kwargs.pop("top_p", None)
-        super().__init__(**kwargs)
+    def invoke(self, input, config=None, **kwargs):
+        return normalize_content(super().invoke(input, config, **kwargs))
+
+# Kwargs forwarded from user config to ChatOpenAI
+_PASSTHROUGH_KWARGS = (
+    "timeout", "max_retries", "reasoning_effort",
+    "api_key", "callbacks", "http_client", "http_async_client",
+)
+
+# Provider base URLs and API key env vars
+_PROVIDER_CONFIG = {
+    "xai": ("https://api.x.ai/v1", "XAI_API_KEY"),
+    "openrouter": ("https://openrouter.ai/api/v1", "OPENROUTER_API_KEY"),
+    "ollama": ("http://localhost:11434/v1", None),
+}
 
 
 class OpenAIClient(BaseLLMClient):
-    """Client for OpenAI, Ollama, OpenRouter, and xAI providers."""
+    """Client for OpenAI, Ollama, OpenRouter, and xAI providers.
+
+    For native OpenAI models, uses the Responses API (/v1/responses) which
+    supports reasoning_effort with function tools across all model families
+    (GPT-4.1, GPT-5). Third-party compatible providers (xAI, OpenRouter,
+    Ollama) use standard Chat Completions.
+    """
 
     def __init__(
         self,
@@ -41,21 +53,19 @@ class OpenAIClient(BaseLLMClient):
 
     def get_llm(self) -> Any:
         """Return configured ChatOpenAI instance."""
+        self.warn_if_unknown_model()
         llm_kwargs = {"model": self.model}
 
-        if self.provider == "xai":
-            llm_kwargs["base_url"] = "https://api.x.ai/v1"
-            api_key = os.environ.get("XAI_API_KEY")
-            if api_key:
-                llm_kwargs["api_key"] = api_key
-        elif self.provider == "openrouter":
-            llm_kwargs["base_url"] = "https://openrouter.ai/api/v1"
-            api_key = os.environ.get("OPENROUTER_API_KEY")
-            if api_key:
-                llm_kwargs["api_key"] = api_key
-        elif self.provider == "ollama":
-            llm_kwargs["base_url"] = "http://localhost:11434/v1"
-            llm_kwargs["api_key"] = "ollama"  # Ollama doesn't require auth
+        # Provider-specific base URL and auth
+        if self.provider in _PROVIDER_CONFIG:
+            base_url, api_key_env = _PROVIDER_CONFIG[self.provider]
+            llm_kwargs["base_url"] = base_url
+            if api_key_env:
+                api_key = os.environ.get(api_key_env)
+                if api_key:
+                    llm_kwargs["api_key"] = api_key
+            else:
+                llm_kwargs["api_key"] = "ollama"
         elif self.provider == "llamacpp":
             base_url = os.environ.get("BACKEND_URL") or os.environ.get("LLAMACPP_BASE_URL", "http://localhost:8080/v1")
             llm_kwargs["base_url"] = base_url
@@ -63,11 +73,17 @@ class OpenAIClient(BaseLLMClient):
         elif self.base_url:
             llm_kwargs["base_url"] = self.base_url
 
-        for key in ("timeout", "max_retries", "reasoning_effort", "api_key", "callbacks", "http_client", "http_async_client"):
+        # Forward user-provided kwargs
+        for key in _PASSTHROUGH_KWARGS:
             if key in self.kwargs:
                 llm_kwargs[key] = self.kwargs[key]
 
-        return UnifiedChatOpenAI(**llm_kwargs)
+        # Native OpenAI: use Responses API for consistent behavior across
+        # all model families. Third-party providers use Chat Completions.
+        if self.provider == "openai":
+            llm_kwargs["use_responses_api"] = True
+
+        return NormalizedChatOpenAI(**llm_kwargs)
 
     def validate_model(self) -> bool:
         """Validate model for the provider."""

tradingagents/llm_clients/validators.py

@@ -1,53 +1,12 @@
-"""Model name validators for each provider.
+"""Model name validators for each provider."""
+
+from .model_catalog import get_known_models
 
-Only validates model names - does NOT enforce limits.
-Let LLM providers use their own defaults for unspecified params.
-"""
 
 VALID_MODELS = {
-    "openai": [
-        # GPT-5 series
-        "gpt-5.4-pro",
-        "gpt-5.4",
-        "gpt-5.2",
-        "gpt-5.1",
-        "gpt-5",
-        "gpt-5-mini",
-        "gpt-5-nano",
-        # GPT-4.1 series
-        "gpt-4.1",
-        "gpt-4.1-mini",
-        "gpt-4.1-nano",
-    ],
-    "anthropic": [
-        # Claude 4.6 series (latest)
-        "claude-opus-4-6",
-        "claude-sonnet-4-6",
-        # Claude 4.5 series
-        "claude-opus-4-5",
-        "claude-sonnet-4-5",
-        "claude-haiku-4-5",
-    ],
-    "google": [
-        # Gemini 3.1 series (preview)
-        "gemini-3.1-pro-preview",
-        "gemini-3.1-flash-lite-preview",
-        # Gemini 3 series (preview)
-        "gemini-3-flash-preview",
-        # Gemini 2.5 series
-        "gemini-2.5-pro",
-        "gemini-2.5-flash",
-        "gemini-2.5-flash-lite",
-    ],
-    "xai": [
-        # Grok 4.1 series
-        "grok-4-1-fast-reasoning",
-        "grok-4-1-fast-non-reasoning",
-        # Grok 4 series
-        "grok-4-0709",
-        "grok-4-fast-reasoning",
-        "grok-4-fast-non-reasoning",
-    ],
+    provider: models
+    for provider, models in get_known_models().items()
+    if provider not in ("ollama", "openrouter")
 }