Merge branch 'main' into roadmap/realistic-timeline-march-2026

README.md

@@ -5,6 +5,10 @@
 
 
 
+## News
+- [2026-03] **TradingAgents v0.2.1** released with GPT-5.4, Gemini 3.1, Claude 4.6 model coverage and improved system stability.
+- [2026-02] **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.
 
 
 

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") as f:
+    with open("./cli/static/welcome.txt", "r", encoding="utf-8") as f:
         welcome_ascii = f.read()
 
     # Create welcome box content
@@ -948,7 +948,7 @@ 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") as f:
+            with open(log_file, "a", encoding="utf-8") as f:
                 f.write(f"{timestamp} [{message_type}] {content}\n")
         return wrapper
     
@@ -959,7 +959,7 @@ 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") as f:
+            with open(log_file, "a", encoding="utf-8") as f:
                 f.write(f"{timestamp} [Tool Call] {tool_name}({args_str})\n")
         return wrapper
 
@@ -972,7 +972,7 @@ def run_analysis():
                 content = obj.report_sections[section_name]
                 if content:
                     file_name = f"{section_name}.md"
-                    with open(report_dir / file_name, "w") as f:
+                    with open(report_dir / file_name, "w", encoding="utf-8") as f:
                         f.write(content)
         return wrapper
 

cli/utils.py

@@ -1,8 +1,12 @@
 import questionary
 from typing import List, Optional, Tuple, Dict
 
+from rich.console import Console
+
 from cli.models import AnalystType
 
+console = Console()
+
 ANALYST_ORDER = [
     ("Market Analyst", AnalystType.MARKET),
     ("Social Media Analyst", AnalystType.SOCIAL),
@@ -126,30 +130,30 @@ 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 - Cost-optimized reasoning", "gpt-5-mini"),
+            ("GPT-5 Mini - Balanced speed, cost, and capability", "gpt-5-mini"),
-            ("GPT-5 Nano - Ultra-fast, high-throughput", "gpt-5-nano"),
+            ("GPT-5 Nano - High-throughput, simple tasks", "gpt-5-nano"),
-            ("GPT-5.2 - Latest flagship", "gpt-5.2"),
+            ("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
-            ("GPT-5.1 - Flexible reasoning", "gpt-5.1"),
+            ("GPT-4.1 - Smartest non-reasoning model", "gpt-4.1"),
-            ("GPT-4.1 - Smartest non-reasoning, 1M context", "gpt-4.1"),
         ],
         "anthropic": [
-            ("Claude Haiku 4.5 - Fast + extended thinking", "claude-haiku-4-5"),
+            ("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
-            ("Claude Sonnet 4.5 - Best for agents/coding", "claude-sonnet-4-5"),
+            ("Claude Haiku 4.5 - Fast, near-instant responses", "claude-haiku-4-5"),
-            ("Claude Sonnet 4 - High-performance", "claude-sonnet-4-20250514"),
+            ("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, recommended", "gemini-2.5-flash"),
+            ("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
-            ("Gemini 3 Pro - Reasoning-first", "gemini-3-pro-preview"),
+            ("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"),
-            ("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
         ],
         "openrouter": [
             ("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
@@ -191,33 +195,32 @@ 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-5.2 - Latest flagship", "gpt-5.2"),
+            ("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
-            ("GPT-5.1 - Flexible reasoning", "gpt-5.1"),
+            ("GPT-5.2 - Strong reasoning, cost-effective", "gpt-5.2"),
-            ("GPT-5 - Advanced reasoning", "gpt-5"),
+            ("GPT-5 Mini - Balanced speed, cost, and capability", "gpt-5-mini"),
-            ("GPT-4.1 - Smartest non-reasoning, 1M context", "gpt-4.1"),
+            ("GPT-5.4 Pro - Most capable, expensive ($30/$180 per 1M tokens)", "gpt-5.4-pro"),
-            ("GPT-5 Mini - Cost-optimized reasoning", "gpt-5-mini"),
-            ("GPT-5 Nano - Ultra-fast, high-throughput", "gpt-5-nano"),
         ],
         "anthropic": [
-            ("Claude Sonnet 4.5 - Best for agents/coding", "claude-sonnet-4-5"),
+            ("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 Opus 4.1 - Most capable model", "claude-opus-4-1-20250805"),
+            ("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
-            ("Claude Haiku 4.5 - Fast + extended thinking", "claude-haiku-4-5"),
+            ("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
-            ("Claude Sonnet 4 - High-performance", "claude-sonnet-4-20250514"),
         ],
         "google": [
-            ("Gemini 3 Pro - Reasoning-first", "gemini-3-pro-preview"),
+            ("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 Flash - Balanced, recommended", "gemini-2.5-flash"),
+            ("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 - Flagship model", "grok-4-0709"),
             ("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"),
         ],
         "openrouter": [
             ("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),

pyproject.toml

@@ -4,14 +4,13 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "tradingagents"
-version = "0.2.0"
+version = "0.2.1"
 description = "TradingAgents: Multi-Agents LLM Financial Trading Framework"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
     "langchain-core>=0.3.81",
     "backtrader>=1.9.78.123",
-    "chainlit>=2.5.5",
     "langchain-anthropic>=0.3.15",
     "langchain-experimental>=0.3.4",
     "langchain-google-genai>=2.1.5",

requirements.txt

@@ -14,7 +14,6 @@ requests
 tqdm
 pytz
 redis
-chainlit
 rich
 typer
 questionary

tradingagents/agents/utils/technical_indicators_tools.py

@@ -10,14 +10,22 @@ def get_indicators(
     look_back_days: Annotated[int, "how many days to look back"] = 30,
 ) -> str:
     """
-    Retrieve technical indicators for a given ticker symbol.
+    Retrieve a single technical indicator for a given ticker symbol.
     Uses the configured technical_indicators vendor.
     Args:
         symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
-        indicator (str): Technical indicator to get the analysis and report of
+        indicator (str): A single technical indicator name, e.g. 'rsi', 'macd'. Call this tool once per indicator.
         curr_date (str): The current trading date you are trading on, YYYY-mm-dd
         look_back_days (int): How many days to look back, default is 30
     Returns:
         str: A formatted dataframe containing the technical indicators for the specified ticker symbol and indicator.
     """
-    return route_to_vendor("get_indicators", symbol, indicator, curr_date, look_back_days)
+    # 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:
+            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)

tradingagents/dataflows/stockstats_utils.py

@@ -6,6 +6,19 @@ import os
 from .config import get_config
 
 
+def _clean_dataframe(data: pd.DataFrame) -> pd.DataFrame:
+    """Normalize a stock DataFrame for stockstats: parse dates, drop invalid rows, fill price gaps."""
+    data["Date"] = pd.to_datetime(data["Date"], errors="coerce")
+    data = data.dropna(subset=["Date"])
+
+    price_cols = [c for c in ["Open", "High", "Low", "Close", "Volume"] if c in data.columns]
+    data[price_cols] = data[price_cols].apply(pd.to_numeric, errors="coerce")
+    data = data.dropna(subset=["Close"])
+    data[price_cols] = data[price_cols].ffill().bfill()
+
+    return data
+
+
 class StockstatsUtils:
     @staticmethod
     def get_stock_stats(
@@ -36,8 +49,7 @@ class StockstatsUtils:
         )
 
         if os.path.exists(data_file):
-            data = pd.read_csv(data_file)
+            data = pd.read_csv(data_file, on_bad_lines="skip")
-            data["Date"] = pd.to_datetime(data["Date"])
         else:
             data = yf.download(
                 symbol,
@@ -50,6 +62,7 @@ class StockstatsUtils:
             data = data.reset_index()
             data.to_csv(data_file, index=False)
 
+        data = _clean_dataframe(data)
         df = wrap(data)
         df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
         curr_date_str = curr_date_dt.strftime("%Y-%m-%d")

tradingagents/dataflows/y_finance.py

@@ -3,7 +3,7 @@ from datetime import datetime
 from dateutil.relativedelta import relativedelta
 import yfinance as yf
 import os
-from .stockstats_utils import StockstatsUtils
+from .stockstats_utils import StockstatsUtils, _clean_dataframe
 
 def get_YFin_data_online(
     symbol: Annotated[str, "ticker symbol of the company"],
@@ -209,9 +209,9 @@ def _get_stock_stats_bulk(
                 os.path.join(
                     config.get("data_cache_dir", "data"),
                     f"{symbol}-YFin-data-2015-01-01-2025-03-25.csv",
+                ),
+                on_bad_lines="skip",
             )
-            )
-            df = wrap(data)
         except FileNotFoundError:
             raise Exception("Stockstats fail: Yahoo Finance data not fetched yet!")
     else:
@@ -232,8 +232,7 @@ def _get_stock_stats_bulk(
         )
 
         if os.path.exists(data_file):
-            data = pd.read_csv(data_file)
+            data = pd.read_csv(data_file, on_bad_lines="skip")
-            data["Date"] = pd.to_datetime(data["Date"])
         else:
             data = yf.download(
                 symbol,
@@ -246,6 +245,7 @@ def _get_stock_stats_bulk(
             data = data.reset_index()
             data.to_csv(data_file, index=False)
 
+    data = _clean_dataframe(data)
     df = wrap(data)
     df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
     

tradingagents/graph/propagation.py

@@ -24,14 +24,26 @@ class Propagator:
             "company_of_interest": company_name,
             "trade_date": str(trade_date),
             "investment_debate_state": InvestDebateState(
-                {"history": "", "current_response": "", "count": 0}
+                {
+                    "bull_history": "",
+                    "bear_history": "",
+                    "history": "",
+                    "current_response": "",
+                    "judge_decision": "",
+                    "count": 0,
+                }
             ),
             "risk_debate_state": RiskDebateState(
                 {
+                    "aggressive_history": "",
+                    "conservative_history": "",
+                    "neutral_history": "",
                     "history": "",
+                    "latest_speaker": "",
                     "current_aggressive_response": "",
                     "current_conservative_response": "",
                     "current_neutral_response": "",
+                    "judge_decision": "",
                     "count": 0,
                 }
             ),

tradingagents/graph/trading_graph.py

@@ -105,7 +105,10 @@ class TradingAgentsGraph:
         self.tool_nodes = self._create_tool_nodes()
 
         # Initialize components
-        self.conditional_logic = ConditionalLogic()
+        self.conditional_logic = ConditionalLogic(
+            max_debate_rounds=self.config["max_debate_rounds"],
+            max_risk_discuss_rounds=self.config["max_risk_discuss_rounds"],
+        )
         self.graph_setup = GraphSetup(
             self.quick_thinking_llm,
             self.deep_thinking_llm,
@@ -257,6 +260,7 @@ class TradingAgentsGraph:
         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)
 

tradingagents/llm_clients/anthropic_client.py

@@ -16,7 +16,7 @@ class AnthropicClient(BaseLLMClient):
         """Return configured ChatAnthropic instance."""
         llm_kwargs = {"model": self.model}
 
-        for key in ("timeout", "max_retries", "api_key", "max_tokens", "callbacks"):
+        for key in ("timeout", "max_retries", "api_key", "max_tokens", "callbacks", "http_client", "http_async_client"):
             if key in self.kwargs:
                 llm_kwargs[key] = self.kwargs[key]
 

tradingagents/llm_clients/factory.py

@@ -19,6 +19,12 @@ def create_llm_client(
         model: Model name/identifier
         base_url: Optional base URL for API endpoint
         **kwargs: Additional provider-specific arguments
+            - http_client: Custom httpx.Client for SSL proxy or certificate customization
+            - http_async_client: Custom httpx.AsyncClient for async operations
+            - timeout: Request timeout in seconds
+            - max_retries: Maximum retry attempts
+            - api_key: API key for the provider
+            - callbacks: LangChain callbacks
 
     Returns:
         Configured BaseLLMClient instance

tradingagents/llm_clients/google_client.py

@@ -38,7 +38,7 @@ class GoogleClient(BaseLLMClient):
         """Return configured ChatGoogleGenerativeAI instance."""
         llm_kwargs = {"model": self.model}
 
-        for key in ("timeout", "max_retries", "google_api_key", "callbacks"):
+        for key in ("timeout", "max_retries", "google_api_key", "callbacks", "http_client", "http_async_client"):
             if key in self.kwargs:
                 llm_kwargs[key] = self.kwargs[key]
 

tradingagents/llm_clients/openai_client.py

@@ -8,25 +8,23 @@ from .validators import validate_model
 
 
 class UnifiedChatOpenAI(ChatOpenAI):
-    """ChatOpenAI subclass that strips incompatible params for certain models."""
+    """ChatOpenAI subclass that strips temperature/top_p for GPT-5 family models.
+
+    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.
+    """
 
     def __init__(self, **kwargs):
-        model = kwargs.get("model", "")
+        if "gpt-5" in kwargs.get("model", "").lower():
-        if self._is_reasoning_model(model):
             kwargs.pop("temperature", None)
             kwargs.pop("top_p", None)
         super().__init__(**kwargs)
 
-    @staticmethod
-    def _is_reasoning_model(model: str) -> bool:
-        """Check if model is a reasoning model that doesn't support temperature."""
-        model_lower = model.lower()
-        return (
-            model_lower.startswith("o1")
-            or model_lower.startswith("o3")
-            or "gpt-5" in model_lower
-        )
-
 
 class OpenAIClient(BaseLLMClient):
     """Client for OpenAI, Ollama, OpenRouter, and xAI providers."""
@@ -61,7 +59,7 @@ 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"):
+        for key in ("timeout", "max_retries", "reasoning_effort", "api_key", "callbacks", "http_client", "http_async_client"):
             if key in self.kwargs:
                 llm_kwargs[key] = self.kwargs[key]
 

tradingagents/llm_clients/validators.py

@@ -6,59 +6,44 @@ Let LLM providers use their own defaults for unspecified params.
 
 VALID_MODELS = {
     "openai": [
-        # GPT-5 series (2025)
+        # 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 (2025)
+        # GPT-4.1 series
         "gpt-4.1",
         "gpt-4.1-mini",
         "gpt-4.1-nano",
-        # o-series reasoning models
-        "o4-mini",
-        "o3",
-        "o3-mini",
-        "o1",
-        "o1-preview",
-        # GPT-4o series (legacy but still supported)
-        "gpt-4o",
-        "gpt-4o-mini",
     ],
     "anthropic": [
-        # Claude 4.5 series (2025)
+        # 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",
-        # Claude 4.x series
-        "claude-opus-4-1-20250805",
-        "claude-sonnet-4-20250514",
-        # Claude 3.7 series
-        "claude-3-7-sonnet-20250219",
-        # Claude 3.5 series (legacy)
-        "claude-3-5-haiku-20241022",
-        "claude-3-5-sonnet-20241022",
     ],
     "google": [
+        # Gemini 3.1 series (preview)
+        "gemini-3.1-pro-preview",
+        "gemini-3.1-flash-lite-preview",
         # Gemini 3 series (preview)
-        "gemini-3-pro-preview",
         "gemini-3-flash-preview",
         # Gemini 2.5 series
         "gemini-2.5-pro",
         "gemini-2.5-flash",
         "gemini-2.5-flash-lite",
-        # Gemini 2.0 series
-        "gemini-2.0-flash",
-        "gemini-2.0-flash-lite",
     ],
     "xai": [
         # Grok 4.1 series
-        "grok-4-1-fast",
         "grok-4-1-fast-reasoning",
         "grok-4-1-fast-non-reasoning",
         # Grok 4 series
-        "grok-4",
         "grok-4-0709",
         "grok-4-fast-reasoning",
         "grok-4-fast-non-reasoning",