From 8a606620706d61aeda9da42b59410af9b128efda Mon Sep 17 00:00:00 2001
From: Yijia-Xiao <yijia-xiao@outlook.com>
Date: Sun, 15 Mar 2026 16:16:42 +0000
Subject: [PATCH 01/21] chore: remove unused chainlit dependency
 (CVE-2026-22218)

---
 pyproject.toml   | 1 -
 requirements.txt | 1 -
 2 files changed, 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 9213d7f6..73231704 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,7 +11,6 @@ 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",
diff --git a/requirements.txt b/requirements.txt
index 9e51ed98..184468b8 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -14,7 +14,6 @@ requests
 tqdm
 pytz
 redis
-chainlit
 rich
 typer
 questionary

From 907bc8022a2e9ad44e0b8eca6c5f05cb2bc13dc1 Mon Sep 17 00:00:00 2001
From: makk9 <117951691+makk9@users.noreply.github.com>
Date: Sun, 15 Mar 2026 12:31:59 -0400
Subject: [PATCH 02/21] fix: pass debate round config to ConditionalLogic
 (#361)

* fix: pass max_debate_rounds and max_risk_discuss_rounds config to ConditionalLogic

* use config values
---
 tradingagents/graph/trading_graph.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tradingagents/graph/trading_graph.py b/tradingagents/graph/trading_graph.py
index 44ecca0c..7944e5f5 100644
--- a/tradingagents/graph/trading_graph.py
+++ b/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,

From 3642f5917c2072ab6e990eede7c6ef1b2b82b8ce Mon Sep 17 00:00:00 2001
From: Yijia-Xiao <yijia-xiao@outlook.com>
Date: Sun, 15 Mar 2026 16:43:37 +0000
Subject: [PATCH 03/21] fix: add explicit UTF-8 encoding to all file open()
 calls

Prevents UnicodeEncodeError on Windows where the default encoding
(cp1252/gbk) cannot handle Unicode characters in LLM output.

Closes #77, closes #114, closes #126, closes #215, closes #332
---
 cli/main.py                          | 8 ++++----
 tradingagents/graph/trading_graph.py | 1 +
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/cli/main.py b/cli/main.py
index fb97d189..adda48fc 100644
--- a/cli/main.py
+++ b/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
 
diff --git a/tradingagents/graph/trading_graph.py b/tradingagents/graph/trading_graph.py
index 7944e5f5..c7ef0f98 100644
--- a/tradingagents/graph/trading_graph.py
+++ b/tradingagents/graph/trading_graph.py
@@ -260,6 +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)
 

From eec6ca4b53eee475f9111bb4cc46637640ce867a Mon Sep 17 00:00:00 2001
From: Yijia-Xiao <yijia-xiao@outlook.com>
Date: Sun, 15 Mar 2026 17:54:32 +0000
Subject: [PATCH 04/21] fix: initialize all debate state fields in
 propagation.py

InvestDebateState was missing bull_history, bear_history, judge_decision.
RiskDebateState was missing aggressive_history, conservative_history,
neutral_history, latest_speaker, judge_decision. This caused KeyError
in _log_state() and reflection, especially with edge-case config values.
---
 tradingagents/graph/propagation.py | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/tradingagents/graph/propagation.py b/tradingagents/graph/propagation.py
index 7aba5258..0fd10c0c 100644
--- a/tradingagents/graph/propagation.py
+++ b/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,
                 }
             ),

From fe9c8d5d31848fcad7d78e04560dfbe6ca98c932 Mon Sep 17 00:00:00 2001
From: Yijia-Xiao <yijia-xiao@outlook.com>
Date: Sun, 15 Mar 2026 18:05:36 +0000
Subject: [PATCH 05/21] fix: handle comma-separated indicators in
 get_indicators tool

LLMs (especially smaller models) sometimes pass multiple indicator
names as a single comma-separated string instead of making separate
tool calls. Split and process each individually at the tool boundary.
---
 .../agents/utils/technical_indicators_tools.py     | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/tradingagents/agents/utils/technical_indicators_tools.py b/tradingagents/agents/utils/technical_indicators_tools.py
index c6c08bca..77acf09c 100644
--- a/tradingagents/agents/utils/technical_indicators_tools.py
+++ b/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)
\ No newline at end of file
+    # 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)
\ No newline at end of file

From 9cc283ac22e938cb917f5887bdd6ae2b1c6cf5be Mon Sep 17 00:00:00 2001
From: Yijia-Xiao <yijia-xiao@outlook.com>
Date: Sun, 15 Mar 2026 18:21:05 +0000
Subject: [PATCH 06/21] fix: add missing console import to cli/utils.py

Seven error-handling paths used console.print() but console was never
imported, causing NameError on invalid user input.
---
 cli/utils.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/cli/utils.py b/cli/utils.py
index aa097fb5..d25c6d2b 100644
--- a/cli/utils.py
+++ b/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),

From b0f9d180f90e50fd1eb354386ca4d9a7936564a2 Mon Sep 17 00:00:00 2001
From: Yijia-Xiao <yijia-xiao@outlook.com>
Date: Sun, 15 Mar 2026 18:29:43 +0000
Subject: [PATCH 07/21] fix: harden stock data parsing against malformed CSV
 and NaN values

Add _clean_dataframe() to normalize stock DataFrames before stockstats:
coerce invalid dates/prices, drop rows missing Close, fill price gaps.
Also add on_bad_lines="skip" to all cached CSV reads.
---
 tradingagents/dataflows/stockstats_utils.py | 17 +++++++++++++--
 tradingagents/dataflows/y_finance.py        | 24 ++++++++++-----------
 2 files changed, 27 insertions(+), 14 deletions(-)

diff --git a/tradingagents/dataflows/stockstats_utils.py b/tradingagents/dataflows/stockstats_utils.py
index b31935b7..467156a2 100644
--- a/tradingagents/dataflows/stockstats_utils.py
+++ b/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["Date"] = pd.to_datetime(data["Date"])
+            data = pd.read_csv(data_file, on_bad_lines="skip")
         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")
diff --git a/tradingagents/dataflows/y_finance.py b/tradingagents/dataflows/y_finance.py
index bc78d8b3..b915490d 100644
--- a/tradingagents/dataflows/y_finance.py
+++ b/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"],
@@ -209,31 +209,30 @@ 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:
         # 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)
-            data["Date"] = pd.to_datetime(data["Date"])
+            data = pd.read_csv(data_file, on_bad_lines="skip")
         else:
             data = yf.download(
                 symbol,
@@ -245,9 +244,10 @@ def _get_stock_stats_bulk(
             )
             data = data.reset_index()
             data.to_csv(data_file, index=False)
-        
-        df = wrap(data)
-        df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
+
+    data = _clean_dataframe(data)
+    df = wrap(data)
+    df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
     
     # Calculate the indicator for all rows at once
     df[indicator]  # This triggers stockstats to calculate the indicator

From 551fd7f074fab8e1080d4ca30efaa4b6b4cd7517 Mon Sep 17 00:00:00 2001
From: Yijia-Xiao <yijia-xiao@outlook.com>
Date: Sun, 15 Mar 2026 23:34:50 +0000
Subject: [PATCH 08/21] chore: update model lists, bump to v0.2.1, fix package
 build

- OpenAI: add GPT-5.4, GPT-5.4 Pro; remove o-series and legacy GPT-4o
- Anthropic: add Claude Opus 4.6, Sonnet 4.6; remove legacy 4.1/4.0/3.x
- Google: add Gemini 3.1 Pro, 3.1 Flash Lite; remove deprecated
  gemini-3-pro-preview and Gemini 2.0 series
- xAI: clean up model list to match current API
- Simplify UnifiedChatOpenAI GPT-5 temperature handling
- Add missing tradingagents/__init__.py (fixes pip install building)
---
 cli/utils.py                               | 49 +++++++++++-----------
 pyproject.toml                             |  2 +-
 tradingagents/__init__.py                  |  0
 tradingagents/llm_clients/openai_client.py | 24 +++++------
 tradingagents/llm_clients/validators.py    | 37 +++++-----------
 5 files changed, 47 insertions(+), 65 deletions(-)
 create mode 100644 tradingagents/__init__.py

diff --git a/cli/utils.py b/cli/utils.py
index d25c6d2b..5a8ec16c 100644
--- a/cli/utils.py
+++ b/cli/utils.py
@@ -130,30 +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 Nano - Ultra-fast, high-throughput", "gpt-5-nano"),
-            ("GPT-5.2 - Latest flagship", "gpt-5.2"),
-            ("GPT-5.1 - Flexible reasoning", "gpt-5.1"),
-            ("GPT-4.1 - Smartest non-reasoning, 1M context", "gpt-4.1"),
+            ("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 Haiku 4.5 - Fast + extended thinking", "claude-haiku-4-5"),
-            ("Claude Sonnet 4.5 - Best for agents/coding", "claude-sonnet-4-5"),
-            ("Claude Sonnet 4 - High-performance", "claude-sonnet-4-20250514"),
+            ("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, recommended", "gemini-2.5-flash"),
-            ("Gemini 3 Pro - Reasoning-first", "gemini-3-pro-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"),
-            ("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
         ],
         "openrouter": [
             ("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
@@ -195,33 +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-5.1 - Flexible reasoning", "gpt-5.1"),
-            ("GPT-5 - Advanced reasoning", "gpt-5"),
-            ("GPT-4.1 - Smartest non-reasoning, 1M context", "gpt-4.1"),
-            ("GPT-5 Mini - Cost-optimized reasoning", "gpt-5-mini"),
-            ("GPT-5 Nano - Ultra-fast, high-throughput", "gpt-5-nano"),
+            ("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 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 Haiku 4.5 - Fast + extended thinking", "claude-haiku-4-5"),
-            ("Claude Sonnet 4 - High-performance", "claude-sonnet-4-20250514"),
+            ("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 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"),
diff --git a/pyproject.toml b/pyproject.toml
index 73231704..4c91a733 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ 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"
diff --git a/tradingagents/__init__.py b/tradingagents/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tradingagents/llm_clients/openai_client.py b/tradingagents/llm_clients/openai_client.py
index 7011895f..924f24b0 100644
--- a/tradingagents/llm_clients/openai_client.py
+++ b/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 self._is_reasoning_model(model):
+        if "gpt-5" in kwargs.get("model", "").lower():
             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."""
diff --git a/tradingagents/llm_clients/validators.py b/tradingagents/llm_clients/validators.py
index 3c0f2290..1e2388b3 100644
--- a/tradingagents/llm_clients/validators.py
+++ b/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",

From b19c5c18fb24b4b7a78310f905f619a856cdf2f3 Mon Sep 17 00:00:00 2001
From: Yijia-Xiao <yijia-xiao@outlook.com>
Date: Sun, 15 Mar 2026 23:39:05 +0000
Subject: [PATCH 09/21] docs: add v0.2.1 release note to README

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 34310010..8cf085e8 100644
--- a/README.md
+++ b/README.md
@@ -28,6 +28,7 @@
 # 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.
 - [2026-02] **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.
 

From 64f07671b9736f44abdcc22eb33ef64ccdea7167 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E9=98=B3=E8=99=8E?= <yanghu@yanghudeMacBook-Pro.local>
Date: Mon, 16 Mar 2026 07:41:20 +0800
Subject: [PATCH 10/21] fix: add http_client support for SSL certificate
 customization

- Add http_client and http_async_client parameters to all LLM clients
- OpenAIClient, GoogleClient, AnthropicClient now support custom httpx clients
- Fixes SSL certificate verification errors on Windows Conda environments
- Users can now pass custom httpx.Client with verify=False or custom certs

Fixes #369
---
 tradingagents/llm_clients/anthropic_client.py | 2 +-
 tradingagents/llm_clients/factory.py          | 6 ++++++
 tradingagents/llm_clients/google_client.py    | 2 +-
 tradingagents/llm_clients/openai_client.py    | 2 +-
 4 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/tradingagents/llm_clients/anthropic_client.py b/tradingagents/llm_clients/anthropic_client.py
index e2f1abba..8539c752 100644
--- a/tradingagents/llm_clients/anthropic_client.py
+++ b/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]
 
diff --git a/tradingagents/llm_clients/factory.py b/tradingagents/llm_clients/factory.py
index 028c88a2..93c2a7d3 100644
--- a/tradingagents/llm_clients/factory.py
+++ b/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
diff --git a/tradingagents/llm_clients/google_client.py b/tradingagents/llm_clients/google_client.py
index a1bd386b..3dd85e3f 100644
--- a/tradingagents/llm_clients/google_client.py
+++ b/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]
 
diff --git a/tradingagents/llm_clients/openai_client.py b/tradingagents/llm_clients/openai_client.py
index 924f24b0..4605c1f9 100644
--- a/tradingagents/llm_clients/openai_client.py
+++ b/tradingagents/llm_clients/openai_client.py
@@ -59,7 +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]
 

From 08bfe70a6990a2756e7fefdd48a45c41e0f2f42f Mon Sep 17 00:00:00 2001
From: CadeYu <yu83612457@gmail.com>
Date: Sat, 21 Mar 2026 13:10:09 +0800
Subject: [PATCH 11/21] fix: preserve exchange-qualified tickers across agent
 prompts

---
 cli/main.py                                    |  4 +++-
 cli/utils.py                                   |  9 ++++++++-
 tests/test_ticker_symbol_handling.py           | 18 ++++++++++++++++++
 .../agents/analysts/fundamentals_analyst.py    | 17 ++++++++++++-----
 .../agents/analysts/market_analyst.py          | 14 +++++++++-----
 tradingagents/agents/analysts/news_analyst.py  | 13 +++++++++----
 .../agents/analysts/social_media_analyst.py    | 12 ++++++------
 .../agents/managers/research_manager.py        |  6 ++++++
 tradingagents/agents/managers/risk_manager.py  |  5 +++++
 tradingagents/agents/trader/trader.py          |  5 ++++-
 tradingagents/agents/utils/agent_utils.py      | 12 +++++++++++-
 11 files changed, 91 insertions(+), 24 deletions(-)
 create mode 100644 tests/test_ticker_symbol_handling.py

diff --git a/cli/main.py b/cli/main.py
index adda48fc..e51a0e7b 100644
--- a/cli/main.py
+++ b/cli/main.py
@@ -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()
diff --git a/cli/utils.py b/cli/utils.py
index 5a8ec16c..b276cc0d 100644
--- a/cli/utils.py
+++ b/cli/utils.py
@@ -7,6 +7,8 @@ from cli.models import AnalystType
 
 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 +20,7 @@ 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 +34,11 @@ 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()
 
 
diff --git a/tests/test_ticker_symbol_handling.py b/tests/test_ticker_symbol_handling.py
new file mode 100644
index 00000000..858d26cd
--- /dev/null
+++ b/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()
diff --git a/tradingagents/agents/analysts/fundamentals_analyst.py b/tradingagents/agents/analysts/fundamentals_analyst.py
index 22d91848..abbe70eb 100644
--- a/tradingagents/agents/analysts/fundamentals_analyst.py
+++ b/tradingagents/agents/analysts/fundamentals_analyst.py
@@ -1,7 +1,14 @@
 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,
+)
 from tradingagents.dataflows.config import get_config
 
 
@@ -9,7 +16,7 @@ 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(ticker)
 
         tools = [
             get_fundamentals,
@@ -19,7 +26,7 @@ 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. Always preserve the exact ticker symbol provided by the user, including any exchange suffix, and never merge fundamentals for similarly named companies from other exchanges. 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."
             + " Use the available tools: `get_fundamentals` for comprehensive company analysis, `get_balance_sheet`, `get_cashflow`, and `get_income_statement` for specific financial statements.",
         )
@@ -35,7 +42,7 @@ 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 +51,7 @@ 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)
 
diff --git a/tradingagents/agents/analysts/market_analyst.py b/tradingagents/agents/analysts/market_analyst.py
index e175b94e..e3f09ab4 100644
--- a/tradingagents/agents/analysts/market_analyst.py
+++ b/tradingagents/agents/analysts/market_analyst.py
@@ -1,7 +1,11 @@
 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_stock_data,
+)
 from tradingagents.dataflows.config import get_config
 
 
@@ -10,7 +14,7 @@ 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(ticker)
 
         tools = [
             get_stock_data,
@@ -42,7 +46,7 @@ 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. Always preserve the exact ticker symbol provided by the user, including any exchange suffix, and never mix in similarly named companies from other exchanges. 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."""
             + """ 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."""
         )
 
@@ -57,7 +61,7 @@ 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 +70,7 @@ 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)
 
diff --git a/tradingagents/agents/analysts/news_analyst.py b/tradingagents/agents/analysts/news_analyst.py
index 03b4fae4..c65de0b8 100644
--- a/tradingagents/agents/analysts/news_analyst.py
+++ b/tradingagents/agents/analysts/news_analyst.py
@@ -1,7 +1,11 @@
 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_news,
+)
 from tradingagents.dataflows.config import get_config
 
 
@@ -9,6 +13,7 @@ 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(ticker)
 
         tools = [
             get_news,
@@ -16,7 +21,7 @@ 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. Always preserve the exact ticker symbol provided by the user, including any exchange suffix, and never merge news for similarly named companies from other exchanges. 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."""
         )
 
@@ -31,7 +36,7 @@ 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 +45,7 @@ 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"])
diff --git a/tradingagents/agents/analysts/social_media_analyst.py b/tradingagents/agents/analysts/social_media_analyst.py
index b25712d7..de57dfe3 100644
--- a/tradingagents/agents/analysts/social_media_analyst.py
+++ b/tradingagents/agents/analysts/social_media_analyst.py
@@ -1,7 +1,7 @@
 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_news
 from tradingagents.dataflows.config import get_config
 
 
@@ -9,15 +9,15 @@ 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(ticker)
 
         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. Always preserve the exact ticker symbol provided by the user, including any exchange suffix, and never merge commentary for similarly named companies from other exchanges. 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."""
         )
 
         prompt = ChatPromptTemplate.from_messages(
@@ -31,7 +31,7 @@ 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 +40,7 @@ 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)
 
diff --git a/tradingagents/agents/managers/research_manager.py b/tradingagents/agents/managers/research_manager.py
index c537fa2f..86abf195 100644
--- a/tradingagents/agents/managers/research_manager.py
+++ b/tradingagents/agents/managers/research_manager.py
@@ -1,9 +1,13 @@
 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:
+        ticker = state["company_of_interest"]
+        instrument_context = build_instrument_context(ticker)
         history = state["investment_debate_state"].get("history", "")
         market_research_report = state["market_report"]
         sentiment_report = state["sentiment_report"]
@@ -33,6 +37,8 @@ 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}"""
diff --git a/tradingagents/agents/managers/risk_manager.py b/tradingagents/agents/managers/risk_manager.py
index 1f2334cc..5c3e0543 100644
--- a/tradingagents/agents/managers/risk_manager.py
+++ b/tradingagents/agents/managers/risk_manager.py
@@ -1,11 +1,14 @@
 import time
 import json
 
+from tradingagents.agents.utils.agent_utils import build_instrument_context
+
 
 def create_risk_manager(llm, memory):
     def risk_manager_node(state) -> dict:
 
         company_name = state["company_of_interest"]
+        instrument_context = build_instrument_context(company_name)
 
         history = state["risk_debate_state"]["history"]
         risk_debate_state = state["risk_debate_state"]
@@ -34,6 +37,8 @@ Deliverables:
 - A clear and actionable recommendation: Buy, Sell, or Hold.
 - Detailed reasoning anchored in the debate and past reflections.
 
+{instrument_context}
+
 ---
 
 **Analysts Debate History:**  
diff --git a/tradingagents/agents/trader/trader.py b/tradingagents/agents/trader/trader.py
index 1b05c35d..a40eb22a 100644
--- a/tradingagents/agents/trader/trader.py
+++ b/tradingagents/agents/trader/trader.py
@@ -2,10 +2,13 @@ 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"]
@@ -24,7 +27,7 @@ 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 = [
diff --git a/tradingagents/agents/utils/agent_utils.py b/tradingagents/agents/utils/agent_utils.py
index b329a3e9..073b209f 100644
--- a/tradingagents/agents/utils/agent_utils.py
+++ b/tradingagents/agents/utils/agent_utils.py
@@ -19,6 +19,16 @@ from tradingagents.agents.utils.news_data_tools import (
     get_global_news
 )
 
+
+def build_instrument_context(ticker: str) -> str:
+    """Describe the exact instrument so agents avoid cross-exchange symbol mixups."""
+    return (
+        f"The exact listed instrument to analyze is `{ticker}`. "
+        "Use this exact ticker in every tool call, report, and recommendation. "
+        "If it includes an exchange suffix such as `.TO`, `.L`, `.HK`, or `.T`, preserve that suffix and do not mix in companies from other exchanges that share the same root symbol. "
+        "If it does not include a suffix, do not invent one."
+    )
+
 def create_msg_delete():
     def delete_messages(state):
         """Clear messages and add placeholder for Anthropic compatibility"""
@@ -35,4 +45,4 @@ def create_msg_delete():
     return delete_messages
 
 
-        
\ No newline at end of file
+        

From 7d200d834ab8cbd109d518d01d9f2103edfa0a5f Mon Sep 17 00:00:00 2001
From: CadeYu <yu83612457@gmail.com>
Date: Sat, 21 Mar 2026 21:31:38 +0800
Subject: [PATCH 12/21] style: inline single-use instrument context vars

---
 tradingagents/agents/analysts/fundamentals_analyst.py | 3 +--
 tradingagents/agents/analysts/market_analyst.py       | 3 +--
 tradingagents/agents/analysts/news_analyst.py         | 3 +--
 tradingagents/agents/analysts/social_media_analyst.py | 3 +--
 tradingagents/agents/managers/research_manager.py     | 3 +--
 tradingagents/agents/managers/risk_manager.py         | 3 +--
 6 files changed, 6 insertions(+), 12 deletions(-)

diff --git a/tradingagents/agents/analysts/fundamentals_analyst.py b/tradingagents/agents/analysts/fundamentals_analyst.py
index abbe70eb..ddf57abd 100644
--- a/tradingagents/agents/analysts/fundamentals_analyst.py
+++ b/tradingagents/agents/analysts/fundamentals_analyst.py
@@ -15,8 +15,7 @@ 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"]
-        instrument_context = build_instrument_context(ticker)
+        instrument_context = build_instrument_context(state["company_of_interest"])
 
         tools = [
             get_fundamentals,
diff --git a/tradingagents/agents/analysts/market_analyst.py b/tradingagents/agents/analysts/market_analyst.py
index e3f09ab4..8c1a9ab7 100644
--- a/tradingagents/agents/analysts/market_analyst.py
+++ b/tradingagents/agents/analysts/market_analyst.py
@@ -13,8 +13,7 @@ def create_market_analyst(llm):
 
     def market_analyst_node(state):
         current_date = state["trade_date"]
-        ticker = state["company_of_interest"]
-        instrument_context = build_instrument_context(ticker)
+        instrument_context = build_instrument_context(state["company_of_interest"])
 
         tools = [
             get_stock_data,
diff --git a/tradingagents/agents/analysts/news_analyst.py b/tradingagents/agents/analysts/news_analyst.py
index c65de0b8..2a3a3433 100644
--- a/tradingagents/agents/analysts/news_analyst.py
+++ b/tradingagents/agents/analysts/news_analyst.py
@@ -12,8 +12,7 @@ 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(ticker)
+        instrument_context = build_instrument_context(state["company_of_interest"])
 
         tools = [
             get_news,
diff --git a/tradingagents/agents/analysts/social_media_analyst.py b/tradingagents/agents/analysts/social_media_analyst.py
index de57dfe3..4a6e0074 100644
--- a/tradingagents/agents/analysts/social_media_analyst.py
+++ b/tradingagents/agents/analysts/social_media_analyst.py
@@ -8,8 +8,7 @@ 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"]
-        instrument_context = build_instrument_context(ticker)
+        instrument_context = build_instrument_context(state["company_of_interest"])
 
         tools = [
             get_news,
diff --git a/tradingagents/agents/managers/research_manager.py b/tradingagents/agents/managers/research_manager.py
index 86abf195..3ac4b150 100644
--- a/tradingagents/agents/managers/research_manager.py
+++ b/tradingagents/agents/managers/research_manager.py
@@ -6,8 +6,7 @@ from tradingagents.agents.utils.agent_utils import build_instrument_context
 
 def create_research_manager(llm, memory):
     def research_manager_node(state) -> dict:
-        ticker = state["company_of_interest"]
-        instrument_context = build_instrument_context(ticker)
+        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"]
diff --git a/tradingagents/agents/managers/risk_manager.py b/tradingagents/agents/managers/risk_manager.py
index 5c3e0543..3dab49fe 100644
--- a/tradingagents/agents/managers/risk_manager.py
+++ b/tradingagents/agents/managers/risk_manager.py
@@ -7,8 +7,7 @@ from tradingagents.agents.utils.agent_utils import build_instrument_context
 def create_risk_manager(llm, memory):
     def risk_manager_node(state) -> dict:
 
-        company_name = state["company_of_interest"]
-        instrument_context = build_instrument_context(company_name)
+        instrument_context = build_instrument_context(state["company_of_interest"])
 
         history = state["risk_debate_state"]["history"]
         risk_debate_state = state["risk_debate_state"]

From 3ff28f3559c3598cd5efb6f3469735e14ce7f3e0 Mon Sep 17 00:00:00 2001
From: Yijia-Xiao <yijia-xiao@outlook.com>
Date: Sun, 22 Mar 2026 20:34:03 +0000
Subject: [PATCH 13/21] fix: use OpenAI Responses API for native models

Enable use_responses_api for native OpenAI provider, which supports
reasoning_effort with function tools across all model families.
Removes the UnifiedChatOpenAI subclass workaround.

Closes #403
---
 tradingagents/llm_clients/openai_client.py | 69 +++++++++++-----------
 1 file changed, 36 insertions(+), 33 deletions(-)

diff --git a/tradingagents/llm_clients/openai_client.py b/tradingagents/llm_clients/openai_client.py
index 4605c1f9..c314d077 100644
--- a/tradingagents/llm_clients/openai_client.py
+++ b/tradingagents/llm_clients/openai_client.py
@@ -6,28 +6,28 @@ from langchain_openai import ChatOpenAI
 from .base_client import BaseLLMClient
 from .validators import validate_model
 
+# Kwargs forwarded from user config to ChatOpenAI
+_PASSTHROUGH_KWARGS = (
+    "timeout", "max_retries", "reasoning_effort",
+    "api_key", "callbacks", "http_client", "http_async_client",
+)
 
-class UnifiedChatOpenAI(ChatOpenAI):
-    """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):
-        if "gpt-5" in kwargs.get("model", "").lower():
-            kwargs.pop("temperature", None)
-            kwargs.pop("top_p", None)
-        super().__init__(**kwargs)
+# 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,
@@ -43,27 +43,30 @@ class OpenAIClient(BaseLLMClient):
         """Return configured ChatOpenAI instance."""
         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.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 ChatOpenAI(**llm_kwargs)
 
     def validate_model(self) -> bool:
         """Validate model for the provider."""

From 0b13145dc0e145fad9e09298ae7c66ba729f38b0 Mon Sep 17 00:00:00 2001
From: Yijia-Xiao <yijia-xiao@outlook.com>
Date: Sun, 22 Mar 2026 20:40:18 +0000
Subject: [PATCH 14/21] fix: handle list content when writing report sections

Closes #400
---
 cli/main.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/cli/main.py b/cli/main.py
index adda48fc..df6dc891 100644
--- a/cli/main.py
+++ b/cli/main.py
@@ -972,8 +972,9 @@ def run_analysis():
                 content = obj.report_sections[section_name]
                 if content:
                     file_name = f"{section_name}.md"
+                    text = "\n".join(str(item) for item in content) if isinstance(content, list) else content
                     with open(report_dir / file_name, "w", encoding="utf-8") as f:
-                        f.write(content)
+                        f.write(text)
         return wrapper
 
     message_buffer.add_message = save_message_decorator(message_buffer, "add_message")

From 77755f0431b284690ddc835ee66684dab25fbb33 Mon Sep 17 00:00:00 2001
From: Yijia-Xiao <yijia-xiao@outlook.com>
Date: Sun, 22 Mar 2026 21:38:01 +0000
Subject: [PATCH 15/21] chore: consolidate install, fix CLI portability,
 normalize LLM responses

- Point requirements.txt to pyproject.toml as single source of truth
- Resolve welcome.txt path relative to module for CLI portability
- Include cli/static files in package build
- Extract shared normalize_content for OpenAI Responses API and
  Gemini 3 list-format responses into base_client.py
- Update README install and CLI usage instructions
---
 README.md                                  | 11 ++++++-----
 cli/main.py                                |  2 +-
 pyproject.toml                             |  3 +++
 requirements.txt                           | 22 +---------------------
 tradingagents/llm_clients/base_client.py   | 19 +++++++++++++++++++
 tradingagents/llm_clients/google_client.py | 17 +++--------------
 tradingagents/llm_clients/openai_client.py | 16 ++++++++++++++--
 7 files changed, 47 insertions(+), 43 deletions(-)

diff --git a/README.md b/README.md
index 8cf085e8..e31c43ad 100644
--- a/README.md
+++ b/README.md
@@ -112,9 +112,9 @@ 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 .
 ```
 
 ### Required APIs
@@ -139,11 +139,12 @@ 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%;">
diff --git a/cli/main.py b/cli/main.py
index df6dc891..a706f11d 100644
--- a/cli/main.py
+++ b/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", encoding="utf-8") as f:
         welcome_ascii = f.read()
 
     # Create welcome box content
diff --git a/pyproject.toml b/pyproject.toml
index 4c91a733..256d21d9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -37,3 +37,6 @@ tradingagents = "cli.main:app"
 
 [tool.setuptools.packages.find]
 include = ["tradingagents*", "cli*"]
+
+[tool.setuptools.package-data]
+cli = ["static/*"]
diff --git a/requirements.txt b/requirements.txt
index 184468b8..9c558e35 100644
--- a/requirements.txt
+++ b/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
+.
diff --git a/tradingagents/llm_clients/base_client.py b/tradingagents/llm_clients/base_client.py
index 43845575..9c3dd17c 100644
--- a/tradingagents/llm_clients/base_client.py
+++ b/tradingagents/llm_clients/base_client.py
@@ -2,6 +2,25 @@ from abc import ABC, abstractmethod
 from typing import Any, Optional
 
 
+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):
     """Abstract base class for LLM clients."""
 
diff --git a/tradingagents/llm_clients/google_client.py b/tradingagents/llm_clients/google_client.py
index 3dd85e3f..7401df0e 100644
--- a/tradingagents/llm_clients/google_client.py
+++ b/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):
diff --git a/tradingagents/llm_clients/openai_client.py b/tradingagents/llm_clients/openai_client.py
index c314d077..fd9b4e33 100644
--- a/tradingagents/llm_clients/openai_client.py
+++ b/tradingagents/llm_clients/openai_client.py
@@ -3,9 +3,21 @@ 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 NormalizedChatOpenAI(ChatOpenAI):
+    """ChatOpenAI with normalized content output.
+
+    The Responses API returns content as a list of typed blocks
+    (reasoning, text, etc.). This normalizes to string for consistent
+    downstream handling.
+    """
+
+    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",
@@ -66,7 +78,7 @@ class OpenAIClient(BaseLLMClient):
         if self.provider == "openai":
             llm_kwargs["use_responses_api"] = True
 
-        return ChatOpenAI(**llm_kwargs)
+        return NormalizedChatOpenAI(**llm_kwargs)
 
     def validate_model(self) -> bool:
         """Validate model for the provider."""

From bd9b1e5efa1fabcfd34d49a07bdc9f0b018f2847 Mon Sep 17 00:00:00 2001
From: Yijia-Xiao <yijia-xiao@outlook.com>
Date: Sun, 22 Mar 2026 21:57:05 +0000
Subject: [PATCH 16/21] feat: add Anthropic effort level support for Claude
 models

Add effort parameter (high/medium/low) for Claude 4.5+ and 4.6 models,
consistent with OpenAI reasoning_effort and Google thinking_level.
Also add content normalization for Anthropic responses.
---
 cli/main.py                                   | 11 +++++++++
 cli/utils.py                                  | 20 ++++++++++++++++
 tradingagents/default_config.py               |  1 +
 tradingagents/graph/trading_graph.py          |  5 ++++
 tradingagents/llm_clients/anthropic_client.py | 23 ++++++++++++++++---
 5 files changed, 57 insertions(+), 3 deletions(-)

diff --git a/cli/main.py b/cli/main.py
index a706f11d..f6e2c44a 100644
--- a/cli/main.py
+++ b/cli/main.py
@@ -556,6 +556,7 @@ def get_user_selections():
     # Step 7: Provider-specific thinking configuration
     thinking_level = None
     reasoning_effort = None
+    anthropic_effort = None
 
     provider_lower = selected_llm_provider.lower()
     if provider_lower == "google":
@@ -574,6 +575,14 @@ def get_user_selections():
             )
         )
         reasoning_effort = ask_openai_reasoning_effort()
+    elif provider_lower == "anthropic":
+        console.print(
+            create_question_box(
+                "Step 7: Effort Level",
+                "Configure Claude effort level"
+            )
+        )
+        anthropic_effort = ask_anthropic_effort()
 
     return {
         "ticker": selected_ticker,
@@ -586,6 +595,7 @@ def get_user_selections():
         "deep_thinker": selected_deep_thinker,
         "google_thinking_level": thinking_level,
         "openai_reasoning_effort": reasoning_effort,
+        "anthropic_effort": anthropic_effort,
     }
 
 
@@ -911,6 +921,7 @@ 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")
 
     # Create stats callback handler for tracking LLM/tool calls
     stats_handler = StatsCallbackHandler()
diff --git a/cli/utils.py b/cli/utils.py
index 5a8ec16c..18efe1d6 100644
--- a/cli/utils.py
+++ b/cli/utils.py
@@ -311,6 +311,26 @@ 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.
 
diff --git a/tradingagents/default_config.py b/tradingagents/default_config.py
index ecf0dc29..898e1e1e 100644
--- a/tradingagents/default_config.py
+++ b/tradingagents/default_config.py
@@ -15,6 +15,7 @@ DEFAULT_CONFIG = {
     # Provider-specific thinking configuration
     "google_thinking_level": None,      # "high", "minimal", etc.
     "openai_reasoning_effort": None,    # "medium", "high", "low"
+    "anthropic_effort": None,           # "high", "medium", "low"
     # Debate and discussion settings
     "max_debate_rounds": 1,
     "max_risk_discuss_rounds": 1,
diff --git a/tradingagents/graph/trading_graph.py b/tradingagents/graph/trading_graph.py
index c7ef0f98..306f7f38 100644
--- a/tradingagents/graph/trading_graph.py
+++ b/tradingagents/graph/trading_graph.py
@@ -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]:
diff --git a/tradingagents/llm_clients/anthropic_client.py b/tradingagents/llm_clients/anthropic_client.py
index 8539c752..2c1e5a67 100644
--- a/tradingagents/llm_clients/anthropic_client.py
+++ b/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."""
@@ -16,11 +33,11 @@ class AnthropicClient(BaseLLMClient):
         """Return configured ChatAnthropic instance."""
         llm_kwargs = {"model": self.model}
 
-        for key in ("timeout", "max_retries", "api_key", "max_tokens", "callbacks", "http_client", "http_async_client"):
+        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."""

From 7cca9c924e786a7f8340179a9710164161988ef8 Mon Sep 17 00:00:00 2001
From: Yijia-Xiao <yijia-xiao@outlook.com>
Date: Sun, 22 Mar 2026 22:11:08 +0000
Subject: [PATCH 17/21] fix: add exponential backoff retry for yfinance rate
 limits (#426)

---
 tradingagents/dataflows/stockstats_utils.py | 29 ++++++++++++++++++--
 tradingagents/dataflows/y_finance.py        | 30 ++++++++++-----------
 2 files changed, 42 insertions(+), 17 deletions(-)

diff --git a/tradingagents/dataflows/stockstats_utils.py b/tradingagents/dataflows/stockstats_utils.py
index 467156a2..47d5460a 100644
--- a/tradingagents/dataflows/stockstats_utils.py
+++ b/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."""
@@ -51,14 +76,14 @@ class StockstatsUtils:
         if os.path.exists(data_file):
             data = pd.read_csv(data_file, on_bad_lines="skip")
         else:
-            data = yf.download(
+            data = yf_retry(lambda: 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)
 
diff --git a/tradingagents/dataflows/y_finance.py b/tradingagents/dataflows/y_finance.py
index b915490d..3682a01d 100644
--- a/tradingagents/dataflows/y_finance.py
+++ b/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, _clean_dataframe
+from .stockstats_utils import StockstatsUtils, _clean_dataframe, yf_retry
 
 def get_YFin_data_online(
     symbol: Annotated[str, "ticker symbol of the company"],
@@ -18,7 +18,7 @@ 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:
@@ -234,14 +234,14 @@ def _get_stock_stats_bulk(
         if os.path.exists(data_file):
             data = pd.read_csv(data_file, on_bad_lines="skip")
         else:
-            data = yf.download(
+            data = yf_retry(lambda: 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)
 
@@ -300,7 +300,7 @@ 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}'"
@@ -358,11 +358,11 @@ def get_balance_sheet(
     """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)
             
         if data.empty:
             return f"No balance sheet data found for symbol '{ticker}'"
@@ -388,11 +388,11 @@ def get_cashflow(
     """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)
             
         if data.empty:
             return f"No cash flow data found for symbol '{ticker}'"
@@ -418,11 +418,11 @@ def get_income_statement(
     """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)
             
         if data.empty:
             return f"No income statement data found for symbol '{ticker}'"
@@ -446,7 +446,7 @@ 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}'"

From 318adda0c64ec04985f36a81834b2fe8c75dd717 Mon Sep 17 00:00:00 2001
From: Yijia-Xiao <yijia-xiao@outlook.com>
Date: Sun, 22 Mar 2026 23:07:20 +0000
Subject: [PATCH 18/21] refactor: five-tier rating scale and streamlined agent
 prompts

---
 .../agents/analysts/fundamentals_analyst.py   |  2 +-
 .../agents/analysts/market_analyst.py         |  2 +-
 tradingagents/agents/analysts/news_analyst.py |  2 +-
 .../agents/analysts/social_media_analyst.py   |  2 +-
 tradingagents/agents/managers/risk_manager.py | 35 ++++++++++++-------
 .../agents/risk_mgmt/aggressive_debator.py    |  2 +-
 .../agents/risk_mgmt/conservative_debator.py  |  2 +-
 .../agents/risk_mgmt/neutral_debator.py       |  2 +-
 tradingagents/agents/trader/trader.py         |  2 +-
 tradingagents/agents/utils/agent_utils.py     |  9 +++--
 tradingagents/graph/signal_processing.py      |  6 ++--
 11 files changed, 38 insertions(+), 28 deletions(-)

diff --git a/tradingagents/agents/analysts/fundamentals_analyst.py b/tradingagents/agents/analysts/fundamentals_analyst.py
index ddf57abd..990398a6 100644
--- a/tradingagents/agents/analysts/fundamentals_analyst.py
+++ b/tradingagents/agents/analysts/fundamentals_analyst.py
@@ -25,7 +25,7 @@ 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. Always preserve the exact ticker symbol provided by the user, including any exchange suffix, and never merge fundamentals for similarly named companies from other exchanges. 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.",
         )
diff --git a/tradingagents/agents/analysts/market_analyst.py b/tradingagents/agents/analysts/market_analyst.py
index 8c1a9ab7..f5d17acd 100644
--- a/tradingagents/agents/analysts/market_analyst.py
+++ b/tradingagents/agents/analysts/market_analyst.py
@@ -45,7 +45,7 @@ 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. Always preserve the exact ticker symbol provided by the user, including any exchange suffix, and never mix in similarly named companies from other exchanges. 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."""
         )
 
diff --git a/tradingagents/agents/analysts/news_analyst.py b/tradingagents/agents/analysts/news_analyst.py
index 2a3a3433..3697c6f6 100644
--- a/tradingagents/agents/analysts/news_analyst.py
+++ b/tradingagents/agents/analysts/news_analyst.py
@@ -20,7 +20,7 @@ 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. Always preserve the exact ticker symbol provided by the user, including any exchange suffix, and never merge news for similarly named companies from other exchanges. 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."""
         )
 
diff --git a/tradingagents/agents/analysts/social_media_analyst.py b/tradingagents/agents/analysts/social_media_analyst.py
index 4a6e0074..43df2258 100644
--- a/tradingagents/agents/analysts/social_media_analyst.py
+++ b/tradingagents/agents/analysts/social_media_analyst.py
@@ -15,7 +15,7 @@ def create_social_media_analyst(llm):
         ]
 
         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. Always preserve the exact ticker symbol provided by the user, including any exchange suffix, and never merge commentary for similarly named companies from other exchanges. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
+            "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."""
         )
 
diff --git a/tradingagents/agents/managers/risk_manager.py b/tradingagents/agents/managers/risk_manager.py
index 3dab49fe..d827c2e8 100644
--- a/tradingagents/agents/managers/risk_manager.py
+++ b/tradingagents/agents/managers/risk_manager.py
@@ -24,28 +24,37 @@ def create_risk_manager(llm, memory):
         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.
+        prompt = f"""As the Risk Management Judge, evaluate the debate between the Aggressive, Neutral, and Conservative analysts and deliver a final trading decision.
 
 {instrument_context}
 
 ---
 
-**Analysts Debate History:**  
+**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
+
+**Guidelines:**
+1. Extract the strongest points from each analyst, focusing on relevance to the current context.
+2. Start with the trader's original plan: **{trader_plan}**, and refine it based on the analysts' insights.
+3. Apply lessons from past decisions to strengthen this analysis: **{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. Keep this brief and actionable.
+3. **Investment Thesis**: 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."""
+Be decisive and ground every conclusion in specific evidence from the analysts."""
 
         response = llm.invoke(prompt)
 
diff --git a/tradingagents/agents/risk_mgmt/aggressive_debator.py b/tradingagents/agents/risk_mgmt/aggressive_debator.py
index 3905d3d1..651114a7 100644
--- a/tradingagents/agents/risk_mgmt/aggressive_debator.py
+++ b/tradingagents/agents/risk_mgmt/aggressive_debator.py
@@ -28,7 +28,7 @@ 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."""
 
diff --git a/tradingagents/agents/risk_mgmt/conservative_debator.py b/tradingagents/agents/risk_mgmt/conservative_debator.py
index 6b106b1b..7c8c0fd1 100644
--- a/tradingagents/agents/risk_mgmt/conservative_debator.py
+++ b/tradingagents/agents/risk_mgmt/conservative_debator.py
@@ -29,7 +29,7 @@ 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."""
 
diff --git a/tradingagents/agents/risk_mgmt/neutral_debator.py b/tradingagents/agents/risk_mgmt/neutral_debator.py
index f6aa888d..9ed490da 100644
--- a/tradingagents/agents/risk_mgmt/neutral_debator.py
+++ b/tradingagents/agents/risk_mgmt/neutral_debator.py
@@ -28,7 +28,7 @@ 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."""
 
diff --git a/tradingagents/agents/trader/trader.py b/tradingagents/agents/trader/trader.py
index a40eb22a..6298f239 100644
--- a/tradingagents/agents/trader/trader.py
+++ b/tradingagents/agents/trader/trader.py
@@ -33,7 +33,7 @@ def create_trader(llm, memory):
         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,
         ]
diff --git a/tradingagents/agents/utils/agent_utils.py b/tradingagents/agents/utils/agent_utils.py
index 073b209f..e4abc4cd 100644
--- a/tradingagents/agents/utils/agent_utils.py
+++ b/tradingagents/agents/utils/agent_utils.py
@@ -21,12 +21,11 @@ from tradingagents.agents.utils.news_data_tools import (
 
 
 def build_instrument_context(ticker: str) -> str:
-    """Describe the exact instrument so agents avoid cross-exchange symbol mixups."""
+    """Describe the exact instrument so agents preserve exchange-qualified tickers."""
     return (
-        f"The exact listed instrument to analyze is `{ticker}`. "
-        "Use this exact ticker in every tool call, report, and recommendation. "
-        "If it includes an exchange suffix such as `.TO`, `.L`, `.HK`, or `.T`, preserve that suffix and do not mix in companies from other exchanges that share the same root symbol. "
-        "If it does not include a suffix, do not invent one."
+        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():
diff --git a/tradingagents/graph/signal_processing.py b/tradingagents/graph/signal_processing.py
index 903e8529..f96c1efa 100644
--- a/tradingagents/graph/signal_processing.py
+++ b/tradingagents/graph/signal_processing.py
@@ -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),
         ]

From b8b2825783f490faeebfef5ddaf0178f70b2b0ba Mon Sep 17 00:00:00 2001
From: Yijia-Xiao <yijia-xiao@outlook.com>
Date: Sun, 22 Mar 2026 23:30:29 +0000
Subject: [PATCH 19/21] refactor: standardize portfolio manager, five-tier
 rating scale, fix analyst status tracking

---
 cli/main.py                                   | 13 +++++++---
 tradingagents/agents/__init__.py              |  4 ++--
 .../{risk_manager.py => portfolio_manager.py} | 24 ++++++++-----------
 tradingagents/graph/conditional_logic.py      |  2 +-
 tradingagents/graph/reflection.py             |  8 +++----
 tradingagents/graph/setup.py                  | 18 +++++++-------
 tradingagents/graph/trading_graph.py          |  8 +++----
 7 files changed, 40 insertions(+), 37 deletions(-)
 rename tradingagents/agents/managers/{risk_manager.py => portfolio_manager.py} (74%)

diff --git a/cli/main.py b/cli/main.py
index f26ae4c5..f975bac1 100644
--- a/cli/main.py
+++ b/cli/main.py
@@ -800,9 +800,11 @@ 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
@@ -817,11 +819,16 @@ 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
diff --git a/tradingagents/agents/__init__.py b/tradingagents/agents/__init__.py
index 8a169f22..1f03642c 100644
--- a/tradingagents/agents/__init__.py
+++ b/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",
diff --git a/tradingagents/agents/managers/risk_manager.py b/tradingagents/agents/managers/portfolio_manager.py
similarity index 74%
rename from tradingagents/agents/managers/risk_manager.py
rename to tradingagents/agents/managers/portfolio_manager.py
index d827c2e8..acdf940b 100644
--- a/tradingagents/agents/managers/risk_manager.py
+++ b/tradingagents/agents/managers/portfolio_manager.py
@@ -1,11 +1,8 @@
-import time
-import json
-
 from tradingagents.agents.utils.agent_utils import build_instrument_context
 
 
-def create_risk_manager(llm, memory):
-    def risk_manager_node(state) -> dict:
+def create_portfolio_manager(llm, memory):
+    def portfolio_manager_node(state) -> dict:
 
         instrument_context = build_instrument_context(state["company_of_interest"])
 
@@ -24,7 +21,7 @@ def create_risk_manager(llm, memory):
         for i, rec in enumerate(past_memories, 1):
             past_memory_str += rec["recommendation"] + "\n\n"
 
-        prompt = f"""As the Risk Management Judge, evaluate the debate between the Aggressive, Neutral, and Conservative analysts and deliver a final trading decision.
+        prompt = f"""As the Portfolio Manager, synthesize the risk analysts' debate and deliver the final trading decision.
 
 {instrument_context}
 
@@ -37,19 +34,18 @@ def create_risk_manager(llm, memory):
 - **Underweight**: Reduce exposure, take partial profits
 - **Sell**: Exit position or avoid entry
 
-**Guidelines:**
-1. Extract the strongest points from each analyst, focusing on relevance to the current context.
-2. Start with the trader's original plan: **{trader_plan}**, and refine it based on the analysts' insights.
-3. Apply lessons from past decisions to strengthen this analysis: **{past_memory_str}**
+**Context:**
+- Trader's proposed plan: **{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. Keep this brief and actionable.
-3. **Investment Thesis**: Detailed reasoning anchored in the debate and past reflections.
+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.
 
 ---
 
-**Analysts Debate History:**
+**Risk Analysts Debate History:**
 {history}
 
 ---
@@ -76,4 +72,4 @@ Be decisive and ground every conclusion in specific evidence from the analysts."
             "final_trade_decision": response.content,
         }
 
-    return risk_manager_node
+    return portfolio_manager_node
diff --git a/tradingagents/graph/conditional_logic.py b/tradingagents/graph/conditional_logic.py
index 7b1b1f90..48371793 100644
--- a/tradingagents/graph/conditional_logic.py
+++ b/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"):
diff --git a/tradingagents/graph/reflection.py b/tradingagents/graph/reflection.py
index 33303231..85438595 100644
--- a/tradingagents/graph/reflection.py
+++ b/tradingagents/graph/reflection.py
@@ -110,12 +110,12 @@ 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)])
diff --git a/tradingagents/graph/setup.py b/tradingagents/graph/setup.py
index 772efe7f..e0771c65 100644
--- a/tradingagents/graph/setup.py
+++ b/tradingagents/graph/setup.py
@@ -23,7 +23,7 @@ class GraphSetup:
         bear_memory,
         trader_memory,
         invest_judge_memory,
-        risk_manager_memory,
+        portfolio_manager_memory,
         conditional_logic: ConditionalLogic,
     ):
         """Initialize with required components."""
@@ -34,7 +34,7 @@ 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 +101,8 @@ 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 +124,7 @@ 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 +176,7 @@ 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 +184,7 @@ 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 +192,11 @@ 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()
diff --git a/tradingagents/graph/trading_graph.py b/tradingagents/graph/trading_graph.py
index 306f7f38..c8cd7492 100644
--- a/tradingagents/graph/trading_graph.py
+++ b/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,
         )
 
@@ -283,8 +283,8 @@ 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):

From 6c9c9ce1fdc3053381bf2b7f3bca41f62d12c098 Mon Sep 17 00:00:00 2001
From: Yijia-Xiao <yijia-xiao@outlook.com>
Date: Sun, 22 Mar 2026 23:42:37 +0000
Subject: [PATCH 20/21] fix: set process-level UTF-8 default for cross-platform
 consistency

---
 cli/main.py               | 8 ++++----
 tradingagents/__init__.py | 2 ++
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/cli/main.py b/cli/main.py
index f975bac1..53837db2 100644
--- a/cli/main.py
+++ b/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(Path(__file__).parent / "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
@@ -968,7 +968,7 @@ 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
     
@@ -979,7 +979,7 @@ 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
 
@@ -993,7 +993,7 @@ def run_analysis():
                 if content:
                     file_name = f"{section_name}.md"
                     text = "\n".join(str(item) for item in content) if isinstance(content, list) else content
-                    with open(report_dir / file_name, "w", encoding="utf-8") as f:
+                    with open(report_dir / file_name, "w") as f:
                         f.write(text)
         return wrapper
 
diff --git a/tradingagents/__init__.py b/tradingagents/__init__.py
index e69de29b..43a2b439 100644
--- a/tradingagents/__init__.py
+++ b/tradingagents/__init__.py
@@ -0,0 +1,2 @@
+import os
+os.environ.setdefault("PYTHONUTF8", "1")

From 589b351f2ab55a8a37d846848479cebc810a5a36 Mon Sep 17 00:00:00 2001
From: Yijia-Xiao <yijia-xiao@outlook.com>
Date: Sun, 22 Mar 2026 23:47:56 +0000
Subject: [PATCH 21/21] TradingAgents v0.2.2

---
 README.md      | 2 +-
 pyproject.toml | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index e31c43ad..4c4856d1 100644
--- a/README.md
+++ b/README.md
@@ -28,7 +28,7 @@
 # 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 v0.2.2** 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.
 
diff --git a/pyproject.toml b/pyproject.toml
index 256d21d9..de27a2b9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "tradingagents"
-version = "0.2.1"
+version = "0.2.2"
 description = "TradingAgents: Multi-Agents LLM Financial Trading Framework"
 readme = "README.md"
 requires-python = ">=3.10"