global news OpenAI bug fixed

evaluation/run_evaluation.py

@@ -83,12 +83,12 @@ def run_evaluation(
         try:
             cfg = (config or DEFAULT_CONFIG).copy()
             # Fast eval defaults (you can override from CLI)
-            cfg["deep_think_llm"] = cfg.get("deep_think_llm", "gpt-5-nano")
-            cfg["quick_think_llm"] = cfg.get("quick_think_llm", "gpt-5-nano")
+            cfg["deep_think_llm"] = cfg.get("deep_think_llm", "o4-mini")
+            cfg["quick_think_llm"] = cfg.get("quick_think_llm", "gpt-4o-mini")
             cfg["max_debate_rounds"] = cfg.get("max_debate_rounds", 1)
             cfg["max_risk_discuss_rounds"] = cfg.get("max_risk_discuss_rounds", 1)
             # Deterministic-ish decoding for reproducibility
-            cfg.setdefault("llm_params", {}).update({"temperature": 0, "top_p": 1.0, "seed": 42})
+            cfg.setdefault("llm_params", {}).update({"temperature": 0.7, "top_p": 1.0, "seed": 42})
 
             print(f"\nInitializing TradingAgents...")
             print(f"  Deep Thinking LLM: {cfg['deep_think_llm']}")
@@ -161,7 +161,7 @@ def main():
     parser.add_argument("--no-tradingagents", action="store_true", help="Skip TradingAgents")
     parser.add_argument("--output-dir", type=str, default=None, help="Output directory for results")
     parser.add_argument("--deep-llm", type=str, default="gpt-4o-mini", help="Deep thinking LLM model")
-    parser.add_argument("--quick-llm", type=str, default="gpt-4o-mini", help="Quick thinking LLM model")
+    parser.add_argument("--quick-llm", type=str, default="gpt-5-nano", help="Quick thinking LLM model")
     parser.add_argument("--debate-rounds", type=int, default=1, help="Number of debate rounds (default: 1)")
 
     # Used for debugging
@@ -169,16 +169,16 @@ def main():
     if is_debugging():
         config = DEFAULT_CONFIG.copy()
         config.update({
-            "deep_think_llm": "gpt-5-nano",
-            "quick_think_llm": "gpt-5-nano",
+            "deep_think_llm": "o4-mini",
+            "quick_think_llm": "gpt-4o-mini",
             "max_debate_rounds": 1,
             "max_risk_discuss_rounds": 1,
-            "llm_params": {"temperature": 0, "top_p": 1.0, "seed": 42},
+            "llm_params": {"temperature": 0.7, "top_p": 1.0, "seed": 42},
         })
         run_evaluation(
             ticker="AAPL",
             start_date="2024-01-01",
-            end_date="2024-01-04",
+            end_date="2024-01-10",
             initial_capital=1000,
             include_tradingagents=True,
             output_dir="./evaluation/results",

tradingagents/dataflows/openai.py

@@ -1,3 +1,5 @@
+from datetime import datetime, timedelta
+
 from openai import OpenAI
 from .config import get_config
 
@@ -38,38 +40,60 @@ def get_stock_news_openai(query, start_date, end_date):
 
 
 def get_global_news_openai(curr_date, look_back_days=7, limit=5):
+
+    def _extract_text(resp):
+        # 1) Preferred field for the Responses API
+        if hasattr(resp, "output_text") and resp.output_text:
+            return resp.output_text
+
+        # 2) Structured outputs (some SDK builds)
+        try:
+            if resp.output and len(resp.output) > 0:
+                parts = resp.output[0].content or []
+                texts = []
+                for p in parts:
+                    # p may be a plain object with .text, or a dict
+                    t = getattr(p, "text", None) or (p.get("text") if isinstance(p, dict) else None)
+                    if t:
+                        texts.append(t)
+                if texts:
+                    return "\n".join(texts)
+        except Exception:
+            pass
+
+        # 3) Chat Completions style fallback (just in case)
+        try:
+            return resp.choices[0].message["content"]
+        except Exception:
+            pass
+
+        # 4) Last resort: stringify the whole object
+        return str(resp)
+
     config = get_config()
     client = OpenAI(base_url=config["backend_url"])
 
-    response = client.responses.create(
-        model=config["quick_think_llm"],
-        input=[
-            {
-                "role": "system",
-                "content": [
-                    {
-                        "type": "input_text",
-                        "text": f"Can you search global or macroeconomics news from {look_back_days} days before {curr_date} to {curr_date} that would be informative for trading purposes? Make sure you only get the data posted during that period. Limit the results to {limit} articles.",
-                    }
-                ],
-            }
-        ],
-        text={"format": {"type": "text"}},
-        reasoning={},
-        tools=[
-            {
-                "type": "web_search_preview",
-                "user_location": {"type": "approximate"},
-                "search_context_size": "low",
-            }
-        ],
-        temperature=1,
-        max_output_tokens=4096,
-        top_p=1,
-        store=True,
+    # Build a clean date window
+    end = datetime.strptime(curr_date, "%Y-%m-%d").date()
+    start = end - timedelta(days=look_back_days)
+
+    prompt = (
+        f"List {limit} global or macroeconomic news items helpful for trading, "
+        f"strictly published between {start.isoformat()} and {end.isoformat()} (inclusive). "
+        "For each item, give: date, headline, 1-2 sentence trading relevance. "
+        "Do not include articles outside the window."
     )
 
-    return response.output[1].content[0].text
+    resp = client.responses.create(
+        model=config["quick_think_llm"],
+        input=prompt,
+        reasoning={},
+        tools=[{"type": "web_search_preview"}],
+        max_output_tokens=4096,
+        store=False,
+    )
+
+    return _extract_text(resp)
 
 
 def get_fundamentals_openai(ticker, curr_date):