fix(filter): replace tqdm with logger in batch news functions to fix I/O error

tqdm writes to stderr immediately on __enter__, before any loop iteration.
In Streamlit's thread/subprocess context stderr can be a closed pipe, causing
'I/O operation on closed file' which _run_call catches and returns {} — so
the entire news enrichment step was silently skipped every run.

Replaced tqdm progress bars with logger.info() calls in:
- get_batch_stock_news_google() in openai.py
- get_batch_stock_news_openai() in openai.py
- Reddit DD parallel evaluation in reddit_api.py

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

tradingagents/dataflows/openai.py

@@ -113,15 +113,15 @@ def get_batch_stock_news_openai(
     class PortfolioUpdate(BaseModel):
         items: List[TickerNews]
 
-    from tqdm import tqdm
-
     client = _get_openai_client()
     results = {}
+    total_batches = (len(tickers) + batch_size - 1) // batch_size
 
     # Process in batches to avoid output token limits
-    with tqdm(total=len(tickers), desc="📰 OpenAI batch news", unit="ticker") as pbar:
     for i in range(0, len(tickers), batch_size):
         batch = tickers[i : i + batch_size]
+        batch_num = i // batch_size + 1
+        logger.info(f"📰 OpenAI news batch {batch_num}/{total_batches}: {batch}")
 
         # Request comprehensive news summaries for better ranker LLM context
         prompt = f"""Find the most significant news stories for {batch} from {start_date} to {end_date}.
@@ -159,9 +159,6 @@ For each ticker, provide a comprehensive summary (5-8 sentences) covering:
             for ticker in batch:
                 results[ticker.upper()] = ""
 
-            # Update progress bar
-            pbar.update(len(batch))
-
     return results
 
 
@@ -218,12 +215,13 @@ def get_batch_stock_news_google(
     ).with_structured_output(PortfolioUpdate, method="json_schema")
     results = {}
 
-    from tqdm import tqdm
+    total_batches = (len(tickers) + batch_size - 1) // batch_size
 
     # Process in batches
-    with tqdm(total=len(tickers), desc="📰 Google batch news", unit="ticker") as pbar:
     for i in range(0, len(tickers), batch_size):
         batch = tickers[i : i + batch_size]
+        batch_num = i // batch_size + 1
+        logger.info(f"📰 Google news batch {batch_num}/{total_batches}: {batch}")
 
         # Request comprehensive news summaries for better ranker LLM context
         prompt = f"""Find the most significant news stories for {batch} from {start_date} to {end_date}.
@@ -263,7 +261,4 @@ For each ticker, provide a comprehensive summary (5-8 sentences) covering:
             for ticker in batch:
                 results[ticker.upper()] = ""
 
-            # Update progress bar
-            pbar.update(len(batch))
-
     return results

tradingagents/dataflows/reddit_api.py

@@ -342,7 +342,8 @@ def get_reddit_undiscovered_dd(
     top_n: Annotated[int, "Number of top DD posts to return"] = 10,
     num_comments: Annotated[int, "Number of top comments to include"] = 10,
     llm_evaluator=None,  # Will be passed from discovery graph
-) -> str:
+    as_list: bool = False,
+) -> str | list:
     """
     Find high-quality undiscovered DD using LLM evaluation.
 
@@ -383,10 +384,11 @@ def get_reddit_undiscovered_dd(
             if not submission.selftext or len(submission.selftext) < 200:
                 continue
 
+            top_comments = []
+            if llm_evaluator:
                 # Get top comments for community validation
                 submission.comment_sort = "top"
                 submission.comments.replace_more(limit=0)
-            top_comments = []
                 for comment in submission.comments[:num_comments]:
                     if hasattr(comment, "body") and hasattr(comment, "score"):
                         top_comments.append(
@@ -517,26 +519,10 @@ Extract all stock ticker symbols mentioned in the post or comments."""
 
                 return post
 
-            # Parallel evaluation with progress tracking
+            # Parallel evaluation
-            try:
+            logger.info(f"Scanning {len(candidate_posts)} Reddit posts with LLM...")
-                from tqdm import tqdm
-
-                use_tqdm = True
-            except ImportError:
-                use_tqdm = False
-
             with ThreadPoolExecutor(max_workers=10) as executor:
                 futures = [executor.submit(evaluate_post, post) for post in candidate_posts]
-
-                if use_tqdm:
-                    # With progress bar
-                    evaluated = []
-                    for future in tqdm(
-                        as_completed(futures), total=len(futures), desc="   Evaluating posts"
-                    ):
-                        evaluated.append(future.result())
-                else:
-                    # Without progress bar (fallback)
                 evaluated = [f.result() for f in as_completed(futures)]
 
             # Filter quality threshold (55+ = decent DD)
@@ -559,6 +545,18 @@ Extract all stock ticker symbols mentioned in the post or comments."""
             candidate_posts.sort(key=lambda x: x["full_length"] + (x["score"] * 10), reverse=True)
             top_dd = candidate_posts[:top_n]
 
+        if as_list:
+            if not llm_evaluator:
+                import re
+
+                ticker_pattern = r"\$([A-Z]{2,5})\b|^([A-Z]{2,5})\s"
+                for post in top_dd:
+                    matches = re.findall(ticker_pattern, post["title"] + " " + post["text"])
+                    tickers = list(set([t[0] or t[1] for t in matches if t[0] or t[1]]))
+                    post["ticker"] = tickers[0] if tickers else ""
+                    post["quality_score"] = 75  # default to Medium priority
+            return top_dd
+
         if not top_dd:
             return f"# Undiscovered DD\n\nNo high-quality DD found (scanned {len(candidate_posts)} posts)."