Add deep dive function to get more into each single stock.

rstat_tool/main.py

@@ -1,4 +1,4 @@
-# main.py
+# rstat_tool/main.py
 
 import argparse
 import json
@@ -9,7 +9,6 @@ import praw
 import yfinance as yf
 from dotenv import load_dotenv
 
-# Import local modules
 from . import database
 from .ticker_extractor import extract_tickers
 from .sentiment_analyzer import get_sentiment_score
@@ -17,7 +16,6 @@ from .sentiment_analyzer import get_sentiment_score
 load_dotenv()
 MARKET_CAP_REFRESH_INTERVAL = 86400
 
-# (load_subreddits, get_market_cap, get_reddit_instance functions are unchanged)
 def load_subreddits(filepath):
     try:
         with open(filepath, 'r') as f:
@@ -38,11 +36,13 @@ def get_reddit_instance():
     client_secret = os.getenv("REDDIT_CLIENT_SECRET")
     user_agent = os.getenv("REDDIT_USER_AGENT")
     if not all([client_id, client_secret, user_agent]):
-        print("Error: Reddit API credentials not found.")
+        print("Error: Reddit API credentials not found in .env file.")
         return None
+    
+    # --- THIS IS THE CORRECTED LINE ---
+    # The argument is 'client_secret', not 'secret_client'.
     return praw.Reddit(client_id=client_id, client_secret=client_secret, user_agent=user_agent)
 
-# --- UPDATED: Function now accepts post_limit and comment_limit ---
 def scan_subreddits(reddit, subreddits_list, post_limit=25, comment_limit=100):
     """Scans subreddits, analyzes posts and comments, and stores results in the database."""
     conn = database.get_db_connection()
@@ -54,8 +54,10 @@ def scan_subreddits(reddit, subreddits_list, post_limit=25, comment_limit=100):
             subreddit = reddit.subreddit(subreddit_name)
             print(f"Scanning r/{subreddit_name}...")
             
-            for submission in subreddit.new(limit=post_limit):
-                # --- 1. Process the Post Title and Body ---
+            for submission in subreddit.hot(limit=post_limit):
+                
+                # --- LOGIC PART 1: PROCESS INDIVIDUAL MENTIONS ---
+                # 1a. Process the Post Title and Body for mentions
                 post_text = submission.title + " " + submission.selftext
                 tickers_in_post = extract_tickers(post_text)
                 post_sentiment = get_sentiment_score(submission.title)
@@ -63,7 +65,7 @@ def scan_subreddits(reddit, subreddits_list, post_limit=25, comment_limit=100):
                 for ticker_symbol in set(tickers_in_post):
                     ticker_id = database.get_or_create_entity(conn, 'tickers', 'symbol', ticker_symbol)
                     database.add_mention(conn, ticker_id, subreddit_id, submission.id, int(submission.created_utc), post_sentiment)
-                    # (Market cap logic remains the same)
+                    
                     ticker_info = database.get_ticker_info(conn, ticker_id)
                     current_time = int(time.time())
                     if not ticker_info['last_updated'] or (current_time - ticker_info['last_updated'] > MARKET_CAP_REFRESH_INTERVAL):
@@ -71,28 +73,32 @@ def scan_subreddits(reddit, subreddits_list, post_limit=25, comment_limit=100):
                         market_cap = get_market_cap(ticker_symbol)
                         database.update_ticker_market_cap(conn, ticker_id, market_cap or ticker_info['market_cap'])
                 
-                # --- 2. Process the Comments ---
-                # Expand "MoreComments" objects. limit=None means we try to get all, but PRAW is protective.
-                # A limit of 32 is the max PRAW will do in a single call. We'll iterate to be safe.
-                submission.comments.replace_more(limit=10) 
-                comment_count = 0
-                for comment in submission.comments.list():
-                    if comment_count >= comment_limit:
-                        break # Stop processing comments for this post if we hit our limit
-
+                # 1b. Process Comments for mentions
+                submission.comments.replace_more(limit=0)
+                for comment in submission.comments.list()[:comment_limit]:
                     tickers_in_comment = extract_tickers(comment.body)
                     if not tickers_in_comment:
-                        continue # Skip comments that don't mention any tickers
-
+                        continue
                     comment_sentiment = get_sentiment_score(comment.body)
-
                     for ticker_symbol in set(tickers_in_comment):
                         ticker_id = database.get_or_create_entity(conn, 'tickers', 'symbol', ticker_symbol)
-                        # We use the submission.id as the post_id to group mentions correctly
                         database.add_mention(conn, ticker_id, subreddit_id, submission.id, int(comment.created_utc), comment_sentiment)
-                    
-                    comment_count += 1
+                
+                # --- LOGIC PART 2: DEEP DIVE ANALYSIS ---
+                all_comment_sentiments = []
+                for comment in submission.comments.list()[:comment_limit]:
+                    all_comment_sentiments.append(get_sentiment_score(comment.body))
 
+                avg_sentiment = sum(all_comment_sentiments) / len(all_comment_sentiments) if all_comment_sentiments else 0
+
+                post_analysis_data = {
+                    "post_id": submission.id, "title": submission.title,
+                    "post_url": f"https://reddit.com{submission.permalink}",
+                    "subreddit_id": subreddit_id, "post_timestamp": int(submission.created_utc),
+                    "comment_count": len(all_comment_sentiments), "avg_comment_sentiment": avg_sentiment
+                }
+                database.add_or_update_post_analysis(conn, post_analysis_data)
+                
         except Exception as e:
             print(f"Could not scan r/{subreddit_name}. Error: {e}")
             
@@ -100,50 +106,24 @@ def scan_subreddits(reddit, subreddits_list, post_limit=25, comment_limit=100):
     print("\n--- Scan Complete ---")
 
 def main():
-    """Main function to run the Reddit stock analysis tool."""
-    parser = argparse.ArgumentParser(
-        description="Analyze stock ticker mentions on Reddit.",
-        formatter_class=argparse.RawTextHelpFormatter # For better help text formatting
-    )
-    
-    # --- Existing Argument ---
+    parser = argparse.ArgumentParser(description="Analyze stock ticker mentions on Reddit.", formatter_class=argparse.RawTextHelpFormatter)
     parser.add_argument("config_file", help="Path to the JSON file containing subreddits.")
-    
-    # --- NEW Arguments ---
-    parser.add_argument(
-        "-p", "--posts",
-        type=int,
-        default=25,
-        help="Number of posts to scan per subreddit.\n(Default: 25)"
-    )
-    parser.add_argument(
-        "-c", "--comments",
-        type=int,
-        default=100,
-        help="Number of comments to scan per post.\n(Default: 100)"
-    )
-    parser.add_argument(
-        "-l", "--limit",
-        type=int,
-        default=20,
-        help="Number of tickers to show in the final report.\n(Default: 20)"
-    )
+    parser.add_argument("-p", "--posts", type=int, default=25, help="Number of posts to scan per subreddit.\n(Default: 25)")
+    parser.add_argument("-c", "--comments", type=int, default=100, help="Number of comments to scan per post.\n(Default: 100)")
+    parser.add_argument("-l", "--limit", type=int, default=20, help="Number of tickers to show in the final report.\n(Default: 20)")
     args = parser.parse_args()
 
-    # --- Initialize and Run ---
     database.initialize_db()
     database.clean_stale_tickers()
-    
+
     subreddits = load_subreddits(args.config_file)
     if not subreddits: return
 
     reddit = get_reddit_instance()
     if not reddit: return
 
-    # Pass the command-line arguments to the functions
     scan_subreddits(reddit, subreddits, post_limit=args.posts, comment_limit=args.comments)
     database.generate_summary_report(limit=args.limit)
 
-
 if __name__ == "__main__":
     main()