From 6577085c0b0be1d3c6c9fdd7b520b659806cdd93 Mon Sep 17 00:00:00 2001
From: MuslemRahimi <moslem_rahimi@hotmail.de>
Date: Sun, 28 Jul 2024 16:45:32 +0200
Subject: [PATCH] update cron job

---
 app/cron_reddit_statistics.py | 70 ++++++++++++++++++++++++++---------
 1 file changed, 53 insertions(+), 17 deletions(-)

diff --git a/app/cron_reddit_statistics.py b/app/cron_reddit_statistics.py
index a67e4c7..0747b11 100644
--- a/app/cron_reddit_statistics.py
+++ b/app/cron_reddit_statistics.py
@@ -7,6 +7,14 @@ from collections import defaultdict
 import os
 from dotenv import load_dotenv
 import sqlite3
+import nltk
+from nltk.sentiment import SentimentIntensityAnalyzer
+
+# Download required NLTK data
+nltk.download('vader_lexicon', quiet=True)
+
+# Initialize the NLTK sentiment analyzer
+sia = SentimentIntensityAnalyzer()
 
 con = sqlite3.connect('stocks.db')
 
@@ -24,7 +32,6 @@ etf_symbols = [row[0] for row in etf_cursor.fetchall()]
 con.close()
 etf_con.close()
 
-
 load_dotenv()
 client_key = os.getenv('REDDIT_API_KEY')
 client_secret = os.getenv('REDDIT_API_SECRET')
@@ -37,12 +44,6 @@ reddit = praw.Reddit(
     user_agent=user_agent
 )
 
-# Get subscriber count and active user count
-#subreddit = reddit.subreddit("wallstreetbets")
-#subscriber_count = subreddit.subscribers
-#active_user_count = subreddit.active_user_count
-
-
 # Function to save data
 def save_data(data, filename):
     with open(f'json/reddit-tracker/wallstreetbets/{filename}', 'w', encoding='utf-8') as f:
@@ -57,12 +58,14 @@ def compute_daily_statistics(file_path):
     daily_stats = defaultdict(lambda: {
         'post_count': 0, 
         'total_comments': 0, 
-        'ticker_mentions': defaultdict(int),
+        'ticker_mentions': defaultdict(lambda: {'total': 0, 'PUT': 0, 'CALL': 0, 'sentiment': []}),
         'unique_tickers': set()
     })
     
-    # Compile regex pattern for finding tickers
+    # Compile regex patterns for finding tickers, PUT, and CALL
     ticker_pattern = re.compile(r'\$([A-Z]+)')
+    put_pattern = re.compile(r'\b(PUT|PUTS)\b', re.IGNORECASE)
+    call_pattern = re.compile(r'\b(CALL|CALLS)\b', re.IGNORECASE)
     
     # Process each post
     for post in data:
@@ -77,9 +80,23 @@ def compute_daily_statistics(file_path):
         text_to_search = post['title'] + ' ' + post['selftext']
         tickers = ticker_pattern.findall(text_to_search)
         
+        # Check for PUT and CALL mentions
+        put_mentions = len(put_pattern.findall(text_to_search))
+        call_mentions = len(call_pattern.findall(text_to_search))
+        
+        # Perform sentiment analysis
+        sentiment_scores = sia.polarity_scores(text_to_search)
+        
         for ticker in tickers:
-            daily_stats[post_date]['ticker_mentions'][ticker] += 1
+            daily_stats[post_date]['ticker_mentions'][ticker]['total'] += 1
             daily_stats[post_date]['unique_tickers'].add(ticker)
+            
+            # Add PUT and CALL counts
+            daily_stats[post_date]['ticker_mentions'][ticker]['PUT'] += put_mentions
+            daily_stats[post_date]['ticker_mentions'][ticker]['CALL'] += call_mentions
+            
+            # Add sentiment score
+            daily_stats[post_date]['ticker_mentions'][ticker]['sentiment'].append(sentiment_scores['compound'])
     
     # Calculate averages and format the results
     formatted_stats = []
@@ -88,26 +105,45 @@ def compute_daily_statistics(file_path):
             'date': date.isoformat(),
             'totalPosts': stats['post_count'],
             'totalComments': stats['total_comments'],
-            'totalMentions': sum(stats['ticker_mentions'].values()),
+            'totalMentions': sum(mentions['total'] for mentions in stats['ticker_mentions'].values()),
             'companySpread': len(stats['unique_tickers']),
-            'tickerMentions': dict(stats['ticker_mentions'])  # Optional: include detailed ticker mentions
+            'tickerMentions': [
+                {
+                    'symbol': ticker,
+                    'count': mentions['total'],
+                    'put': mentions['PUT'],
+                    'call': mentions['CALL']
+                }
+                for ticker, mentions in stats['ticker_mentions'].items()
+            ]
         })
     
     return formatted_stats, daily_stats
 
-# Function to compute trending tickers
 def compute_trending_tickers(daily_stats):
     today = datetime.now().date()
     seven_days_ago = today - timedelta(days=14)
     
-    trending = defaultdict(int)
+    trending = defaultdict(lambda: {'total': 0, 'PUT': 0, 'CALL': 0, 'sentiment': []})
     
     for date, stats in daily_stats.items():
         if seven_days_ago <= date <= today:
-            for ticker, count in stats['ticker_mentions'].items():
-                trending[ticker] += count
+            for ticker, counts in stats['ticker_mentions'].items():
+                trending[ticker]['total'] += counts['total']
+                trending[ticker]['PUT'] += counts['PUT']
+                trending[ticker]['CALL'] += counts['CALL']
+                trending[ticker]['sentiment'].extend(counts['sentiment'])
     
-    trending_list = [{'symbol': symbol, 'count': count} for symbol, count in trending.items()]
+    trending_list = [
+        {
+            'symbol': symbol,
+            'count': counts['total'],
+            'put': counts['PUT'],
+            'call': counts['CALL'],
+            'avgSentiment': sum(counts['sentiment']) / len(counts['sentiment']) if counts['sentiment'] else 0
+        }
+        for symbol, counts in trending.items()
+    ]
     trending_list.sort(key=lambda x: x['count'], reverse=True)
 
     for item in trending_list: