update top analyst rule

app/cron_analyst_db.py

@@ -187,9 +187,8 @@ def get_top_stocks():
 
     # Filter analysts with a score >= 4
     filtered_data = [item for item in analyst_stats_list if item['analystScore'] >= 4]
-
-    # Define the date range for the past 12 months
     end_date = datetime.now().date()
+    # Define the date range for the past 12 months
     start_date = end_date - timedelta(days=365)
 
     # Track unique analyst-stock pairs and get the latest Strong Buy for each pair
@@ -203,10 +202,7 @@ def get_top_stocks():
             rating_date = datetime.strptime(rating['date'], '%Y-%m-%d').date()
             ticker = rating['ticker']
             
-            if (
-                rating['rating_current'] == 'Strong Buy' and
-                start_date <= rating_date <= end_date
-            ):
+            if rating['rating_current'] == 'Strong Buy' and start_date <= rating_date:
                 # Keep the latest rating for each stock by this analyst
                 if ticker not in ticker_ratings or rating_date > ticker_ratings[ticker]['date']:
                     ticker_ratings[ticker] = {
@@ -264,7 +260,7 @@ def get_top_stocks():
             'topAnalystUpside': round((info['median'] / info.get('price') - 1) * 100, 2) if info.get('price') else None,
             'topAnalystPriceTarget': info['median'],
             'topAnalystCounter': len(info['analyst_ids']),
-            'analystRating': "Strong Buy",
+            'topAnalystRating': "Strong Buy",
             'marketCap': info['marketCap'],
             'name': info['name']
         }

app/cron_analyst_ticker.py

@@ -323,8 +323,7 @@ try:
     with open(f"json/analyst/all-analyst-data.json", 'r') as file:
     	analyst_stats_list = ujson.load(file)
 
-    chunk_size = len(stock_symbols) // 100  # Divide the list into N chunks
-
+    chunk_size = len(stock_symbols) // 300  # Divide the list into N chunks
     chunks = [stock_symbols[i:i + chunk_size] for i in range(0, len(stock_symbols), chunk_size)]
     #chunks = [['NVDA']]
     for chunk in chunks:

app/restart_json.py

@@ -60,6 +60,8 @@ time_frames = {
     'change3Y': (datetime.now() - timedelta(days=365 * 3)).strftime('%Y-%m-%d'),
 }
 
+one_year_ago = datetime.now() - timedelta(days=365)
+
 def calculate_price_changes(symbol, item, con):
     try:
         # Loop through each time frame to calculate the change
@@ -202,12 +204,7 @@ def filter_latest_analyst_unique_rating(data):
 
 def process_top_analyst_data(data, current_price):
     data = [item for item in data if item.get('analystScore', 0) >= 4] if data else []
-    if symbol == 'AMD':
-        print(data)
     data = filter_latest_analyst_unique_rating(data)
-
-    one_year_ago = datetime.now() - timedelta(days=365)
-
     # Filter recent data from the last 12 months
     recent_data = [
         item for item in data
@@ -257,7 +254,7 @@ def process_top_analyst_data(data, current_price):
 
         # Calculate average rating score
         average_rating_score = (
-            total_rating_score / filtered_analyst_count
+            round(total_rating_score / filtered_analyst_count,2)
             if filtered_analyst_count > 0 else 0
         )
 
@@ -270,8 +267,10 @@ def process_top_analyst_data(data, current_price):
             consensus_rating = "Hold"
         elif average_rating_score >= 1.5:
             consensus_rating = "Sell"
-        else:
+        elif average_rating_score >= 1.0:
             consensus_rating = "Strong Sell"
+        else:
+            consensus_rating = None
 
         return {
             "topAnalystCounter": filtered_analyst_count,