update share statistics

app/cron_share_statistics.py

@@ -85,20 +85,24 @@ async def run():
 
     cursor = con.cursor()
     cursor.execute("PRAGMA journal_mode = wal")
-    cursor.execute("SELECT DISTINCT symbol FROM stocks")
+    cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE symbol NOT LIKE '%.%'")
     stock_symbols = [row[0] for row in cursor.fetchall()]
     
     counter = 0
 
     for ticker in tqdm(stock_symbols):
+        try:
             forward_pe_dict, short_dict = await get_data(ticker, con)
             if forward_pe_dict.keys() and short_dict.keys():
                 await save_as_json(ticker, forward_pe_dict, short_dict)
 
             counter += 1
-        if counter % 100 == 0:
-            print(f"Processed {counter} tickers, waiting for 10 seconds...")
-            await asyncio.sleep(10)
+            if counter % 20 == 0:
+                print(f"Processed {counter} tickers, waiting for 5 seconds...")
+                await asyncio.sleep(5)
+        except Exception as e:
+            print(ticker, e)
+
 
     con.close()
 

app/ml_models/fundamental_predictor.py

@@ -144,7 +144,7 @@ class FundamentalPredictor:
 
         
         # Second LSTM layer with dropout and batch normalization
-        model.add(LSTM(128, return_sequences=True, kernel_regularizer=regularizers.l2(0.01)))
+        model.add(LSTM(256, return_sequences=True, kernel_regularizer=regularizers.l2(0.01)))
         model.add(Dropout(0.5))
         model.add(BatchNormalization())
         
@@ -153,7 +153,7 @@ class FundamentalPredictor:
         model.add(Dropout(0.5))
         model.add(BatchNormalization())
 
-        model.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
+        model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
         model.add(Dropout(0.2))
         model.add(BatchNormalization())