Update app.py

app.py

@@ -91,32 +91,48 @@ for company in companies:
     df_c['Day'] = df_c['date'].dt.date
     df_c['Month'] = df_c['date'].dt.to_period('M').dt.to_timestamp()
     df_c['Year'] = df_c['date'].dt.year
-
+    
     # Strategy A: Custom Sentiment
     df_c['StrategyA_Cumulative'] = 0.0
     for i in range(1, len(df_c)):
         pct = df_c.loc[i, 'PctChangeDaily'] if pd.notnull(df_c.loc[i,'PctChangeDaily']) else 0
+        price = df_c.loc[i-1, f'Close_{TICKERS[c]}']  # prezzo di acquisto del giorno precedente
+        
         if df_c.loc[i, 'Sentiment'] == "UP" and df_c.loc[i,'Confidence'] > 0.8:
-            df_c.loc[i,'StrategyA_Cumulative'] = df_c.loc[i-1,'StrategyA_Cumulative'] + pct
+            df_c.loc[i,'StrategyA_Cumulative'] = df_c.loc[i-1,'StrategyA_Cumulative'] + price * pct
         elif df_c.loc[i, 'Sentiment'] == "DOWN" and df_c.loc[i,'Confidence'] > 0.8:
-            df_c.loc[i,'StrategyA_Cumulative'] = df_c.loc[i-1,'StrategyA_Cumulative'] - pct
+            df_c.loc[i,'StrategyA_Cumulative'] = df_c.loc[i-1,'StrategyA_Cumulative'] - price * pct
         else:
             df_c.loc[i,'StrategyA_Cumulative'] = df_c.loc[i-1,'StrategyA_Cumulative']
+    # Strategy B: Regression (buy if >1, sell if <1)
+    df_c['StrategyB_Cumulative'] = 0.0
+    for i in range(1, len(df_c)):
+        pct = df_c.loc[i, 'PctChangeDaily'] if pd.notnull(df_c.loc[i,'PctChangeDaily']) else 0
+        price = df_c.loc[i-1, f'Close_{TICKERS[c]}']
+        predicted = df_c.loc[i, 'Predicted']
+    
+        if predicted > 1:
+            df_c.loc[i,'StrategyB_Cumulative'] = df_c.loc[i-1,'StrategyB_Cumulative'] + price * pct
+        elif predicted < 1:
+            df_c.loc[i,'StrategyB_Cumulative'] = df_c.loc[i-1,'StrategyB_Cumulative'] - price * pct
+        else:
+            df_c.loc[i,'StrategyB_Cumulative'] = df_c.loc[i-1,'StrategyB_Cumulative']
 
-    # Strategy B: Regression
-    df_c['StrategyB_Cumulative'] = (df_c['Predicted'] * df_c['PctChangeDaily']).cumsum()
-
+    
     # Strategy C: FinBERT
     df_c['StrategyC_Cumulative'] = 0.0
     for i in range(1, len(df_c)):
         pct = df_c.loc[i, 'PctChangeDaily'] if pd.notnull(df_c.loc[i,'PctChangeDaily']) else 0
+        price = df_c.loc[i-1, f'Close_{TICKERS[c]}']
+        
         if df_c.loc[i, 'FinBERT_Sentiment'] == "POSITIVE" and df_c.loc[i,'FinBERT_Confidence'] > 0.8:
-            df_c.loc[i,'StrategyC_Cumulative'] = df_c.loc[i-1,'StrategyC_Cumulative'] + pct
+            df_c.loc[i,'StrategyC_Cumulative'] = df_c.loc[i-1,'StrategyC_Cumulative'] + price * pct
         elif df_c.loc[i, 'FinBERT_Sentiment'] == "NEGATIVE" and df_c.loc[i,'FinBERT_Confidence'] > 0.8:
-            df_c.loc[i,'StrategyC_Cumulative'] = df_c.loc[i-1,'StrategyC_Cumulative'] - pct
+            df_c.loc[i,'StrategyC_Cumulative'] = df_c.loc[i-1,'StrategyC_Cumulative'] - price * pct
         else:
             df_c.loc[i,'StrategyC_Cumulative'] = df_c.loc[i-1,'StrategyC_Cumulative']
 
+
     dfs_final[company] = df_c.drop(columns=["date", "date_merge"], errors="ignore")
 
 # --- FUNZIONE PER GRADIO ---