diff --git a/src/screener/t_atr_ema.py b/src/screener/t_atr_ema.py
index 38a74eb..7acbcf8 100644
--- a/src/screener/t_atr_ema.py
+++ b/src/screener/t_atr_ema.py
@@ -120,17 +120,17 @@ def run_atr_ema_scanner(min_price: float, max_price: float, min_volume: int, por
                         if position['shares'] > 0:
                             signal_data = {
                                 'ticker': ticker,
-                                'entry': current_price,
-                                'target': target_price,
+                                'entry_price': current_price,
+                                'target_price': target_price,
                                 'signal_date': signal_date,
                                 'volume': current_volume,
                                 'last_update': datetime.fromtimestamp(last_update/1000000000),
                                 'shares': position['shares'],
                                 'position_size': position['position_value'],
                                 'stop_loss': position['stop_loss'],
-                                'risk': position['potential_loss'],
-                                'reward': position['potential_profit'],
-                                'r_r': position['risk_reward_ratio']
+                                'risk_amount': position['potential_loss'],
+                                'profit_amount': position['potential_profit'],
+                                'risk_reward_ratio': position['risk_reward_ratio']
                             }
                             bullish_signals.append(signal_data)
                             dollar_risk = signal_data['risk'] * -1
diff --git a/src/screener/t_atr_ema_v2.py b/src/screener/t_atr_ema_v2.py
index 8c6f026..f4489e1 100644
--- a/src/screener/t_atr_ema_v2.py
+++ b/src/screener/t_atr_ema_v2.py
@@ -126,20 +126,23 @@ def run_atr_ema_scanner_v2(min_price: float, max_price: float, min_volume: int,
                 if signal:
                     entry_data = {
                         'ticker': ticker,
-                        'price': signal_data['price'],
+                        'entry_price': signal_data['price'],
+                        'target_price': signal_data['ema'],
                         'volume': current_volume,
                         'signal_date': signal_date,
-                        'last_update': datetime.fromtimestamp(last_update/1000000000),
-                        'ema': signal_data['ema']  # Make sure we store the EMA value
+                        'last_update': datetime.fromtimestamp(last_update/1000000000)
                     }
                     
                     if calculator:
-                        position = calculator.calculate_position_size(entry_data['price'])
+                        position = calculator.calculate_position_size(entry_data['entry_price'])
+                        potential_profit = (entry_data['target_price'] - entry_data['entry_price']) * position['shares']
                         entry_data.update({
                             'shares': position['shares'],
                             'position_size': position['position_value'],
                             'stop_loss': position['stop_loss'],
-                            'risk': position['potential_loss']
+                            'risk_amount': position['potential_loss'],
+                            'profit_amount': potential_profit,
+                            'risk_reward_ratio': abs(potential_profit / position['potential_loss']) if position['potential_loss'] != 0 else 0
                         })
                     
                     entry_signals.append(entry_data)
diff --git a/src/screener/t_sunnyband.py b/src/screener/t_sunnyband.py
index 5374589..aabd093 100644
--- a/src/screener/t_sunnyband.py
+++ b/src/screener/t_sunnyband.py
@@ -252,16 +252,17 @@ def run_sunny_scanner(min_price: float, max_price: float, min_volume: int, portf
                                 # Update signal data with proper stop loss calculation
                                 signal_data = {
                                     'ticker': ticker,
-                                    'entry': current_price,
-                                    'target': target_price,
+                                    'entry_price': current_price,
+                                    'target_price': target_price,
+                                    'signal_date': signal_date,
                                     'volume': current_volume,
                                     'last_update': datetime.fromtimestamp(last_update/1000000000),
                                     'shares': position['shares'],
                                     'position_size': position['position_value'],
                                     'stop_loss': current_price * 0.93,  # 7% stop loss
-                                    'risk': position['potential_loss'],
-                                    'reward': position['potential_profit'],
-                                    'r_r': position['risk_reward_ratio']
+                                    'risk_amount': position['potential_loss'],
+                                    'profit_amount': position['potential_profit'],
+                                    'risk_reward_ratio': position['risk_reward_ratio']
                                 }
                                 bullish_signals.append(signal_data)
                                 # Update print output format
@@ -299,17 +300,11 @@ def run_sunny_scanner(min_price: float, max_price: float, min_volume: int, portf
         
         if bullish_signals:
             df_bullish = pd.DataFrame(bullish_signals)
-            output_file = f'{output_dir}/sunny_bullish_{output_date}.csv'
+            output_file = f'{output_dir}/sunny_signals_{output_date}.csv'
             df_bullish.to_csv(output_file, index=False)
-            print(f"\nSaved bullish signals to {output_file}")
-        
-        if bearish_signals:
-            df_bearish = pd.DataFrame(bearish_signals)
-            output_file = f'{output_dir}/sunny_bearish_{output_date}.csv'
-            df_bearish.to_csv(output_file, index=False)
-            print(f"\nSaved bearish signals to {output_file}")
-        
-        print(f"\nFound {len(bullish_signals)} bullish and {len(bearish_signals)} bearish signals")
+            print(f"\nSaved {len(bullish_signals)} signals to {output_file}")
+        else:
+            print("\nNo signals found")
         
     except Exception as e:
         print(f"Error during scan: {str(e)}")