refactor: Centralize signal output and CSV saving in data_utils.py

src/screener/t_atr_ema.py

@@ -4,7 +4,7 @@ from datetime import datetime, timedelta
 import pandas as pd
 from db.db_connection import create_client
 from trading.position_calculator import PositionCalculator
-from utils.data_utils import get_stock_data, validate_signal_date
+from utils.data_utils import get_stock_data, validate_signal_date, print_signal, save_signals_to_csv
 from indicators.three_atr_ema import ThreeATREMAIndicator
 
 def check_atr_ema_bullish_signal(df: pd.DataFrame) -> bool:
@@ -144,18 +144,7 @@ def run_atr_ema_scanner(min_price: float, max_price: float, min_volume: int, por
                 print(f"Error processing {ticker}: {str(e)}")
                 continue
         
-        # Save results
+        save_signals_to_csv(bullish_signals, 'atr_ema')
-        output_dir = 'reports'
-        os.makedirs(output_dir, exist_ok=True)
-        output_date = datetime.now().strftime("%Y%m%d_%H%M")
-        
-        if bullish_signals:
-            df_bullish = pd.DataFrame(bullish_signals)
-            output_file = f'{output_dir}/atr_ema_bullish_{output_date}.csv'
-            df_bullish.to_csv(output_file, index=False)
-            print(f"\nSaved bullish signals to {output_file}")
-        else:
-            print("No bullish signals found")
             
     except Exception as e:
         print(f"Error during scan: {str(e)}")

src/screener/t_atr_ema_v2.py

@@ -3,7 +3,7 @@ import pandas as pd
 import os
 from db.db_connection import create_client
 from trading.position_calculator import PositionCalculator
-from utils.data_utils import get_stock_data, validate_signal_date
+from utils.data_utils import get_stock_data, validate_signal_date, print_signal, save_signals_to_csv
 from screener.user_input import get_interval_choice, get_date_range
 from indicators.three_atr_ema import ThreeATREMAIndicator
 
@@ -148,32 +148,13 @@ def run_atr_ema_scanner_v2(min_price: float, max_price: float, min_volume: int,
                     entry_signals.append(entry_data)
                     
                     # Print signal information with date
-                    print(f"\n🔍 {ticker} @ ${entry_data['price']:.2f} on {signal_date.strftime('%Y-%m-%d %H:%M')}")
+                    print_signal(entry_data)
-                    if calculator:
-                        print(f"   Size: {entry_data['shares']} shares (${entry_data['position_size']:.2f})")
-                        print(f"   Stop: ${entry_data['stop_loss']:.2f} (7%)")
-                        print(f"   Risk: ${abs(entry_data['risk']):.2f}")
-                        # Add potential profit calculation and display
-                        target_price = entry_data['ema']  # Use stored EMA as target
-                        potential_profit = (target_price - entry_data['price']) * entry_data['shares']
-                        print(f"   Target: ${target_price:.2f} | Profit: ${potential_profit:.2f}")
                     
             except Exception as e:
                 print(f"Error processing {ticker}: {str(e)}")
                 continue
         
-        # Save results
+        save_signals_to_csv(entry_signals, 'atr_ema_v2')
-        if entry_signals:
-            output_dir = 'reports'
-            os.makedirs(output_dir, exist_ok=True)
-            output_date = datetime.now().strftime("%Y%m%d_%H%M")
-            output_file = f'{output_dir}/atr_ema_v2_{output_date}.csv'
-            
-            df_signals = pd.DataFrame(entry_signals)
-            df_signals.to_csv(output_file, index=False)
-            print(f"\nSaved {len(entry_signals)} signals to {output_file}")
-        else:
-            print("\nNo entry signals found")
             
     except Exception as e:
         print(f"Error during scan: {str(e)}")

src/screener/t_sunnyband.py

@@ -6,7 +6,7 @@ from db.db_connection import create_client
 from indicators.sunny_bands import SunnyBands
 from trading.position_calculator import PositionCalculator
 from screener.user_input import get_interval_choice, get_date_range
-from utils.data_utils import get_stock_data, validate_signal_date
+from utils.data_utils import get_stock_data, validate_signal_date, print_signal, save_signals_to_csv
 
 
 
@@ -271,11 +271,7 @@ def run_sunny_scanner(min_price: float, max_price: float, min_volume: int, portf
                                 dollar_risk = position['potential_loss'] * -1
                                 signal_date = validate_signal_date(df.iloc[-1]['date'])  # Get and validate the date
                                 signal_data['signal_date'] = signal_date  # Add to signal data
-                                print(f"\n🟢 {ticker} @ ${current_price:.2f} on {signal_date.strftime('%Y-%m-%d %H:%M')}")
+                                print_signal(signal_data, "🟢")
-                                print(f"   Size: {position['shares']} shares (${position['position_value']:.2f})")
-                                print(f"   Stop: ${signal_data['stop_loss']:.2f} (-7%) | Target: ${target_price:.2f}")
-                                print(f"   Risk/Reward: 1:{position['risk_reward_ratio']:.1f} | Risk: ${dollar_risk:.2f}")
-                                print(f"   Potential Profit: ${position['potential_profit']:.2f}")
                         except ValueError as e:
                             print(f"Skipping {ticker} position: {str(e)}")
                             continue
@@ -295,18 +291,7 @@ def run_sunny_scanner(min_price: float, max_price: float, min_volume: int, portf
                 print(f"Error processing {ticker}: {str(e)}")
                 continue
         
-        # Save results
+        save_signals_to_csv(bullish_signals, 'sunny')
-        output_dir = 'reports'
-        os.makedirs(output_dir, exist_ok=True)
-        output_date = datetime.now().strftime("%Y%m%d_%H%M")
-        
-        if bullish_signals:
-            df_bullish = pd.DataFrame(bullish_signals)
-            output_file = f'{output_dir}/sunny_signals_{output_date}.csv'
-            df_bullish.to_csv(output_file, index=False)
-            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)}")

src/utils/data_utils.py

@@ -17,6 +17,44 @@ def validate_signal_date(signal_date: datetime) -> datetime:
         return current_date
     return signal_date
 
+def print_signal(signal_data: dict, signal_type: str = "🔍") -> None:
+    """
+    Print standardized signal output
+    
+    Args:
+        signal_data (dict): Dictionary containing signal information
+        signal_type (str): Emoji indicator for signal type (default: 🔍)
+    """
+    try:
+        print(f"\n{signal_type} {signal_data['ticker']} @ ${signal_data['entry_price']:.2f} on {signal_data['signal_date'].strftime('%Y-%m-%d %H:%M')}")
+        print(f"   Size: {signal_data['shares']} shares (${signal_data['position_size']:.2f})")
+        print(f"   Stop: ${signal_data['stop_loss']:.2f} (7%) | Target: ${signal_data['target_price']:.2f}")
+        print(f"   Risk/Reward: 1:{signal_data['risk_reward_ratio']:.1f} | Risk: ${abs(signal_data['risk_amount']):.2f}")
+        print(f"   Potential Profit: ${signal_data['profit_amount']:.2f}")
+    except KeyError as e:
+        print(f"Error printing signal: Missing key {e}")
+
+def save_signals_to_csv(signals: list, scanner_name: str) -> None:
+    """
+    Save signals to CSV file with standardized format and naming
+    
+    Args:
+        signals (list): List of signal dictionaries
+        scanner_name (str): Name of the scanner for file naming
+    """
+    if not signals:
+        print("\nNo signals found")
+        return
+        
+    output_dir = 'reports'
+    os.makedirs(output_dir, exist_ok=True)
+    output_date = datetime.now().strftime("%Y%m%d_%H%M")
+    output_file = f'{output_dir}/{scanner_name}_{output_date}.csv'
+    
+    df_signals = pd.DataFrame(signals)
+    df_signals.to_csv(output_file, index=False)
+    print(f"\nSaved {len(signals)} signals to {output_file}")
+
 def get_stock_data(ticker: str, start_date: datetime, end_date: datetime, interval: str) -> pd.DataFrame:
     """
     Fetch stock data from the database with enhanced fallback logic