feat: Add signal dates to ATR EMA screeners for backtesting

src/screener/t_atr_ema.py

@@ -24,7 +24,7 @@ def check_atr_ema_bullish_signal(df: pd.DataFrame) -> bool:
     print(f"Lower Band: ${last_bands['lower_band']:.2f}")
     print(f"Bullish Signal: {'Yes' if last_bands['signal'] else 'No'}")
 
-def check_atr_ema_buy_condition(df: pd.DataFrame) -> bool:
+def check_atr_ema_buy_condition(df: pd.DataFrame) -> tuple:
     """Check if price is below EMA and moving up through lower ATR band"""
     # Get latest values from DataFrame
     last_price = df.iloc[-1]
@@ -35,12 +35,14 @@ def check_atr_ema_buy_condition(df: pd.DataFrame) -> bool:
     ema = results['ema'].iloc[-1]
     lower_band = results['lower_band'].iloc[-1]
     
-    return (
+    signal = (
         last_price['close'] < ema and
         previous_price['close'] <= lower_band and
         last_price['close'] > previous_price['close']
     )
     
+    return signal, last_price['date'] if signal else None, results.iloc[-1]
+
 def run_atr_ema_scanner(min_price: float, max_price: float, min_volume: int, portfolio_size: float = None) -> None:
     print(f"\nScanning for stocks ${min_price:.2f}-${max_price:.2f} with min volume {min_volume:,}")
     
@@ -108,8 +110,9 @@ def run_atr_ema_scanner(min_price: float, max_price: float, min_volume: int, por
                 results = indicator.calculate(df)
                 
                 # Check for signals
-                if results['bullish_signal'].iloc[-1]:
+                signal, signal_date, indicator_values = check_atr_ema_buy_condition(df)
-                    target_price = results['upper_band'].iloc[-1]
+                if signal:
+                    target_price = indicator_values['upper_band']
                     
                     if calculator:
                         position = calculator.calculate_position_size(current_price, target_price)
@@ -118,6 +121,7 @@ def run_atr_ema_scanner(min_price: float, max_price: float, min_volume: int, por
                                 'ticker': ticker,
                                 'entry': current_price,
                                 'target': target_price,
+                                'signal_date': signal_date,
                                 'volume': current_volume,
                                 'last_update': datetime.fromtimestamp(last_update/1000000000),
                                 'shares': position['shares'],
@@ -129,7 +133,7 @@ def run_atr_ema_scanner(min_price: float, max_price: float, min_volume: int, por
                             }
                             bullish_signals.append(signal_data)
                             dollar_risk = signal_data['risk'] * -1
-                            print(f"\n🟢 {ticker} @ ${current_price:.2f}")
+                            print(f"\n🟢 {ticker} @ ${current_price:.2f} on {signal_date.strftime('%Y-%m-%d %H:%M')}")
                             print(f"   Size: {signal_data['shares']} shares (${signal_data['position_size']:.2f})")
                             print(f"   Stop: ${signal_data['stop_loss']:.2f} (7%) | Target: ${target_price:.2f}")
                             print(f"   Risk/Reward: 1:{signal_data['r_r']:.1f} | Risk: ${dollar_risk:.2f}")

src/screener/t_atr_ema_v2.py

@@ -7,7 +7,7 @@ from utils.data_utils import get_stock_data
 from screener.user_input import get_interval_choice, get_date_range
 from indicators.three_atr_ema import ThreeATREMAIndicator
 
-def check_entry_signal(df: pd.DataFrame) -> bool:
+def check_entry_signal(df: pd.DataFrame) -> tuple:
     """
     Check for entry signal based on Three ATR EMA strategy
     
@@ -15,16 +15,16 @@ def check_entry_signal(df: pd.DataFrame) -> bool:
         df (pd.DataFrame): DataFrame with price data
         
     Returns:
-        bool: True if entry signal is present, False otherwise
+        tuple: (bool, datetime, dict) Entry signal, signal date, and signal data
     """
     if len(df) < 2:  # Need at least 2 bars for comparison
-        return False
+        return False, None, None
         
     indicator = ThreeATREMAIndicator()
     results = indicator.calculate(df)
     
     if len(results) < 2:
-        return False
+        return False, None, None
         
     # Get latest values
     current = df.iloc[-1]
@@ -36,16 +36,19 @@ def check_entry_signal(df: pd.DataFrame) -> bool:
     prev_lower_band = results['lower_band'].iloc[-2]
     
     # Entry conditions from Pine script:
-    # 1. Price is below EMA
-    # 2. Previous close was at or below lower band
-    # 3. Current close is higher than previous close
     entry_signal = (
         current['close'] < ema and
         previous['close'] <= prev_lower_band and
         current['close'] > previous['close']
     )
     
-    return entry_signal
+    signal_data = {
+        'price': current['close'],
+        'ema': ema,
+        'lower_band': lower_band
+    } if entry_signal else None
+    
+    return entry_signal, current['date'] if entry_signal else None, signal_data
 
 def run_atr_ema_scanner_v2(min_price: float, max_price: float, min_volume: int, portfolio_size: float = None) -> None:
     """
@@ -118,16 +121,18 @@ def run_atr_ema_scanner_v2(min_price: float, max_price: float, min_volume: int,
                 if df.empty or len(df) < 21:  # Need at least 21 bars for EMA
                     continue
                 
-                if check_entry_signal(df):
+                signal, signal_date, signal_data = check_entry_signal(df)
+                if signal:
                     signal_data = {
                         'ticker': ticker,
-                        'price': current_price,
+                        'price': signal_data['price'],
                         'volume': current_volume,
+                        'signal_date': signal_date,
                         'last_update': datetime.fromtimestamp(last_update/1000000000)
                     }
                     
                     if calculator:
-                        position = calculator.calculate_position_size(current_price)
+                        position = calculator.calculate_position_size(signal_data['price'])
                         signal_data.update({
                             'shares': position['shares'],
                             'position_size': position['position_value'],
@@ -137,8 +142,8 @@ def run_atr_ema_scanner_v2(min_price: float, max_price: float, min_volume: int,
                     
                     entry_signals.append(signal_data)
                     
-                    # Print signal information
+                    # Print signal information with date
-                    print(f"\n🔍 {ticker} @ ${current_price:.2f}")
+                    print(f"\n🔍 {ticker} @ ${signal_data['price']:.2f} on {signal_date.strftime('%Y-%m-%d %H:%M')}")
                     if calculator:
                         print(f"   Size: {signal_data['shares']} shares (${signal_data['position_size']:.2f})")
                         print(f"   Stop: ${signal_data['stop_loss']:.2f} (7%)")