feat: Add Sunny-SMA scanner combining Sunny Bands and 21 SMA strategy

src/pages/screener/technical_scanner_page.py

@@ -13,7 +13,7 @@ def technical_scanner_page():
     with scanner_tab:
         scanner_type = st.selectbox(
             "Select Scanner",
-            ["SunnyBands", "ATR-EMA", "ATR-EMA v2", "Heikin-Ashi", "Candlestick"],
+            ["SunnyBands", "ATR-EMA", "ATR-EMA v2", "Heikin-Ashi", "Candlestick", "Sunny-SMA"],
             key="tech_scanner_type"
         )
 

src/screener/scanner_controller.py

@@ -4,6 +4,7 @@ from screener.t_atr_ema import run_atr_ema_scanner
 from screener.t_atr_ema_v2 import run_atr_ema_scanner_v2
 from screener.t_heikinashi import run_heikin_ashi_scanner
 from screener.t_candlestick import run_candlestick_scanner
+from screener.t_sunny_sma import run_sunny_sma_scanner
 
 def run_technical_scanner(scanner_choice: str, start_date: str, end_date: str, 
                          min_price: float, max_price: float, min_volume: int, 
@@ -31,7 +32,8 @@ def run_technical_scanner(scanner_choice: str, start_date: str, end_date: str,
         "atr-ema": lambda: run_atr_ema_scanner(min_price, max_price, min_volume, portfolio_size, interval, start_dt, end_dt),
         "atr-ema_v2": lambda: run_atr_ema_scanner_v2(min_price, max_price, min_volume, portfolio_size, interval, start_dt, end_dt),
         "heikin-ashi": lambda: run_heikin_ashi_scanner(min_price, max_price, min_volume, portfolio_size, interval, start_dt, end_dt),
-        "candlestick": lambda: run_candlestick_scanner(min_price, max_price, min_volume, portfolio_size, interval, start_dt, end_dt, selected_patterns)
+        "candlestick": lambda: run_candlestick_scanner(min_price, max_price, min_volume, portfolio_size, interval, start_dt, end_dt, selected_patterns),
+        "sunny-sma": lambda: run_sunny_sma_scanner(min_price, max_price, min_volume, portfolio_size, interval, start_dt, end_dt)
     }
     
     scanner_func = scanner_map.get(scanner_choice)

src/screener/t_sunny_sma.py

@@ -0,0 +1,125 @@
+import pandas as pd
+import talib
+from datetime import datetime
+from utils.data_utils import (
+    get_stock_data, validate_signal_date, print_signal, 
+    save_signals_to_csv, get_qualified_stocks
+)
+from utils.scanner_utils import initialize_scanner, process_signal_data
+from indicators.sunny_bands import SunnyBands
+
+def check_entry_signal(df: pd.DataFrame) -> list:
+    """
+    Check for entry signals based on combined Sunny Bands and SMA strategy
+    
+    Conditions:
+    1. 21 SMA below lowest Sunny Band
+    2. Price crosses above 21 SMA
+    3. Price still below Sunny Bands
+    
+    Args:
+        df (pd.DataFrame): DataFrame with price data
+        
+    Returns:
+        list: List of tuples (signal, date, signal_data) for each signal found
+    """
+    if len(df) < 21:  # Need at least 21 bars for SMA
+        return []
+        
+    # Calculate Sunny Bands
+    sunny = SunnyBands()
+    sunny_results = sunny.calculate(df)
+    
+    # Calculate 21 day SMA
+    sma21 = talib.SMA(df['close'].values, timeperiod=21)
+    
+    signals = []
+    
+    # Start from index 21 to ensure we have enough data for SMA
+    for i in range(21, len(df)):
+        current = df.iloc[i]
+        prev = df.iloc[i-1]
+        current_bands = sunny_results.iloc[i]
+        current_sma = sma21[i]
+        
+        # Check conditions:
+        # 1. SMA below lower band
+        sma_below_band = current_sma < current_bands['lower_band']
+        
+        # 2. Price crosses above SMA
+        price_cross_sma = (current['close'] > current_sma) and (prev['close'] < sma21[i-1])
+        
+        # 3. Price still below lower band
+        price_below_band = current['close'] < current_bands['lower_band']
+        
+        if sma_below_band and price_cross_sma and price_below_band:
+            signal_data = {
+                'price': current['close'],
+                'sma21': current_sma,
+                'upper_band': current_bands['upper_band'],
+                'lower_band': current_bands['lower_band'],
+                'dma': current_bands['dma']
+            }
+            signals.append((True, current['date'], signal_data))
+    
+    return signals
+
+def run_sunny_sma_scanner(min_price: float, max_price: float, min_volume: int, 
+                         portfolio_size: float = None, interval: str = "1d",
+                         start_date: datetime = None, end_date: datetime = None) -> None:
+    """
+    Run scanner combining Sunny Bands and 21 SMA strategy
+    """
+    try:
+        # Initialize scanner components
+        interval, start_date, end_date, qualified_stocks, calculator = initialize_scanner(
+            min_price=min_price,
+            max_price=max_price,
+            min_volume=min_volume,
+            portfolio_size=portfolio_size,
+            interval=interval,
+            start_date=start_date,
+            end_date=end_date
+        )
+        
+        if not qualified_stocks:
+            return
+            
+        bullish_signals = []
+        
+        for ticker, current_price, current_volume, last_update, stock_type in qualified_stocks:
+            try:
+                df = get_stock_data(ticker, start_date, end_date, interval)
+                
+                if df.empty or len(df) < 50:  # Need at least 50 bars for the indicators
+                    continue
+                    
+                signals = check_entry_signal(df)
+                for signal, signal_date, signal_data in signals:
+                    # Custom print for Sunny-SMA signals
+                    print(f"🌞 {ticker}: SMA-21 Cross at ${signal_data['price']:.2f} on {signal_date.strftime('%Y-%m-%d')}")
+                    print(f"    SMA: ${signal_data['sma21']:.2f}")
+                    print(f"    Lower Band: ${signal_data['lower_band']:.2f}")
+                    
+                    entry_data = {
+                        'ticker': ticker,
+                        'signal_date': signal_date,
+                        'entry_price': signal_data['price'],
+                        'sma21': signal_data['sma21'],
+                        'lower_band': signal_data['lower_band'],
+                        'volume': current_volume,
+                        'last_update': last_update,
+                        'stock_type': stock_type
+                    }
+                    bullish_signals.append(entry_data)
+                    
+            except Exception as e:
+                print(f"Error processing {ticker}: {str(e)}")
+                continue
+        
+        save_signals_to_csv(bullish_signals, 'sunny_sma')
+        return bullish_signals
+            
+    except Exception as e:
+        print(f"Error during scan: {str(e)}")
+        return []