fix: Improve error handling and data resampling in indicator calculation

src/screener/t_atr_ema_v2.py

@@ -138,29 +138,48 @@ def run_atr_ema_target_scanner(min_price: float, max_price: float, min_volume: i
                 # Make a clean copy for indicator calculation
                 calc_df = df.copy()
                 
+                # Make a clean copy for indicator calculation
+                calc_df = df.copy()
+                
+                # Resample to daily data if we have intraday
+                if interval != "daily":
+                    calc_df = calc_df.resample('D', on='date').agg({
+                        'open': 'first',
+                        'high': 'max',
+                        'low': 'min',
+                        'close': 'last',
+                        'volume': 'sum'
+                    }).dropna()
+                
+                # Ensure minimum data points after resampling
+                if len(calc_df) < 50:
+                    print(f"⚠️ {ticker}: Insufficient data points after resampling ({len(calc_df)})")
+                    continue
+                
                 # Calculate indicator with validated data
                 try:
                     results = indicator.calculate(calc_df)
                     if results.empty:
                         print(f"⚠️ {ticker}: No valid indicator results")
                         continue
+                        
+                    # Get the last two rows for signal checking
+                    last_row = results.iloc[-1]
+                    prev_row = results.iloc[-2]
+
+                    bullish_entry = (
+                        last_row["close"] < last_row["ema"] and 
+                        prev_row["close"] <= prev_row["lower_band"] and 
+                        last_row["close"] > prev_row["close"]
+                    )
+
                 except Exception as e:
-                    print(f"⚠️ {ticker}: Error calculating indicator:")
+                    print(f"⚠️ {ticker}: Error in indicator calculation:")
                     print(f"Error details: {str(e)}")
                     print(f"Data shape: {calc_df.shape}")
                     print(f"Sample data:\n{calc_df.head()}")
                     continue
 
-                results = indicator.calculate(df)
-                last_row = results.iloc[-1]
-                prev_row = results.iloc[-2]
-
-                bullish_entry = (
-                    last_row["close"] < last_row["ema"] and 
-                    prev_row["close"] <= prev_row["lower_band"] and 
-                    last_row["close"] > prev_row["close"]
-                )
-
                 if bullish_entry:
                     entry_price = last_row["close"]
                     target_1 = entry_price * 1.10  # 10% profit