diff --git a/src/screener/t_atr_ema_v2.py b/src/screener/t_atr_ema_v2.py
index 3cb350b..c0d9e58 100644
--- a/src/screener/t_atr_ema_v2.py
+++ b/src/screener/t_atr_ema_v2.py
@@ -11,7 +11,7 @@ def run_atr_ema_target_scanner(min_price: float, max_price: float, min_volume: i
     
     # Set time range
     end_date = datetime.now()
-    start_date = end_date - timedelta(days=1)  # Last trading day
+    start_date = end_date - timedelta(days=30)  # Changed from 1 day to 30 days
     start_ts = int(start_date.timestamp() * 1000000000)
     end_ts = int(end_date.timestamp() * 1000000000)
     
@@ -48,7 +48,11 @@ def run_atr_ema_target_scanner(min_price: float, max_price: float, min_volume: i
             print("❌ No stocks found matching criteria.")
             return
 
-        print(f"\n✅ Found {len(stocks)} stocks matching criteria")
+        print("\n🔍 Verifying data availability...")
+        with client.query_stream(f"SELECT ticker FROM stock_db.stock_prices WHERE window_start BETWEEN {start_ts} AND {end_ts} GROUP BY ticker HAVING count() > 50") as stream:
+            valid_symbols = {row[0] for row in stream.result_rows}
+        qualified_stocks = [s for s in stocks if s[0] in valid_symbols]
+        print(f"\n✅ Found {len(qualified_stocks)} stocks with sufficient historical data")
 
         indicator = ThreeATREMAIndicator()
         calculator = PositionCalculator(portfolio_size, risk_percentage=1.0, stop_loss_percentage=7.0) if portfolio_size else None
@@ -62,16 +66,15 @@ def run_atr_ema_target_scanner(min_price: float, max_price: float, min_volume: i
                 try:
                     df = get_stock_data(ticker, start_date, end_date, "1d")
                     if df.empty or len(df) < 50:
-                        print(f"⚠️ {ticker}: Insufficient historical data ({len(df)} bars)")
+                        df = get_stock_data(ticker, start_date - timedelta(days=30), end_date, "1d")  # Try wider range
+                    if df.empty or len(df) < 50:
+                        print(f"⚠️ {ticker}: No valid data in extended timeframe")
                         continue
-                    if "1d" not in VALID_INTERVALS:
-                        raise ValueError(f"Invalid interval format. Must be one of: {VALID_INTERVALS}")
-
                 except Exception as e:
-                    print(f"❌ Data fetch failed for {ticker}: {str(e)}")
+                    print(f"❌ Data fetch failed for {ticker}: {str(e)[:100]}")
                     continue
 
-                results = indicator.calculate(df)
+                results = indicator.calculate(df).dropna().tail(30)  # Use only most recent valid data
                 last_row = results.iloc[-1]
                 prev_row = results.iloc[-2]