diff --git a/backtesting/filter/volatility_regime_filter.py b/backtesting/filter/volatility_regime_filter.py
index 1cc1b67..9d38098 100644
--- a/backtesting/filter/volatility_regime_filter.py
+++ b/backtesting/filter/volatility_regime_filter.py
@@ -1,7 +1,7 @@
 from datetime import datetime, timedelta
 from dotenv import load_dotenv
 from os import getenv
-from pandas import DataFrame, Series
+from pandas import concat, DataFrame, Series
 
 from database.ohlc import ohlc
 
@@ -17,15 +17,34 @@ class VolatilityRegimeFilter(BacktestFilter):
     def filter(self) -> DataFrame:
         data_start_date = datetime.strptime(getenv('OPTION_DATA_START_DATE'), '%Y-%m-%d')
 
+        now = datetime.now()
         vix_data = ohlc(
             symbol = 'VIX.XO', 
             timeframe = '1d', 
             start_date = data_start_date - timedelta(weeks = 52), 
-            end_date = datetime.now()
+            end_date = now
         )
 
         vix_data.rename(columns = {'Timestamp': 'Date'}, inplace = True)
-        vix_data['Date'] = vix_data['Date']
+
+        """""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
+            Filtering is based on the previous day's close, so the current date can be included even though the 
+            data may not be availble yet.
+
+            This allows for utilizing the filter in live trading to decide whether to trade prior to market open.
+        """""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
+        vix_data = concat([
+            vix_data,
+            DataFrame({
+                'Date': [datetime.combine(now, datetime.min.time())],
+                'Open': [0.0],
+                'High': [0.0],
+                'Low': [0.0],
+                'Close': [0.0],
+                'Volume': [0.0]
+            })], 
+            ignore_index = True
+        )
 
         percent_rank = lambda x: Series(x).rank(pct = True).iloc[-1]
         vix_data['Percent Rank'] = vix_data['Close'].shift(1).rolling(window = 252).apply(percent_rank)