diff --git a/WebServer/AIPython/features.py b/WebServer/AIPython/features.py
index 7ed7b957..03e9d48d 100644
--- a/WebServer/AIPython/features.py
+++ b/WebServer/AIPython/features.py
@@ -17,7 +17,7 @@ def MakeFeatures(df):
     df.drop('Low', axis=1, inplace=True)
 
      # Is volume 2x higher than the 20-day average?
-    df['Vol_Intensity'] = df['Volume'] / df['Volume'].rolling(20).mean()
+    df['Vol_Intensity'] = df['Volume'] / df['Volume'].shift(1).rolling(20).mean()
     # Volume Change
     df['Volume_Chg'] = df['Volume'].pct_change()
 
@@ -25,15 +25,15 @@ def MakeFeatures(df):
     df.drop('Volume', axis=1, inplace=True)
 
     # Moving Average Crossover (Golden/Death Cross logic)
-    df['Moving_Average_5'] = df['Close'].rolling(window=20).mean()
-    df['Moving_Average_20'] = df['Close'].rolling(window=20).mean()
+    df['Moving_Average_5'] = df['Close'].shift(1).rolling(window=20).mean()
+    df['Moving_Average_20'] = df['Close'].shift(1).rolling(window=20).mean()
     # if short term > long term (bullish), else 0
     df['Trend_Signal'] = (df['Moving_Average_5'] > df['Moving_Average_20']).astype(int)
     # Distance from MA (How overextended are we?)
     df['Dist_From_MA20'] = (df['Close'] / df['Moving_Average_20']) - 1
 
     # Bollinger Band Position (Where are we relative to volatility?)
-    std_20 = df['Close'].rolling(20).std()
+    std_20 = df['Close'].shift(1).rolling(20).std()
     upper_band = df['Moving_Average_20'] + (std_20 * 2)
     lower_band = df['Moving_Average_20'] - (std_20 * 2)
     df['BB_Pos'] = (df['Close'] - lower_band) / (upper_band - lower_band)
@@ -48,14 +48,14 @@ def MakeFeatures(df):
     df['Log_Return'] = np.log(df['Close'] / df['Close'].shift(1))
 
     # Add feature for volitility last 5
-    df['Volatility_5'] = df['Return'].transform(lambda x: x.rolling(5).std())
+    df['Volatility_5'] = df['Return'].transform(lambda x: x.shift(1).rolling(5).std())
     # Add feature for volitility last 20
-    df['Volatility_20'] = df['Return'].transform(lambda x: x.rolling(20).std())
+    df['Volatility_20'] = df['Return'].transform(lambda x: x.shift(1).rolling(20).std())
 
     # RSI (Relative Strength Index)
     delta = df['Close'].diff()
-    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
-    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
+    gain = (delta.where(delta > 0, 0)).shift(1).rolling(window=14).mean()
+    loss = (-delta.where(delta < 0, 0)).shift(1).rolling(window=14).mean()
     rs = gain / loss
     df['RSI'] = 100 - (100 / (1 + rs))
 
@@ -65,8 +65,7 @@ def MakeFeatures(df):
         df[f'Vol_Lag_{lag}'] = df['Volume_Chg'].shift(lag)
 
     # This is our training metric of price difference 5 days ahead
-    df['Target_Close'] = np.log(df['Close'].shift(-5) / df['Close'])
-    df['Target_Close'] = np.log(df['Close'].shift(-5) / df['Close']) / df['Volatility_20']
+    df['Target_Close'] = np.log(df['Close'].shift(-5) / df['Close']) / df['Volatility_5']
 
 # Remove Unused Symbols to save ram
     df.drop('Close', axis=1, inplace=True)