Split out AI features

WebServer/AIPython/features.py

@@ -0,0 +1,38 @@
+import pandas as pd
+
+def MakeFeatures(df, i):
+    # Remove the ticker column
+    df.columns = df.columns.get_level_values(0)
+    
+    # Make sure Date is a number object
+    df = df.reset_index()
+    df['Date'] = pd.to_numeric(pd.to_datetime(df['Date']))
+
+    # Add the Symbol column for tracking | as an int 1 hot encoded
+    df['Symbol'] = i
+
+    # Add feature Spread
+    df['Spread'] = abs( df['High'] - df['Low'] )
+
+    # Add feature for Returns
+    df['Return'] = df['Close'].pct_change()
+
+    # Add feature for volitility last 5
+    df['Volatility_5'] = df['Return'].transform(lambda x: x.rolling(5).std())
+
+    # Add feature for volitility last 20
+    df['Volatility_20'] = df['Return'].transform(lambda x: x.rolling(20).std())
+
+    # Return new df with new features
+    return df
+
+
+def CleanDF(df):
+    # Make date the index so it doesnt influence the training
+    df.set_index('Date', inplace=True)
+
+    # Drop rows with null values
+    df.dropna(inplace=True)
+
+    # Return new df thats cleaned
+    return df