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())

    # This is our training metric
    df['Target_Close_Tomorrow'] = df['Close'].shift(-1).pct_change()

    # 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