def get_data (symbol='spy', interval='1d', first_date='max', last_date='max'):

    """
    Params:   (symbol, interval, first_date, last_date).
    Defaults: ('SPY' , '1d'    , 'max'     , 'max'    ).

    Pulls timeseries data from yfinance and returns a DataFrame with the
    following columns: [Symbol, Open, High, Low, Close, Log Return, lnRange, lnvolume]

    Enter dates as str 'YYYY-MM-DD'.

    Valid Intervals:
    1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo

    """

    #imports
    import numpy as np
    import yfinance as yf

    #no dates case
    if first_date == 'max' and last_date == 'max':

        #get data from yahoo
        data = yf.download(symbol.upper(), interval=interval.lower(),
                           period='max')

        #create df
        data = data[['Open', 'High', 'Low', 'Close', 'Volume']]

        #copy and structure
        df = data.copy()
        df['log'] = np.log(df['Close'])
        df['log_return'] = df['log'].pct_change()
        df['lnrange'] = np.log(df['High']/df['Low'])-1
        df = df[['Open', 'High', 'Low', 'Close', 'Volume', 'log', 'log_return',
                 'lnrange']]
        df['lnvolume'] = np.log(df['Volume'])
        df['log_return_volume'] = df['lnvolume'].pct_change()
        df['range'] = (df["High"] / df["Low"]) - 1
        df.dropna(inplace=True)

        #append symbol and interval columns handling defaults
        if symbol == 'spy':
            df.insert(0, 'symbol', 'SPY')
        else:
            df.insert(0, 'symbol', symbol.upper())

        if interval == '1d':
            df.insert(1,'interval','1D')
        else:
            df.insert(1, 'interval', interval.upper())

        return df

    #both dates case
    if first_date != 'max' and last_date != 'max':

        #get data from yahoo
        data = yf.download(symbol.upper(), interval=interval,
                           start=first_date, end=last_date)

        #create df
        data = data[['Open', 'High', 'Low', 'Close', 'Volume']]

        #copy and structure
        df = data.copy()
        df['log'] = np.log(df['Close'])
        df['log_return'] = df['log'].pct_change()
        df['lnrange'] = np.log(df['High']/df['Low'])-1
        df = df[['Open', 'High', 'Low', 'Close', 'Volume', 'log', 'log_return',
                 'lnrange']]
        df['lnvolume'] = np.log(df['Volume'])
        df['log_return_volume'] = df['lnvolume'].pct_change()
        df['range'] = (df["High"] / df["Low"]) - 1
        df.dropna(inplace=True)

        #append symbol and interval columns handling defaults
        if symbol == 'spy':
            df.insert(0, 'symbol', 'SPY')
        else:
            df.insert(0, 'symbol', symbol.upper())

        if interval == '1d':
            df.insert(1,'interval','1D')
        else:
            df.insert(1, 'interval', interval.upper())

        return df

    #first date only case
    if first_date != 'max' and last_date == 'max':

        #import
        from datetime import datetime, timedelta

        #handle when non-trading day
        today = datetime.today()
        offset = max(1,(today.weekday()+6) %7-3)
        timed = timedelta(offset)
        today_business = today - timed
        end_date = today_business.strftime('%Y-%m-%d')

        #get data from yahoo
        data = yf.download(symbol.upper(), interval=interval, start=first_date,
                           end=end_date)

        #create df
        data = data[['Open', 'High', 'Low', 'Close', 'Volume']]

        #copy and structure
        df = data.copy()
        df['log'] = np.log(df['Close'])
        df['log_return'] = df['log'].pct_change()
        df['lnrange'] = np.log(df['High']/df['Low'])-1
        df = df[['Open', 'High', 'Low', 'Close', 'Volume', 'log', 'log_return',
                 'lnrange']]
        df['lnvolume'] = np.log(df['Volume'])
        df['log_return_volume'] = df['lnvolume'].pct_change()
        df['range'] = (df["High"] / df["Low"]) - 1
        df.dropna(inplace=True)

        #append symbol and interval columns handling defaults
        if symbol == 'spy':
            df.insert(0, 'symbol', 'SPY')
        else:
            df.insert(0, 'symbol', symbol.upper())

        if interval == '1d':
            df.insert(1,'interval','1D')
        else:
            df.insert(1, 'interval', interval.upper())

        return df

    #last date only case
    if first_date == 'max' and last_date != 'max':

        #get data from yahoo
        data = yf.download(symbol.upper(), interval=interval,
                           start='1970-01-02', end=last_date)

        #create df
        data = data[['Open', 'High', 'Low', 'Close', 'Volume']]

        #copy and structure
        df = data.copy()
        df['log'] = np.log(df['Close'])
        df['log_return'] = df['log'].pct_change()
        df['lnrange'] = np.log(df['High']/df['Low'])-1
        df = df[['Open', 'High', 'Low', 'Close', 'Volume', 'log', 'log_return',
                 'lnrange']]
        df['lnvolume'] = np.log(df['Volume'])
        df['log_return_volume'] = df['lnvolume'].pct_change()
        df['range'] = (df["High"] / df["Low"]) - 1
        df.dropna(inplace=True)

        #append symbol and interval columns handling defaults
        if symbol == 'spy':
            df.insert(0, 'symbol', 'SPY')
        else:
            df.insert(0, 'symbol', symbol.upper())

        if interval == '1d':
            df.insert(1,'interval','1D')
        else:
            df.insert(1, 'interval', interval.upper())

        return df