def chart_series(series, market_sym='$SPX', price='actual_close', normalize=True):
    """Display a graph of the price history for the list of ticker symbols provided


    Arguments:
      series (dataframe, list of str, or list of tuples): 
        datafram (Timestamp or Datetime for index)
          other columns are float y-axis values to be plotted
        list of str: 1st 3 comma or slash-separated integers are the year, month, day
          others are float y-axis values
        list of tuples: 1st 3 integers are year, month, day 
          others are float y-axis values
      market_sym (str): ticker symbol of equity or comodity to plot along side the series
      price (str): which market data value ('close', 'actual_close', 'volume', etc) to use 
         for the market symbol for comparison to the series 
      normalize (bool): Whether to normalize prices to 1 at the start of the time series.
    """
    series = util.make_dataframe(series)
    start = util.normalize_date(series.index[0] or datetime.datetime(2008, 1, 1))
    end = util.normalize_date(series.index[-1] or datetime.datetime(2009, 12, 28))
    timestamps = du.getNYSEdays(start, end, datetime.timedelta(hours=16))

    if market_sym:
        if isinstance(market_sym, basestring):
            market_sym = [market_sym.upper().strip()]
        reference_prices = da.get_data(timestamps, market_sym, [price])[0]
        reference_dict = dict(zip(market_sym, reference_prices))
        for sym, market_data in reference_dict.iteritems():
            series[sym] = pd.Series(market_data, index=timestamps)
    # na_price = reference_dict[price].values
    # if normalize:
    #     na_price /= na_price[0, :]
    series.plot()
    # plt.clf()
    # plt.plot(timestamps, na_price)
    # plt.legend(symbols)
    # plt.ylabel(price.title())
    # plt.xlabel('Date')
    # # plt.savefig('portfolio.chart_series.pdf', format='pdf')
    plt.grid(True)
    plt.show()
    return series