In the following elaborate example, we are going to apply the many techniques that we have covered thus far to create a more comprehensive stock chart. In addition to the preceding plot, we will add a line chart to display the Relative Strength Index (RSI) and a bar chart to show trade volume. A special market event (http://markets.businessinsider.com/news/stocks/apple-stock-price-falling-new-iphone-speed-2017-6-1002082799) is going to be annotated on the chart as well:

import datetime
import matplotlib.pyplot as plt
from matplotlib.dates import date2num, WeekdayLocator, DayLocator, DateFormatter, MONDAY
from matplotlib.finance import candlestick_ohlc
from matplotlib.ticker import FuncFormatter


# FuncFormatter to convert tick values to Millions
def millions(x, pos):
    return '%dM' % (x/1e6)

# Create 3 subplots spread acrosee three rows, with shared x-axis. 
# The height ratio is specified via gridspec_kw
fig, axarr = plt.subplots(nrows=3, ncols=1, sharex=True, figsize=(8,8),
                          gridspec_kw={'height_ratios':[3,1,1]})

# Prepare a candlestick plot in the first axes
candlestick_ohlc(axarr[0], candlestick_data.values, width=0.6)

# Overlay stock indicators in the first axes
axarr[0].plot(candlestick_data["Datetime"], ema_5, lw=1, label='EMA (5)')
axarr[0].plot(candlestick_data["Datetime"], ema_20, lw=1, label='EMA (20)')
axarr[0].plot(candlestick_data["Datetime"], ema_50, lw=1, label='EMA (50)')
axarr[0].plot(candlestick_data["Datetime"], boll_ub, lw=2, linestyle="--", label='Bollinger upper')
axarr[0].plot(candlestick_data["Datetime"], boll_lb, lw=2, linestyle="--", label='Bollinger lower')

# Display RSI in the second axes
axarr[1].axhline(y=30, lw=2, color = '0.7') # Line for oversold threshold
axarr[1].axhline(y=50, lw=2, linestyle="--", color = '0.8') # Neutral RSI
axarr[1].axhline(y=70, lw=2, color = '0.7') # Line for overbought threshold
axarr[1].plot(candlestick_data["Datetime"], rsi_7, lw=2, label='RSI (7)')
axarr[1].plot(candlestick_data["Datetime"], rsi_14, lw=2, label='RSI (14)')

# Display trade volume in the third axes
axarr[2].bar(candlestick_data["Datetime"], candlestick_data['Volume'])

# Mark the market reaction to the Bloomberg news
# https://www.bloomberg.com/news/articles/2017-06-09/apple-s-new
# -iphones-said-to-miss-out-on-higher-speed-data-links
# http://markets.businessinsider.com/news/stocks/apple-stock-price
# -falling-new-iphone-speed-2017-6-1002082799
axarr[0].annotate("Bloomberg News",
                  xy=(datetime.date(2017, 6, 9), 155), xycoords='data',
                  xytext=(25, 10), textcoords='offset points', size=12,
                  arrowprops=dict(arrowstyle="simple",
                  fc="green", ec="none"))

# Label the axes
axarr[0].set_ylabel('Price (US $)')
axarr[1].set_ylabel('RSI')
axarr[2].set_ylabel('Volume (US $)')

axarr[2].xaxis.set_major_locator(WeekdayLocator(MONDAY)) # major ticks on the mondays
axarr[2].xaxis.set_minor_locator(DayLocator()) # minor ticks on the days
axarr[2].xaxis.set_major_formatter(DateFormatter('%Y-%m-%d'))
axarr[2].xaxis_date() # treat the x data as dates
axarr[2].yaxis.set_major_formatter(FuncFormatter(millions)) # Change the y-axis ticks to millions
plt.setp(axarr[2].get_xticklabels(), rotation=90, horizontalalignment='right') # Rotate x-tick labels by 90 degree

# Limit the x-axis range from 2017-4-23 to 2017-7-1
datemin = datetime.date(2017, 4, 23)
datemax = datetime.date(2017, 7, 1)
axarr[2].set_xlim(datemin, datemax)

# Show figure legend
axarr[0].legend()
axarr[1].legend()

# Show figure title
axarr[0].set_title("AAPL (Apple Inc.) NASDAQ", loc='left')

# Reduce unneccesary white space
plt.tight_layout()
plt.show()