为此，首先我们需要定义一个新的轴域，但是这个轴域是仅带有x轴的『双生子』。

    这足以创建轴域了。我们叫它ax2v，因为这个轴域是ax2加交易量。

    现在，我们在轴域上定义绘图，我们将添加：

    我们在 0 和当前交易量之间填充，给予它蓝色的前景色，然后给予它一个透明度。 我们想要应用幽冥毒，以防交易量最终覆盖其它东西，所以我们仍然可以看到这两个元素。

    1. import matplotlib.pyplot as plt
    2. import matplotlib.dates as mdates
    3. import matplotlib.ticker as mticker
    4. from matplotlib.finance import candlestick_ohlc
    5. from matplotlib import style
    7. import numpy as np
    8. import urllib
    9. import datetime as dt
    11. style.use('fivethirtyeight')
    12. print(plt.style.available)
    14. print(plt.__file__)
    16. MA1 = 10
    17. MA2 = 30
    19. def moving_average(values, window):
    20.     weights = np.repeat(1.0, window)/window
    21.     smas = np.convolve(values, weights, 'valid')
    22.     return smas
    24. def high_minus_low(highs, lows):
    25.     return highs-lows
    28. def bytespdate2num(fmt, encoding='utf-8'):
    29.     strconverter = mdates.strpdate2num(fmt)
    30.     def bytesconverter(b):
    31.         s = b.decode(encoding)
    32.         return strconverter(s)
    33.     return bytesconverter
    36. def graph_data(stock):
    38.     fig = plt.figure()
    39.     ax1 = plt.subplot2grid((6,1), (0,0), rowspan=1, colspan=1)
    40.     plt.title(stock)
    41.     plt.ylabel('H-L')
    42.     ax2 = plt.subplot2grid((6,1), (1,0), rowspan=4, colspan=1, sharex=ax1)
    43.     plt.ylabel('Price')
    44.     ax2v = ax2.twinx()
    46.     ax3 = plt.subplot2grid((6,1), (5,0), rowspan=1, colspan=1, sharex=ax1)
    47.     plt.ylabel('MAvgs')
    50.     stock_price_url = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=1y/csv'
    51.     source_code = urllib.request.urlopen(stock_price_url).read().decode()
    52.     stock_data = []
    53.     split_source = source_code.split('\n')
    54.     for line in split_source:
    55.         split_line = line.split(',')
    56.         if len(split_line) == 6:
    57.             if 'values' not in line and 'labels' not in line:
    58.                 stock_data.append(line)
    61.     date, closep, highp, lowp, openp, volume = np.loadtxt(stock_data,
    62.                                                           delimiter=',',
    63.                                                           unpack=True,
    64.                                                           converters={0: bytespdate2num('%Y%m%d')})
    66.     x = 0
    67.     y = len(date)
    68.     ohlc = []
    70.     while x < y:
    71.         append_me = date[x], openp[x], highp[x], lowp[x], closep[x], volume[x]
    72.         ohlc.append(append_me)
    73.         x+=1
    75.     ma1 = moving_average(closep,MA1)
    76.     ma2 = moving_average(closep,MA2)
    77.     start = len(date[MA2-1:])
    79.     h_l = list(map(high_minus_low, highp, lowp))
    82.     ax1.plot_date(date[-start:],h_l[-start:],'-')
    83.     ax1.yaxis.set_major_locator(mticker.MaxNLocator(nbins=4, prune='lower'))
    86.     candlestick_ohlc(ax2, ohlc[-start:], width=0.4, colorup='#77d879', colordown='#db3f3f')
    90.     ax2.yaxis.set_major_locator(mticker.MaxNLocator(nbins=7, prune='upper'))
    91.     ax2.grid(True)
    93.     bbox_props = dict(boxstyle='round',fc='w', ec='k',lw=1)
    95.                  xytext = (date[-1]+4, closep[-1]), bbox=bbox_props)
    97. ##    # Annotation example with arrow
    98. ##    ax2.annotate('Bad News!',(date[11],highp[11]),
    99. ##                 xytext=(0.8, 0.9), textcoords='axes fraction',
    100. ##                 arrowprops = dict(facecolor='grey',color='grey'))
    101. ##
    102. ##    
    103. ##    # Font dict example
    104. ##    font_dict = {'family':'serif',
    105. ##                 'color':'darkred',
    106. ##                 'size':15}
    107. ##    # Hard coded text 
    108. ##    ax2.text(date[10], closep[1],'Text Example', fontdict=font_dict)
    109.     ax2v.fill_between(date[-start:],0, volume[-start:], facecolor='#0079a3', alpha=0.4)
    112.     ax3.plot(date[-start:], ma1[-start:], linewidth=1)
    113.     ax3.plot(date[-start:], ma2[-start:], linewidth=1)
    115.     ax3.fill_between(date[-start:], ma2[-start:], ma1[-start:],
    116.                      where=(ma1[-start:] < ma2[-start:]),
    117.                      facecolor='r', edgecolor='r', alpha=0.5)
    119.     ax3.fill_between(date[-start:], ma2[-start:], ma1[-start:],
    120.                      where=(ma1[-start:] > ma2[-start:]),
    121.                      facecolor='g', edgecolor='g', alpha=0.5)
    123.     ax3.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
    124.     ax3.xaxis.set_major_locator(mticker.MaxNLocator(10))
    125.     ax3.yaxis.set_major_locator(mticker.MaxNLocator(nbins=4, prune='upper'))
    127.     for label in ax3.xaxis.get_ticklabels():
    128.         label.set_rotation(45)
    132.     plt.setp(ax1.get_xticklabels(), visible=False)
    133.     plt.setp(ax2.get_xticklabels(), visible=False)
    134.     plt.subplots_adjust(left=0.11, bottom=0.24, right=0.90, top=0.90, wspace=0.2, hspace=0)
    135.     plt.show()
    138. graph_data('GOOG')

    会生成：

    太棒了，到目前为止还不错。 接下来，我们可能要删除新y轴上的标签，然后我们也可能不想让交易量占用太多空间。 没问题：

    首先：

    接下来，我们可能要将网格设置为false，使轴域上不会有双网格：

    1. ax2v.grid(False)

    最后，为了处理交易量占用很多空间，我们可以做以下操作：

    所以这设置y轴显示范围从 0 到交易量的最大值的 3 倍。 这意味着，在最高点，交易量最多可占据图形的33％。 所以，增加volume.max的倍数越多，空间就越小/越少。

    1. import matplotlib.pyplot as plt
    2. import matplotlib.dates as mdates
    3. import matplotlib.ticker as mticker
    4. from matplotlib.finance import candlestick_ohlc
    5. from matplotlib import style
    7. import numpy as np
    8. import urllib
    9. import datetime as dt
    11. style.use('fivethirtyeight')
    12. print(plt.style.available)
    14. print(plt.__file__)
    16. MA1 = 10
    17. MA2 = 30
    19. def moving_average(values, window):
    20.     weights = np.repeat(1.0, window)/window
    21.     smas = np.convolve(values, weights, 'valid')
    22.     return smas
    24. def high_minus_low(highs, lows):
    25.     return highs-lows
    28. def bytespdate2num(fmt, encoding='utf-8'):
    29.     strconverter = mdates.strpdate2num(fmt)
    30.     def bytesconverter(b):
    31.         s = b.decode(encoding)
    32.         return strconverter(s)
    33.     return bytesconverter
    36. def graph_data(stock):
    38.     fig = plt.figure()
    39.     ax1 = plt.subplot2grid((6,1), (0,0), rowspan=1, colspan=1)
    40.     plt.title(stock)
    41.     plt.ylabel('H-L')
    42.     ax2 = plt.subplot2grid((6,1), (1,0), rowspan=4, colspan=1, sharex=ax1)
    43.     plt.ylabel('Price')
    44.     ax2v = ax2.twinx()
    47.     plt.ylabel('MAvgs')
    50.     source_code = urllib.request.urlopen(stock_price_url).read().decode()
    51.     stock_data = []
    52.     split_source = source_code.split('\n')
    53.     for line in split_source:
    54.         split_line = line.split(',')
    55.         if len(split_line) == 6:
    56.             if 'values' not in line and 'labels' not in line:
    57.                 stock_data.append(line)
    60.     date, closep, highp, lowp, openp, volume = np.loadtxt(stock_data,
    61.                                                           delimiter=',',
    62.                                                           unpack=True,
    63.                                                           converters={0: bytespdate2num('%Y%m%d')})
    65.     x = 0
    66.     y = len(date)
    67.     ohlc = []
    69.     while x < y:
    70.         append_me = date[x], openp[x], highp[x], lowp[x], closep[x], volume[x]
    71.         ohlc.append(append_me)
    72.         x+=1
    74.     ma1 = moving_average(closep,MA1)
    75.     ma2 = moving_average(closep,MA2)
    76.     start = len(date[MA2-1:])
    78.     h_l = list(map(high_minus_low, highp, lowp))
    81.     ax1.plot_date(date[-start:],h_l[-start:],'-')
    82.     ax1.yaxis.set_major_locator(mticker.MaxNLocator(nbins=4, prune='lower'))
    85.     candlestick_ohlc(ax2, ohlc[-start:], width=0.4, colorup='#77d879', colordown='#db3f3f')
    89.     ax2.yaxis.set_major_locator(mticker.MaxNLocator(nbins=7, prune='upper'))
    90.     ax2.grid(True)
    92.     bbox_props = dict(boxstyle='round',fc='w', ec='k',lw=1)
    94.     ax2.annotate(str(closep[-1]), (date[-1], closep[-1]),
    95.                  xytext = (date[-1]+5, closep[-1]), bbox=bbox_props)
    97. ##    # Annotation example with arrow
    98. ##    ax2.annotate('Bad News!',(date[11],highp[11]),
    99. ##                 xytext=(0.8, 0.9), textcoords='axes fraction',
    100. ##                 arrowprops = dict(facecolor='grey',color='grey'))
    101. ##
    102. ##    
    103. ##    # Font dict example
    104. ##    font_dict = {'family':'serif',
    105. ##                 'color':'darkred',
    106. ##                 'size':15}
    107. ##    # Hard coded text 
    108. ##    ax2.text(date[10], closep[1],'Text Example', fontdict=font_dict)
    110.     ax2v.fill_between(date[-start:],0, volume[-start:], facecolor='#0079a3', alpha=0.4)
    111.     ax2v.axes.yaxis.set_ticklabels([])
    112.     ax2v.grid(False)
    113.     ax2v.set_ylim(0, 3*volume.max())
    117.     ax3.plot(date[-start:], ma1[-start:], linewidth=1)
    118.     ax3.plot(date[-start:], ma2[-start:], linewidth=1)
    120.     ax3.fill_between(date[-start:], ma2[-start:], ma1[-start:],
    121.                      where=(ma1[-start:] < ma2[-start:]),
    122.                      facecolor='r', edgecolor='r', alpha=0.5)
    124.     ax3.fill_between(date[-start:], ma2[-start:], ma1[-start:],
    125.                      where=(ma1[-start:] > ma2[-start:]),
    126.                      facecolor='g', edgecolor='g', alpha=0.5)
    128.     ax3.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
    129.     ax3.xaxis.set_major_locator(mticker.MaxNLocator(10))
    130.     ax3.yaxis.set_major_locator(mticker.MaxNLocator(nbins=4, prune='upper'))
    132.     for label in ax3.xaxis.get_ticklabels():
    133.         label.set_rotation(45)
    137.     plt.setp(ax1.get_xticklabels(), visible=False)
    138.     plt.setp(ax2.get_xticklabels(), visible=False)
    139.     plt.subplots_adjust(left=0.11, bottom=0.24, right=0.90, top=0.90, wspace=0.2, hspace=0)
    140.     plt.show()
    143. graph_data('GOOG')

    到这里，我们差不多完成了。 这里唯一的缺陷是一个好的图例。 一些线条是显而易见的，但人们可能会好奇移动均值的参数是什么，我们这里是 10 和 30。 添加自定义图例是下一个教程中涉及的内容。