python - 如何在 matplotlib 中用日期时间绘制 ohlc 烛台？

Question

我需要每 5 分钟绘制一次交易数据(一根蜡烛)

这是我目前所拥有的:

from matplotlib.finance import candlestick2_ohlc
fig, ax = plt.subplots()
candlestick2_ohlc(ax,quotes['open'],quotes['high'],quotes['low'],quotes['close'],width=0.6)

它看起来像这样:

我需要改进它:

1. 蓝色标记表示 xticks 以 int 显示，我希望它们采用 datetime 格式。

2. 红色标记显示状态栏中的 x 值。我也希望它采用 datetime 格式。

这是quotes演示数据:

array([ (1459388100, 29.799999237060547, 29.799999237060547, 29.799999237060547, 29.799999237060547, 148929.0, 450030016.0),
   (1459388400, 29.799999237060547, 29.979999542236328, 29.709999084472656, 29.920000076293945, 10395.0, 31069984.0),
   (1459388700, 29.959999084472656, 30.18000030517578, 29.719999313354492, 30.149999618530273, 38522.0, 114999968.0),
   (1459389000, 30.170000076293945, 30.479999542236328, 30.0, 30.149999618530273, 29823.0, 90220032.0),
   (1459389300, 30.149999618530273, 30.75, 30.1299991607666, 30.549999237060547, 38903.0, 118620032.0),
   (1459389600, 30.59000015258789, 30.93000030517578, 30.559999465942383, 30.65999984741211, 42308.0, 130000000.0),
   (1459389900, 30.6200008392334, 30.690000534057617, 30.3799991607666, 30.3799991607666, 20209.0, 61689984.0),
   (1459390200, 30.3700008392334, 30.489999771118164, 30.18000030517578, 30.18000030517578, 18491.0, 56169984.0),
   (1459390500, 30.190000534057617, 30.329999923706055, 30.010000228881836, 30.010000228881836, 17641.0, 53200000.0),
   (1459390800, 30.030000686645508, 30.399999618530273, 30.030000686645508, 30.280000686645508, 9526.0, 28899968.0),
   (1459391100, 30.299999237060547, 30.31999969482422, 30.200000762939453, 30.209999084472656, 9282.0, 28100096.0),
   (1459391400, 30.190000534057617, 30.280000686645508, 30.049999237060547, 30.1200008392334, 8663.0, 26099968.0),
   (1459391700, 30.110000610351562, 30.110000610351562, 29.959999084472656, 30.100000381469727, 15677.0, 47099904.0),
   (1459392000, 30.1200008392334, 30.260000228881836, 30.0, 30.059999465942383, 5649.0, 17000064.0),
   (1459392300, 30.079999923706055, 30.299999237060547, 30.0, 30.280000686645508, 6057.0, 18199936.0),
   (1459392600, 30.290000915527344, 30.34000015258789, 30.1200008392334, 30.1200008392334, 7914.0, 24000000.0),
   (1459392900, 30.1299991607666, 30.15999984741211, 30.079999923706055, 30.139999389648438, 4521.0, 13600000.0),
   (1459393200, 30.139999389648438, 30.139999389648438, 29.829999923706055, 29.899999618530273, 16255.0, 48600064.0),
   (1459393500, 29.93000030517578, 30.1200008392334, 29.889999389648438, 30.1200008392334, 6877.0, 20600064.0),
   (1459393800, 30.1299991607666, 30.15999984741211, 29.979999542236328, 30.030000686645508, 3803.0, 11499904.0),
   (1459394100, 30.040000915527344, 30.1299991607666, 30.0, 30.030000686645508, 4421.0, 13300096.0),
   (1459394400, 29.989999771118164, 30.389999389648438, 29.989999771118164, 30.389999389648438, 7011.0, 21099904.0),
   (1459394700, 30.399999618530273, 30.450000762939453, 30.270000457763672, 30.299999237060547, 12095.0, 36800000.0),
   (1459395000, 30.34000015258789, 30.450000762939453, 30.280000686645508, 30.43000030517578, 9284.0, 28099968.0),
   (1459400700, 30.510000228881836, 30.729999542236328, 30.5, 30.600000381469727, 17139.0, 52500096.0),
   (1459401000, 30.600000381469727, 30.799999237060547, 30.530000686645508, 30.790000915527344, 11888.0, 36400000.0),
   (1459401300, 30.809999465942383, 31.100000381469727, 30.809999465942383, 31.049999237060547, 30692.0, 95099904.0),
   (1459401600, 31.06999969482422, 31.559999465942383, 30.93000030517578, 31.559999465942383, 24473.0, 76200064.0),
   (1459401900, 31.600000381469727, 31.860000610351562, 31.299999237060547, 31.450000762939453, 34497.0, 109200000.0),
   (1459402200, 31.43000030517578, 31.600000381469727, 31.18000030517578, 31.18000030517578, 18525.0, 58200064.0),
   (1459402500, 31.18000030517578, 31.350000381469727, 31.040000915527344, 31.18000030517578, 10153.0, 31599872.0),
   (1459402800, 31.200000762939453, 31.399999618530273, 31.010000228881836, 31.389999389648438, 9668.0, 30100096.0),
   (1459403100, 31.399999618530273, 31.399999618530273, 31.110000610351562, 31.360000610351562, 8445.0, 26499968.0),
   (1459403400, 31.360000610351562, 31.399999618530273, 31.040000915527344, 31.100000381469727, 9538.0, 29799936.0),
   (1459403700, 31.1200008392334, 31.399999618530273, 31.100000381469727, 31.270000457763672, 7996.0, 25000064.0),
   (1459404000, 31.270000457763672, 31.399999618530273, 31.15999984741211, 31.399999618530273, 6760.0, 21100032.0),
   (1459404300, 31.389999389648438, 32.400001525878906, 31.389999389648438, 32.189998626708984, 26108.0, 83700096.0),
   (1459404600, 32.209999084472656, 32.400001525878906, 31.860000610351562, 32.29999923706055, 15736.0, 50599936.0),
   (1459404900, 32.29999923706055, 32.310001373291016, 31.489999771118164, 31.489999771118164, 12945.0, 41399808.0),
   (1459405200, 31.5, 32.0, 31.40999984741211, 31.81999969482422, 11901.0, 37700096.0),
   (1459405500, 31.809999465942383, 31.940000534057617, 31.719999313354492, 31.770000457763672, 6503.0, 20700160.0),
   (1459405800, 31.760000228881836, 31.790000915527344, 31.399999618530273, 31.790000915527344, 10103.0, 31899904.0),
   (1459406100, 31.780000686645508, 32.029998779296875, 31.780000686645508, 31.850000381469727, 12033.0, 38500096.0),
   (1459406400, 31.809999465942383, 33.310001373291016, 31.809999465942383, 33.029998779296875, 58238.0, 192199936.0),
   (1459406700, 33.029998779296875, 33.310001373291016, 32.79999923706055, 32.79999923706055, 36689.0, 121900032.0),
   (1459407000, 32.79999923706055, 32.869998931884766, 32.61000061035156, 32.70000076293945, 15245.0, 49799936.0),
   (1459407300, 32.68000030517578, 32.689998626708984, 31.799999237060547, 32.0099983215332, 20507.0, 65999872.0),
   (1459407600, 32.02000045776367, 32.02000045776367, 31.760000228881836, 31.799999237060547, 29610.0, 94300160.0)], 
  dtype=[('time', '<i4'), ('open', '<f4'), ('high', '<f4'), ('low', '<f4'), ('close', '<f4'), ('volume', '<f4'), ('amount', '<f4')])

Answer 1

这是一些有效的代码。

首先，我们使用 datetime.datetime.fromtimestamp 将时间戳转换为日期时间对象.

然后，我们使用 ticker.MaxNLocator 设置刻度位置.

然后我创建了一个函数来提供给 ticker.FuncFormatter 以使用 datetime 对象作为刻度标签，并使用刻度的整数值来索引我们之前创建的 xdate 列表。

try...except 子句在那里，以防在您的 quotes 数组中有超出最终时间戳的滴答，在这种情况下函数将失败。

我还添加了 autofmt_xdate() 来旋转刻度，并添加了 tight_layout() 来为它们腾出空间

from matplotlib.finance import candlestick2_ohlc
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import datetime as datetime
import numpy as np

quotes = np.array(...)

fig, ax = plt.subplots()
candlestick2_ohlc(ax,quotes['open'],quotes['high'],quotes['low'],quotes['close'],width=0.6)

xdate = [datetime.datetime.fromtimestamp(i) for i in quotes['time']]

ax.xaxis.set_major_locator(ticker.MaxNLocator(6))

def mydate(x,pos):
    try:
        return xdate[int(x)]
    except IndexError:
        return ''

ax.xaxis.set_major_formatter(ticker.FuncFormatter(mydate))

fig.autofmt_xdate()
fig.tight_layout()

plt.show()