Python 如何在matplotlib中用日期时间绘制ohlc烛台？

how to plot ohlc candlestick with datetime in matplotlib?

提问by dindom

I need to plot trade data every 5 minutes (one candle)

我需要每 5 分钟绘制一次交易数据（一根蜡烛）

Here is what I have so far:

这是我到目前为止所拥有的：

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

And it looks like this:

它看起来像这样：

I need to improve it:

我需要改进它：

1. The blue mark shows that the xticksdisplay with int, I would like them to be in datetimeformat.

2. The red mark shows the x value in the status bar. I would like that to be in datetimeformat too.

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

2. 红色标记在状态栏中显示 x 值。我也希望它是datetime格式的。

Here is the quotesdemo data:

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')])

回答by tmdavison

Here is some code that works.

这是一些有效的代码。

First, we convert the timestamp to a datetime object using datetime.datetime.fromtimestamp.

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

Then, we set the tick locations using a ticker.MaxNLocator.

然后，我们使用ticker.MaxNLocator.

I've then created a function to feed to ticker.FuncFormatterto use the datetimeobject as the tick label, and use the integer value of the tick to index the xdatelist we created earlier.

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

The try... exceptclause is in there in case there is a tick beyond the final timestamp in your quotesarray, in which case the function would fail.

该try... except子句在那里，以防在quotes数组中的最终时间戳之外有一个刻度，在这种情况下函数将失败。

I also added autofmt_xdate()to rotate the ticks, and tight_layout()to make room for them

我还添加autofmt_xdate()了旋转刻度，并tight_layout()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()

回答by Daniele

Plot ohlc candles WITHOUT matplotlib.finance

在没有 matplotlib.finance 的情况下绘制 ohlc 蜡烛图

Assuming that pricesis a Dataframe

假设价格是一个数据框

import pandas as pd
import matplotlib.pyplot as plt

plt.figure()
width=1
width2=0.1
pricesup=prices[prices.close>=prices.open]
pricesdown=prices[prices.close<prices.open]

plt.bar(pricesup.index,pricesup.close-pricesup.open,width,bottom=pricesup.open,color='g')
plt.bar(pricesup.index,pricesup.high-pricesup.close,width2,bottom=pricesup.close,color='g')
plt.bar(pricesup.index,pricesup.low-pricesup.open,width2,bottom=pricesup.open,color='g')

plt.bar(pricesdown.index,pricesdown.close-pricesdown.open,width,bottom=pricesdown.open,color='r')
plt.bar(pricesdown.index,pricesdown.high-pricesdown.open,width2,bottom=pricesdown.open,color='r')
plt.bar(pricesdown.index,pricesdown.low-pricesdown.close,width2, bottom=pricesdown.close,color='r')
plt.grid()

Widths should be adjusted for different timeframes

宽度应针对不同的时间范围进行调整

回答by alec_djinn

You should convert the datestamp in your array to datetime object first and then convert it using date2num.

您应该先将数组中的日期戳转换为日期时间对象，然后使用date2num.

As specified in http://matplotlib.org/api/finance_api.html

如http://matplotlib.org/api/finance_api.html 中所述

matplotlib.finance.candlestick_ochl(ax, quotes, width=0.2, colorup='k', colordown='r', alpha=1.0)

matplotlib.finance.candlestick_ochl(ax, quotes,width=0.2,colorup='k',colordown='r',alpha=1.0)

quotes: sequence of (time, open, close, high, low, ...) sequences

报价：（时间，开盘，收盘，高，低，...）序列的序列

As long as the first 5 elements are these values, the record can be as long as you want (e.g., it may store volume).

只要前 5 个元素是这些值，记录就可以任意长（例如，它可以存储音量）。

time must be in float days format - see date2num

时间必须为浮点数格式 - 请参阅 date2num

import datetime
from matplotlib.dates import date2num

a = your_array
d = [date2num(datetime.datetime.fromtimestamp(x[0])) for x in a]

回答by Matt Allen

Here I would like to expand the code on this page by Daniele, as some people want to see how the DataFrame (prices) would look like. Here is my take (btw thanks to Daniele for this very good idea).

在这里，我想扩展 Daniele 在此页面上的代码，因为有些人想看看 DataFrame（价格）会是什么样子。这是我的看法（顺便说一句，感谢 Daniele 提出的这个好主意）。

listTimestamp = list(<timestamp data>)
listOpen = list(<Open data>)
listHigh = list(<High data>)
listLow = list(<Low data>)
listClose = list(<Close data>)

dictdata = {'Timestamp':listTimestamp,'Open':listOpen,
          'High':listHigh,'Low':listLow,'Close':listClose}
prices = pd.DataFrame(dictdata,columns=['Timestamp','Open','High','Low','Close'])

width=0.9
width2=0.1
pricesup=prices[prices.Close>=prices.Open]
pricesdown=prices[prices.Close<prices.Open]
plt.bar(pricesup.index,pricesup.Close-pricesup.Open,width,bottom=pricesup.Open,color='g')
plt.bar(pricesup.index,pricesup.High-pricesup.Close,width2,bottom=pricesup.Close,color='g')
plt.bar(pricesup.index,pricesup.Low-pricesup.Open,width2,bottom=pricesup.Open,color='g')
plt.bar(pricesdown.index,pricesdown.Close-pricesdown.Open,width,bottom=pricesdown.Open,color='r')
plt.bar(pricesdown.index,pricesdown.High-pricesdown.Open,width2,bottom=pricesdown.Open,color='r')
plt.bar(pricesdown.index,pricesdown.Low-pricesdown.Close,width2, bottom=pricesdown.Close,color='r')
plt.grid()
plt.show()