Matplotlib烛台在几分钟内

一尘不染

Matplotlib烛台在几分钟内

python

下午好，

我想看看你们中谁能在几分钟内帮我做个蜡烛图。我已经设法在几天内绘制出它们的图形，但是我不知道如何在几分钟内完成它们。

附加代码。

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import dates, ticker
import matplotlib as mpl
from mpl_finance import candlestick_ohlc

mpl.style.use('default')

data = [('2017-01-02 02:00:00', '1.05155', '1.05197', '1.05155', '1.0519'),
    ('2017-01-02 02:01:00', '1.05209', '1.05209', '1.05177', '1.05179'),
    ('2017-01-02 02:02:00', '1.05177', '1.05198', '1.05177', '1.05178'),
    ('2017-01-02 02:03:00', '1.05188', '1.052', '1.05188', '1.052'),
    ('2017-01-02 02:04:00', '1.05196', '1.05204', '1.05196', '1.05203'),
    ('2017-01-02 02:06:00', '1.05196', '1.05204', '1.05196', '1.05204'),
    ('2017-01-02 02:07:00', '1.05205', '1.0521', '1.05205', '1.05209'),
    ('2017-01-02 02:08:00', '1.0521', '1.0521', '1.05209', '1.05209'),
    ('2017-01-02 02:09:00', '1.05208', '1.05209', '1.05208', '1.05209'),
    ('2017-01-02 02:10:00', '1.05208', '1.05211', '1.05207', '1.05209')]

ohlc_data = []

for line in data:
    ohlc_data.append((dates.datestr2num(line[0]), np.float64(line[1]), np.float64(line[2]), np.float64(line[3]), np.float64(line[4])))

fig, ax1 = plt.subplots()
candlestick_ohlc(ax1, ohlc_data, width = 0.5, colorup = 'g', colordown = 'r', alpha = 0.8)

ax1.xaxis.set_major_formatter(dates.DateFormatter('%d/%m/%Y %H:%M'))
ax1.xaxis.set_major_locator(ticker.MaxNLocator(10))

plt.xticks(rotation = 30)
plt.grid()
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Historical Data EURUSD')
plt.tight_layout()
plt.show()

阅读 200

2021-01-20

共1个答案

一尘不染

如此接近，但只有反复试验才能使您更进一步。糟糕的文档不是很好吗？

只需除以width一天中的分钟数即可。完整的代码，供您在下面复制和粘贴，但我所做的只是更改width = 0.5为width = 0.5/(24*60)。

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import dates, ticker
import matplotlib as mpl
from mpl_finance import candlestick_ohlc

mpl.style.use('default')

data = [('2017-01-02 02:00:00', '1.05155', '1.05197', '1.05155', '1.0519'),
    ('2017-01-02 02:01:00', '1.05209', '1.05209', '1.05177', '1.05179'),
    ('2017-01-02 02:02:00', '1.05177', '1.05198', '1.05177', '1.05178'),
    ('2017-01-02 02:03:00', '1.05188', '1.052', '1.05188', '1.052'),
    ('2017-01-02 02:04:00', '1.05196', '1.05204', '1.05196', '1.05203'),
    ('2017-01-02 02:06:00', '1.05196', '1.05204', '1.05196', '1.05204'),
    ('2017-01-02 02:07:00', '1.05205', '1.0521', '1.05205', '1.05209'),
    ('2017-01-02 02:08:00', '1.0521', '1.0521', '1.05209', '1.05209'),
    ('2017-01-02 02:09:00', '1.05208', '1.05209', '1.05208', '1.05209'),
    ('2017-01-02 02:10:00', '1.05208', '1.05211', '1.05207', '1.05209')]

ohlc_data = []

for line in data:
    ohlc_data.append((dates.datestr2num(line[0]), np.float64(line[1]), np.float64(line[2]), np.float64(line[3]), np.float64(line[4])))

fig, ax1 = plt.subplots()
candlestick_ohlc(ax1, ohlc_data, width = 0.5/(24*60), colorup = 'g', colordown = 'r', alpha = 0.8)

ax1.xaxis.set_major_formatter(dates.DateFormatter('%d/%m/%Y %H:%M'))
ax1.xaxis.set_major_locator(ticker.MaxNLocator(10))

plt.xticks(rotation = 30)
plt.grid()
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Historical Data EURUSD')
plt.tight_layout()
plt.show()

2021-01-20