模块：Numpy

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准备工作：

抓取某一只股票的信息，每30min为一组数据，可以参考上一篇：

Note: 只为演示如何统计，更精准的可以抓取每5min为一组数据

PS：如有需要Python学习资料的小伙伴可以加点击下方链接自行获取

目标：输出每天的开盘，收盘，最高，最低价格，以其中的某一周(5 days, 40组数据)为例

1， 从csv中导入数据，需要有 open, high, close 和low

#import numpy as np

open = np.loadtxt('30min.csv', dtype = float, skiprows = 1, usecols = 2, delimiter=',', encoding = 'utf-8')

high = np.loadtxt('30min.csv', dtype = float, skiprows = 1, usecols = 3, delimiter=',', encoding = 'utf-8')

close = np.loadtxt('30min.csv', dtype = float, skiprows = 1, usecols = 4, delimiter=',', encoding = 'utf-8')

low = np.loadtxt('30min.csv', dtype = float, skiprows = 1, usecols = 5, delimiter=',', encoding = 'utf-8')

打印测试可以得到如下数据，

2，从csv中导入date数据，需要将date转化输出成工作日的格式

from datetime import datetime

def datestr(s):

return datetime.strptime(s, '%Y/%m/%d').isoweekday()

#print(datestr('2020/12/7'))

dates = np.loadtxt('30min.csv',dtype = str, skiprows = 1, usecols = 0, converters = {0: datestr}, delimiter = ',', encoding = 'utf-8')

打印测试可以得到如下数据，

3， 找到某一周的40组数据，确认第一天的开盘时间和最后一天的收盘时间

close = close[0:40]

dates = dates[0:40]

first_monday = np.ravel(np.where(dates == 1))[-1] #根据csv中的排序找到某一周的第一天

last_friday = np.ravel(np.where(dates == 5))[0] #根据csv中的排序找到某一周的最后一天

打印测试得到如下某一周的40个时间

4， 创建一个数组，用于存储一周内每一天的索引值

day_indices = np.arange(last_friday, first_monday +1)[::-1]

weeks_indices = np.split(day_indices, 5)

打印测试得到如下输出，

5，编写summarize函数，返回一个元组包含这一周每天对应的open, close, high, low

def summarize(a, o, h, c, l):

monday_open = o[a[0]]  #monday open是最后一个价格

day_high = np.max( np.take(h, a) )  #每天的最高价格

day_low = np.min( np.take(l, a) )#每天的最低价格

friday_close = c[a[-1]]  #friday close是第一个价格

return('lux', monday_open, day_high, friday_close, day_low)

6，生成每周的数据

weeksummary = np.apply_along_axis(summarize, 1, weeks_indices, open, high, close, low)

print(' ****** open,  high,  close,  low \n', weeksummary)

对比一下表格中的数据信息，结果是匹配的

7， 如果有需要可以保存

np.savetxt('cw36_lux.csv', weeksummary, delimiter = ',', fmt = '%s') #同30min.csv在同一文件夹下

打开csv之后保存的数据如下

好了，完整的代码如下：

import numpy as np

from datetime import datetime

def datestr(s):

return datetime.strptime(s, '%Y/%m/%d').isoweekday()

dates, open, high, close, low = np.loadtxt('30min.csv', skiprows = 1, usecols = (0, 2, 3, 4, 5), converters = {0:datestr}, delimiter = ',', unpack = True, encoding = 'utf-8')

close = close[0:40]

dates = dates[0:40]

#print(dates)

first_monday = np.ravel(np.where(dates == 1))[-1]

#print(first_monday)

last_friday = np.ravel(np.where(dates == 5))[0]

#print(last_friday)

day_indices = np.arange(last_friday, first_monday +1)[::-1]

#print(day_indices)

weeks_indices = np.split(day_indices, 5)

#print(weeks_indices)

def summarize(a, o, h, c, l):

monday_open = o[a[0]]

day_high = np.max( np.take(h, a) )

day_low = np.min( np.take(l, a) )

friday_close = c[a[-1]]

return('lux', monday_open, day_high, friday_close, day_low)

weeksummary = np.apply_along_axis(summarize, 1, weeks_indices, open, high, close, low)

#print(' ****** open,  high,  close,  low \n', weeksummary)

np.savetxt('cw36_lux.csv', weeksummary, delimiter = ',', fmt = '%s')

欢迎大家一起讨论学习。