目标：不做蜡烛图，只用折线图绘图，绘出四条线之间的关系。
注：未使用接口，仅爬虫学习，不做任何违法操作。
1 """
2 新浪财经，爬取历史股票数据
3 """
4
5 # -*- coding:utf-8 -*-
6
7 import numpy as np
8 import urllib.request, lxml.html
9 from urllib.request import urlopen
10 from bs4 import BeautifulSoup
11 import re, time
12 import matplotlib.pyplot as plt
13 from datetime import datetime
14 # 绘图显示中文设置
15 plt.rcParams['font.sans-serif'] = ['SimHei']
16 plt.rcParams['axes.unicode_minus'] = False
17
18
19 # 公共模块，请求头信息
20 def public(link):
21 r = urllib.request.Request(link)
22
23 ug = 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/49.0.2623.221 Safari/537.36 SE 2.X MetaSr 1.0'
24
25 r.add_header('User-Agent', ug)
26
27 cookie = "SUB=_2AkMsqZjif8NxqwJRmfkRxG7nZYpzyg_EieKa9Wk5JRMyHRl-yD83qkJatRB6Bym2DDqPE870e3uMsySIjHjrMbMNxNqk; "
28 "SUBP=0033WrSXqPxfM72-Ws9jqgMF55529P9D9WFXmxLGpAG5k05lCJw6qgYe; "
29 "SINAGLOBAL=172.16.92.24_1542789082.401113; "
30 "Apache=172.16.92.24_1542789082.401115; UOR=www.baidu.com,blog.sina.com.cn,; "
31 "ULV=1542789814434:1:1:1:172.16.92.24_1542789082.401115:; U_TRS1=000000d1.1f4d3546.5bf53673.955fa32e; "
32 "U_TRS2=000000d1.1f593546.5bf53673.736853cc; FINANCE2=661413ac85cadaab72ec7e3d842d6a3a; _s_upa=1"
33
34 r.add_header("Cookie", cookie)
35
36 html = urllib.request.urlopen(r, timeout=500).read()
37
38 bsObj = BeautifulSoup(html, "lxml") # 将html对象转化为BeautifulSoup对象
39
40 return bsObj
41
42
43 # 获取股票价格
44 def shares_price(code, year, quarter):
45 link = "http://money.finance.sina.com.cn/corp/go.php/vMS_MarketHistory/stockid/%s.phtml?year=%d&jidu=%d" % (code, year, quarter)
46
47 bsObj = public(link)
48 # print(bsObj)
49
50 a = 0
51 # date_list为日期列表，open_list为开盘价列表，high_list为最高价列表，close_list为收盘价列表，low_list为最低价列表
52 price_list, date_list, open_list, high_list, close_list, low_list = [], [], [], [], [], []
53 # 获取股票信息
54 jpg_title = re.findall("(.*?))", bsObj.title.text)
55
56 prices_bs = bsObj.find_all(name='div', attrs={"align": 'center'})
57 # 获取并处理价格信息
58 for price_bs in prices_bs:
59 # 去除空格
60 price_bs_1 = price_bs.text.replace("
", "")
61 price_bs_2 = price_bs_1.replace("
", "")
62
63 # 6个字符串为一个列表
64 if a != 6:
65 price_list.append(price_bs_2)
66 a = a + 1
67 else:
68 date_list.append(price_list[0])
69 open_list.append(price_list[1])
70 high_list.append(price_list[2])
71 close_list.append(price_list[3])
72 low_list.append(price_list[4])
73 a = 0
74 price_list = []
75 # 删除列表头
76 for b in (date_list, open_list, high_list, close_list, low_list):
77 b.pop(0)
78
79 # 全部倒序排列（由日期远到近，从左到右排列）
80 for c in (date_list, open_list, high_list, close_list, low_list):
81 c.reverse()
82
83 return date_list, open_list, high_list, close_list, low_list, jpg_title
84
85
86 # 输入股票代码，年份，季度
87 code = "002925"
88 year = "2018"
89 quarter = 4
90 # 以下为手动输入模式，因调试方便默认上面固定模式。
91 # code = input("code：") # 002925
92 # year = input("year：") # 2018
93 # quarter = int(input("quarter："))
94
95 # 列表字符串转为数值date
96 x = [datetime.strptime(d, '%Y-%m-%d').date() for d in shares_price(code, int(year), quarter)[0]]
97 # 将爬取的数据（字符串）转化为浮点型
98 open_list = [float(i) for i in shares_price(code, int(year), quarter)[1]]
99 high_list = [float(i) for i in shares_price(code, int(year), quarter)[2]]
100 close_list = [float(i) for i in shares_price(code, int(year), quarter)[3]]
101 low_list = [float(i) for i in shares_price(code, int(year), quarter)[4]]
102
103 # 线条设置
104 plt.plot(x, open_list, label='open', linewidth=1, color='red', marker='o', markerfacecolor='blue', markersize=2)
105 plt.plot(x, high_list, label='high', linewidth=1, color='green', marker='o', markerfacecolor='blue', markersize=2)
106 plt.plot(x, close_list, label='close', linewidth=1, color='blue', marker='o', markerfacecolor='blue', markersize=2)
107 plt.plot(x, low_list, label='low', linewidth=1, color='black', marker='o', markerfacecolor='blue', markersize=2)
108
109 # 取数列最大数值与最小值做图表的边界值。
110 plt.ylim(min(low_list)-1, max(high_list)+1)
111 plt.gcf().autofmt_xdate() # 自动旋转日期标记
112
113 # 打印表头
114 plt.xlabel('time')
115 plt.ylabel('price')
116 # shares_price(code, int(year), quarter)[5][0]为title中的股票名称与代码
117 plt.title('gp_1_{0}.jpg'.format(shares_price(code, int(year), quarter)[5][0]))
118 plt.legend()
119 plt.show()
效果如下：
是不是有另一种看法的感觉？如：黑线下跌后向上的第一个大拐点为买入点。