首先需要安装Python软件，以及pyecharts库
相关教程链接：
龙哥量化：文档目录（股票，期货，通达信、同花顺、文华等软件使用，Python量化交易，策略编写，学习文档，策略案例等等）
1 """
2 复刻的 Echarts 的 demo 链接
3 https://gallery.echartsjs.com/editor.html?c=xByOFPcjBe
4
5 @Author: sunhailin-Leo
6 @Time: 2019年7月14日
7 """
8 from typing import List, Sequence, Union
9
10 from pyecharts import options as opts
11 from pyecharts.commons.utils import JsCode
12 from pyecharts.charts import Kline, Line, Bar, Grid
13 import os
14
15 # 数据
16 echarts_data = [
17 ["2015-10-16", 18.4, 18.58, 18.33, 18.79, 67.00, 1, 0.04, 0.11, 0.09],
18 ["2015-10-19", 18.56, 18.25, 18.19, 18.56, 55.00, 0, -0.00, 0.08, 0.09],
19 ["2015-10-20", 18.3, 18.22, 18.05, 18.41, 37.00, 0, 0.01, 0.09, 0.09],
20 ["2015-10-21", 18.18, 18.69, 18.02, 18.98, 89.00, 0, 0.03, 0.10, 0.08],
21 ["2015-10-22", 18.42, 18.29, 18.22, 18.48, 43.00, 0, -0.06, 0.05, 0.08],
22 ["2015-10-23", 18.26, 18.19, 18.08, 18.36, 46.00, 0, -0.10, 0.03, 0.09],
23 ["2015-10-26", 18.33, 18.07, 17.98, 18.35, 65.00, 0, -0.15, 0.03, 0.10],
24 ["2015-10-27", 18.08, 18.04, 17.88, 18.13, 37.00, 0, -0.19, 0.03, 0.12],
25 ["2015-10-28", 17.96, 17.86, 17.82, 17.99, 35.00, 0, -0.24, 0.03, 0.15],
26 ["2015-10-29", 17.85, 17.81, 17.8, 17.93, 27.00, 0, -0.24, 0.06, 0.18],
27 ["2015-10-30", 17.79, 17.93, 17.78, 18.08, 43.00, 0, -0.22, 0.11, 0.22],
28 ["2015-11-02", 17.78, 17.83, 17.78, 18.04, 27.00, 0, -0.20, 0.15, 0.25],
29 ["2015-11-03", 17.84, 17.9, 17.84, 18.06, 34.00, 0, -0.12, 0.22, 0.28],
30 ["2015-11-04", 17.97, 18.36, 17.85, 18.39, 62.00, 0, -0.00, 0.30, 0.30],
31 ["2015-11-05", 18.3, 18.57, 18.18, 19.08, 177.00, 0, 0.07, 0.33, 0.30],
32 ["2015-11-06", 18.53, 18.68, 18.3, 18.71, 95.00, 0, 0.12, 0.35, 0.29],
33 ["2015-11-09", 18.75, 19.08, 18.75, 19.98, 202.00, 1, 0.16, 0.35, 0.27],
34 ["2015-11-10", 18.85, 18.64, 18.56, 18.99, 85.00, 0, 0.09, 0.29, 0.25],
35 ["2015-11-11", 18.64, 18.44, 18.31, 18.64, 50.00, 0, 0.06, 0.27, 0.23],
36 ["2015-11-12", 18.55, 18.27, 18.17, 18.57, 43.00, 0, 0.05, 0.25, 0.23],
37 ["2015-11-13", 18.13, 18.14, 18.09, 18.34, 35.00, 0, 0.05, 0.24, 0.22],
38 ["2015-11-16", 18.01, 18.1, 17.93, 18.17, 34.00, 0, 0.07, 0.25, 0.21],
39 ["2015-11-17", 18.2, 18.14, 18.08, 18.45, 58.00, 0, 0.11, 0.25, 0.20],
40 ["2015-11-18", 18.23, 18.16, 18.0, 18.45, 47.00, 0, 0.13, 0.25, 0.19],
41 ["2015-11-19", 18.08, 18.2, 18.05, 18.25, 32.00, 0, 0.15, 0.24, 0.17],
42 ["2015-11-20", 18.15, 18.15, 18.11, 18.29, 36.00, 0, 0.13, 0.21, 0.15],
43 ["2015-11-23", 18.16, 18.19, 18.12, 18.34, 47.00, 0, 0.11, 0.18, 0.13],
44 ["2015-11-24", 18.23, 17.88, 17.7, 18.23, 62.00, 0, 0.03, 0.13, 0.11],
45 ["2015-11-25", 17.85, 17.73, 17.56, 17.85, 66.00, 0, -0.03, 0.09, 0.11],
46 ["2015-11-26", 17.79, 17.53, 17.5, 17.92, 63.00, 0, -0.10, 0.06, 0.11],
47 ["2015-11-27", 17.51, 17.04, 16.9, 17.51, 67.00, 0, -0.16, 0.05, 0.13],
48 ["2015-11-30", 17.07, 17.2, 16.98, 17.32, 55.00, 0, -0.12, 0.09, 0.15],
49 ["2015-12-01", 17.28, 17.11, 16.91, 17.28, 39.00, 0, -0.09, 0.12, 0.16],
50 ["2015-12-02", 17.13, 17.91, 17.05, 17.99, 102.00, 0, -0.01, 0.17, 0.18],
51 ["2015-12-03", 17.8, 17.78, 17.61, 17.98, 71.00, 0, -0.09, 0.14, 0.18],
52 ["2015-12-04", 17.6, 17.25, 17.13, 17.69, 51.00, 0, -0.18, 0.10, 0.19],
53 ["2015-12-07", 17.2, 17.39, 17.15, 17.45, 43.00, 0, -0.19, 0.12, 0.22],
54 ["2015-12-08", 17.3, 17.42, 17.18, 17.62, 45.00, 0, -0.23, 0.13, 0.24],
55 ["2015-12-09", 17.33, 17.39, 17.32, 17.59, 44.00, 0, -0.29, 0.13, 0.28],
56 ["2015-12-10", 17.39, 17.26, 17.21, 17.65, 44.00, 0, -0.37, 0.13, 0.32],
57 ["2015-12-11", 17.23, 16.92, 16.66, 17.26, 114.00, 1, -0.44, 0.15, 0.37],
58 ["2015-12-14", 16.75, 17.06, 16.5, 17.09, 94.00, 0, -0.44, 0.21, 0.44],
59 ["2015-12-15", 17.03, 17.03, 16.9, 17.06, 46.00, 0, -0.44, 0.28, 0.50],
60 ["2015-12-16", 17.08, 16.96, 16.87, 17.09, 30.00, 0, -0.40, 0.36, 0.56],
61 ["2015-12-17", 17.0, 17.1, 16.95, 17.12, 50.00, 0, -0.30, 0.47, 0.62],
62 ["2015-12-18", 17.09, 17.52, 17.04, 18.06, 156.00, 0, -0.14, 0.59, 0.66],
63 ["2015-12-21", 17.43, 18.23, 17.35, 18.45, 152.00, 1, 0.02, 0.69, 0.68],
64 ["2015-12-22", 18.14, 18.27, 18.06, 18.32, 94.00, 0, 0.08, 0.72, 0.68],
65 ["2015-12-23", 18.28, 18.19, 18.17, 18.71, 108.00, 0, 0.13, 0.73, 0.67],
66 ["2015-12-24", 18.18, 18.14, 18.01, 18.31, 37.00, 0, 0.19, 0.74, 0.65],
67 ["2015-12-25", 18.22, 18.33, 18.2, 18.36, 48.00, 0, 0.26, 0.75, 0.62],
68 ["2015-12-28", 18.35, 17.84, 17.8, 18.39, 48.00, 0, 0.27, 0.72, 0.59],
69 ["2015-12-29", 17.83, 17.94, 17.71, 17.97, 36.00, 0, 0.36, 0.73, 0.55],
70 ["2015-12-30", 17.9, 18.26, 17.55, 18.3, 71.00, 1, 0.43, 0.71, 0.50],
71 ["2015-12-31", 18.12, 17.99, 17.91, 18.33, 72.00, 0, 0.40, 0.63, 0.43],
72 ["2016-01-04", 17.91, 17.28, 17.16, 17.95, 37.00, 1, 0.34, 0.55, 0.38],
73 ["2016-01-05", 17.17, 17.23, 17.0, 17.55, 51.00, 0, 0.37, 0.51, 0.33],
74 ["2016-01-06", 17.2, 17.31, 17.06, 17.33, 31.00, 0, 0.37, 0.46, 0.28],
75 ["2016-01-07", 17.15, 16.67, 16.51, 17.15, 19.00, 0, 0.30, 0.37, 0.22],
76 ["2016-01-08", 16.8, 16.81, 16.61, 17.06, 60.00, 0, 0.29, 0.32, 0.18],
77 ["2016-01-11", 16.68, 16.04, 16.0, 16.68, 65.00, 0, 0.20, 0.24, 0.14],
78 ["2016-01-12", 16.03, 15.98, 15.88, 16.25, 46.00, 0, 0.20, 0.21, 0.11],
79 ["2016-01-13", 16.21, 15.87, 15.78, 16.21, 57.00, 0, 0.20, 0.18, 0.08],
80 ["2016-01-14", 15.55, 15.89, 15.52, 15.96, 42.00, 0, 0.20, 0.16, 0.05],
81 ["2016-01-15", 15.87, 15.48, 15.45, 15.92, 34.00, 1, 0.17, 0.11, 0.02],
82 ["2016-01-18", 15.39, 15.42, 15.36, 15.7, 26.00, 0, 0.21, 0.10, -0.00],
83 ["2016-01-19", 15.58, 15.71, 15.35, 15.77, 38.00, 0, 0.25, 0.09, -0.03],
84 ["2016-01-20", 15.56, 15.52, 15.24, 15.68, 38.00, 0, 0.23, 0.05, -0.07],
85 ["2016-01-21", 15.41, 15.3, 15.28, 15.68, 35.00, 0, 0.21, 0.00, -0.10],
86 ["2016-01-22", 15.48, 15.28, 15.13, 15.49, 30.00, 0, 0.21, -0.02, -0.13],
87 ["2016-01-25", 15.29, 15.48, 15.2, 15.49, 21.00, 0, 0.20, -0.06, -0.16],
88 ["2016-01-26", 15.33, 14.86, 14.78, 15.39, 30.00, 0, 0.12, -0.13, -0.19],
89 ["2016-01-27", 14.96, 15.0, 14.84, 15.22, 51.00, 0, 0.13, -0.14, -0.20],
90 ["2016-01-28", 14.96, 14.72, 14.62, 15.06, 25.00, 0, 0.10, -0.17, -0.22],
91 ["2016-01-29", 14.75, 14.99, 14.62, 15.08, 36.00, 0, 0.13, -0.17, -0.24],
92 ["2016-02-01", 14.98, 14.72, 14.48, 15.18, 27.00, 0, 0.10, -0.21, -0.26],
93 ["2016-02-02", 14.65, 14.85, 14.65, 14.95, 18.00, 0, 0.11, -0.21, -0.27],
94 ["2016-02-03", 14.72, 14.67, 14.55, 14.8, 23.00, 0, 0.10, -0.24, -0.29],
95 ["2016-02-04", 14.79, 14.88, 14.69, 14.93, 22.00, 0, 0.13, -0.24, -0.30],
96 ["2016-02-05", 14.9, 14.86, 14.78, 14.93, 16.00, 0, 0.12, -0.26, -0.32],
97 ["2016-02-15", 14.5, 14.66, 14.47, 14.82, 19.00, 0, 0.11, -0.28, -0.34],
98 ["2016-02-16", 14.77, 14.94, 14.72, 15.05, 26.00, 0, 0.14, -0.28, -0.35],
99 ["2016-02-17", 14.95, 15.03, 14.88, 15.07, 38.00, 0, 0.12, -0.31, -0.37],
100 ["2016-02-18", 14.95, 14.9, 14.87, 15.06, 28.00, 0, 0.07, -0.35, -0.39],
101 ["2016-02-19", 14.9, 14.75, 14.68, 14.94, 22.00, 0, 0.03, -0.38, -0.40],
102 ["2016-02-22", 14.88, 15.01, 14.79, 15.11, 38.00, 1, 0.01, -0.40, -0.40],
103 ["2016-02-23", 15.01, 14.83, 14.72, 15.01, 24.00, 0, -0.09, -0.45, -0.40],
104 ["2016-02-24", 14.75, 14.81, 14.67, 14.87, 21.00, 0, -0.17, -0.48, -0.39],
105 ["2016-02-25", 14.81, 14.25, 14.21, 14.81, 51.00, 1, -0.27, -0.50, -0.37],
106 ["2016-02-26", 14.35, 14.45, 14.28, 14.57, 28.00, 0, -0.26, -0.46, -0.33],
107 ["2016-02-29", 14.43, 14.56, 14.04, 14.6, 48.00, 0, -0.25, -0.41, -0.29],
108 ["2016-03-01", 14.56, 14.65, 14.36, 14.78, 32.00, 0, -0.21, -0.36, -0.25],
109 ["2016-03-02", 14.79, 14.96, 14.72, 14.97, 60.00, 0, -0.13, -0.29, -0.22],
110 ["2016-03-03", 14.95, 15.15, 14.91, 15.19, 53.00, 1, -0.05, -0.23, -0.21],
111 ["2016-03-04", 15.14, 15.97, 15.02, 16.02, 164.00, 1, 0.06, -0.17, -0.20],
112 ["2016-03-07", 15.9, 15.78, 15.65, 16.0, 41.00, 0, 0.04, -0.19, -0.21],
113 ["2016-03-08", 15.78, 15.96, 15.21, 15.99, 45.00, 0, 0.05, -0.19, -0.21],
114 ["2016-03-09", 15.73, 16.05, 15.41, 16.08, 74.00, 0, 0.03, -0.20, -0.22],
115 ["2016-03-10", 15.82, 15.66, 15.65, 15.98, 19.00, 0, -0.02, -0.23, -0.22],
116 ["2016-03-11", 15.59, 15.76, 15.42, 15.78, 32.00, 0, 0.01, -0.22, -0.22],
117 ["2016-03-14", 15.78, 15.72, 15.65, 16.04, 31.00, 0, 0.03, -0.20, -0.22],
118 ["2016-03-15", 15.81, 15.86, 15.6, 15.99, 35.00, 0, 0.10, -0.18, -0.23],
119 ["2016-03-16", 15.88, 16.42, 15.79, 16.45, 123.00, 0, 0.17, -0.16, -0.24],
120 ["2016-03-17", 16.39, 16.23, 16.11, 16.4, 46.00, 0, 0.14, -0.20, -0.26],
121 ["2016-03-18", 16.39, 16.17, 16.04, 16.4, 59.00, 0, 0.13, -0.22, -0.28],
122 ["2016-03-21", 16.21, 16.22, 16.11, 16.44, 50.00, 0, 0.12, -0.24, -0.30],
123 ["2016-03-22", 16.27, 16.19, 16.16, 16.42, 33.00, 0, 0.10, -0.27, -0.32],
124 ["2016-03-23", 16.26, 16.18, 16.18, 16.29, 19.00, 0, 0.08, -0.30, -0.33],
125 ["2016-03-24", 16.18, 16.11, 16.01, 16.23, 23.00, 0, 0.04, -0.33, -0.35],
126 ["2016-03-25", 16.12, 16.13, 16.1, 16.2, 15.00, 0, 0.00, -0.35, -0.35],
127 ["2016-03-28", 16.15, 15.85, 15.81, 16.2, 22.00, 0, -0.06, -0.38, -0.35],
128 ["2016-03-29", 15.9, 15.79, 15.76, 16.05, 19.00, 0, -0.06, -0.37, -0.34],
129 ["2016-03-30", 15.79, 16.24, 15.78, 16.3, 29.00, 0, -0.03, -0.35, -0.33],
130 ["2016-03-31", 16.3, 16.09, 16.02, 16.35, 25.00, 0, -0.07, -0.37, -0.33],
131 ["2016-04-01", 16.18, 16.27, 15.98, 16.3, 38.00, 0, -0.08, -0.36, -0.32],
132 ["2016-04-05", 16.13, 16.34, 16.07, 16.37, 39.00, 0, -0.13, -0.37, -0.31],
133 ["2016-04-06", 16.21, 16.26, 16.19, 16.35, 30.00, 0, -0.20, -0.39, -0.29],
134 ["2016-04-07", 16.32, 16.1, 16.05, 16.35, 29.00, 1, -0.26, -0.39, -0.26],
135 ["2016-04-08", 16.0, 16.16, 15.98, 16.21, 22.00, 0, -0.28, -0.37, -0.23],
136 ["2016-04-11", 16.16, 16.31, 16.15, 16.57, 31.00, 0, -0.30, -0.33, -0.19],
137 ["2016-04-12", 16.41, 16.29, 16.12, 16.41, 17.00, 0, -0.31, -0.30, -0.14],
138 ["2016-04-13", 16.39, 16.48, 16.0, 16.68, 40.00, 0, -0.30, -0.25, -0.10],
139 ["2016-04-14", 16.5, 16.46, 16.37, 16.68, 22.00, 0, -0.27, -0.19, -0.06],
140 ["2016-04-15", 16.56, 16.93, 16.46, 17.04, 58.00, 0, -0.20, -0.12, -0.02],
141 ["2016-04-18", 16.76, 17.06, 16.72, 17.27, 50.00, 0, -0.16, -0.07, 0.01],
142 ["2016-04-19", 17.21, 17.11, 17.02, 17.23, 30.00, 0, -0.12, -0.02, 0.03],
143 ["2016-04-20", 17.11, 17.33, 16.8, 17.36, 78.00, 0, -0.04, 0.03, 0.05],
144 ["2016-04-21", 17.27, 17.69, 17.17, 17.93, 79.00, 0, 0.05, 0.08, 0.06],
145 ["2016-04-22", 17.6, 17.87, 17.56, 18.02, 55.00, 0, 0.09, 0.10, 0.05],
146 ["2016-04-25", 17.75, 17.9, 17.41, 17.96, 39.00, 1, 0.11, 0.09, 0.04],
147 ["2016-04-26", 17.81, 17.91, 17.6, 17.95, 39.00, 0, 0.12, 0.08, 0.02],
148 ["2016-04-27", 17.9, 17.88, 17.81, 17.95, 25.00, 0, 0.12, 0.06, 0.00],
149 ["2016-04-28", 17.93, 17.88, 17.67, 17.93, 28.00, 0, 0.11, 0.04, -0.01],
150 ["2016-04-29", 17.87, 17.75, 17.73, 17.92, 19.00, 0, 0.08, 0.01, -0.03],
151 ["2016-05-03", 17.79, 17.7, 17.56, 17.85, 35.00, 0, 0.05, -0.01, -0.04],
152 ["2016-05-04", 17.7, 17.65, 17.59, 17.71, 24.00, 0, 0.02, -0.04, -0.05],
153 ["2016-05-05", 17.65, 17.62, 17.46, 17.7, 20.00, 0, -0.03, -0.06, -0.05],
154 ["2016-05-06", 17.62, 17.32, 17.3, 17.65, 29.00, 0, -0.10, -0.09, -0.05],
155 ["2016-05-09", 17.33, 17.3, 17.21, 17.45, 23.00, 0, -0.13, -0.10, -0.03],
156 ["2016-05-10", 17.11, 17.04, 16.98, 17.41, 28.00, 0, -0.15, -0.09, -0.01],
157 ["2016-05-11", 17.06, 17.15, 17.06, 17.32, 20.00, 0, -0.12, -0.05, 0.01],
158 ["2016-05-12", 17.02, 17.46, 17.02, 17.58, 26.00, 0, -0.07, -0.01, 0.03],
159 ["2016-05-13", 17.41, 17.57, 17.34, 17.62, 23.00, 0, -0.06, 0.01, 0.03],
160 ["2016-05-16", 17.55, 17.5, 17.48, 17.64, 37.00, 0, -0.06, 0.01, 0.04],
161 ["2016-05-17", 17.49, 17.48, 17.39, 17.53, 13.00, 0, -0.03, 0.03, 0.05],
162 ["2016-05-18", 17.41, 17.82, 17.39, 17.87, 46.00, 0, 0.01, 0.06, 0.06],
163 ["2016-05-19", 17.74, 17.81, 17.67, 17.86, 17.00, 0, -0.01, 0.05, 0.05],
164 ["2016-05-20", 17.76, 17.88, 17.7, 17.93, 14.00, 0, -0.03, 0.04, 0.06],
165 ["2016-05-23", 17.88, 17.52, 17.48, 17.97, 16.00, 0, -0.09, 0.02, 0.06],
166 ["2016-05-24", 17.51, 17.33, 17.32, 17.51, 8.00, 0, -0.09, 0.03, 0.07],
167 ["2016-05-25", 17.59, 17.55, 17.44, 17.59, 10.00, 0, -0.03, 0.07, 0.08],
168 ["2016-05-26", 17.5, 17.69, 17.5, 17.8, 12.00, 0, 0.00, 0.09, 0.09],
169 ["2016-05-27", 17.77, 17.66, 17.62, 17.77, 7.00, 0, 0.03, 0.10, 0.09],
170 ["2016-05-30", 17.75, 17.84, 17.62, 17.87, 20.00, 0, 0.08, 0.12, 0.08],
171 ["2016-05-31", 17.88, 18.0, 17.81, 18.03, 41.00, 0, 0.10, 0.12, 0.07],
172 ["2016-06-01", 18.09, 17.83, 17.73, 18.09, 22.00, 0, 0.08, 0.10, 0.06],
173 ["2016-06-02", 17.82, 17.73, 17.66, 17.88, 10.00, 0, 0.07, 0.08, 0.05],
174 ["2016-06-03", 17.8, 17.78, 17.71, 17.83, 9.00, 0, 0.08, 0.08, 0.04],
175 ["2016-06-06", 17.73, 17.64, 17.56, 17.83, 12.00, 0, 0.07, 0.06, 0.03],
176 ["2016-06-07", 17.76, 17.8, 17.59, 17.87, 11.00, 0, 0.08, 0.06, 0.02],
177 ["2016-06-08", 17.75, 17.77, 17.65, 17.84, 9.00, 0, 0.04, 0.03, 0.01],
178 ["2016-06-13", 17.58, 17.32, 17.29, 17.79, 16.00, 0, -0.02, -0.01, 0.00],
179 ["2016-06-14", 17.33, 17.38, 17.29, 17.5, 10.00, 0, -0.01, 0.00, 0.00],
180 ["2016-06-15", 17.25, 17.39, 17.25, 17.46, 18.00, 0, 0.00, 0.01, 0.00],
181 ["2016-06-16", 17.26, 17.4, 17.26, 17.46, 22.00, 0, 0.01, 0.01, 0.00],
182 ["2016-06-17", 17.38, 17.5, 17.37, 17.67, 13.00, 0, 0.03, 0.02, 0.00],
183 ["2016-06-20", 17.62, 17.51, 17.42, 17.63, 15.00, 0, 0.03, 0.01, -0.00],
184 ["2016-06-21", 17.53, 17.54, 17.5, 17.7, 11.00, 0, 0.02, 0.00, -0.01],
185 ["2016-06-22", 17.5, 17.5, 17.46, 17.6, 10.00, 0, -0.01, -0.01, -0.01],
186 ["2016-06-23", 17.52, 17.26, 17.24, 17.53, 16.00, 0, -0.04, -0.03, -0.01],
187 ["2016-06-24", 17.26, 17.25, 17.18, 17.38, 60.00, 0, -0.03, -0.02, -0.00],
188 ["2016-06-27", 17.25, 17.28, 17.18, 17.33, 19.00, 0, -0.01, -0.00, 0.00],
189 ["2016-06-28", 17.25, 17.29, 17.21, 17.32, 13.00, 0, 0.02, 0.01, 0.00],
190 ["2016-06-29", 17.31, 17.45, 17.27, 17.49, 21.00, 0, 0.07, 0.04, 0.00],
191 ["2016-06-30", 17.47, 17.5, 17.39, 17.55, 17.00, 0, 0.11, 0.04, -0.01],
192 ["2016-07-01", 17.5, 17.63, 17.49, 17.66, 10.00, 0, 0.14, 0.05, -0.03],
193 ["2016-07-04", 17.63, 17.72, 17.63, 17.92, 17.00, 0, 0.16, 0.03, -0.05],
194 ["2016-07-05", 17.79, 17.56, 17.45, 17.79, 18.00, 0, 0.14, 0.00, -0.07],
195 ["2016-07-06", 17.53, 17.42, 17.31, 17.54, 20.00, 0, 0.14, -0.02, -0.09],
196 ["2016-07-07", 17.41, 17.51, 17.35, 17.52, 15.00, 0, 0.16, -0.03, -0.11],
197 ["2016-07-08", 17.5, 17.39, 17.35, 17.51, 15.00, 0, 0.16, -0.05, -0.13],
198 ["2016-07-11", 17.49, 17.48, 17.4, 17.55, 16.00, 0, 0.17, -0.07, -0.15],
199 ["2016-07-12", 17.48, 17.71, 17.46, 17.75, 25.00, 0, 0.16, -0.10, -0.18],
200 ["2016-07-13", 17.13, 17.05, 17.02, 17.39, 28.00, 0, 0.07, -0.17, -0.20],
201 ["2016-07-14", 17.07, 17.09, 17.0, 17.16, 12.00, 0, 0.08, -0.17, -0.21],
202 ["2016-07-15", 17.08, 17.14, 17.08, 17.17, 11.00, 0, 0.09, -0.18, -0.22],
203 ["2016-07-18", 17.15, 17.26, 17.13, 17.49, 24.00, 0, 0.10, -0.19, -0.23],
204 ["2016-07-19", 17.26, 17.12, 17.09, 17.33, 13.00, 0, 0.07, -0.21, -0.25],
205 ["2016-07-20", 17.1, 17.07, 17.02, 17.14, 11.00, 0, 0.06, -0.23, -0.26],
206 ["2016-07-21", 17.07, 17.24, 17.07, 17.27, 14.00, 0, 0.07, -0.23, -0.27],
207 ["2016-07-22", 17.25, 17.08, 17.03, 17.25, 10.00, 0, 0.04, -0.26, -0.28],
208 ["2016-07-25", 17.09, 17.12, 17.01, 17.18, 8.00, 0, 0.04, -0.26, -0.28],
209 ["2016-07-26", 17.05, 17.17, 17.05, 17.2, 11.00, 0, 0.04, -0.27, -0.29],
210 ["2016-07-27", 17.2, 17.37, 16.89, 17.38, 32.00, 0, 0.02, -0.28, -0.29],
211 ["2016-07-28", 17.19, 17.14, 17.09, 17.29, 19.00, 0, -0.04, -0.32, -0.30],
212 ["2016-07-29", 17.15, 17.16, 17.04, 17.23, 12.00, 0, -0.08, -0.33, -0.29],
213 ["2016-08-01", 17.15, 17.18, 17.1, 17.24, 19.00, 0, -0.13, -0.34, -0.28],
214 ["2016-08-02", 17.21, 17.15, 17.12, 17.25, 9.00, 0, -0.19, -0.36, -0.26],
215 ["2016-08-03", 17.08, 17.07, 17.01, 17.16, 9.00, 0, -0.25, -0.36, -0.24],
216 ["2016-08-04", 17.11, 17.06, 16.98, 17.12, 11.00, 1, -0.29, -0.35, -0.20],
217 ["2016-08-05", 17.06, 17.1, 17.05, 17.15, 16.00, 0, -0.33, -0.32, -0.16],
218 ["2016-08-08", 17.14, 17.13, 17.07, 17.15, 13.00, 0, -0.35, -0.29, -0.11],
219 ["2016-08-09", 17.13, 17.17, 17.1, 17.2, 25.00, 0, -0.35, -0.24, -0.06],
220 ["2016-08-10", 17.17, 17.28, 17.15, 17.29, 18.00, 0, -0.31, -0.17, -0.01],
221 ["2016-08-11", 17.3, 17.45, 17.26, 17.87, 31.00, 0, -0.24, -0.09, 0.03],
222 ["2016-08-12", 17.51, 17.99, 17.47, 18.0, 44.00, 0, -0.14, -0.00, 0.07],
223 ["2016-08-15", 18.1, 18.42, 18.02, 18.99, 81.00, 0, -0.09, 0.04, 0.09],
224 ["2016-08-16", 18.64, 18.31, 18.12, 18.87, 60.00, 0, -0.10, 0.05, 0.10],
225 ["2016-08-17", 18.43, 18.4, 18.31, 18.68, 21.00, 0, -0.08, 0.08, 0.11],
226 ["2016-08-18", 18.33, 18.23, 18.13, 18.65, 32.00, 0, -0.07, 0.09, 0.13],
227 ["2016-08-19", 18.34, 18.62, 18.31, 18.75, 39.00, 0, 0.00, 0.14, 0.14],
228 ["2016-08-22", 18.62, 18.69, 18.51, 18.8, 20.00, 0, 0.01, 0.14, 0.13],
229 ["2016-08-23", 18.61, 18.66, 18.52, 19.0, 28.00, 0, 0.01, 0.14, 0.13],
230 ["2016-08-24", 18.66, 18.62, 18.43, 18.7, 19.00, 0, 0.00, 0.13, 0.13],
231 ["2016-08-25", 18.57, 18.51, 18.19, 18.64, 19.00, 0, -0.00, 0.13, 0.13],
232 ["2016-08-26", 18.49, 18.55, 18.44, 18.6, 16.00, 0, 0.01, 0.13, 0.13],
233 ["2016-08-29", 18.46, 18.27, 18.03, 18.48, 20.00, 0, 0.01, 0.13, 0.13],
234 ["2016-08-30", 18.24, 18.44, 18.23, 18.52, 19.00, 0, 0.07, 0.17, 0.13],
235 ["2016-08-31", 18.36, 18.63, 18.36, 18.76, 15.00, 0, 0.13, 0.18, 0.12],
236 ["2016-09-01", 18.6, 18.62, 18.55, 18.78, 15.00, 0, 0.16, 0.18, 0.10],
237 ["2016-09-02", 18.52, 18.68, 18.48, 18.72, 17.00, 0, 0.19, 0.17, 0.08],
238 ["2016-09-05", 18.68, 18.75, 18.57, 18.82, 19.00, 0, 0.20, 0.15, 0.05],
239 ["2016-09-06", 18.75, 18.51, 18.43, 18.78, 17.00, 0, 0.18, 0.11, 0.02],
240 ["2016-09-07", 18.51, 18.56, 18.4, 18.62, 17.00, 0, 0.17, 0.08, -0.00],
241 ["2016-09-08", 18.58, 18.53, 18.48, 18.63, 8.00, 0, 0.13, 0.04, -0.03],
242 ["2016-09-09", 18.52, 18.33, 18.31, 18.57, 8.00, 0, 0.06, -0.02, -0.05],
243 ["2016-09-12", 18.16, 17.9, 17.81, 18.18, 28.00, 0, -0.02, -0.07, -0.06],
244 ["2016-09-13", 17.91, 17.91, 17.9, 18.08, 13.00, 0, -0.05, -0.08, -0.05],
245 ["2016-09-14", 17.99, 17.54, 17.48, 17.99, 22.00, 0, -0.09, -0.09, -0.05],
246 ["2016-09-19", 17.55, 17.81, 17.55, 17.88, 16.00, 0, -0.06, -0.06, -0.03],
247 ["2016-09-20", 17.8, 17.74, 17.67, 17.85, 10.00, 0, -0.06, -0.05, -0.02],
248 ["2016-09-21", 17.75, 17.88, 17.75, 17.95, 7.00, 0, -0.03, -0.03, -0.02],
249 ["2016-09-22", 17.99, 17.97, 17.88, 18.17, 12.00, 0, -0.02, -0.02, -0.01],
250 ["2016-09-23", 17.99, 17.98, 17.93, 18.09, 13.00, 0, -0.01, -0.01, -0.01],
251 ["2016-09-26", 17.91, 18.0, 17.85, 18.09, 14.00, 0, -0.00, -0.01, -0.01],
252 ["2016-09-27", 17.97, 18.07, 17.94, 18.1, 10.00, 0, 0.00, -0.01, -0.01],
253 ["2016-09-28", 18.06, 17.89, 17.83, 18.06, 10.00, 0, -0.00, -0.01, -0.01],
254 ["2016-09-29", 17.96, 18.0, 17.92, 18.07, 10.00, 0, 0.03, 0.01, -0.01],
255 ["2016-09-30", 17.96, 18.0, 17.95, 18.1, 8.00, 0, 0.06, 0.02, -0.01],
256 ["2016-10-10", 18.03, 18.3, 18.03, 18.38, 19.00, 0, 0.11, 0.04, -0.02],
257 ["2016-10-11", 18.33, 18.33, 18.26, 18.49, 12.00, 0, 0.10, 0.02, -0.04],
258 ["2016-10-12", 18.28, 18.15, 18.1, 18.31, 10.00, 0, 0.07, -0.02, -0.05],
259 ["2016-10-13", 18.15, 18.09, 18.05, 18.21, 10.00, 0, 0.06, -0.03, -0.06],
260 ["2016-10-14", 18.1, 18.1, 18.0, 18.15, 12.00, 0, 0.04, -0.05, -0.07],
261 ["2016-10-17", 18.07, 17.86, 17.83, 18.1, 12.00, 0, 0.01, -0.07, -0.08],
262 ["2016-10-18", 17.86, 17.93, 17.84, 17.99, 14.00, 0, 0.03, -0.07, -0.08],
263 ["2016-10-19", 17.93, 17.88, 17.83, 18.05, 11.00, 0, 0.03, -0.07, -0.08],
264 ["2016-10-20", 17.9, 17.89, 17.83, 17.98, 12.00, 0, 0.05, -0.06, -0.09],
265 ["2016-10-21", 17.91, 17.91, 17.82, 17.93, 12.00, 0, 0.07, -0.06, -0.09],
266 ["2016-10-24", 17.93, 18.31, 17.86, 18.42, 29.00, 0, 0.11, -0.05, -0.10],
267 ["2016-10-25", 18.31, 18.13, 18.09, 18.46, 19.00, 0, 0.06, -0.09, -0.12],
268 ["2016-10-26", 18.12, 17.97, 17.95, 18.15, 14.00, 0, 0.02, -0.12, -0.13],
269 ["2016-10-27", 18.06, 17.81, 17.77, 18.06, 21.00, 0, -0.01, -0.13, -0.13],
270 ["2016-10-28", 17.8, 17.9, 17.8, 18.05, 20.00, 0, -0.01, -0.13, -0.13],
271 ["2016-10-31", 17.87, 17.86, 17.72, 17.96, 12.00, 0, -0.02, -0.14, -0.13],
272 ["2016-11-01", 17.87, 17.98, 17.79, 17.99, 18.00, 0, -0.03, -0.14, -0.12],
273 ["2016-11-02", 17.86, 17.84, 17.76, 17.94, 30.00, 0, -0.06, -0.15, -0.12],
274 ["2016-11-03", 17.83, 17.93, 17.79, 17.97, 27.00, 0, -0.07, -0.14, -0.11],
275 ["2016-11-04", 17.9, 17.91, 17.87, 18.0, 26.00, 0, -0.09, -0.15, -0.10],
276 ["2016-11-07", 17.91, 17.89, 17.85, 17.93, 20.00, 0, -0.11, -0.14, -0.09],
277 ["2016-11-08", 17.92, 17.99, 17.89, 18.06, 26.00, 0, -0.12, -0.13, -0.07],
278 ["2016-11-09", 18.0, 17.89, 17.77, 18.08, 34.00, 0, -0.15, -0.13, -0.06],
279 ["2016-11-10", 17.95, 18.0, 17.94, 18.11, 27.00, 0, -0.15, -0.11, -0.03],
280 ["2016-11-11", 17.95, 18.02, 17.93, 18.08, 27.00, 0, -0.17, -0.10, -0.01],
281 ["2016-11-14", 18.0, 18.04, 17.95, 18.25, 35.00, 0, -0.18, -0.08, 0.01],
282 ["2016-11-15", 18.1, 18.18, 18.03, 18.24, 25.00, 0, -0.18, -0.06, 0.04],
283 ["2016-11-16", 18.23, 18.12, 18.05, 18.29, 23.00, 0, -0.21, -0.04, 0.06],
284 ["2016-11-17", 18.11, 18.12, 18.01, 18.14, 27.00, 0, -0.21, -0.01, 0.09],
285 ["2016-11-18", 18.12, 18.1, 18.03, 18.16, 18.00, 0, -0.19, 0.03, 0.12],
286 ["2016-11-21", 18.08, 18.34, 18.08, 18.68, 41.00, 0, -0.13, 0.08, 0.15],
287 ["2016-11-22", 18.37, 18.37, 18.28, 18.49, 52.00, 0, -0.09, 0.12, 0.17],
288 ["2016-11-23", 18.4, 18.84, 18.37, 18.9, 66.00, 0, -0.02, 0.17, 0.18],
289 ["2016-11-24", 18.77, 18.74, 18.61, 18.97, 26.00, 0, -0.02, 0.17, 0.18],
290 ["2016-11-25", 18.8, 18.99, 18.66, 19.02, 40.00, 0, -0.01, 0.18, 0.19],
291 ["2016-11-28", 19.1, 18.65, 18.52, 19.2, 85.00, 0, -0.06, 0.16, 0.19],
292 ["2016-11-29", 18.65, 18.75, 18.51, 18.76, 49.00, 0, -0.06, 0.17, 0.20],
293 ["2016-11-30", 18.76, 18.55, 18.47, 18.82, 39.00, 0, -0.08, 0.17, 0.21],
294 ["2016-12-01", 18.55, 18.49, 18.41, 18.64, 53.00, 0, -0.06, 0.19, 0.22],
295 ["2016-12-02", 18.53, 18.49, 18.24, 18.54, 48.00, 0, -0.02, 0.21, 0.23],
296 ["2016-12-05", 18.39, 18.66, 18.34, 18.67, 50.00, 0, 0.03, 0.25, 0.23],
297 ["2016-12-06", 18.66, 18.6, 18.57, 18.78, 31.00, 0, 0.08, 0.26, 0.23],
298 ["2016-12-07", 18.65, 18.62, 18.58, 18.71, 12.00, 0, 0.15, 0.29, 0.21],
299 ["2016-12-08", 18.67, 18.76, 18.62, 18.88, 26.00, 0, 0.25, 0.32, 0.19],
300 ["2016-12-09", 18.76, 19.2, 18.75, 19.34, 62.00, 0, 0.34, 0.33, 0.16],
301 ["2016-12-12", 19.16, 19.25, 18.9, 19.65, 79.00, 1, 0.34, 0.28, 0.11],
302 ["2016-12-13", 19.09, 18.88, 18.81, 19.2, 24.00, 0, 0.27, 0.20, 0.06],
303 ["2016-12-14", 18.8, 18.82, 18.8, 19.14, 32.00, 0, 0.23, 0.13, 0.02],
304 ["2016-12-15", 18.73, 18.24, 18.2, 18.73, 36.00, 0, 0.13, 0.05, -0.01],
305 ["2016-12-16", 18.24, 18.18, 18.12, 18.4, 24.00, 0, 0.10, 0.02, -0.03],
306 ["2016-12-19", 18.15, 18.01, 17.93, 18.18, 24.00, 0, 0.06, -0.02, -0.05],
307 ["2016-12-20", 17.99, 17.79, 17.7, 17.99, 29.00, 1, 0.02, -0.05, -0.05],
308 ["2016-12-21", 17.83, 17.81, 17.77, 17.98, 30.00, 0, 0.00, -0.05, -0.06],
309 ["2016-12-22", 17.85, 17.72, 17.65, 17.85, 21.00, 0, -0.03, -0.07, -0.06],
310 ["2016-12-23", 17.77, 17.6, 17.54, 17.77, 18.00, 0, -0.04, -0.08, -0.05],
311 ["2016-12-26", 17.56, 17.75, 17.39, 17.77, 16.00, 0, -0.04, -0.07, -0.05],
312 ["2016-12-27", 17.73, 17.71, 17.65, 17.82, 10.00, 0, -0.06, -0.07, -0.04],
313 ["2016-12-28", 17.72, 17.62, 17.49, 17.77, 26.00, 0, -0.09, -0.07, -0.03],
314 ["2016-12-29", 17.6, 17.49, 17.43, 17.62, 28.00, 0, -0.09, -0.06, -0.02],
315 ["2016-12-30", 17.53, 17.6, 17.47, 17.61, 22.00, 0, -0.05, -0.03, -0.01],
316 ["2017-01-03", 17.6, 17.92, 17.57, 17.98, 28.00, 1, 0.00, 0.00, 0.00],
317 ]
318
319
320 def split_data(origin_data) -> dict:
321 datas = []
322 times = []
323 vols = []
324 macds = []
325 difs = []
326 deas = []
327
328 for i in range(len(origin_data)):
329 datas.append(origin_data[i][1:])
330 times.append(origin_data[i][0:1][0])
331 vols.append(origin_data[i][5])
332 macds.append(origin_data[i][7])
333 difs.append(origin_data[i][8])
334 deas.append(origin_data[i][9])
335 vols = [int(v) for v in vols]
336
337 return {
338 "datas": datas,
339 "times": times,
340 "vols": vols,
341 "macds": macds,
342 "difs": difs,
343 "deas": deas,
344 }
345
346
347 def split_data_part() -> Sequence:
348 mark_line_data = []
349 idx = 0
350 tag = 0
351 vols = 0
352 for i in range(len(data["times"])):
353 if data["datas"][i][5] != 0 and tag == 0:
354 idx = i
355 vols = data["datas"][i][4]
356 tag = 1
357 if tag == 1:
358 vols += data["datas"][i][4]
359 if data["datas"][i][5] != 0 or tag == 1:
360 mark_line_data.append(
361 [
362 {
363 "xAxis": idx,
364 "yAxis": float("%.2f" % data["datas"][idx][3])
365 if data["datas"][idx][1] > data["datas"][idx][0]
366 else float("%.2f" % data["datas"][idx][2]),
367 "value": vols,
368 },
369 {
370 "xAxis": i,
371 "yAxis": float("%.2f" % data["datas"][i][3])
372 if data["datas"][i][1] > data["datas"][i][0]
373 else float("%.2f" % data["datas"][i][2]),
374 },
375 ]
376 )
377 idx = i
378 vols = data["datas"][i][4]
379 tag = 2
380 if tag == 2:
381 vols += data["datas"][i][4]
382 if data["datas"][i][5] != 0 and tag == 2:
383 mark_line_data.append(
384 [
385 {
386 "xAxis": idx,
387 "yAxis": float("%.2f" % data["datas"][idx][3])
388 if data["datas"][i][1] > data["datas"][i][0]
389 else float("%.2f" % data["datas"][i][2]),
390 "value": str(float("%.2f" % (vols / (i - idx + 1)))) + " M",
391 },
392 {
393 "xAxis": i,
394 "yAxis": float("%.2f" % data["datas"][i][3])
395 if data["datas"][i][1] > data["datas"][i][0]
396 else float("%.2f" % data["datas"][i][2]),
397 },
398 ]
399 )
400 idx = i
401 vols = data["datas"][i][4]
402 return mark_line_data
403
404
405 def calculate_ma(day_count: int):
406 result: List[Union[float, str]] = []
407
408 for i in range(len(data["times"])):
409 if i < day_count:
410 result.append("-")
411 continue
412 sum_total = 0.0
413 for j in range(day_count):
414 sum_total += float(data["datas"][i - j][1])
415 result.append(abs(float("%.2f" % (sum_total / day_count))))
416 return result
417
418
419 def draw_chart():
420 kline = (
421 Kline()
422 .add_xaxis(xaxis_data=data["times"])
423 .add_yaxis(
424 series_name="",
425 y_axis=data["datas"],
426 itemstyle_opts=opts.ItemStyleOpts( #K线颜色
427 color="#ef232a",
428 color0="#14b143",
429 border_color="#ef232a",
430 border_color0="#14b143",
431 ),
432 markpoint_opts=opts.MarkPointOpts( # 标记点配置项
433 data=[
434 opts.MarkPointItem(type_="max", name="最大值", symbol="diamond"),
435 opts.MarkPointItem(type_="min", name="最小值", symbol="pin"),
436 ]
437 ),
438 # markline_opts=opts.MarkLineOpts( #标记线配置项
439 # label_opts=opts.LabelOpts(position="middle", color="blue", font_size=15), #加图标
440 # data=split_data_part(), # 标记线数据
441 # symbol=["circle", "none"],
442 # ),
443 )
444 # .set_series_opts( #系列配置项
445 # markarea_opts=opts.MarkAreaOpts(is_silent=True, data=split_data_part()) # 标记区域配置项
446 # )
447 .set_global_opts( #全局配置项
448 title_opts=opts.TitleOpts(title="600690_1m", pos_left="0"), #图表的标题，这里可以加变量吗，传参数
449 xaxis_opts=opts.AxisOpts(
450 type_="category",
451 is_scale=True,
452 boundary_gap=True, #K线和成交量对齐
453 axisline_opts=opts.AxisLineOpts(is_on_zero=False),
454 splitline_opts=opts.SplitLineOpts(is_show=False),
455 split_number=20,
456 min_="dataMin",
457 max_="dataMax",
458 ),
459 yaxis_opts=opts.AxisOpts(
460 is_scale=True, splitline_opts=opts.SplitLineOpts(is_show=True)
461 ),
462 tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line"),
463 datazoom_opts=[
464 opts.DataZoomOpts(is_show=False, type_="inside", xaxis_index=[0, 0], range_start=20, range_end=100),
465 opts.DataZoomOpts(is_show=True, xaxis_index=[0, 1], pos_top="97%", range_start=20, range_end=100),
466 opts.DataZoomOpts(is_show=False, xaxis_index=[0, 2], range_start=20, range_end=100),
467 ],
468
469 axispointer_opts=opts.AxisPointerOpts( # 三个图的 竖光标axis 连在一块 #坐标轴指示器配置项
470 is_show=True,
471 link=[{"xAxisIndex": "all"}],
472 label=opts.LabelOpts(background_color="#777"),
473 ),
474
475 brush_opts=opts.BrushOpts( #区域选择组件配置项
476 x_axis_index="all",
477 brush_link="all",
478 out_of_brush={"colorAlpha": 0.1},
479 brush_type="lineX",
480 ),
481
482 )
483 )
484
485 kline_line = (
486 Line()
487 .add_xaxis(xaxis_data=data["times"])
488 .add_yaxis(
489 series_name="MA10",
490 y_axis=calculate_ma(day_count=10),
491 is_smooth=True, # # 是否平滑曲线
492 is_hover_animation=False, #均线上的动画效果
493 linestyle_opts=opts.LineStyleOpts(width=1, opacity=1, type_= "solid",),
494 label_opts=opts.LabelOpts(is_show=False),
495 )
496 .add_yaxis(
497 series_name="MA30",
498 y_axis=calculate_ma(day_count=30),
499 is_smooth=True,
500 is_hover_animation=False,
501 linestyle_opts=opts.LineStyleOpts(width=1, opacity=1, type_= "solid",),
502 label_opts=opts.LabelOpts(is_show=False),
503 )
504 .set_global_opts( # 不知道为啥是类目轴 # 适用于离散的类目数据，为该类型时必须通过 data 设置类目数据
505 xaxis_opts=opts.AxisOpts(
506 type_="category",
507 grid_index=1,
508 axislabel_opts=opts.LabelOpts(is_show=False),
509 ),
510 yaxis_opts=opts.AxisOpts(
511 grid_index=1,
512 split_number=3,
513 axisline_opts=opts.AxisLineOpts(is_on_zero=False), #LabelOpts：标签配置项
514 axistick_opts=opts.AxisTickOpts(is_show=False), #坐标轴轴线配置项
515 splitline_opts=opts.SplitLineOpts(is_show=False), #坐标轴刻度配置项
516 axislabel_opts=opts.LabelOpts(is_show=True), # 分割线配置项
517 ),
518 )
519 )
520 # Overlap Kline + Line
521 overlap_kline_line = kline.overlap(kline_line) #Overlap：层叠多图
522
523 # Bar-1
524 bar_1 = (
525 Bar()
526 .add_xaxis(xaxis_data=data["times"])
527 .add_yaxis(
528 series_name="Volumn",
529 y_axis=data["vols"],
530 xaxis_index=1,
531 yaxis_index=1,
532 label_opts=opts.LabelOpts(is_show=False),
533 itemstyle_opts=opts.ItemStyleOpts(
534 color=JsCode(
535 """
536 function(params) {
537 var colorList;
538 if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
539 colorList = '#ef232a';
540 } else {
541 colorList = '#14b143';
542 }
543 return colorList;
544 }
545 """
546 )
547 ),
548 )
549 .set_global_opts(
550 xaxis_opts=opts.AxisOpts(
551 type_="category",
552 grid_index=1,
553 axislabel_opts=opts.LabelOpts(is_show=False),
554 ),
555 legend_opts=opts.LegendOpts(is_show=False),
556 )
557 )
558
559 # Bar-2 (Overlap Bar + Line)
560 bar_2 = (
561 Bar()
562 .add_xaxis(xaxis_data=data["times"])
563 .add_yaxis(
564 series_name="MACD",
565 y_axis=data["macds"],
566 xaxis_index=2,
567 yaxis_index=2,
568 label_opts=opts.LabelOpts(is_show=False),
569 itemstyle_opts=opts.ItemStyleOpts(
570 color=JsCode(
571 """
572 function(params) {
573 var colorList;
574 if (params.data >= 0) {
575 colorList = '#ef232a';
576 } else {
577 colorList = '#14b143';
578 }
579 return colorList;
580 }
581 """
582 )
583 ),
584 )
585 .set_global_opts(
586 xaxis_opts=opts.AxisOpts(
587 type_="category",
588 grid_index=2,
589 axislabel_opts=opts.LabelOpts(is_show=False),
590 ),
591 yaxis_opts=opts.AxisOpts(
592 grid_index=2,
593 split_number=4,
594 axisline_opts=opts.AxisLineOpts(is_on_zero=False),
595 axistick_opts=opts.AxisTickOpts(is_show=False),
596 splitline_opts=opts.SplitLineOpts(is_show=False),
597 axislabel_opts=opts.LabelOpts(is_show=True),
598 ),
599 legend_opts=opts.LegendOpts(is_show=False),
600 )
601 )
602
603 line_2 = (
604 Line()
605 .add_xaxis(xaxis_data=data["times"])
606 .add_yaxis(
607 series_name="DIF",
608 y_axis=data["difs"],
609 xaxis_index=2,
610 yaxis_index=2,
611 color="#000000",
612 label_opts=opts.LabelOpts(is_show=False),
613 )
614 .add_yaxis(
615 series_name="DEA",
616 y_axis=data["deas"],
617 xaxis_index=2,
618 yaxis_index=2,
619 color="#ffff000",
620 label_opts=opts.LabelOpts(is_show=False),
621 )
622 .set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
623 )
624 # 最下面的柱状图和折线图
625 overlap_bar_line = bar_2.overlap(line_2)
626
627 # 最后的 Grid
628 grid_chart = Grid(init_opts=opts.InitOpts(width="1400px", height="800px"))
629
630 # 这个是为了把 data.datas 这个数据写入到 html 中,还没想到怎么跨 series 传值
631 # demo 中的代码也是用全局变量传的
632 grid_chart.add_js_funcs("var barData = {}".format(data["datas"]))
633
634 # K线图和 MA5 的折线图
635 grid_chart.add(
636 overlap_kline_line,
637 grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="60%"),
638 )
639 # Volumn 柱状图
640 grid_chart.add(
641 bar_1,
642 grid_opts=opts.GridOpts(
643 pos_left="3%", pos_right="1%", pos_top="71%", height="10%"
644 ),
645 )
646 # MACD DIFS DEAS
647 grid_chart.add(
648 overlap_bar_line,
649 grid_opts=opts.GridOpts(
650 pos_left="3%", pos_right="1%", pos_top="82%", height="14%"
651 ),
652 )
653 grid_chart.render("professional_kline_chart.html")
654
655 os.system("professional_kline_chart.html")
656 if __name__ == "__main__":
657 data = split_data(origin_data=echarts_data)
658 draw_chart()
标签：12,pyecharts,示例,Python,0.01,2015,2016,data,opts From： https://www.cnblogs.com/long136/p/16888758.html