100天精通Python（可视化篇）——第99天：Pyecharts绘制多种炫酷K线图参数说明+代码实战
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![在这里插入图片描述]()
![在这里插入图片描述]()
一、K线图介绍
1. 说明
K线图是一种常用的金融图表，用于展示股票、期货、外汇等金融市场的价格走势。它由四个关键价格组成，分别是开盘价（Open）、最高价（High）、最低价（Low）和收盘价（Close）。K线图通过绘制矩形或蜡烛形状的图形来表示每个交易周期（如一天、一周或一个月）的价格波动情况。
K线图的主要特点是能够直观地显示价格的波动情况和交易行为，包括价格的涨跌、高低点、趋势等。它提供了丰富的信息，可以帮助分析师和投资者进行技术分析和决策。
2. 应用场景
- 技术分析：K线图是技术分析的重要工具，通过观察K线图的形态和走势，可以判断市场的趋势，识别价格的支撑和阻力位，预测价格的变化趋势。
- 交易决策：基于K线图的技术分析，可以制定交易策略，如买入或卖出的时机、止损和止盈的设置等，帮助投资者做出更明智的决策。
- 风险管理：K线图可以帮助投资者识别市场的风险和机会，及时调整仓位和风险控制措施，降低投资风险。
- 市场监测：K线图可以用于监测市场的整体情况和行业走势，帮助投资者了解市场的热点和趋势，进行市场分析和预测。
总之，K线图是金融市场分析和决策的重要工具，广泛应用于股票、期货、外汇等金融市场，帮助投资者更好地理解市场走势和价格波动，做出明智的投资决策。
二、配置说明
在Pyecharts中，绘制K线图时可以使用以下配置类来自定义图表的样式和交互效果：
Kline：K线图类，用于创建K线图对象。opts.AxisOpts：坐标轴配置类，用于设置横坐标轴和纵坐标轴的样式和属性。opts.DataZoomOpts：数据缩放配置类，用于设置数据缩放的样式和属性。opts.SplitAreaOpts：分割区域配置类，用于设置分割区域的样式和属性。opts.AreaStyleOpts：区域样式配置类，用于设置区域的填充颜色和透明度。opts.ItemStyleOpts：图表元素样式配置类，用于设置图表元素的颜色、边框颜色等属性。opts.MarkLineOpts：标线配置类，用于设置标线的样式和属性。opts.MarkLineItem：标线项类，用于设置标线的类型和数值。opts.MarkPointOpts：标点配置类，用于设置标点的样式和属性。opts.TooltipOpts：提示框配置类，用于设置提示框的样式和属性。opts.TitleOpts：标题配置类，用于设置图表的标题样式和属性。opts.InitOpts：初始化配置类，用于设置图表的初始化属性。opts.RenderOpts：渲染初始化配置类，用于设置图表的渲染初始化属性。
以上是一些常用的配置类，可以通过实例化这些类并设置相应的属性来自定义K线图的样式和交互效果。根据具体需求，可以灵活使用这些配置类来定制自己想要的K线图。
三、K线图实战
1. 普通k线图
下面我们绘制一个最简单的K线图：
from pyecharts import options as optsfrom pyecharts.charts import Kline# 准备K线图的数据x_data = ["2023-01-01", "2023-01-02", "2023-01-03", "2023-01-04", "2023-01-05"]y_data = [ [100, 120, 80, 110], # 第一天的K线数据：开盘价、最高价、最低价、收盘价 [110, 130, 100, 120], # 第二天的K线数据 [120, 140, 90, 110], # 第三天的K线数据 [110, 130, 100, 120], # 第四天的K线数据 [120, 140, 90, 110], # 第五天的K线数据]# 创建K线图对象c = ( Kline() .add_xaxis(xaxis_data=x_data) # 设置x轴数据 .add_yaxis( series_name="K线图", # 设置数据系列的名称 y_axis=y_data, # 设置y轴数据 itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"), # 设置K线图的颜色 ) .set_global_opts( xaxis_opts=opts.AxisOpts(is_scale=True), # 设置x轴选项，使其自适应 yaxis_opts=opts.AxisOpts(is_scale=True), # 设置y轴选项，使其自适应 title_opts=opts.TitleOpts(title="普通K线图"), # 设置标题选项 ))# 渲染图表c.render("kline.html")c.render_notebook()运行结果：
2. 添加辅助线
这里我们可以添加辅助线：
import randomfrom pyecharts import options as optsfrom pyecharts.charts import Kline# 随机数据data = []# 使用嵌套的循环结构生成双层随机嵌套列表for _ in range(30): inner_list = [] # 内层列表个数 for _ in range(4): random_num = random.randint(2000, 2500) inner_list.append(random_num) data.append(inner_list)# 创建K线图对象c = ( Kline() # 添加横坐标数据 .add_xaxis(["2023/7/{}".format(i + 1) for i in range(31)]) # 添加纵坐标数据 .add_yaxis( "kline", data, # 设置标线配置项，标记最大值 markline_opts=opts.MarkLineOpts( data=[opts.MarkLineItem(type_="max", value_dim="close")] ), ) # 设置全局配置项 .set_global_opts( xaxis_opts=opts.AxisOpts(is_scale=True), # 设置横坐标��配置项，is_scale=True表示自适应刻度 yaxis_opts=opts.AxisOpts( is_scale=True, # 设置纵坐标轴配置项，is_scale=True表示自适应刻度 splitarea_opts=opts.SplitAreaOpts( is_show=True, # 设置分割区域配置项，is_show=True表示显示分割区域 areastyle_opts=opts.AreaStyleOpts(opacity=1) # 设置分割区域样式配置项，opacity=1表示不透明 ), ), title_opts=opts.TitleOpts(title="K线图添加辅助线"), # 设置标题配置项 ))# 渲染为HTML文件c.render("K线图添加辅助线.html")# 在Jupyter Notebook中显示图表c.render_notebook()运行结果：
3. k线图鼠标缩放
当数据量很大的时候，我们可以不全部展示，可以通过鼠标缩放：
import randomfrom pyecharts import options as optsfrom pyecharts.charts import Kline# 随机数据data = []# 使用嵌套的循环结构生成双层随机嵌套列表for _ in range(30): inner_list = [] # 内层列表个数 for _ in range(4): random_num = random.randint(2000, 2500) inner_list.append(random_num) data.append(inner_list)# 创建K线图对象c = ( Kline() # 添加横坐标数据 .add_xaxis(["2023/7/{}".format(i + 1) for i in range(31)]) # 添加纵坐标数据 .add_yaxis( "kline", data, # 设置图表元素样式 itemstyle_opts=opts.ItemStyleOpts( color="#ec0000", color0="#00da3c", border_color="#8A0000", border_color0="#008F28", ), ) # 设置全局配置项 .set_global_opts( # 设置横坐标轴配置项，is_scale=True表示自适应刻度 xaxis_opts=opts.AxisOpts(is_scale=True), # 设置纵坐标轴配置项，is_scale=True表示自适应刻度 yaxis_opts=opts.AxisOpts( is_scale=True, # 设置分割区域配置项，is_show=True表示显示分割区域 splitarea_opts=opts.SplitAreaOpts( is_show=True, # 设置分割区域样式配置项，opacity=1表示不透明 areastyle_opts=opts.AreaStyleOpts(opacity=1) ), ), # 设置数据缩放配置项，type_="inside"表示内置缩放 datazoom_opts=[opts.DataZoomOpts(type_="inside")], # 设置标题配置项，title="K线图鼠标缩放"为标题内容 title_opts=opts.TitleOpts(title="K线图鼠标缩放"), ))# 渲染为HTML文件c.render("K线图鼠标缩放.html")# 在Jupyter Notebook中显示图表c.render_notebook()运行结果：
4. 添加数据缩放滑块
可以在K线图底部添加数据缩放滑块用于缩放数据：
import randomfrom pyecharts import options as optsfrom pyecharts.charts import Kline# 随机数据data = []# 使用嵌套的循环结构生成双层随机嵌套列表for _ in range(30): inner_list = [] # 内层列表个数 for _ in range(4): random_num = random.randint(2000, 2500) inner_list.append(random_num) data.append(inner_list)# 创建K线图对象c = ( Kline() # 添加横坐标数据 .add_xaxis(["2023/7/{}".format(i + 1) for i in range(31)]) # 添加纵坐标数据 .add_yaxis("kline", data) # 设置全局配置项 .set_global_opts( xaxis_opts=opts.AxisOpts(is_scale=True), # 设置横坐标轴配置项，is_scale=True表示自适应刻度 yaxis_opts=opts.AxisOpts( is_scale=True, # 设置纵坐标轴配置项，is_scale=True表示自适应刻度 splitarea_opts=opts.SplitAreaOpts( is_show=True, # 设置分割区域配置项，is_show=True表示显示分割区域 areastyle_opts=opts.AreaStyleOpts(opacity=1) # 设置分割区域样式配置项，opacity=1表示不透明 ), ), datazoom_opts=[opts.DataZoomOpts()], # 设置数据缩放配置项位置在底部偏下 title_opts=opts.TitleOpts(title="K线图数据缩放滑块"), # 设置标题配置项 ))# 渲染为HTML文件c.render("K线图数据缩放滑块.html")# 在Jupyter Notebook中显示图表c.render_notebook()运行结果：
5. K线周期图表
下面我们绘制一个K线周期图表（开源）：
from typing import List, Sequence, Unionfrom pyecharts import options as optsfrom pyecharts.commons.utils import JsCodefrom pyecharts.charts import Kline, Line, Bar, Grid# 数据echarts_data = [ ["2022-10-16", 18.4, 18.58, 18.33, 18.79, 67.00, 1, 0.04, 0.11, 0.09], ["2022-10-19", 18.56, 18.25, 18.19, 18.56, 55.00, 0, -0.00, 0.08, 0.09], ["2022-10-20", 18.3, 18.22, 18.05, 18.41, 37.00, 0, 0.01, 0.09, 0.09], ["2022-10-21", 18.18, 18.69, 18.02, 18.98, 89.00, 0, 0.03, 0.10, 0.08], ["2022-10-22", 18.42, 18.29, 18.22, 18.48, 43.00, 0, -0.06, 0.05, 0.08], ["2022-10-23", 18.26, 18.19, 18.08, 18.36, 46.00, 0, -0.10, 0.03, 0.09], ["2022-10-26", 18.33, 18.07, 17.98, 18.35, 65.00, 0, -0.15, 0.03, 0.10], ["2022-10-27", 18.08, 18.04, 17.88, 18.13, 37.00, 0, -0.19, 0.03, 0.12], ["2022-10-28", 17.96, 17.86, 17.82, 17.99, 35.00, 0, -0.24, 0.03, 0.15], ["2022-10-29", 17.85, 17.81, 17.8, 17.93, 27.00, 0, -0.24, 0.06, 0.18], ["2022-10-30", 17.79, 17.93, 17.78, 18.08, 43.00, 0, -0.22, 0.11, 0.22], ["2022-11-02", 17.78, 17.83, 17.78, 18.04, 27.00, 0, -0.20, 0.15, 0.25], ["2022-11-03", 17.84, 17.9, 17.84, 18.06, 34.00, 0, -0.12, 0.22, 0.28], ["2022-11-04", 17.97, 18.36, 17.85, 18.39, 62.00, 0, -0.00, 0.30, 0.30], ["2022-11-05", 18.3, 18.57, 18.18, 19.08, 177.00, 0, 0.07, 0.33, 0.30], ["2022-11-06", 18.53, 18.68, 18.3, 18.71, 95.00, 0, 0.12, 0.35, 0.29], ["2022-11-09", 18.75, 19.08, 18.75, 19.98, 202.00, 1, 0.16, 0.35, 0.27], ["2022-11-10", 18.85, 18.64, 18.56, 18.99, 85.00, 0, 0.09, 0.29, 0.25], ["2022-11-11", 18.64, 18.44, 18.31, 18.64, 50.00, 0, 0.06, 0.27, 0.23], ["2022-11-12", 18.55, 18.27, 18.17, 18.57, 43.00, 0, 0.05, 0.25, 0.23], ["2022-11-13", 18.13, 18.14, 18.09, 18.34, 35.00, 0, 0.05, 0.24, 0.22], ["2022-11-16", 18.01, 18.1, 17.93, 18.17, 34.00, 0, 0.07, 0.25, 0.21], ["2022-11-17", 18.2, 18.14, 18.08, 18.45, 58.00, 0, 0.11, 0.25, 0.20], ["2022-11-18", 18.23, 18.16, 18.0, 18.45, 47.00, 0, 0.13, 0.25, 0.19], ["2022-11-19", 18.08, 18.2, 18.05, 18.25, 32.00, 0, 0.15, 0.24, 0.17], ["2022-11-20", 18.15, 18.15, 18.11, 18.29, 36.00, 0, 0.13, 0.21, 0.15], ["2022-11-23", 18.16, 18.19, 18.12, 18.34, 47.00, 0, 0.11, 0.18, 0.13], ["2022-11-24", 18.23, 17.88, 17.7, 18.23, 62.00, 0, 0.03, 0.13, 0.11], ["2022-11-25", 17.85, 17.73, 17.56, 17.85, 66.00, 0, -0.03, 0.09, 0.11], ["2022-11-26", 17.79, 17.53, 17.5, 17.92, 63.00, 0, -0.10, 0.06, 0.11], ["2022-11-27", 17.51, 17.04, 16.9, 17.51, 67.00, 0, -0.16, 0.05, 0.13], ["2022-11-30", 17.07, 17.2, 16.98, 17.32, 55.00, 0, -0.12, 0.09, 0.15], ["2022-12-01", 17.28, 17.11, 16.91, 17.28, 39.00, 0, -0.09, 0.12, 0.16], ["2022-12-02", 17.13, 17.91, 17.05, 17.99, 102.00, 0, -0.01, 0.17, 0.18], ["2022-12-03", 17.8, 17.78, 17.61, 17.98, 71.00, 0, -0.09, 0.14, 0.18], ["2022-12-04", 17.6, 17.25, 17.13, 17.69, 51.00, 0, -0.18, 0.10, 0.19], ["2022-12-07", 17.2, 17.39, 17.15, 17.45, 43.00, 0, -0.19, 0.12, 0.22], ["2022-12-08", 17.3, 17.42, 17.18, 17.62, 45.00, 0, -0.23, 0.13, 0.24], ["2022-12-09", 17.33, 17.39, 17.32, 17.59, 44.00, 0, -0.29, 0.13, 0.28], ["2022-12-10", 17.39, 17.26, 17.21, 17.65, 44.00, 0, -0.37, 0.13, 0.32], ["2022-12-11", 17.23, 16.92, 16.66, 17.26, 114.00, 1, -0.44, 0.15, 0.37], ["2022-12-14", 16.75, 17.06, 16.5, 17.09, 94.00, 0, -0.44, 0.21, 0.44], ["2022-12-15", 17.03, 17.03, 16.9, 17.06, 46.00, 0, -0.44, 0.28, 0.50], ["2022-12-16", 17.08, 16.96, 16.87, 17.09, 30.00, 0, -0.40, 0.36, 0.56], ["2022-12-17", 17.0, 17.1, 16.95, 17.12, 50.00, 0, -0.30, 0.47, 0.62], ["2022-12-18", 17.09, 17.52, 17.04, 18.06, 156.00, 0, -0.14, 0.59, 0.66], ["2022-12-21", 17.43, 18.23, 17.35, 18.45, 152.00, 1, 0.02, 0.69, 0.68], ["2022-12-22", 18.14, 18.27, 18.06, 18.32, 94.00, 0, 0.08, 0.72, 0.68], ["2022-12-23", 18.28, 18.19, 18.17, 18.71, 108.00, 0, 0.13, 0.73, 0.67], ["2022-12-24", 18.18, 18.14, 18.01, 18.31, 37.00, 0, 0.19, 0.74, 0.65], ["2022-12-25", 18.22, 18.33, 18.2, 18.36, 48.00, 0, 0.26, 0.75, 0.62], ["2022-12-28", 18.35, 17.84, 17.8, 18.39, 48.00, 0, 0.27, 0.72, 0.59], ["2022-12-29", 17.83, 17.94, 17.71, 17.97, 36.00, 0, 0.36, 0.73, 0.55], ["2022-12-30", 17.9, 18.26, 17.55, 18.3, 71.00, 1, 0.43, 0.71, 0.50], ["2022-12-31", 18.12, 17.99, 17.91, 18.33, 72.00, 0, 0.40, 0.63, 0.43], ["2023-01-04", 17.91, 17.28, 17.16, 17.95, 37.00, 1, 0.34, 0.55, 0.38], ["2023-01-05", 17.17, 17.23, 17.0, 17.55, 51.00, 0, 0.37, 0.51, 0.33], ["2023-01-06", 17.2, 17.31, 17.06, 17.33, 31.00, 0, 0.37, 0.46, 0.28], ["2023-01-07", 17.15, 16.67, 16.51, 17.15, 19.00, 0, 0.30, 0.37, 0.22], ["2023-01-08", 16.8, 16.81, 16.61, 17.06, 60.00, 0, 0.29, 0.32, 0.18], ["2023-01-11", 16.68, 16.04, 16.0, 16.68, 65.00, 0, 0.20, 0.24, 0.14], ["2023-01-12", 16.03, 15.98, 15.88, 16.25, 46.00, 0, 0.20, 0.21, 0.11], ["2023-01-13", 16.21, 15.87, 15.78, 16.21, 57.00, 0, 0.20, 0.18, 0.08], ["2023-01-14", 15.55, 15.89, 15.52, 15.96, 42.00, 0, 0.20, 0.16, 0.05], ["2023-01-15", 15.87, 15.48, 15.45, 15.92, 34.00, 1, 0.17, 0.11, 0.02], ["2023-01-18", 15.39, 15.42, 15.36, 15.7, 26.00, 0, 0.21, 0.10, -0.00], ["2023-01-19", 15.58, 15.71, 15.35, 15.77, 38.00, 0, 0.25, 0.09, -0.03], ["2023-01-20", 15.56, 15.52, 15.24, 15.68, 38.00, 0, 0.23, 0.05, -0.07], ["2023-01-21", 15.41, 15.3, 15.28, 15.68, 35.00, 0, 0.21, 0.00, -0.10], ["2023-01-22", 15.48, 15.28, 15.13, 15.49, 30.00, 0, 0.21, -0.02, -0.13], ["2023-01-25", 15.29, 15.48, 15.2, 15.49, 21.00, 0, 0.20, -0.06, -0.16], ["2023-01-26", 15.33, 14.86, 14.78, 15.39, 30.00, 0, 0.12, -0.13, -0.19], ["2023-01-27", 14.96, 15.0, 14.84, 15.22, 51.00, 0, 0.13, -0.14, -0.20], ["2023-01-28", 14.96, 14.72, 14.62, 15.06, 25.00, 0, 0.10, -0.17, -0.22], ["2023-01-29", 14.75, 14.99, 14.62, 15.08, 36.00, 0, 0.13, -0.17, -0.24], ["2023-02-01", 14.98, 14.72, 14.48, 15.18, 27.00, 0, 0.10, -0.21, -0.26], ["2023-02-02", 14.65, 14.85, 14.65, 14.95, 18.00, 0, 0.11, -0.21, -0.27], ["2023-02-03", 14.72, 14.67, 14.55, 14.8, 23.00, 0, 0.10, -0.24, -0.29], ["2023-02-04", 14.79, 14.88, 14.69, 14.93, 22.00, 0, 0.13, -0.24, -0.30], ["2023-02-05", 14.9, 14.86, 14.78, 14.93, 16.00, 0, 0.12, -0.26, -0.32], 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0.04, 0.06], ["2023-05-23", 17.88, 17.52, 17.48, 17.97, 16.00, 0, -0.09, 0.02, 0.06], ["2023-05-24", 17.51, 17.33, 17.32, 17.51, 8.00, 0, -0.09, 0.03, 0.07], ["2023-05-25", 17.59, 17.55, 17.44, 17.59, 10.00, 0, -0.03, 0.07, 0.08], ["2023-05-26", 17.5, 17.69, 17.5, 17.8, 12.00, 0, 0.00, 0.09, 0.09], ["2023-05-27", 17.77, 17.66, 17.62, 17.77, 7.00, 0, 0.03, 0.10, 0.09], ["2023-05-30", 17.75, 17.84, 17.62, 17.87, 20.00, 0, 0.08, 0.12, 0.08], ["2023-05-31", 17.88, 18.0, 17.81, 18.03, 41.00, 0, 0.10, 0.12, 0.07], ["2023-06-01", 18.09, 17.83, 17.73, 18.09, 22.00, 0, 0.08, 0.10, 0.06], ["2023-06-02", 17.82, 17.73, 17.66, 17.88, 10.00, 0, 0.07, 0.08, 0.05], ["2023-06-03", 17.8, 17.78, 17.71, 17.83, 9.00, 0, 0.08, 0.08, 0.04], ["2023-06-06", 17.73, 17.64, 17.56, 17.83, 12.00, 0, 0.07, 0.06, 0.03], ["2023-06-07", 17.76, 17.8, 17.59, 17.87, 11.00, 0, 0.08, 0.06, 0.02], ["2023-06-08", 17.75, 17.77, 17.65, 17.84, 9.00, 0, 0.04, 0.03, 0.01], ["2023-06-13", 17.58, 17.32, 17.29, 17.79, 16.00, 0, 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17.66, 10.00, 0, 0.14, 0.05, -0.03], ["2023-07-04", 17.63, 17.72, 17.63, 17.92, 17.00, 0, 0.16, 0.03, -0.05], ["2023-07-05", 17.79, 17.56, 17.45, 17.79, 18.00, 0, 0.14, 0.00, -0.07], ["2023-07-06", 17.53, 17.42, 17.31, 17.54, 20.00, 0, 0.14, -0.02, -0.09], ["2023-07-07", 17.41, 17.51, 17.35, 17.52, 15.00, 0, 0.16, -0.03, -0.11], ["2023-07-08", 17.5, 17.39, 17.35, 17.51, 15.00, 0, 0.16, -0.05, -0.13], ["2023-07-11", 17.49, 17.48, 17.4, 17.55, 16.00, 0, 0.17, -0.07, -0.15], ["2023-07-12", 17.48, 17.71, 17.46, 17.75, 25.00, 0, 0.16, -0.10, -0.18], ["2023-07-13", 17.13, 17.05, 17.02, 17.39, 28.00, 0, 0.07, -0.17, -0.20], ["2023-07-14", 17.07, 17.09, 17.0, 17.16, 12.00, 0, 0.08, -0.17, -0.21], ["2023-07-15", 17.08, 17.14, 17.08, 17.17, 11.00, 0, 0.09, -0.18, -0.22], ["2023-07-18", 17.15, 17.26, 17.13, 17.49, 24.00, 0, 0.10, -0.19, -0.23], ["2023-07-19", 17.26, 17.12, 17.09, 17.33, 13.00, 0, 0.07, -0.21, -0.25], ["2023-07-20", 17.1, 17.07, 17.02, 17.14, 11.00, 0, 0.06, -0.23, -0.26], ["2023-07-21", 17.07, 17.24, 17.07, 17.27, 14.00, 0, 0.07, -0.23, -0.27], ["2023-07-22", 17.25, 17.08, 17.03, 17.25, 10.00, 0, 0.04, -0.26, -0.28], ["2023-07-25", 17.09, 17.12, 17.01, 17.18, 8.00, 0, 0.04, -0.26, -0.28], ["2023-07-26", 17.05, 17.17, 17.05, 17.2, 11.00, 0, 0.04, -0.27, -0.29], ["2023-07-27", 17.2, 17.37, 16.89, 17.38, 32.00, 0, 0.02, -0.28, -0.29], ["2023-07-28", 17.19, 17.14, 17.09, 17.29, 19.00, 0, -0.04, -0.32, -0.30], ["2023-07-29", 17.15, 17.16, 17.04, 17.23, 12.00, 0, -0.08, -0.33, -0.29], ["2023-08-01", 17.15, 17.18, 17.1, 17.24, 19.00, 0, -0.13, -0.34, -0.28], ["2023-08-02", 17.21, 17.15, 17.12, 17.25, 9.00, 0, -0.19, -0.36, -0.26], ["2023-08-03", 17.08, 17.07, 17.01, 17.16, 9.00, 0, -0.25, -0.36, -0.24], ["2023-08-04", 17.11, 17.06, 16.98, 17.12, 11.00, 1, -0.29, -0.35, -0.20], ["2023-08-05", 17.06, 17.1, 17.05, 17.15, 16.00, 0, -0.33, -0.32, -0.16], ["2023-08-08", 17.14, 17.13, 17.07, 17.15, 13.00, 0, -0.35, -0.29, -0.11], ["2023-08-09", 17.13, 17.17, 17.1, 17.2, 25.00, 0, -0.35, -0.24, -0.06], ["2023-08-10", 17.17, 17.28, 17.15, 17.29, 18.00, 0, -0.31, -0.17, -0.01], ["2023-08-11", 17.3, 17.45, 17.26, 17.87, 31.00, 0, -0.24, -0.09, 0.03], ["2023-08-12", 17.51, 17.99, 17.47, 18.0, 44.00, 0, -0.14, -0.00, 0.07], ["2023-08-15", 18.1, 18.42, 18.02, 18.99, 81.00, 0, -0.09, 0.04, 0.09], ["2023-08-16", 18.64, 18.31, 18.12, 18.87, 60.00, 0, -0.10, 0.05, 0.10], ["2023-08-17", 18.43, 18.4, 18.31, 18.68, 21.00, 0, -0.08, 0.08, 0.11], ["2023-08-18", 18.33, 18.23, 18.13, 18.65, 32.00, 0, -0.07, 0.09, 0.13], ["2023-08-19", 18.34, 18.62, 18.31, 18.75, 39.00, 0, 0.00, 0.14, 0.14], ["2023-08-22", 18.62, 18.69, 18.51, 18.8, 20.00, 0, 0.01, 0.14, 0.13], ["2023-08-23", 18.61, 18.66, 18.52, 19.0, 28.00, 0, 0.01, 0.14, 0.13], ["2023-08-24", 18.66, 18.62, 18.43, 18.7, 19.00, 0, 0.00, 0.13, 0.13], ["2023-08-25", 18.57, 18.51, 18.19, 18.64, 19.00, 0, -0.00, 0.13, 0.13], ["2023-08-26", 18.49, 18.55, 18.44, 18.6, 16.00, 0, 0.01, 0.13, 0.13], ["2023-08-29", 18.46, 18.27, 18.03, 18.48, 20.00, 0, 0.01, 0.13, 0.13], ["2023-08-30", 18.24, 18.44, 18.23, 18.52, 19.00, 0, 0.07, 0.17, 0.13], ["2023-08-31", 18.36, 18.63, 18.36, 18.76, 15.00, 0, 0.13, 0.18, 0.12], ["2023-09-01", 18.6, 18.62, 18.55, 18.78, 15.00, 0, 0.16, 0.18, 0.10], ["2023-09-02", 18.52, 18.68, 18.48, 18.72, 17.00, 0, 0.19, 0.17, 0.08], ["2023-09-05", 18.68, 18.75, 18.57, 18.82, 19.00, 0, 0.20, 0.15, 0.05], ["2023-09-06", 18.75, 18.51, 18.43, 18.78, 17.00, 0, 0.18, 0.11, 0.02], ["2023-09-07", 18.51, 18.56, 18.4, 18.62, 17.00, 0, 0.17, 0.08, -0.00], ["2023-09-08", 18.58, 18.53, 18.48, 18.63, 8.00, 0, 0.13, 0.04, -0.03], ["2023-09-09", 18.52, 18.33, 18.31, 18.57, 8.00, 0, 0.06, -0.02, -0.05], ["2023-09-12", 18.16, 17.9, 17.81, 18.18, 28.00, 0, -0.02, -0.07, -0.06], ["2023-09-13", 17.91, 17.91, 17.9, 18.08, 13.00, 0, -0.05, -0.08, -0.05], ["2023-09-14", 17.99, 17.54, 17.48, 17.99, 22.00, 0, -0.09, -0.09, -0.05], ["2023-09-19", 17.55, 17.81, 17.55, 17.88, 16.00, 0, -0.06, -0.06, -0.03], ["2023-09-20", 17.8, 17.74, 17.67, 17.85, 10.00, 0, -0.06, -0.05, -0.02], ["2023-09-21", 17.75, 17.88, 17.75, 17.95, 7.00, 0, -0.03, -0.03, -0.02], ["2023-09-22", 17.99, 17.97, 17.88, 18.17, 12.00, 0, -0.02, -0.02, -0.01], ["2023-09-23", 17.99, 17.98, 17.93, 18.09, 13.00, 0, -0.01, -0.01, -0.01], ["2023-09-26", 17.91, 18.0, 17.85, 18.09, 14.00, 0, -0.00, -0.01, -0.01], ["2023-09-27", 17.97, 18.07, 17.94, 18.1, 10.00, 0, 0.00, -0.01, -0.01], ["2023-09-28", 18.06, 17.89, 17.83, 18.06, 10.00, 0, -0.00, -0.01, -0.01], ["2023-09-29", 17.96, 18.0, 17.92, 18.07, 10.00, 0, 0.03, 0.01, -0.01], ["2023-09-30", 17.96, 18.0, 17.95, 18.1, 8.00, 0, 0.06, 0.02, -0.01], ["2023-10-10", 18.03, 18.3, 18.03, 18.38, 19.00, 0, 0.11, 0.04, -0.02], ["2023-10-11", 18.33, 18.33, 18.26, 18.49, 12.00, 0, 0.10, 0.02, -0.04], ["2023-10-12", 18.28, 18.15, 18.1, 18.31, 10.00, 0, 0.07, -0.02, -0.05], ["2023-10-13", 18.15, 18.09, 18.05, 18.21, 10.00, 0, 0.06, -0.03, -0.06], ["2023-10-14", 18.1, 18.1, 18.0, 18.15, 12.00, 0, 0.04, -0.05, -0.07], ["2023-10-17", 18.07, 17.86, 17.83, 18.1, 12.00, 0, 0.01, -0.07, -0.08], ["2023-10-18", 17.86, 17.93, 17.84, 17.99, 14.00, 0, 0.03, -0.07, -0.08], ["2023-10-19", 17.93, 17.88, 17.83, 18.05, 11.00, 0, 0.03, -0.07, -0.08], ["2023-10-20", 17.9, 17.89, 17.83, 17.98, 12.00, 0, 0.05, -0.06, -0.09], ["2023-10-21", 17.91, 17.91, 17.82, 17.93, 12.00, 0, 0.07, -0.06, -0.09], ["2023-10-24", 17.93, 18.31, 17.86, 18.42, 29.00, 0, 0.11, -0.05, -0.10], ["2023-10-25", 18.31, 18.13, 18.09, 18.46, 19.00, 0, 0.06, -0.09, -0.12], ["2023-10-26", 18.12, 17.97, 17.95, 18.15, 14.00, 0, 0.02, -0.12, -0.13], ["2023-10-27", 18.06, 17.81, 17.77, 18.06, 21.00, 0, -0.01, -0.13, -0.13], ["2023-10-28", 17.8, 17.9, 17.8, 18.05, 20.00, 0, -0.01, -0.13, -0.13], ["2023-10-31", 17.87, 17.86, 17.72, 17.96, 12.00, 0, -0.02, -0.14, -0.13], ["2023-11-01", 17.87, 17.98, 17.79, 17.99, 18.00, 0, -0.03, -0.14, -0.12], ["2023-11-02", 17.86, 17.84, 17.76, 17.94, 30.00, 0, -0.06, -0.15, -0.12], ["2023-11-03", 17.83, 17.93, 17.79, 17.97, 27.00, 0, -0.07, -0.14, -0.11], ["2023-11-04", 17.9, 17.91, 17.87, 18.0, 26.00, 0, -0.09, -0.15, -0.10], ["2023-11-07", 17.91, 17.89, 17.85, 17.93, 20.00, 0, -0.11, -0.14, -0.09], ["2023-11-08", 17.92, 17.99, 17.89, 18.06, 26.00, 0, -0.12, -0.13, -0.07], ["2023-11-09", 18.0, 17.89, 17.77, 18.08, 34.00, 0, -0.15, -0.13, -0.06], ["2023-11-10", 17.95, 18.0, 17.94, 18.11, 27.00, 0, -0.15, -0.11, -0.03], ["2023-11-11", 17.95, 18.02, 17.93, 18.08, 27.00, 0, -0.17, -0.10, -0.01], ["2023-11-14", 18.0, 18.04, 17.95, 18.25, 35.00, 0, -0.18, -0.08, 0.01], ["2023-11-15", 18.1, 18.18, 18.03, 18.24, 25.00, 0, -0.18, -0.06, 0.04], ["2023-11-16", 18.23, 18.12, 18.05, 18.29, 23.00, 0, -0.21, -0.04, 0.06], ["2023-11-17", 18.11, 18.12, 18.01, 18.14, 27.00, 0, -0.21, -0.01, 0.09], ["2023-11-18", 18.12, 18.1, 18.03, 18.16, 18.00, 0, -0.19, 0.03, 0.12], ["2023-11-21", 18.08, 18.34, 18.08, 18.68, 41.00, 0, -0.13, 0.08, 0.15], ["2023-11-22", 18.37, 18.37, 18.28, 18.49, 52.00, 0, -0.09, 0.12, 0.17], ["2023-11-23", 18.4, 18.84, 18.37, 18.9, 66.00, 0, -0.02, 0.17, 0.18], ["2023-11-24", 18.77, 18.74, 18.61, 18.97, 26.00, 0, -0.02, 0.17, 0.18], ["2023-11-25", 18.8, 18.99, 18.66, 19.02, 40.00, 0, -0.01, 0.18, 0.19], ["2023-11-28", 19.1, 18.65, 18.52, 19.2, 85.00, 0, -0.06, 0.16, 0.19], ["2023-11-29", 18.65, 18.75, 18.51, 18.76, 49.00, 0, -0.06, 0.17, 0.20], ["2023-11-30", 18.76, 18.55, 18.47, 18.82, 39.00, 0, -0.08, 0.17, 0.21], ["2023-12-01", 18.55, 18.49, 18.41, 18.64, 53.00, 0, -0.06, 0.19, 0.22], ["2023-12-02", 18.53, 18.49, 18.24, 18.54, 48.00, 0, -0.02, 0.21, 0.23], ["2023-12-05", 18.39, 18.66, 18.34, 18.67, 50.00, 0, 0.03, 0.25, 0.23], ["2023-12-06", 18.66, 18.6, 18.57, 18.78, 31.00, 0, 0.08, 0.26, 0.23], ["2023-12-07", 18.65, 18.62, 18.58, 18.71, 12.00, 0, 0.15, 0.29, 0.21], ["2023-12-08", 18.67, 18.76, 18.62, 18.88, 26.00, 0, 0.25, 0.32, 0.19], ["2023-12-09", 18.76, 19.2, 18.75, 19.34, 62.00, 0, 0.34, 0.33, 0.16], ["2023-12-12", 19.16, 19.25, 18.9, 19.65, 79.00, 1, 0.34, 0.28, 0.11], ["2023-12-13", 19.09, 18.88, 18.81, 19.2, 24.00, 0, 0.27, 0.20, 0.06], ["2023-12-14", 18.8, 18.82, 18.8, 19.14, 32.00, 0, 0.23, 0.13, 0.02], ["2023-12-15", 18.73, 18.24, 18.2, 18.73, 36.00, 0, 0.13, 0.05, -0.01], ["2023-12-16", 18.24, 18.18, 18.12, 18.4, 24.00, 0, 0.10, 0.02, -0.03], ["2023-12-19", 18.15, 18.01, 17.93, 18.18, 24.00, 0, 0.06, -0.02, -0.05], ["2023-12-20", 17.99, 17.79, 17.7, 17.99, 29.00, 1, 0.02, -0.05, -0.05], ["2023-12-21", 17.83, 17.81, 17.77, 17.98, 30.00, 0, 0.00, -0.05, -0.06], ["2023-12-22", 17.85, 17.72, 17.65, 17.85, 21.00, 0, -0.03, -0.07, -0.06], ["2023-12-23", 17.77, 17.6, 17.54, 17.77, 18.00, 0, -0.04, -0.08, -0.05], ["2023-12-26", 17.56, 17.75, 17.39, 17.77, 16.00, 0, -0.04, -0.07, -0.05], ["2023-12-27", 17.73, 17.71, 17.65, 17.82, 10.00, 0, -0.06, -0.07, -0.04], ["2023-12-28", 17.72, 17.62, 17.49, 17.77, 26.00, 0, -0.09, -0.07, -0.03], ["2023-12-29", 17.6, 17.49, 17.43, 17.62, 28.00, 0, -0.09, -0.06, -0.02], ["2023-12-30", 17.53, 17.6, 17.47, 17.61, 22.00, 0, -0.05, -0.03, -0.01], ["2017-01-03", 17.6, 17.92, 17.57, 17.98, 28.00, 1, 0.00, 0.00, 0.00],]def split_data(origin_data) -> dict: datas = [] times = [] vols = [] macds = [] difs = [] deas = [] for i in range(len(origin_data)): datas.append(origin_data[i][1:]) times.append(origin_data[i][0:1][0]) vols.append(origin_data[i][5]) macds.append(origin_data[i][7]) difs.append(origin_data[i][8]) deas.append(origin_data[i][9]) vols = [int(v) for v in vols] return { "datas": datas, "times": times, "vols": vols, "macds": macds, "difs": difs, "deas": deas, }def split_data_part() -> Sequence: mark_line_data = [] idx = 0 tag = 0 vols = 0 for i in range(len(data["times"])): if data["datas"][i][5] != 0 and tag == 0: idx = i vols = data["datas"][i][4] tag = 1 if tag == 1: vols += data["datas"][i][4] if data["datas"][i][5] != 0 or tag == 1: mark_line_data.append( [ { "xAxis": idx, "yAxis": float("%.2f" % data["datas"][idx][3]) if data["datas"][idx][1] > data["datas"][idx][0] else float("%.2f" % data["datas"][idx][2]), "value": vols, }, { "xAxis": i, "yAxis": float("%.2f" % data["datas"][i][3]) if data["datas"][i][1] > data["datas"][i][0] else float("%.2f" % data["datas"][i][2]), }, ] ) idx = i vols = data["datas"][i][4] tag = 2 if tag == 2: vols += data["datas"][i][4] if data["datas"][i][5] != 0 and tag == 2: mark_line_data.append( [ { "xAxis": idx, "yAxis": float("%.2f" % data["datas"][idx][3]) if data["datas"][i][1] > data["datas"][i][0] else float("%.2f" % data["datas"][i][2]), "value": str(float("%.2f" % (vols / (i - idx + 1)))) + " M", }, { "xAxis": i, "yAxis": float("%.2f" % data["datas"][i][3]) if data["datas"][i][1] > data["datas"][i][0] else float("%.2f" % data["datas"][i][2]), }, ] ) idx = i vols = data["datas"][i][4] return mark_line_datadef calculate_ma(day_count: int): result: List[Union[float, str]] = [] for i in range(len(data["times"])): if i < day_count: result.append("-") continue sum_total = 0.0 for j in range(day_count): sum_total += float(data["datas"][i - j][1]) result.append(abs(float("%.2f" % (sum_total / day_count)))) return resultdef draw_chart(): kline = ( Kline() .add_xaxis(xaxis_data=data["times"]) .add_yaxis( series_name="", y_axis=data["datas"], itemstyle_opts=opts.ItemStyleOpts( color="#ef232a", color0="#14b143", border_color="#ef232a", border_color0="#14b143", ), markpoint_opts=opts.MarkPointOpts( data=[ opts.MarkPointItem(type_="max", name="最大值"), opts.MarkPointItem(type_="min", name="最小值"), ] ), markline_opts=opts.MarkLineOpts( label_opts=opts.LabelOpts( position="middle", color="blue", font_size=15 ), data=split_data_part(), symbol=["circle", "none"], ), ) .set_series_opts( markarea_opts=opts.MarkAreaOpts(is_silent=True, data=split_data_part()) ) .set_global_opts( title_opts=opts.TitleOpts(title="K线周期图表", pos_left="0"), xaxis_opts=opts.AxisOpts( type_="category", is_scale=True, boundary_gap=False, axisline_opts=opts.AxisLineOpts(is_on_zero=False), splitline_opts=opts.SplitLineOpts(is_show=False), split_number=20, min_="dataMin", max_="dataMax", ), yaxis_opts=opts.AxisOpts( is_scale=True, splitline_opts=opts.SplitLineOpts(is_show=True) ), tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line"), datazoom_opts=[ opts.DataZoomOpts( is_show=False, type_="inside", xaxis_index=[0, 0], range_end=100 ), opts.DataZoomOpts( # is_show=True, xaxis_index=[0, 1], pos_top="100%", range_end=100 ), opts.DataZoomOpts(is_show=False, xaxis_index=[0, 2], range_end=100), ], # 三个图的 axis 连在一块 # axispointer_opts=opts.AxisPointerOpts( # is_show=True, # link=[{"xAxisIndex": "all"}], # label=opts.LabelOpts(background_color="#777"), # ), ) ) kline_line = ( Line() .add_xaxis(xaxis_data=data["times"]) .add_yaxis( series_name="MA5", y_axis=calculate_ma(day_count=5), is_smooth=True, linestyle_opts=opts.LineStyleOpts(opacity=0.5), label_opts=opts.LabelOpts(is_show=False), ) .set_global_opts( xaxis_opts=opts.AxisOpts( type_="category", grid_index=1, axislabel_opts=opts.LabelOpts(is_show=False), ), yaxis_opts=opts.AxisOpts( grid_index=1, split_number=3, axisline_opts=opts.AxisLineOpts(is_on_zero=False), axistick_opts=opts.AxisTickOpts(is_show=False), splitline_opts=opts.SplitLineOpts(is_show=False), axislabel_opts=opts.LabelOpts(is_show=True), ), ) ) # Overlap Kline + Line overlap_kline_line = kline.overlap(kline_line) # Bar-1 bar_1 = ( Bar() .add_xaxis(xaxis_data=data["times"]) .add_yaxis( series_name="Volumn", y_axis=data["vols"], xaxis_index=1, yaxis_index=1, label_opts=opts.LabelOpts(is_show=False), # 根据 echarts demo 的原版是这么写的 # itemstyle_opts=opts.ItemStyleOpts( # color=JsCode(""" # function(params) { # var colorList; # if (data.datas[params.dataIndex][1]>data.datas[params.dataIndex][0]) { # colorList = '#ef232a'; # } else { # colorList = '#14b143'; # } # return colorList; # } # """) # ) # 改进后在 grid 中 add_js_funcs 后变成如下 itemstyle_opts=opts.ItemStyleOpts( color=JsCode( """ function(params) { var colorList; if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) { colorList = '#ef232a'; } else { colorList = '#14b143'; } return colorList; } """ ) ), ) .set_global_opts( xaxis_opts=opts.AxisOpts( type_="category", grid_index=1, axislabel_opts=opts.LabelOpts(is_show=False), ), legend_opts=opts.LegendOpts(is_show=False), ) ) # Bar-2 (Overlap Bar + Line) bar_2 = ( Bar() .add_xaxis(xaxis_data=data["times"]) .add_yaxis( series_name="MACD", y_axis=data["macds"], xaxis_index=2, yaxis_index=2, label_opts=opts.LabelOpts(is_show=False), itemstyle_opts=opts.ItemStyleOpts( color=JsCode( """ function(params) {var colorList;if (params.data >= 0) { colorList = '#ef232a';} else { colorList = '#14b143';}return colorList; } """ ) ), ) .set_global_opts( xaxis_opts=opts.AxisOpts( type_="category", grid_index=2, axislabel_opts=opts.LabelOpts(is_show=False), ), yaxis_opts=opts.AxisOpts( grid_index=2, split_number=4, axisline_opts=opts.AxisLineOpts(is_on_zero=False), axistick_opts=opts.AxisTickOpts(is_show=False), splitline_opts=opts.SplitLineOpts(is_show=False), axislabel_opts=opts.LabelOpts(is_show=True), ), legend_opts=opts.LegendOpts(is_show=False), ) ) line_2 = ( Line() .add_xaxis(xaxis_data=data["times"]) .add_yaxis( series_name="DIF", y_axis=data["difs"], xaxis_index=2, yaxis_index=2, label_opts=opts.LabelOpts(is_show=False), ) .add_yaxis( series_name="DIF", y_axis=data["deas"], xaxis_index=2, yaxis_index=2, label_opts=opts.LabelOpts(is_show=False), ) .set_global_opts(legend_opts=opts.LegendOpts(is_show=False)) ) # 最下面的柱状图和折线图 overlap_bar_line = bar_2.overlap(line_2) # 最后的 Grid grid_chart = Grid() # 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 # demo 中的代码也是用全局变量传的 grid_chart.add_js_funcs("var barData = {}".format(data["datas"])) # K线图和 MA5 的折线图 grid_chart.add( overlap_kline_line, grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="60%"), ) # Volumn 柱状图 grid_chart.add( bar_1, grid_opts=opts.GridOpts( pos_left="3%", pos_right="1%", pos_top="71%", height="10%" ), ) # MACD DIFS DEAS grid_chart.add( overlap_bar_line, grid_opts=opts.GridOpts( pos_left="3%", pos_right="1%", pos_top="82%", height="14%" ), ) grid_chart.render("professional_kline_chart.html")if __name__ == "__main__": data = split_data(origin_data=echarts_data) draw_chart()运行结果：
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来源地址：https://blog.csdn.net/yuan2019035055/article/details/132124804
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