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

文章目录

• 专栏导读
• 一、K线图介绍
• • 1. 说明
  • 2. 应用场景
• 二、配置说明
• 三、K线图实战
• • 1. 普通k线图
  • 2. 添加辅助线
  • 3. k线图鼠标缩放
  • 4. 添加数据缩放滑块
  • 5. K线周期图表
• 书籍推荐

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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

1. 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。
2. 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。
3. 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。
4. 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。

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：

1. Kline：K线图类，用于创建K线图对象。
2. 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。
3. opts.DataZoomOpts：数据缩放配置类，用于设置数据缩放的样式和属性。
4. opts.SplitAreaOpts：分割区域配置类，用于设置分割区域的样式和属性。
5. opts.AreaStyleOpts：区域样式配置类，用于设置区域的填充颜色和透明度。
6. 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。
7. opts.MarkLineOpts：标线配置类，用于设置标线的样式和属性。
8. opts.MarkLineItem：标线项类，用于设置标线的类型和数值。
9. opts.MarkPointOpts：标点配置类，用于设置标点的样式和属性。
10. opts.TooltipOpts：提示框配置类，用于设置提示框的样式和属性。
11. opts.TitleOpts：配置类，用于设置图表的样式和属性。
12. opts.InitOpts：初始化配置类，用于设置图表的初始化属性。
13. 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。

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")],        # 设置配置项，title="K线图鼠标缩放"为内容        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],    ["2023-02-15", 14.5, 14.66, 14.47, 14.82, 19.00, 0, 0.11, -0.28, -0.34],    ["2023-02-16", 14.77, 14.94, 14.72, 15.05, 26.00, 0, 0.14, -0.28, -0.35],    ["2023-02-17", 14.95, 15.03, 14.88, 15.07, 38.00, 0, 0.12, -0.31, -0.37],    ["2023-02-18", 14.95, 14.9, 14.87, 15.06, 28.00, 0, 0.07, -0.35, -0.39],    ["2023-02-19", 14.9, 14.75, 14.68, 14.94, 22.00, 0, 0.03, -0.38, -0.40],    ["2023-02-22", 14.88, 15.01, 14.79, 15.11, 38.00, 1, 0.01, -0.40, -0.40],    ["2023-02-23", 15.01, 14.83, 14.72, 15.01, 24.00, 0, -0.09, -0.45, -0.40],    ["2023-02-24", 14.75, 14.81, 14.67, 14.87, 21.00, 0, -0.17, -0.48, -0.39],    ["2023-02-25", 14.81, 14.25, 14.21, 14.81, 51.00, 1, -0.27, -0.50, -0.37],    ["2023-02-26", 14.35, 14.45, 14.28, 14.57, 28.00, 0, -0.26, -0.46, -0.33],    ["2023-02-29", 14.43, 14.56, 14.04, 14.6, 48.00, 0, -0.25, -0.41, -0.29],    ["2023-03-01", 14.56, 14.65, 14.36, 14.78, 32.00, 0, -0.21, -0.36, -0.25],    ["2023-03-02", 14.79, 14.96, 14.72, 14.97, 60.00, 0, -0.13, -0.29, -0.22],    ["2023-03-03", 14.95, 15.15, 14.91, 15.19, 53.00, 1, -0.05, -0.23, -0.21],    ["2023-03-04", 15.14, 15.97, 15.02, 16.02, 164.00, 1, 0.06, -0.17, -0.20],    ["2023-03-07", 15.9, 15.78, 15.65, 16.0, 41.00, 0, 0.04, -0.19, -0.21],    ["2023-03-08", 15.78, 15.96, 15.21, 15.99, 45.00, 0, 0.05, -0.19, -0.21],    ["2023-03-09", 15.73, 16.05, 15.41, 16.08, 74.00, 0, 0.03, -0.20, -0.22],    ["2023-03-10", 15.82, 15.66, 15.65, 15.98, 19.00, 0, -0.02, -0.23, -0.22],    ["2023-03-11", 15.59, 15.76, 15.42, 15.78, 32.00, 0, 0.01, -0.22, -0.22],    ["2023-03-14", 15.78, 15.72, 15.65, 16.04, 31.00, 0, 0.03, -0.20, -0.22],    ["2023-03-15", 15.81, 15.86, 15.6, 15.99, 35.00, 0, 0.10, -0.18, -0.23],    ["2023-03-16", 15.88, 16.42, 15.79, 16.45, 123.00, 0, 0.17, -0.16, -0.24], 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["2023-04-06", 16.21, 16.26, 16.19, 16.35, 30.00, 0, -0.20, -0.39, -0.29],    ["2023-04-07", 16.32, 16.1, 16.05, 16.35, 29.00, 1, -0.26, -0.39, -0.26],    ["2023-04-08", 16.0, 16.16, 15.98, 16.21, 22.00, 0, -0.28, -0.37, -0.23],    ["2023-04-11", 16.16, 16.31, 16.15, 16.57, 31.00, 0, -0.30, -0.33, -0.19],    ["2023-04-12", 16.41, 16.29, 16.12, 16.41, 17.00, 0, -0.31, -0.30, -0.14],    ["2023-04-13", 16.39, 16.48, 16.0, 16.68, 40.00, 0, -0.30, -0.25, -0.10],    ["2023-04-14", 16.5, 16.46, 16.37, 16.68, 22.00, 0, -0.27, -0.19, -0.06],    ["2023-04-15", 16.56, 16.93, 16.46, 17.04, 58.00, 0, -0.20, -0.12, -0.02],    ["2023-04-18", 16.76, 17.06, 16.72, 17.27, 50.00, 0, -0.16, -0.07, 0.01],    ["2023-04-19", 17.21, 17.11, 17.02, 17.23, 30.00, 0, -0.12, -0.02, 0.03],    ["2023-04-20", 17.11, 17.33, 16.8, 17.36, 78.00, 0, -0.04, 0.03, 0.05],    ["2023-04-21", 17.27, 17.69, 17.17, 17.93, 79.00, 0, 0.05, 0.08, 0.06],    ["2023-04-22", 17.6, 17.87, 17.56, 18.02, 55.00, 0, 0.09, 0.10, 0.05],    ["2023-04-25", 17.75, 17.9, 17.41, 17.96, 39.00, 1, 0.11, 0.09, 0.04],    ["2023-04-26", 17.81, 17.91, 17.6, 17.95, 39.00, 0, 0.12, 0.08, 0.02],    ["2023-04-27", 17.9, 17.88, 17.81, 17.95, 25.00, 0, 0.12, 0.06, 0.00],    ["2023-04-28", 17.93, 17.88, 17.67, 17.93, 28.00, 0, 0.11, 0.04, -0.01],    ["2023-04-29", 17.87, 17.75, 17.73, 17.92, 19.00, 0, 0.08, 0.01, -0.03],    ["2023-05-03", 17.79, 17.7, 17.56, 17.85, 35.00, 0, 0.05, -0.01, -0.04],    ["2023-05-04", 17.7, 17.65, 17.59, 17.71, 24.00, 0, 0.02, -0.04, -0.05],    ["2023-05-05", 17.65, 17.62, 17.46, 17.7, 20.00, 0, -0.03, -0.06, -0.05],    ["2023-05-06", 17.62, 17.32, 17.3, 17.65, 29.00, 0, -0.10, -0.09, -0.05],    ["2023-05-09", 17.33, 17.3, 17.21, 17.45, 23.00, 0, -0.13, -0.10, -0.03],    ["2023-05-10", 17.11, 17.04, 16.98, 17.41, 28.00, 0, -0.15, -0.09, -0.01],    ["2023-05-11", 17.06, 17.15, 17.06, 17.32, 20.00, 0, -0.12, -0.05, 0.01],    ["2023-05-12", 17.02, 17.46, 17.02, 17.58, 26.00, 0, -0.07, -0.01, 0.03],    ["2023-05-13", 17.41, 17.57, 17.34, 17.62, 23.00, 0, -0.06, 0.01, 0.03],    ["2023-05-16", 17.55, 17.5, 17.48, 17.64, 37.00, 0, -0.06, 0.01, 0.04],    ["2023-05-17", 17.49, 17.48, 17.39, 17.53, 13.00, 0, -0.03, 0.03, 0.05],    ["2023-05-18", 17.41, 17.82, 17.39, 17.87, 46.00, 0, 0.01, 0.06, 0.06],    ["2023-05-19", 17.74, 17.81, 17.67, 17.86, 17.00, 0, -0.01, 0.05, 0.05],    ["2023-05-20", 17.76, 17.88, 17.7, 17.93, 14.00, 0, -0.03, 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, -0.02, -0.01, 0.00],    ["2023-06-14", 17.33, 17.38, 17.29, 17.5, 10.00, 0, -0.01, 0.00, 0.00],    ["2023-06-15", 17.25, 17.39, 17.25, 17.46, 18.00, 0, 0.00, 0.01, 0.00],    ["2023-06-16", 17.26, 17.4, 17.26, 17.46, 22.00, 0, 0.01, 0.01, 0.00],    ["2023-06-17", 17.38, 17.5, 17.37, 17.67, 13.00, 0, 0.03, 0.02, 0.00],    ["2023-06-20", 17.62, 17.51, 17.42, 17.63, 15.00, 0, 0.03, 0.01, -0.00],    ["2023-06-21", 17.53, 17.54, 17.5, 17.7, 11.00, 0, 0.02, 0.00, -0.01],    ["2023-06-22", 17.5, 17.5, 17.46, 17.6, 10.00, 0, -0.01, -0.01, -0.01],    ["2023-06-23", 17.52, 17.26, 17.24, 17.53, 16.00, 0, -0.04, -0.03, -0.01],    ["2023-06-24", 17.26, 17.25, 17.18, 17.38, 60.00, 0, -0.03, -0.02, -0.00],    ["2023-06-27", 17.25, 17.28, 17.18, 17.33, 19.00, 0, -0.01, -0.00, 0.00],    ["2023-06-28", 17.25, 17.29, 17.21, 17.32, 13.00, 0, 0.02, 0.01, 0.00],    ["2023-06-29", 17.31, 17.45, 17.27, 17.49, 21.00, 0, 0.07, 0.04, 0.00],    ["2023-06-30", 17.47, 17.5, 17.39, 17.55, 17.00, 0, 0.11, 0.04, -0.01],    ["2023-07-01", 17.5, 17.63, 17.49, 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