iis服务器助手广告广告
返回顶部
首页 > 资讯 > 后端开发 > Python >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
  • 412
分享到

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

python开发语言K线图可视化Pyecharts原力计划 2023-09-05 08:09:48 412人浏览 安东尼

Python 官方文档:入门教程 => 点击学习

摘要

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

文章目录

专栏导读

🔥🔥本文已收录于《100天精通Python从入门到就业》:本专栏专门针对零基础和需要进阶提升的同学所准备的一套完整教学,从0到100的不断进阶深入,后续还有实战项目,轻松应对面试,专栏订阅地址:https://blog.csdn.net/yuan2019035055/category_11466020.html

  • 优点订阅限时9.9付费专栏进入千人全栈VIP答疑群,作者优先解答机会(代码指导、远程服务),群里大佬众多可以抱团取暖(大厂内推机会)
  • 专栏福利简历指导、招聘内推、每周送实体书、80G全栈学习视频、300本IT电子书:python、Java、前端大数据数据库算法爬虫数据分析机器学习面试题库等等
    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

  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。

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:

  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:标题配置类,用于设置图表的标题样式和属性。
  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。

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:
在这里插入图片描述

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:
在这里插入图片描述

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:
在这里插入图片描述

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:
在这里插入图片描述

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],    ["2023-03-17", 16.39, 16.23, 16.11, 16.4, 46.00, 0, 0.14, -0.20, -0.26],    ["2023-03-18", 16.39, 16.17, 16.04, 16.4, 59.00, 0, 0.13, -0.22, -0.28],    ["2023-03-21", 16.21, 16.22, 16.11, 16.44, 50.00, 0, 0.12, -0.24, -0.30],    ["2023-03-22", 16.27, 16.19, 16.16, 16.42, 33.00, 0, 0.10, -0.27, -0.32],    ["2023-03-23", 16.26, 16.18, 16.18, 16.29, 19.00, 0, 0.08, -0.30, -0.33],    ["2023-03-24", 16.18, 16.11, 16.01, 16.23, 23.00, 0, 0.04, -0.33, -0.35],    ["2023-03-25", 16.12, 16.13, 16.1, 16.2, 15.00, 0, 0.00, -0.35, -0.35],    ["2023-03-28", 16.15, 15.85, 15.81, 16.2, 22.00, 0, -0.06, -0.38, -0.35],    ["2023-03-29", 15.9, 15.79, 15.76, 16.05, 19.00, 0, -0.06, -0.37, -0.34],    ["2023-03-30", 15.79, 16.24, 15.78, 16.3, 29.00, 0, -0.03, -0.35, -0.33],    ["2023-03-31", 16.3, 16.09, 16.02, 16.35, 25.00, 0, -0.07, -0.37, -0.33],    ["2023-04-01", 16.18, 16.27, 15.98, 16.3, 38.00, 0, -0.08, -0.36, -0.32],    ["2023-04-05", 16.13, 16.34, 16.07, 16.37, 39.00, 0, -0.13, -0.37, -0.31],    ["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:
在这里插入图片描述

书籍推荐

在这里插入图片描述

抽奖送书老规矩(不点赞收藏中奖无效):注意记得关注博主不然中奖了还不知道!!!

  • 1. 点赞收藏文章
  • 2. 评论区留言:人生苦短,我用Python!!!(留言才能进入奖池,每人最多留言三条)
  • 3. 周六八点爬虫抽奖5人

来源地址:https://blog.csdn.net/yuan2019035055/article/details/132124804

--结束END--

本文标题: 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

本文链接: https://www.lsjlt.com/news/394909.html(转载时请注明来源链接)

有问题或投稿请发送至: 邮箱/279061341@qq.com    QQ/279061341

本篇文章演示代码以及资料文档资料下载

下载Word文档到电脑,方便收藏和打印~

下载Word文档
猜你喜欢
软考高级职称资格查询
编程网,编程工程师的家园,是目前国内优秀的开源技术社区之一,形成了由开源软件库、代码分享、资讯、协作翻译、讨论区和博客等几大频道内容,为IT开发者提供了一个发现、使用、并交流开源技术的平台。
  • 官方手机版

  • 微信公众号

  • 商务合作