虚拟会员商城网站分销,网站头部 标签,建筑模板多少钱一张,网站要求wordpress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 专栏导读
#x1f525;#x1f525;本文已收录于《100天精通Python从入门到就业》#xff1a;本专栏专… 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 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
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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
KlineK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 opts
from 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_datax_data) # 设置x轴数据.add_yaxis(series_nameK线图, # 设置数据系列的名称y_axisy_data, # 设置y轴数据itemstyle_optsopts.ItemStyleOpts(color#ec0000, color0#00da3c), # 设置K线图的颜色).set_global_opts(xaxis_optsopts.AxisOpts(is_scaleTrue), # 设置x轴选项使其自适应yaxis_optsopts.AxisOpts(is_scaleTrue), # 设置y轴选项使其自适应title_optsopts.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 random
from pyecharts import options as opts
from 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_optsopts.MarkLineOpts(data[opts.MarkLineItem(type_max, value_dimclose)]),)# 设置全局配置项.set_global_opts(xaxis_optsopts.AxisOpts(is_scaleTrue), # 设置横坐标配置项is_scaleTrue表示自适应刻度yaxis_optsopts.AxisOpts(is_scaleTrue, # 设置纵坐标轴配置项is_scaleTrue表示自适应刻度splitarea_optsopts.SplitAreaOpts(is_showTrue, # 设置分割区域配置项is_showTrue表示显示分割区域areastyle_optsopts.AreaStyleOpts(opacity1) # 设置分割区域样式配置项opacity1表示不透明),),title_optsopts.TitleOpts(titleK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 random
from pyecharts import options as opts
from 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_optsopts.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_scaleTrue表示自适应刻度xaxis_optsopts.AxisOpts(is_scaleTrue),# 设置纵坐标轴配置项is_scaleTrue表示自适应刻度yaxis_optsopts.AxisOpts(is_scaleTrue,# 设置分割区域配置项is_showTrue表示显示分割区域splitarea_optsopts.SplitAreaOpts(is_showTrue,# 设置分割区域样式配置项opacity1表示不透明areastyle_optsopts.AreaStyleOpts(opacity1)),),# 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titleK线图鼠标缩放为标题内容title_optsopts.TitleOpts(titleK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 random
from pyecharts import options as opts
from 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_optsopts.AxisOpts(is_scaleTrue), # 设置横坐标轴配置项is_scaleTrue表示自适应刻度yaxis_optsopts.AxisOpts(is_scaleTrue, # 设置纵坐标轴配置项is_scaleTrue表示自适应刻度splitarea_optsopts.SplitAreaOpts(is_showTrue, # 设置分割区域配置项is_showTrue表示显示分割区域areastyle_optsopts.AreaStyleOpts(opacity1) # 设置分割区域样式配置项opacity1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_optsopts.TitleOpts(titleK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, Union
from pyecharts import options as opts
from pyecharts.commons.utils import JsCode
from 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, 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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 0tag 0vols 0for i in range(len(data[times])):if data[datas][i][5] ! 0 and tag 0:idx ivols data[datas][i][4]tag 1if 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 ivols data[datas][i][4]tag 2if 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 ivols 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(-)continuesum_total 0.0for 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_datadata[times]).add_yaxis(series_name,y_axisdata[datas],itemstyle_optsopts.ItemStyleOpts(color#ef232a,color0#14b143,border_color#ef232a,border_color0#14b143,),markpoint_optsopts.MarkPointOpts(data[opts.MarkPointItem(type_max, name最大值),opts.MarkPointItem(type_min, name最小值),]),markline_optsopts.MarkLineOpts(label_optsopts.LabelOpts(positionmiddle, colorblue, font_size15),datasplit_data_part(),symbol[circle, none],),).set_series_opts(markarea_optsopts.MarkAreaOpts(is_silentTrue, datasplit_data_part())).set_global_opts(title_optsopts.TitleOpts(titleK线周期图表, pos_left0),xaxis_optsopts.AxisOpts(type_category,is_scaleTrue,boundary_gapFalse,axisline_optsopts.AxisLineOpts(is_on_zeroFalse),splitline_optsopts.SplitLineOpts(is_showFalse),split_number20,min_dataMin,max_dataMax,),yaxis_optsopts.AxisOpts(is_scaleTrue, splitline_optsopts.SplitLineOpts(is_showTrue)),tooltip_optsopts.TooltipOpts(triggeraxis, axis_pointer_typeline),datazoom_opts[opts.DataZoomOpts(is_showFalse, type_inside, xaxis_index[0, 0], range_end100),opts.DataZoomOpts(# is_showTrue, xaxis_index[0, 1], pos_top100%, range_end100),opts.DataZoomOpts(is_showFalse, xaxis_index[0, 2], range_end100),],# 三个图的 axis 连在一块# axispointer_optsopts.AxisPointerOpts(# is_showTrue,# link[{xAxisIndex: all}],# labelopts.LabelOpts(background_color#777),# ),))kline_line (Line().add_xaxis(xaxis_datadata[times]).add_yaxis(series_nameMA5,y_axiscalculate_ma(day_count5),is_smoothTrue,linestyle_optsopts.LineStyleOpts(opacity0.5),label_optsopts.LabelOpts(is_showFalse),).set_global_opts(xaxis_optsopts.AxisOpts(type_category,grid_index1,axislabel_optsopts.LabelOpts(is_showFalse),),yaxis_optsopts.AxisOpts(grid_index1,split_number3,axisline_optsopts.AxisLineOpts(is_on_zeroFalse),axistick_optsopts.AxisTickOpts(is_showFalse),splitline_optsopts.SplitLineOpts(is_showFalse),axislabel_optsopts.LabelOpts(is_showTrue),),))# Overlap Kline Lineoverlap_kline_line kline.overlap(kline_line)# Bar-1bar_1 (Bar().add_xaxis(xaxis_datadata[times]).add_yaxis(series_nameVolumn,y_axisdata[vols],xaxis_index1,yaxis_index1,label_optsopts.LabelOpts(is_showFalse),# 根据 echarts demo 的原版是这么写的# itemstyle_optsopts.ItemStyleOpts(# colorJsCode(# 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_optsopts.ItemStyleOpts(colorJsCode(function(params) {var colorList;if (barData[params.dataIndex][1] barData[params.dataIndex][0]) {colorList #ef232a;} else {colorList #14b143;}return colorList;})),).set_global_opts(xaxis_optsopts.AxisOpts(type_category,grid_index1,axislabel_optsopts.LabelOpts(is_showFalse),),legend_optsopts.LegendOpts(is_showFalse),))# Bar-2 (Overlap Bar Line)bar_2 (Bar().add_xaxis(xaxis_datadata[times]).add_yaxis(series_nameMACD,y_axisdata[macds],xaxis_index2,yaxis_index2,label_optsopts.LabelOpts(is_showFalse),itemstyle_optsopts.ItemStyleOpts(colorJsCode(function(params) {var colorList;if (params.data 0) {colorList #ef232a;} else {colorList #14b143;}return colorList;})),).set_global_opts(xaxis_optsopts.AxisOpts(type_category,grid_index2,axislabel_optsopts.LabelOpts(is_showFalse),),yaxis_optsopts.AxisOpts(grid_index2,split_number4,axisline_optsopts.AxisLineOpts(is_on_zeroFalse),axistick_optsopts.AxisTickOpts(is_showFalse),splitline_optsopts.SplitLineOpts(is_showFalse),axislabel_optsopts.LabelOpts(is_showTrue),),legend_optsopts.LegendOpts(is_showFalse),))line_2 (Line().add_xaxis(xaxis_datadata[times]).add_yaxis(series_nameDIF,y_axisdata[difs],xaxis_index2,yaxis_index2,label_optsopts.LabelOpts(is_showFalse),).add_yaxis(series_nameDIF,y_axisdata[deas],xaxis_index2,yaxis_index2,label_optsopts.LabelOpts(is_showFalse),).set_global_opts(legend_optsopts.LegendOpts(is_showFalse)))# 最下面的柱状图和折线图overlap_bar_line bar_2.overlap(line_2)# 最后的 Gridgrid_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_optsopts.GridOpts(pos_left3%, pos_right1%, height60%),)# Volumn 柱状图grid_chart.add(bar_1,grid_optsopts.GridOpts(pos_left3%, pos_right1%, pos_top71%, height10%),)# MACD DIFS DEASgrid_chart.add(overlap_bar_line,grid_optsopts.GridOpts(pos_left3%, pos_right1%, pos_top82%, height14%),)grid_chart.render(professional_kline_chart.html)if __name__ __main__:data split_data(origin_dataecharts_data)draw_chart()运行结果
书籍推荐 购书链接京东https://item.jd.com/14055900.html 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
