[图表]pyecharts-K线图
先来看代码:
import requests
from typing import List, Unionfrom pyecharts import options as opts
from pyecharts.charts import Kline, Line, Bar, Griddef get_data():response = requests.get(url="https://echarts.apache.org/examples/data/asset/data/stock-DJI.json")json_response = response.json()# 解析数据return split_data(data=json_response)def split_data(data):category_data = []values = []volumes = []for i, tick in enumerate(data):category_data.append(tick[0])values.append(tick)volumes.append([i, tick[4], 1 if tick[1] > tick[2] else -1])return {"categoryData": category_data, "values": values, "volumes": volumes}def calculate_ma(day_count: int, data):result: List[Union[float, str]] = []for i in range(len(data["values"])):if i < day_count:result.append("-")continuesum_total = 0.0for j in range(day_count):sum_total += float(data["values"][i - j][1])result.append(abs(float("%.3f" % (sum_total / day_count))))return resultdef draw_charts():kline_data = [data[1:-1] for data in chart_data["values"]]kline = (Kline().add_xaxis(xaxis_data=chart_data["categoryData"]).add_yaxis(series_name="Dow-Jones index",y_axis=kline_data,itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),).set_global_opts(legend_opts=opts.LegendOpts(is_show=False, pos_bottom=10, pos_left="center"),datazoom_opts=[opts.DataZoomOpts(is_show=False,type_="inside",xaxis_index=[0, 1],range_start=98,range_end=100,),opts.DataZoomOpts(is_show=True,xaxis_index=[0, 1],type_="slider",pos_top="85%",range_start=98,range_end=100,),],yaxis_opts=opts.AxisOpts(is_scale=True,splitarea_opts=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),),tooltip_opts=opts.TooltipOpts(trigger="axis",axis_pointer_type="cross",background_color="rgba(245, 245, 245, 0.8)",border_width=1,border_color="#ccc",textstyle_opts=opts.TextStyleOpts(color="#000"),),visualmap_opts=opts.VisualMapOpts(is_show=False,dimension=2,series_index=5,is_piecewise=True,pieces=[{"value": 1, "color": "#00da3c"},{"value": -1, "color": "#ec0000"},],),axispointer_opts=opts.AxisPointerOpts(is_show=True,link=[{"xAxisIndex": "all"}],label=opts.LabelOpts(background_color="#777"),),brush_opts=opts.BrushOpts(x_axis_index="all",brush_link="all",out_of_brush={"colorAlpha": 0.1},brush_type="lineX",),))line = (Line().add_xaxis(xaxis_data=chart_data["categoryData"]).add_yaxis(series_name="MA5",y_axis=calculate_ma(day_count=5, data=chart_data),is_smooth=True,is_hover_animation=False,linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),label_opts=opts.LabelOpts(is_show=False),).add_yaxis(series_name="MA10",y_axis=calculate_ma(day_count=10, data=chart_data),is_smooth=True,is_hover_animation=False,linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),label_opts=opts.LabelOpts(is_show=False),).add_yaxis(series_name="MA20",y_axis=calculate_ma(day_count=20, data=chart_data),is_smooth=True,is_hover_animation=False,linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),label_opts=opts.LabelOpts(is_show=False),).add_yaxis(series_name="MA30",y_axis=calculate_ma(day_count=30, data=chart_data),is_smooth=True,is_hover_animation=False,linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),label_opts=opts.LabelOpts(is_show=False),).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))bar = (Bar().add_xaxis(xaxis_data=chart_data["categoryData"]).add_yaxis(series_name="Volume",y_axis=chart_data["volumes"],xaxis_index=1,yaxis_index=1,label_opts=opts.LabelOpts(is_show=False),).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category",is_scale=True,grid_index=1,boundary_gap=False,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=False),split_number=20,min_="dataMin",max_="dataMax",),yaxis_opts=opts.AxisOpts(grid_index=1,is_scale=True,split_number=2,axislabel_opts=opts.LabelOpts(is_show=False),axisline_opts=opts.AxisLineOpts(is_show=False),axistick_opts=opts.AxisTickOpts(is_show=False),splitline_opts=opts.SplitLineOpts(is_show=False),),legend_opts=opts.LegendOpts(is_show=False),))# Kline And Lineoverlap_kline_line = kline.overlap(line)# Grid Overlap + Bargrid_chart = Grid(init_opts=opts.InitOpts(width="1000px",height="800px",animation_opts=opts.AnimationOpts(animation=False),))grid_chart.add(overlap_kline_line,grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="50%"),)grid_chart.add(bar,grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="63%", height="16%"),)grid_chart.render("professional_kline_brush.html")if __name__ == "__main__":chart_data = get_data()draw_charts()
再来看结果:
再来看不那么详细的解析:(太多了)
这段代码使用了Python中的一些模块来生成一个包含K线图、折线图和柱状图的可视化图表。以下是对代码的详细解析和代码注释:
import requests
from typing import List, Unionfrom pyecharts import options as opts
from pyecharts.charts import Kline, Line, Bar, Grid
导入了需要使用的模块,包括requests
用于发送HTTP请求,List
和Union
用于类型提示,以及Kline
、Line
、Bar
和Grid
模块用于生成图表。
def get_data():response = requests.get(url="https://echarts.apache.org/examples/data/asset/data/stock-DJI.json")json_response = response.json()# 解析数据return split_data(data=json_response)
定义了一个函数get_data()
,该函数使用requests
模块发送HTTP请求获取数据。数据来自"https://echarts.apache.org/examples/data/asset/data/stock-DJI.json"这个URL。然后将获取到的JSON数据解析,并调用split_data()
函数对数据进行处理。
def split_data(data):category_data = []values = []volumes = []for i, tick in enumerate(data):category_data.append(tick[0])values.append(tick)volumes.append([i, tick[4], 1 if tick[1] > tick[2] else -1])return {"categoryData": category_data, "values": values, "volumes": volumes}
定义了一个函数split_data(data)
,该函数将获取到的JSON数据进行处理和分割。将时间序列、数据和成交量分别存储在category_data
、values
和volumes
变量中,然后将它们作为字典的键值返回。
def calculate_ma(day_count: int, data):result: List[Union[float, str]] = []for i in range(len(data["values"])):if i < day_count:result.append("-")continuesum_total = 0.0for j in range(day_count):sum_total += float(data["values"][i - j][1])result.append(abs(float("%.3f" % (sum_total / day_count))))return result
定义了一个函数calculate_ma(day_count, data)
,该函数用于计算移动平均线(Moving Average)。通过遍历数据并根据给定的天数计算移动平均线的值,然后将结果存储在result
列表中并返回。
def draw_charts():kline_data = [data[1:-1] for data in chart_data["values"]]kline = (Kline().add_xaxis(xaxis_data=chart_data["categoryData"]).add_yaxis(series_name="Dow-Jones index",y_axis=kline_data,itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),).set_global_opts(legend_opts=opts.LegendOpts(is_show=False, pos_bottom=10, pos_left="center"),datazoom_opts=[opts.DataZoomOpts(is_show=False,type_="inside",xaxis_index=[0, 1],range_start=98,range_end=100,),opts.DataZoomOpts(is_show=True,xaxis_index=[0, 1],type_="slider",pos_top="85%",range_start=98,range_end=100,),],yaxis_opts=opts.AxisOpts(is_scale=True,splitarea_opts=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),),tooltip_opts=opts.TooltipOpts(trigger="axis",axis_pointer_type="cross",background_color="rgba(245, 245, 245, 0.8)",border_width=1,border_color="#ccc",textstyle_opts=opts.TextStyleOpts(color="#000"),),visualmap_opts=opts.VisualMapOpts(is_show=False,dimension=2,series_index=5,is_piecewise=True,pieces=[{"value": 1, "color": "#00da3c"},{"value": -1, "color": "#ec0000"},],),axispointer_opts=opts.AxisPointerOpts(is_show=True,link=[{"xAxisIndex": "all"}],label=opts.LabelOpts(background_color="#777"),),brush_opts=opts.BrushOpts(x_axis_index="all",brush_link="all",out_of_brush={"colorAlpha": 0.1},brush_type="lineX",),))line = (Line().add_xaxis(xaxis_data=chart_data["categoryData"]).add_yaxis(series_name="MA5",y_axis=calculate_ma(day_count=5, data=chart_data),is_smooth=True,is_hover_animation=False,linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),label_opts=opts.LabelOpts(is_show=False),).add_yaxis(series_name="MA10",y_axis=calculate_ma(day_count=10, data=chart_data),is_smooth=True,is_hover_animation=False,linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),label_opts=opts.LabelOpts(is_show=False),).add_yaxis(series_name="MA20",y_axis=calculate_ma(day_count=20, data=chart_data),is_smooth=True,is_hover_animation=False,linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),label_opts=opts.LabelOpts(is_show=False),).add_yaxis(series_name="MA30",y_axis=calculate_ma(day_count=30, data=chart_data),is_smooth=True,is_hover_animation=False,linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),label_opts=opts.LabelOpts(is_show=False),).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))bar = (Bar().add_xaxis(xaxis_data=chart_data["categoryData"]).add_yaxis(series_name="Volume",y_axis=chart_data["volumes"],xaxis_index=1,yaxis_index=1,label_opts=opts.LabelOpts(is_show=False),).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category",is_scale=True,grid_index=1,boundary_gap=False,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=False),split_number=20,min_="dataMin",max_="dataMax",),yaxis_opts=opts.AxisOpts(grid_index=1,is_scale=True,split_number=2,axislabel_opts=opts.LabelOpts(is_show=False),axisline_opts=opts.AxisLineOpts(is_show=False),axistick_opts=opts.AxisTickOpts(is_show=False),splitline_opts=opts.SplitLineOpts(is_show=False),),legend_opts=opts.LegendOpts(is_show=False),))
创建了一个K线图实例kline
,并使用.add_xaxis()
方法设置x轴数据,使用.add_yaxis()
方法添加K线图的系列数据,其中系列名称为"Dow-Jones index",y轴数据为kline_data
,并设置了颜色样式。使用.set_global_opts()
方法设置图表的全局配置,包括图例、数据缩放、坐标轴、提示框、可视映射等。
创建了一个折线图实例line
,并使用.add_xaxis()
方法设置x轴数据,使用.add_yaxis()
方法添加折线图的系列数据,其中包括"MA5"、“MA10”、"MA20"和"MA30"这四个系列,y轴数据通过调用calculate_ma()
函数计算得到,设置了平滑曲线、不显示标签等样式,并使用.set_global_opts()
方法设置x轴的配置。
创建了一个柱状图实例bar
,并使用.add_xaxis()
方法设置x轴数据,使用.add_yaxis()
方法添加柱状图的系列数据,其中系列名称为"Volume",y轴数据为chart_data["volumes"]
,并设置了一些轴和坐标轴的配置。
overlap_kline_line = kline.overlap(line)grid_chart = Grid(init_opts=opts.InitOpts(width="1000px",height="800px",animation_opts=opts.AnimationOpts(animation=False),))grid_chart.add(overlap_kline_line,grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="50%"),)grid_chart.add(bar,grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="63%", height="16%"),)grid_chart.render("professional_kline_brush.html")
使用.overlap()
方法将K线图和折线图进行重叠叠加得到overlap_kline_line
。创建了一个网格图表grid_chart
,并设置了初始配置,包括宽度、高度和动画效果。使用.add()
方法将叠加后的图表overlap_kline_line
和柱状图bar
添加到网格图表中,并
设置了网格的位置和高度。最后调用.render()
方法将图表保存为HTML文件,文件名为"professional_kline_brush.html"。
以上就是这段代码的解析和注释说明。这段代码使用了pyecharts
库来生成包含K线图、折线图和柱状图的可视化图表,使用了HTTP请求获取数据,并对数据进行处理和分析,最终生成一个交互式的图表页面。
注:图表资源来源于:
pyecharts-gallery
本站只提供常用图表与其解析