版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。
本文链接：https://blog.csdn.net/weixin_44580977/article/details/102211465
介绍下pyecharts库的使用，以契合大家对Web版图表显示的需求。
Kline方法绘制K线图
代码语言：javascript复制from pyecharts import Kline
import pandas_datareader.data as web
import datetime
df_stockload = web.DataReader("000001.SS", "yahoo", datetime.datetime(2018, 1, 1), datetime.datetime(2019, 1, 1))
kline = Kline("行情显示图")
# 数据转换
ohlc = list(zip(df_stockload.Open,df_stockload.Close,df_stockload.Low,df_stockload.High))
dates = df_stockload.index.strftime('%Y-%m-%d')
kline.add("日K", dates, ohlc, is_datazoom_show=True,
mark_line=["max"], mark_point=["max"], xaxis_rotate=30, yaxis_min=0.9*min(df_stockload["Low"]))
kline.show_config()
kline.render(r'k.html')Line方法绘制移动平均线
代码语言：javascript复制from pyecharts import Line
import pandas_datareader.data as web
import datetime
# example1 Line
line = Line("移动平均线图示例")
df_stockload = web.DataReader("000001.SS", "yahoo", datetime.datetime(2018, 1, 1), datetime.date.today())
df_stockload['Ma20'] = df_stockload.Close.rolling(window=20).mean() # pd.rolling_mean(df_stockload.Close,window=20)
df_stockload['Ma30'] = df_stockload.Close.rolling(window=30).mean() # pd.rolling_mean(df_stockload.Close,window=30)
df_stockload['Ma60'] = df_stockload.Close.rolling(window=60).mean() # pd.rolling_mean(df_stockload.Close,window=60)
dates = df_stockload.index.strftime('%Y-%m-%d')
indic_name_list = ['Ma20','Ma30','Ma60']
for indic_ma in indic_name_list:
line.add(indic_ma, dates, df_stockload[indic_ma].tolist(),is_smooth=True,yaxis_min=0.9*min(df_stockload["Low"]))#is_smooth 平滑曲线显示
line.show_config()
line.render(r'average.html')Bar方法绘制成交量
代码语言：javascript复制from pyecharts import Bar
import pandas_datareader.data as web
import datetime
# example1 Bar
bar = Bar('柱状图表渲染', '成交量显示')
df_stockload = web.DataReader("000001.SS", "yahoo", datetime.datetime(2018, 1, 1), datetime.datetime(2019, 1, 1))
# 数据转换
dates = df_stockload.index.strftime('%Y-%m-%d')
volume_rise=[df_stockload.Volume[x] if df_stockload.Close[x] > df_stockload.Open[x] else "0" for x in range(0, len(df_stockload.index))]
volume_drop=[df_stockload.Volume[x] if df_stockload.Close[x] <= df_stockload.Open[x] else "0" for x in range(0, len(df_stockload.index))]
bar.add("rvolume", dates, volume_rise, is_stack=True, label_color=["#218868"], is_datazoom_show=True)
bar.add("dvolume", dates, volume_drop, is_stack=True, label_color=["#FA8072"], is_datazoom_show=True)
bar.show_config()
bar.render(r'volume.html')#渲染图表，指定生成volume.html文件EffectScatter方法绘制买卖点
代码语言：javascript复制from pyecharts import EffectScatter
# example1 EffectScatter
es = EffectScatter("动态散点图示例")
#带有涟漪特效动画的散点图
es.add("buy signal", [10],[10],symbol_size=20,effect_scale=3.5,effect_period=3,symbol="pin")
es.add("buy signal", [30],[30],symbol_size=30,effect_scale=5.5,effect_period=5,symbol="roundRect")
es.add("sell signal", [20],[20],symbol_size=12,effect_scale=4.5,effect_period=4,symbol="rect")
es.add("sell signal", [50],[50],symbol_size=16,effect_scale=5.5,effect_period=3,symbol="arrow")
es.show_config()
es.render(r'signal.html')技术指标在图表上的集成
overlap,grid，是两种绘图区域， 前者是在原来的区域上叠加画图， 后者是分区域
代码语言：javascript复制#Overlap Grid方法绘制交易行情界面
import pandas_datareader.data as web
import datetime
from pyecharts import Grid,Overlap,Line,Bar,EffectScatter,Kline
# example1 senior quotations
df_stockload = web.DataReader("000001.SS", "yahoo", datetime.datetime(2018, 1, 1), datetime.date.today())
df_stockload['Ma20'] = df_stockload.Close.rolling(window=20).mean()
df_stockload['Ma30'] = df_stockload.Close.rolling(window=30).mean()
df_stockload['Ma60'] = df_stockload.Close.rolling(window=60).mean()
# python3.7打印
print(df_stockload.tail()) # 查看前几行
print(df_stockload.columns) # 查看列名
print(df_stockload.index) # 查看索引
print(df_stockload.describe()) # 查看各列数据描述性统计
kline = Kline("行情显示图",title_pos="40%")
ohlc = list(zip(df_stockload.Open,df_stockload.Close,df_stockload.Low,df_stockload.High))
dates = df_stockload.index.strftime('%Y-%m-%d')
print(type(dates))
print(type(df_stockload.index))
#is_datazoom_show=True 图表数据缩放 指定 markLine 位于开盘或者收盘上
kline.add("日K", dates, ohlc, is_datazoom_show=True,is_xaxis_show=False,
legend_pos="85%",legend_orient="vertical",legend_top="45%",mark_line=["max"], mark_point=["max"])
line = Line()
indic_name_list = ['Ma20','Ma30','Ma60']
for indic_ma in indic_name_list:
line.add(indic_ma, dates, df_stockload[indic_ma].tolist(),is_smooth=True)
bar = Bar()
volume_rise=[df_stockload.Volume[x] if df_stockload.Close[x] > df_stockload.Open[x] else "0" for x in range(0, len(df_stockload.index))]
volume_drop=[df_stockload.Volume[x] if df_stockload.Close[x] <= df_stockload.Open[x] else "0" for x in range(0, len(df_stockload.index))]
#is_yaxis_show=True 显示y坐标轴
#datazoom_xaxis_index=[0, 1] 设置dataZoom控制索引为 0,1两个x 轴
bar.add("rvolume", dates, volume_rise, is_stack=True)
bar.add("dvolume", dates, volume_drop, is_stack=True,legend_pos="85%",legend_orient="vertical",legend_top="30%",
is_datazoom_show=True,tooltip_tragger="axis", is_legend_show=True, is_yaxis_show=True, datazoom_xaxis_index=[0, 1])
# buy and sell
v1 = dates[50]
v2 = df_stockload['Low'].iloc[50]
es = EffectScatter("buy")
es.add("buy", [v1], [v2])
v1 = dates[88]
v2 = df_stockload['High'].iloc[88]
es.add( "sell", [v1], [v2], symbol="pin",)
overlap = Overlap()
overlap.add(kline)
overlap.add(line)
overlap.add(es)
grid = Grid()
grid.add(bar, grid_top="70%",grid_right="15%")
grid.add(overlap, grid_bottom="30%",grid_right="15%")
grid.show_config()
grid.render(r'total.html')
