python 实现Web版股票行情界面

忘是亡心i 2023-06-03 08:58 7阅读 0赞

> 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 ####

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 ####

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 ####

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 ####

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

#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')

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDU4MDk3Nw_size_16_color_FFFFFF_t_70]

#### 总结 ####

如果遇到不理解的地方可以结合[官网][Link 1]进行学习

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDU4MDk3Nw_size_16_color_FFFFFF_t_70]: /images/20230601/e5284c629c4947e28d87804453248c7f.png
[Link 1]: https://pyecharts.org/#/