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量化交易吧 /  数理科学 帖子:3364712 新帖:0

研究中完美显示K线图:缩放、拖动、多图MACD叠加

爱德华发表于:5 月 10 日 06:16回复(1)

2018年11月30日更新:
修正了MACD线不显示的问题。

以下是原文:

想展现数据,图无疑是最好的方式。展示股票数据,当然是K线图了,之前论内有人用matplotlib,有人用bokeh画K线,问题多多,图片太小、不支持拖动,导致细节展示难度太大,代码也太复杂等等。
今天把JQData 100万条数据跑完了,闲来无事,共享下用百度echart画K线、MACD图的类。先来个效果图:
000001 上证指数.png
下边是数据滚动轴哦

不多说,上代码,研究内核请用 Python 2 (PacVer 2.0)
有问题留言

注:如果研究中无法显示K线图,请按F5刷新下

#pyecharts文档请见http://pyecharts.org
from pyecharts import Kline, online,Overlap,Scatter,Style,Line,Grid,Bar
import talib
class Kshow:
    def __init__(self,stock_name,jsPath='https://cdn.bootcss.com/echarts/4.1.0.rc2'):
        #使用最新的echarts js文件
        online(jsPath)
        #K线
        self.kline=Kline(stock_name)
        #macd line,即dif、dea
        self.macd_line=Line('')
        self.macd_bar=Bar('')
    
    def chart_init(self,data_df,show_BI=True,show_Line=True,show_Center=True):
        #获取K线数据
        def get_K_data(df):
            kdata=df.loc[:,['open','close','low','high']].to_dict('split')['data']
            xaxis=list(df.index.tolist())
            return xaxis,kdata
        #获取MACD数据
        def get_macd_data(df):
            dif=[l[1] for l in df.dif.iteritems()]
            dea=[l[1] for l in df.dea.iteritems()]
            macd_z=[l for l in df[df.macd>=0].macd.iteritems()]
            macd_f=[l for l in df[df.macd<0].macd.iteritems()]
            return dif,dea,macd_z,macd_f
        
        kx,ky=get_K_data(data_df)
        #添加K线,is_datazoom_show显示时间轴,datazoom_range时间轴范围
        self.kline.add('K',kx,ky,is_datazoom_show=True,datazoom_range=[80,100])
        #修改K线颜色
        self.kline._option['series'][0]['itemStyle']={'normal':{'color':'#ef232a', 'color0': '#14b143','borderColor': '#ef232a', 'borderColor0': '#14b143'}}
        
        dif,dea,macd_z,macd_f=get_macd_data(data_df)
        self.macd_bar.add('macd_z',[t for t,v in macd_z],macd_z,is_datazoom_show=True)
        self.macd_bar.add('macd_f',[t for t,v in macd_f],macd_f,is_datazoom_show=True)
        self.macd_line.add('dif',kx,dif,is_datazoom_show=True, xaxis_type='category',line_width =1,
                           is_symbol_show=False,is_fill=False)
        self.macd_line.add('dea',kx,dea,is_datazoom_show=True, xaxis_type='category',line_width =1,
                           is_symbol_show=False,is_fill=False)
        
        #这个应该是目前版本pyechart的bug,导致区域填充,删除后只显示线
        del chart.macd_line._option['series'][0]['areaStyle']
        del chart.macd_line._option['series'][1]['areaStyle']
    
    #主图
    def get_main_chart(self,height=600,width=1000):
        overlap = Overlap(height=height,width=height)
        overlap.add(self.kline)
        
        return overlap
    
    #辅图
    def get_macd_chart(self,height=200,width=1000):
        overlap = Overlap(height=height,width=height)
        overlap.add(self.macd_line)
        overlap.add(self.macd_bar)
        return overlap
    
    #获取图,并显示
    def show_chart(self,zoom_start=50,height=600,width=1000):
        grid = Grid(height=height,width =width)
        
        main_ov=self.get_main_chart(height-200,width)
        macd_ov=self.get_macd_chart(200,width)
        
        grid.add(main_ov,grid_top=0,grid_bottom=220)
        grid.add(macd_ov,grid_top=height-200)
        
        grid._option['dataZoom'][0]['xAxisIndex']=[0, 1]
        grid._option['dataZoom'][0]['start']=zoom_start
        grid._option['xAxis'][1]['show']=False
        grid._option['legend'][1]['show']=False
        grid._option['color']=['#145b7d','#e0861a','#ef232a','#14b143'] #DIF,DEA,MACD正,MACD负
        return grid
    
        
#股票代码
stock='000001.XSHG'
#名称
stock_name=get_security_info(stock).display_name

#获取dataframe
df=get_price(stock,end_date='2018-06-10',fields=['open','close','high','low'],frequency='1d',count=600)

#计算macd
df['dif'],df['dea'],df['macd']=talib.MACD(df.close.values, 
                                        fastperiod=12 , 
                                        slowperiod=26, 
                                        signalperiod=9 )
#macd最开始一段数据不准,移除
df=df.iloc[100:,:]
#初始化画图函数
chart=Kshow(stock[:6]+' '+stock_name)
#初始化数据
chart.chart_init(df)
#显示图表
chart.show_chart(height=600,width=1000)
require.config({ paths: { 'echarts': 'https://cdn.bootcss.com/echarts/4.1.0.rc2/echarts.min' } });
require(['echarts'], function(echarts) { var myChart_ae173dd9ed154b429ac13e69fef611be = echarts.init(document.getElementById('ae173dd9ed154b429ac13e69fef611be'), 'light', {renderer: 'canvas'}); function kline_tooltip_formatter(params) { var text; text = ((((((((((((params[0].seriesName + "<br/>") + "- open:") + params[0].data[1]) + "<br/>") + "- close:") + params[0].data[2]) + "<br/>") + "- lowest:") + params[0].data[3]) + "<br/>") + "- highest:") + params[0].data[4]); return text; } var option_ae173dd9ed154b429ac13e69fef611be = { "yAxis": [ { "nameLocation": "middle", "axisTick": { "alignWithLabel": false }, "scale": true, "show": true, "axisLabel": { "textStyle": { "fontSize": 12 }, "interval": "auto", "margin": 8, "rotate": 0, "formatter": "{value} " }, "splitLine": { "show": true }, "boundaryGap": true, "nameTextStyle": { "fontSize": 14 }, "nameGap": 25, "axisLine": { "lineStyle": { "width": 1 } }, "inverse": false, "splitArea": { "show": true }, "type": "value" }, { "nameLocation": "middle", "axisTick": { "alignWithLabel": false }, "inverse": false, "show": true, "axisLabel": { "textStyle": { "fontSize": 12 }, "interval": "auto", "margin": 8, "rotate": 0, "formatter": "{value} " }, "splitLine": { "show": true }, "boundaryGap": true, "nameTextStyle": { "fontSize": 14 }, "nameGap": 25, "axisLine": { "lineStyle": { "width": 1 } }, "type": "value", "gridIndex": 4 } ], "color": [ "#145b7d", "#e0861a", "#ef232a", "#14b143" ], "series": [ { "seriesId": 3840874, "markLine": { "data": [] }, "name": "K", "itemStyle": { "normal": { "color": "#ef232a", "color0": "#14b143", "borderColor0": "#14b143", "borderColor": "#ef232a" } }, "xAxisIndex": 0, "yAxisIndex": 0, "data": [ [ 2826.3123, 2843.645, 2826.2562, 2848.0713 ], [ 2839.6823, 2821.6663, 2807.1878, 2839.695 ], [ 2835.0293, 2815.0863, 2807.7488, 2843.165 ], [ 2813.5431, 2822.4429, 2780.7634, 2827.0916 ], [ 2817.9684, 2821.046, 2809.7992, 2832.7994 ], [ 2814.6507, 2822.4509, 2794.6608, 2830.9697 ], [ 2822.5927, 2916.616, 2822.5927, 2917.1351 ], [ 2917.1541, 2913.5077, 2909.5121, 2929.0759 ], [ 2911.2193, 2925.2293, 2906.5242, 2925.6702 ], [ 2929.7881, 2938.682, 2915.185, 2945.5193 ], [ 2940.9943, 2934.0979, 2922.2833, 2945.9416 ], [ 2936.2821, 2936.0449, 2923.502, 2938.444 ], [ 2932.3761, 2927.159, 2908.3666, 2937.9863 ], [ 2897.2731, 2833.0707, 2832.5072, 2911.1582 ], [ 2824.2285, 2842.1886, 2822.0609, 2843.4558 ], [ 2814.6931, 2887.2101, 2811.7808, 2894.2639 ], [ 2878.4023, 2872.8166, 2865.3878, 2887.7426 ], [ 2873.0089, 2885.105, 2872.1845, 2900.2993 ], [ 2887.6358, 2888.8091, 2864.0199, 2891.6583 ], [ 2898.3837, 2878.5575, 2869.1759, 2919.302 ], [ 2872.7337, 2905.5495, 2869.5133, 2905.978 ], [ 2902.3979, 2891.9602, 2878.661, 2904.1115 ], [ 2883.759, 2854.2864, 2807.5976, 2899.5689 ], [ 2840.5585, 2895.7033, 2840.2765, 2895.731 ], [ 2885.0112, 2912.5574, 2878.8243, 2913.5817 ], [ 2918.5337, 2931.5923, 2915.0618, 2933.9974 ], [ 2931.4805, 2929.6057, 2922.3124, 2938.1374 ], [ 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"2018-04-02T00:00:00", "2018-04-03T00:00:00", "2018-04-04T00:00:00", "2018-04-09T00:00:00", "2018-04-10T00:00:00", "2018-04-11T00:00:00", "2018-04-12T00:00:00", "2018-04-13T00:00:00", "2018-04-16T00:00:00", "2018-04-17T00:00:00", "2018-04-18T00:00:00", "2018-04-19T00:00:00", "2018-04-20T00:00:00", "2018-04-23T00:00:00", "2018-04-24T00:00:00", "2018-04-25T00:00:00", "2018-04-26T00:00:00", "2018-04-27T00:00:00", "2018-05-02T00:00:00", "2018-05-03T00:00:00", "2018-05-04T00:00:00", "2018-05-07T00:00:00", "2018-05-08T00:00:00", "2018-05-09T00:00:00", "2018-05-10T00:00:00", "2018-05-11T00:00:00", "2018-05-14T00:00:00", "2018-05-15T00:00:00", "2018-05-16T00:00:00", "2018-05-17T00:00:00", "2018-05-18T00:00:00", "2018-05-21T00:00:00", "2018-05-22T00:00:00", "2018-05-23T00:00:00", "2018-05-24T00:00:00", "2018-05-25T00:00:00", "2018-05-28T00:00:00", "2018-05-29T00:00:00", "2018-05-30T00:00:00", "2018-05-31T00:00:00", "2018-06-01T00:00:00", "2018-06-04T00:00:00", "2018-06-05T00:00:00", "2018-06-06T00:00:00", "2018-06-07T00:00:00", "2018-06-08T00:00:00" ], "gridIndex": 4 } ], "dataZoom": [ { "end": 100, "show": true, "xAxisIndex": [ 0, 1 ], "start": 50, "type": "slider", "orient": "horizontal" } ], "toolbox": { "orient": "vertical", "top": "center", "show": true, "feature": { "restore": { "show": true, "title": "restore" }, "dataView": { "show": true, "title": "data view" }, "saveAsImage": { "show": true, "title": "save as image" } }, "left": "95%" }, "legend": [ { "textStyle": { "fontSize": 12 }, "show": true, "top": "top", "selectedMode": "multiple", "data": [ "K" ], "orient": "horizontal", "left": "center" }, { "textStyle": { "fontSize": 12 }, "show": false, "top": "top", "selectedMode": "multiple", "data": [ "dif", "dea", "macd_z", "macd_f" ], "orient": "horizontal", "left": "center" } ] }; myChart_ae173dd9ed154b429ac13e69fef611be.setOption(option_ae173dd9ed154b429ac13e69fef611be); });
#以下代码可以在研究中生成图表大图 render.html
chart.show_chart(height=600,width=1000).render()
 

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