Python绘制K线图之可视化神器pyecharts的使用
K线图
概念
股市及期货市bai场中的K线图的du画法包含四个zhi数据,即开盘dao价、最高价、最低价zhuan、收盘价,所有的shuk线都是围绕这四个数据展开,反映大势的状况和价格信息。如果把每日的K线图放在一张纸上,就能得到日K线图,同样也可画出周K线图、月K线图。研究金融的小伙伴肯定比较熟悉这个,那么我们看起来比较复杂的K线图,又是这样画出来的,本文我们将一起探索K线图的魅力与神奇之处吧!
用处
K线图用处于股票分析,作为数据分析,以后的进入大数据肯定是一个趋势和热潮,K线图的专业知识,说实话肯定比较的复杂,这里就不做过多的展示了,有兴趣的小伙伴去问问百度小哥哥哟!
K线图系列模板
最简单的K线图绘制
第一个K线图绘制,来看看需要哪些参数吧,数据集都有四个必要的哟!
importpyecharts.optionsasopts frompyecharts.chartsimportCandlestick x_data=["2017-10-24","2017-10-25","2017-10-26","2017-10-27"] y_data=[[20,30,10,35],[40,35,30,55],[33,38,33,40],[40,40,32,42]] ( Candlestick(init_opts=opts.InitOpts(width="1200px",height="600px")) .add_xaxis(xaxis_data=x_data) .add_yaxis(series_name="",y_axis=y_data) .set_series_opts() .set_global_opts( yaxis_opts=opts.AxisOpts( splitline_opts=opts.SplitLineOpts( is_show=True,linestyle_opts=opts.LineStyleOpts(width=1) ) ) ) .render("简单K线图.html") )
K线图鼠标缩放
大量的数据集的时候,我们不可以全部同时展示,我们可以缩放来进行定向展示。
frompyechartsimportoptionsasopts frompyecharts.chartsimportKline data=[ [2320.26,2320.26,2287.3,2362.94], [2300,2291.3,2288.26,2308.38], [2295.35,2346.5,2295.35,2345.92], [2347.22,2358.98,2337.35,2363.8], [2360.75,2382.48,2347.89,2383.76], [2383.43,2385.42,2371.23,2391.82], [2377.41,2419.02,2369.57,2421.15], [2425.92,2428.15,2417.58,2440.38], [2411,2433.13,2403.3,2437.42], [2432.68,2334.48,2427.7,2441.73], [2430.69,2418.53,2394.22,2433.89], [2416.62,2432.4,2414.4,2443.03], [2441.91,2421.56,2418.43,2444.8], [2420.26,2382.91,2373.53,2427.07], [2383.49,2397.18,2370.61,2397.94], [2378.82,2325.95,2309.17,2378.82], [2322.94,2314.16,2308.76,2330.88], [2320.62,2325.82,2315.01,2338.78], [2313.74,2293.34,2289.89,2340.71], [2297.77,2313.22,2292.03,2324.63], [2322.32,2365.59,2308.92,2366.16], [2364.54,2359.51,2330.86,2369.65], [2332.08,2273.4,2259.25,2333.54], [2274.81,2326.31,2270.1,2328.14], [2333.61,2347.18,2321.6,2351.44], [2340.44,2324.29,2304.27,2352.02], [2326.42,2318.61,2314.59,2333.67], [2314.68,2310.59,2296.58,2320.96], [2309.16,2286.6,2264.83,2333.29], [2282.17,2263.97,2253.25,2286.33], [2255.77,2270.28,2253.31,2276.22], ] c=( Kline() .add_xaxis(["2017/7/{}".format(i+1)foriinrange(31)]) .add_yaxis( "kline", data, itemstyle_opts=opts.ItemStyleOpts( color="#ec0000", color0="#00da3c", border_color="#8A0000", border_color0="#008F28", ), ) .set_global_opts( xaxis_opts=opts.AxisOpts(is_scale=True), yaxis_opts=opts.AxisOpts( is_scale=True, splitarea_opts=opts.SplitAreaOpts( is_show=True,areastyle_opts=opts.AreaStyleOpts(opacity=1) ), ), datazoom_opts=[opts.DataZoomOpts(type_="inside")], title_opts=opts.TitleOpts(title="Kline-ItemStyle"), ) .render("K线图鼠标缩放.html") )
有刻度标签的K线图
我们知道一个数据节点,但是我们不能在图像里面一眼看出有哪些数据量超出了它的范围,刻度标签就可以派上用场了。
frompyechartsimportoptionsasopts frompyecharts.chartsimportKline data=[ [2320.26,2320.26,2287.3,2362.94], [2300,2291.3,2288.26,2308.38], [2295.35,2346.5,2295.35,2345.92], [2347.22,2358.98,2337.35,2363.8], [2360.75,2382.48,2347.89,2383.76], [2383.43,2385.42,2371.23,2391.82], [2377.41,2419.02,2369.57,2421.15], [2425.92,2428.15,2417.58,2440.38], [2411,2433.13,2403.3,2437.42], [2432.68,2334.48,2427.7,2441.73], [2430.69,2418.53,2394.22,2433.89], [2416.62,2432.4,2414.4,2443.03], [2441.91,2421.56,2418.43,2444.8], [2420.26,2382.91,2373.53,2427.07], [2383.49,2397.18,2370.61,2397.94], [2378.82,2325.95,2309.17,2378.82], [2322.94,2314.16,2308.76,2330.88], [2320.62,2325.82,2315.01,2338.78], [2313.74,2293.34,2289.89,2340.71], [2297.77,2313.22,2292.03,2324.63], [2322.32,2365.59,2308.92,2366.16], [2364.54,2359.51,2330.86,2369.65], [2332.08,2273.4,2259.25,2333.54], [2274.81,2326.31,2270.1,2328.14], [2333.61,2347.18,2321.6,2351.44], [2340.44,2324.29,2304.27,2352.02], [2326.42,2318.61,2314.59,2333.67], [2314.68,2310.59,2296.58,2320.96], [2309.16,2286.6,2264.83,2333.29], [2282.17,2263.97,2253.25,2286.33], [2255.77,2270.28,2253.31,2276.22], ] c=( Kline() .add_xaxis(["2017/7/{}".format(i+1)foriinrange(31)]) .add_yaxis( "kline", data, markline_opts=opts.MarkLineOpts( data=[opts.MarkLineItem(type_="max",value_dim="close")] ), ) .set_global_opts( xaxis_opts=opts.AxisOpts(is_scale=True), yaxis_opts=opts.AxisOpts( is_scale=True, splitarea_opts=opts.SplitAreaOpts( is_show=True,areastyle_opts=opts.AreaStyleOpts(opacity=1) ), ), title_opts=opts.TitleOpts(title="标题"), ) .render("刻度标签.html") )
K线图鼠标无缩放
前面的是一个有缩放功能的图例代码,但是有时候我们不想要那么修改一下参数就可以了。
frompyechartsimportoptionsasopts frompyecharts.chartsimportKline data=[ [2320.26,2320.26,2287.3,2362.94], [2300,2291.3,2288.26,2308.38], [2295.35,2346.5,2295.35,2345.92], [2347.22,2358.98,2337.35,2363.8], [2360.75,2382.48,2347.89,2383.76], [2383.43,2385.42,2371.23,2391.82], [2377.41,2419.02,2369.57,2421.15], [2425.92,2428.15,2417.58,2440.38], [2411,2433.13,2403.3,2437.42], [2432.68,2334.48,2427.7,2441.73], [2430.69,2418.53,2394.22,2433.89], [2416.62,2432.4,2414.4,2443.03], [2441.91,2421.56,2418.43,2444.8], [2420.26,2382.91,2373.53,2427.07], [2383.49,2397.18,2370.61,2397.94], [2378.82,2325.95,2309.17,2378.82], [2322.94,2314.16,2308.76,2330.88], [2320.62,2325.82,2315.01,2338.78], [2313.74,2293.34,2289.89,2340.71], [2297.77,2313.22,2292.03,2324.63], [2322.32,2365.59,2308.92,2366.16], [2364.54,2359.51,2330.86,2369.65], [2332.08,2273.4,2259.25,2333.54], [2274.81,2326.31,2270.1,2328.14], [2333.61,2347.18,2321.6,2351.44], [2340.44,2324.29,2304.27,2352.02], [2326.42,2318.61,2314.59,2333.67], [2314.68,2310.59,2296.58,2320.96], [2309.16,2286.6,2264.83,2333.29], [2282.17,2263.97,2253.25,2286.33], [2255.77,2270.28,2253.31,2276.22], ] c=( Kline() .add_xaxis(["2017/7/{}".format(i+1)foriinrange(31)]) .add_yaxis("kline",data) .set_global_opts( yaxis_opts=opts.AxisOpts(is_scale=True), xaxis_opts=opts.AxisOpts(is_scale=True), title_opts=opts.TitleOpts(title="Kline-基本示例"), ) .render("鼠标无缩放.html") )
大量数据K线图绘制(X轴鼠标可移动)
虽然有时候缩放可以容纳较多的数据量,但是还是不够智能,可以利用这个
frompyechartsimportoptionsasopts frompyecharts.chartsimportKline data=[ [2320.26,2320.26,2287.3,2362.94], [2300,2291.3,2288.26,2308.38], [2295.35,2346.5,2295.35,2345.92], [2347.22,2358.98,2337.35,2363.8], [2360.75,2382.48,2347.89,2383.76], [2383.43,2385.42,2371.23,2391.82], [2377.41,2419.02,2369.57,2421.15], [2425.92,2428.15,2417.58,2440.38], [2411,2433.13,2403.3,2437.42], [2432.68,2334.48,2427.7,2441.73], [2430.69,2418.53,2394.22,2433.89], [2416.62,2432.4,2414.4,2443.03], [2441.91,2421.56,2418.43,2444.8], [2420.26,2382.91,2373.53,2427.07], [2383.49,2397.18,2370.61,2397.94], [2378.82,2325.95,2309.17,2378.82], [2322.94,2314.16,2308.76,2330.88], [2320.62,2325.82,2315.01,2338.78], [2313.74,2293.34,2289.89,2340.71], [2297.77,2313.22,2292.03,2324.63], [2322.32,2365.59,2308.92,2366.16], [2364.54,2359.51,2330.86,2369.65], [2332.08,2273.4,2259.25,2333.54], [2274.81,2326.31,2270.1,2328.14], [2333.61,2347.18,2321.6,2351.44], [2340.44,2324.29,2304.27,2352.02], [2326.42,2318.61,2314.59,2333.67], [2314.68,2310.59,2296.58,2320.96], [2309.16,2286.6,2264.83,2333.29], [2282.17,2263.97,2253.25,2286.33], [2255.77,2270.28,2253.31,2276.22], ] c=( Kline() .add_xaxis(["2017/7/{}".format(i+1)foriinrange(31)]) .add_yaxis("kline",data) .set_global_opts( xaxis_opts=opts.AxisOpts(is_scale=True), yaxis_opts=opts.AxisOpts( is_scale=True, splitarea_opts=opts.SplitAreaOpts( is_show=True,areastyle_opts=opts.AreaStyleOpts(opacity=1) ), ), datazoom_opts=[opts.DataZoomOpts(pos_bottom="-2%")], title_opts=opts.TitleOpts(title="Kline-DataZoom-slider-Position"), ) .render("大量数据展示.html") )
K线图的绘制需要有专业的基本知识哟,不然可能有点恼火了。
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