股市及期貨市bai場中的K線圖的du畫法包含四個zhi數據，即開盤dao價、最高價、最低價zhuan、收盤價，所有的shuk線都是圍繞這四個數據展開，反映大勢的狀況和價格信息。如果把每日的K線圖放在一張紙上，就能得到日K線圖，同樣也可畫出周K線圖、月K線圖。研究金融的小伙伴肯定比較熟悉這個，那么我們看起來比較復雜的K線圖，又是這樣畫出來的，本文我們將一起探索K線圖的魅力與神奇之處吧！

K線圖

K線圖用處于股票分析，作為數據分析，以后的進入大數據肯定是一個趨勢和熱潮，K線圖的專業知識，說實話肯定比較的復雜，這里就不做過多的展示了，有興趣的小伙伴去問問百度小哥哥喲！

第一個K線圖繪制，來看看需要哪些參數吧，數據集都有四個必要的喲！

import pyecharts.options as optsfrom pyecharts.charts import Candlestick 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('簡單K線圖.html'))

大量的數據集的時候，我們不可以全部同時展示，我們可以縮放來進行定向展示。

from pyecharts import options as optsfrom pyecharts.charts import Kline 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) for i in range(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線圖鼠標縮放.html'))

我們知道一個數據節點，但是我們不能在圖像里面一眼看出有哪些數據量超出了它的范圍，刻度標簽就可以派上用場了。

from pyecharts import options as optsfrom pyecharts.charts import Kline 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) for i in range(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='標題'), ) .render('刻度標簽.html'))

前面的是一個有縮放功能的圖例代碼，但是有時候我們不想要那么修改一下參數就可以了。

from pyecharts import options as optsfrom pyecharts.charts import Kline 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) for i in range(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('鼠標無縮放.html'))

雖然有時候縮放可以容納較多的數據量，但是還是不夠智能，可以利用這個

from pyecharts import options as optsfrom pyecharts.charts import Kline 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) for i in range(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('大量數據展示.html'))

K線圖的繪制需要有專業的基本知識喲，不然可能有點惱火了。

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