股市亏惨了，手把手教你python画K线+找数据......

圆圆的算法笔记

发布于 2022-09-22 11:27:29

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点关注，不迷路，定期不务正业~

2022年梦幻开局，到现在4个月了，A股、美股都在大跌（沪深300到五一为止快跌了20个点了），买的基金、股票都亏惨了。于是最近开始学习“更科学”的投资理财方法，其中K线是分析基金、股票走势的一大利器。虽然目前各大理财APP上都有各个股票和指数的K线，但是当我们想看一些定制化的K线，例如以自己选择的定投日为周期的月线时，这些软件可能就支持不了了。比如，我一般在每个月的15号定投基金，希望看看以15号为周期的月线，但是各个APP上的月线都是以1号为周期的。

其实画个K线对于我们这些算法搬砖师来说并不难，无非就是找到数据+import个K线绘制python包。下面我整理了一下我画K线的过程，给有需要的小伙伴提供一个参考~

什么是K线

为了保证这个简单教程的完整性，还是先简单介绍一下K线的基础知识。每一根K线都由5个参数构成，分别为：开盘价、收盘价、最高价、最低价、日期。4个价格参数都是和纵轴对应的，最高价和最低价之间连线，收盘价和开盘价之间画柱状图。如果收盘价大于开盘价，表示这个周期内的价格是上涨的，因此标成红色，否则标成绿色。上影线表示最高价和收盘价之间的价格差，下影线表示最低价和开盘价之间的价格差。

因此理论上，只要我们能够获取到一个股票每天的开盘价、收盘价、最高价、最低价、日期这5个决定K线的参数，就能绘制K线了。下面给大家分享一下我找到的一个数据汇总网站。

数据获取

在github有一个接口可以通过代码获取数据：https://github.com/akfamily/akshare/blob/master/docs/data/index/index.md。这个接口汇总了新浪、腾讯、东方财富等各个平台的数据获取方式，还是非常方便的。我们以东方财富平台为例，获取中证医疗的历史数据，代码如下：

import akshare as ak

stock_zh_index_daily_em_df = ak.stock_zh_index_daily_em(symbol="sz399989")
print(stock_zh_index_daily_em_df)

其中symbol是股票对应的代码缩写，返回的数据是dataframe格式的。也可以按照不同的周期获取历史数据，例如获取一段时间内每天的详细信息：

import akshare as ak

index_zh_a_hist_df = ak.index_zh_a_hist(symbol="000016", period="daily", start_date="19700101", end_date="22220101")
print(index_zh_a_hist_df)

获取到的数据就张这个样子：

更详细的接口使用方法大家可以直接去github库查看，写的非常详细了~

绘制K线

数据到手了，下面就可以开始进行绘图了。这里我们使用python自带的一个用于绘制K线的算法包mplfinance。这个python包之前改版过多次，一开始是封装在matplotlib.finance里的，后来独立出来变成了mplfinance。使用这个python包绘制K线也是非常简单，只需要传入我们在第一节中介绍的绘制K线的5个参数，直接调用函数就可以了，代码如下（注意这里的数据使用的是虚拟的哟，替换成从接口获取的数据就可以了）：

stock_code = "sz399989"

stock_zh_index_daily_em_df = ak.stock_zh_index_daily_em(symbol="sz399989")

ohlc = stock_zh_index_daily_em_df[['date','open','close','high','low']]
ohlc = ohlc.loc[ohlc["date"] >= "2022-02-01"]

date = [732797.0, 732828.0, 732858.0, 732889.0, 732920.0, 732950.0, 732981.0, 733011.0, 733042.0, 733073.0, 733102.0, 733133.0, 733163.0, 733194.0, 733224.0, 733255.0, 733286.0, 733316.0, 733347.0, 733377.0, 733408.0, 733439.0, 733467.0, 733498.0, 733528.0, 733559.0, 733589.0, 733620.0, 733651.0, 733681.0, 733712.0, 733742.0, 733773.0, 733804.0, 733832.0, 733863.0, 733893.0, 733924.0, 733954.0, 733985.0, 734016.0, 734046.0, 734077.0, 734107.0, 734138.0, 734169.0, 734197.0, 734228.0, 734258.0, 734289.0, 734319.0, 734350.0, 734381.0, 734411.0, 734442.0, 734472.0, 734503.0, 734534.0, 734563.0, 734594.0, 734624.0, 734655.0, 734685.0, 734716.0, 734747.0, 734777.0, 734808.0, 734838.0, 734869.0, 734900.0, 734928.0, 734959.0, 734989.0, 735020.0, 735050.0, 735081.0, 735112.0, 735142.0, 735173.0, 735203.0, 735234.0, 735265.0, 735293.0, 735324.0, 735354.0, 735385.0, 735415.0, 735446.0, 735477.0, 735507.0, 735538.0, 735568.0, 735599.0, 735630.0, 735658.0, 735689.0, 735719.0, 735750.0, 735780.0, 735811.0, 735842.0, 735872.0, 735903.0, 735933.0, 735964.0, 735995.0, 736024.0, 736055.0, 736085.0, 736116.0, 736146.0, 736177.0, 736208.0, 736238.0, 736269.0, 736299.0, 736330.0, 736361.0, 736389.0, 736420.0, 736450.0]
open = [60.0, 68.15, 68.08, 65.01, 66.1, 70.59, 75.69, 69.12, 66.25, 53.15, 54.61, 54.12, 50.81, 49.0, 39.09, 36.5, 39.6, 35.75, 27.56, 24.22, 27.3, 21.83, 17.74, 19.0, 27.57, 26.62, 25.78, 32.4, 31.92, 34.5, 32.7, 34.1, 37.24, 33.0, 31.15, 35.08, 38.31, 40.75, 41.46, 41.14, 38.5, 46.32, 48.1, 50.51, 50.9, 54.0, 51.56, 50.31, 52.3, 49.2, 51.9, 51.52, 37.76, 32.2, 35.52, 33.48, 33.92, 42.42, 44.8, 45.76, 42.6, 37.3, 35.4, 40.44, 38.87, 37.82, 36.05, 38.1, 42.03, 42.9, 45.67, 42.55, 41.83, 48.9, 46.5, 52.77, 52.92, 57.64, 60.46, 61.14, 63.21, 62.13, 65.49, 68.97, 67.02, 70.0, 68.58, 61.51, 62.2, 60.39, 62.0, 67.2, 68.26, 80.66, 86.79, 89.7, 87.07, 86.2, 83.5, 81.25, 70.36, 66.14, 78.08, 85.1, 75.26, 64.23, 62.89, 66.9, 61.15, 61.36, 53.93, 61.4, 62.29, 62.85, 65.26, 62.4, 70.18, 70.25, 69.2, 69.55, 68.51]
high = [68.49, 69.66, 71.0, 67.2, 71.14, 78.85, 76.64, 71.6, 66.61, 57.81, 56.07, 55.94, 53.2, 49.0, 43.8, 44.44, 43.45, 35.75, 28.3, 26.74, 28.4, 25.98, 23.1, 28.2, 29.03, 28.51, 32.84, 33.99, 34.7, 37.9, 36.37, 37.9, 37.67, 34.95, 35.52, 39.9, 41.92, 44.8, 44.7, 42.75, 47.59, 50.05, 52.63, 55.05, 59.09, 57.22, 52.48, 53.69, 53.03, 51.93, 53.95, 51.81, 37.88, 39.85, 36.95, 35.09, 43.79, 48.9, 48.95, 46.46, 42.8, 37.36, 40.9, 42.44, 40.57, 39.82, 38.23, 42.01, 44.31, 46.06, 47.27, 43.42, 50.37, 49.82, 53.95, 56.1, 59.56, 60.96, 61.36, 63.19, 66.85, 67.81, 69.59, 71.27, 70.0, 70.8, 70.65, 63.62, 65.75, 62.38, 67.8, 70.2, 81.3, 86.51, 96.07, 92.7, 91.0, 87.63, 86.59, 85.12, 76.72, 79.89, 84.73, 85.5, 75.26, 65.86, 68.52, 66.95, 62.1, 61.41, 62.49, 63.8, 64.59, 66.5, 66.36, 71.4, 73.23, 70.94, 73.0, 69.68, 69.29]
close = [57.91, 63.53, 63.28, 57.75, 63.55, 70.43, 63.2, 63.88, 46.65, 49.52, 50.51, 48.05, 48.46, 38.65, 35.3, 36.05, 33.7, 17.92, 19.61, 22.15, 20.35, 17.69, 17.2, 18.6, 23.98, 24.03, 23.52, 30.21, 30.1, 32.2, 31.35, 34.0, 32.32, 29.92, 30.74, 34.79, 35.3, 39.47, 39.95, 37.02, 38.3, 43.59, 47.22, 50.09, 50.75, 49.64, 43.56, 48.36, 47.0, 45.7, 49.1, 33.27, 30.92, 30.52, 29.02, 31.1, 33.92, 42.34, 43.11, 39.4, 36.7, 32.86, 34.9, 38.34, 37.36, 35.85, 35.14, 37.74, 41.7, 41.82, 42.1, 38.14, 41.65, 43.16, 45.96, 50.95, 51.89, 56.96, 57.57, 58.05, 60.34, 58.78, 62.65, 63.94, 64.19, 67.46, 61.57, 57.1, 59.34, 55.1, 60.23, 64.13, 65.57, 79.78, 84.65, 84.65, 83.0, 79.03, 77.25, 65.4, 62.06, 62.91, 75.1, 72.48, 62.73, 57.01, 62.36, 58.9, 56.19, 52.0, 50.83, 58.01, 60.14, 62.7, 60.02, 61.61, 68.44, 66.13, 68.91, 64.96, 67.05]
low = [68.15, 68.59, 66.89, 65.18, 70.64, 75.95, 69.55, 66.5, 52.19, 55.79, 54.15, 50.15, 48.92, 39.28, 37.33, 39.9, 35.4, 26.81, 24.66, 26.7, 22.0, 18.01, 19.08, 27.14, 25.85, 25.78, 32.47, 31.53, 34.4, 33.08, 33.72, 37.23, 33.42, 30.66, 34.86, 38.82, 41.0, 41.92, 41.38, 38.36, 46.46, 47.43, 49.87, 51.32, 53.42, 51.05, 49.85, 52.19, 49.1, 51.9, 50.66, 37.67, 33.63, 37.0, 33.61, 33.92, 42.24, 45.4, 45.21, 41.76, 37.43, 35.34, 40.71, 39.0, 37.66, 36.02, 37.98, 41.32, 42.88, 45.66, 42.44, 42.02, 49.41, 46.48, 52.22, 51.92, 57.62, 60.44, 61.0, 62.9, 62.13, 67.52, 68.59, 66.73, 69.7, 68.4, 61.88, 62.24, 60.73, 62.03, 67.8, 68.97, 80.48, 86.51, 89.73, 86.33, 85.28, 81.64, 81.39, 71.66, 64.85, 78.97, 84.73, 77.58, 64.16, 63.1, 67.37, 60.69, 61.39, 53.52, 60.82, 62.08, 62.71, 64.91, 62.76, 70.72, 69.35, 68.64, 69.2, 68.4, 69.07]
ohlc = [tuple([date[i],
                 open[i],
                 high[i],
                 close[i],
                 low[i]]) for i in range(len(date))] #_1
# ohlc["date"] = pd.to_datetime(ohlc['date'])
# ohlc['date'] = ohlc['date'].map(mpdates.date2num)
f1, ax = plt.subplots(figsize = (12,6))                        # 创建图片
#candlestick_ohlc(ax, ohlc.values, width = 0.6, colorup = 'red', colordown = 'green', alpha = 0.8)  # 使用candlestick_ohlc绘图
candlestick_ohlc(ax, ohlc, colorup = 'r', colordown = 'green')  # 使用candlestick_ohlc绘图
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d')) # 设置横轴日期格式
plt.xticks(rotation=30)                                        # 日期显示的旋转角度
plt.title(stock_code,fontsize = 14)                            # 设置图片标题
plt.xlabel('Date',fontsize = 14)                              # 设置横轴标题
plt.ylabel('Price',fontsize = 14)                        # 设置纵轴标题
plt.show()

最后绘制好的图像就长下面这样（能看出个大致的趋势就够用了）：