python 常用股票走势图绘制

版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。

本文链接： https://blog.csdn.net/weixin_44580977/article/details/102136288

常用股票走势图绘制
K线图+移动平均线+成交量+MACD+KDJ

例程如下

#绘制K线图+移动平均线+成交量+MACD+KDJ
import datetime
import talib
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec#分割子图
import mpl_finance as mpf #替换 import matplotlib.finance as mpf
import pandas_datareader.data as web

plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False #用来正常显示负号

df_stockload = web.DataReader("600797.SS", "yahoo", datetime.datetime(2018,6,1), datetime.datetime(2019,1,1))
print(df_stockload.info())

fig = plt.figure(figsize=(8,6), dpi=100,facecolor="white")#创建fig对象

gs = gridspec.GridSpec(4, 1, left=0.08, bottom=0.15, right=0.99, top=0.96, wspace=None, hspace=0, height_ratios=[3.5,1,1,1])
graph_KAV = fig.add_subplot(gs[0,:])
graph_VOL = fig.add_subplot(gs[1,:])
graph_MACD = fig.add_subplot(gs[2,:])
graph_KDJ = fig.add_subplot(gs[3,:])

#绘制K线图
mpf.candlestick2_ochl(graph_KAV, df_stockload.Open, df_stockload.Close, df_stockload.High, df_stockload.Low, width=0.5,
                      colorup='r', colordown='g')  # 绘制K线走势

#绘制移动平均线图
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)

graph_KAV.plot(np.arange(0, len(df_stockload.index)), df_stockload['Ma20'],'black', label='M20',lw=1.0)
graph_KAV.plot(np.arange(0, len(df_stockload.index)), df_stockload['Ma30'],'green',label='M30', lw=1.0)
graph_KAV.plot(np.arange(0, len(df_stockload.index)), df_stockload['Ma60'],'blue',label='M60', lw=1.0)

graph_KAV.legend(loc='best')
graph_KAV.set_title(u"600797 浙大网新-日K线")
graph_KAV.set_ylabel(u"价格")
graph_KAV.set_xlim(0, len(df_stockload.index))  # 设置一下x轴的范围

#绘制成交量图
graph_VOL.bar(np.arange(0, len(df_stockload.index)), df_stockload.Volume,color=['g' if df_stockload.Open[x] > df_stockload.Close[x] else 'r' for x in range(0,len(df_stockload.index))])
graph_VOL.set_ylabel(u"成交量")
graph_VOL.set_xlim(0,len(df_stockload.index)) #设置一下x轴的范围
graph_VOL.set_xticks(range(0,len(df_stockload.index),15))#X轴刻度设定 每15天标一个日期

#绘制MACD
macd_dif, macd_dea, macd_bar = talib.MACD(df_stockload['Close'].values, fastperiod=12, slowperiod=26, signalperiod=9)
graph_MACD.plot(np.arange(0, len(df_stockload.index)), macd_dif, 'red', label='macd dif')  # dif
graph_MACD.plot(np.arange(0, len(df_stockload.index)), macd_dea, 'blue', label='macd dea')  # dea

bar_red = np.where(macd_bar > 0, 2 * macd_bar, 0)# 绘制BAR>0 柱状图
bar_green = np.where(macd_bar < 0, 2 * macd_bar, 0)# 绘制BAR<0 柱状图
graph_MACD.bar(np.arange(0, len(df_stockload.index)), bar_red, facecolor='red')
graph_MACD.bar(np.arange(0, len(df_stockload.index)), bar_green, facecolor='green')

graph_MACD.legend(loc='best',shadow=True, fontsize ='10')
graph_MACD.set_ylabel(u"MACD")
graph_MACD.set_xlim(0,len(df_stockload.index)) #设置一下x轴的范围
graph_MACD.set_xticks(range(0,len(df_stockload.index),15))#X轴刻度设定 每15天标一个日期

#绘制KDJ
df_stockload['K'], df_stockload['D'] = talib.STOCH(df_stockload.High.values, df_stockload.Low.values, df_stockload.Close.values,\
                                       fastk_period=9, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)

df_stockload['J'] = 3 * df_stockload['K'] - 2 * df_stockload['D']

graph_KDJ.plot(np.arange(0, len(df_stockload.index)), df_stockload['K'], 'blue', label='K')  # K
graph_KDJ.plot(np.arange(0, len(df_stockload.index)), df_stockload['D'], 'g--', label='D')  # D
graph_KDJ.plot(np.arange(0, len(df_stockload.index)), df_stockload['J'], 'r-', label='J')  # J
graph_KDJ.legend(loc='best', shadow=True, fontsize='10')

graph_KDJ.set_ylabel(u"KDJ")
graph_KDJ.set_xlabel("日期")
graph_KDJ.set_xlim(0, len(df_stockload.index))  # 设置一下x轴的范围
graph_KDJ.set_xticks(range(0, len(df_stockload.index), 15))  # X轴刻度设定 每15天标一个日期
graph_KDJ.set_xticklabels(
    [df_stockload.index.strftime('%Y-%m-%d')[index] for index in graph_KDJ.get_xticks()])  # 标签设置为日期

# X-轴每个ticker标签都向右倾斜45度
for label in graph_KAV.xaxis.get_ticklabels():
    label.set_visible(False)

for label in graph_VOL.xaxis.get_ticklabels():
    label.set_visible(False)

for label in graph_MACD.xaxis.get_ticklabels():
    label.set_visible(False)

for label in graph_KDJ.xaxis.get_ticklabels():
    label.set_rotation(45)
    label.set_fontsize(10)  # 设置标签字体
plt.show()