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# -*- coding:utf-8 -*-
import matplotlib as mpl
import tushare as ts
import matplotlib.pyplot as plt
import matplotlib.finance as mpf
%matplotlib inline
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start_date = '2017-07-01'
end_date = '2017-11-01'
stock_selected = '000050'
data = ts.get_k_data(stock_selected,start_date,end_date)
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# 导入两个涉及的库
from matplotlib.pylab import date2num
import datetime
import numpy as np
import pandas as pd
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data[data['volume']==0]=np.nan
data=data.dropna()
data.sort_values(by='date',ascending=True,inplace=True)
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data.date=pd.to_datetime(data.date)
#ransfer date to numbers
data.date=data.date.apply(lambda x:date2num(x))
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#re-org the data as requried
data=data[['date','open','close','high','low','volume']]
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#change data to matrix
data_mat=data.as_matrix()
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data_mat[:,5]
#need learn how to operate matrix data here
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fig,ax=plt.subplots(figsize=(1200/72,240/72))
fig.subplots_adjust(bottom=0.5)
mpf.candlestick_ochl(ax,data_mat,colordown='r', colorup='b',width=0.3,alpha=1)
ax.grid(True)
ax.xaxis_date()
plt.show()
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fig,(ax1,ax2)=plt.subplots(2,sharex=True,figsize=(1200/72,480/72))
mpf.candlestick_ochl(ax1,data_mat,colordown='g', colorup='r',width=0.3,alpha=1)
ax1.grid(True)
ax1.xaxis_date()
plt.bar(data_mat[:,0],data_mat[:,5],width=0.5)
ax2.set_ylabel('Volume')
ax2.grid(True)
plt.show()
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data.date.values
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