fig, (ax1, ax2,ax3) = plt.subplots(3,1, figsize=(15,10),facecolor='white')
plt.subplots_adjust(hspace=0.5)
mpf.candlestick_ochl(ax1, k_test_data_values, width=1.0, colorup = 'r', colordown = 'g')
ax1.set_title(test_code)
ax1.set_ylabel('Price')
ax1.grid(True)
x_list=n_division(len(k_test_data),5)
ax1.set_xticks(x_list)
ax1.set_xticklabels(k_test_data.ix[x_list,'str_date'])
mpf.candlestick_ochl(ax2, find_data_values, width=1.0, colorup = 'r', colordown = 'g')
ax2.set_title(find_code)
ax2.set_ylabel('Price')
ax2.grid(True)
x2_list=n_division(len(find_data),5)
ax2.set_xticks(x2_list)
ax2.set_xticklabels(find_data.ix[x2_list,'str_date'])
"""收盘价数据"""
two_closes=pd.DataFrame()
two_closes['test']=k_test_data['dealed_close'].values
two_closes['find']=find_data['dealed_close'].values
two_closes.reset_index(drop=True,inplace=True)
two_closes.plot(ax=ax3)
ax3.text(cnt//2,0.5,'similarity:'+str(result.ix[find_idx,'ratio']),family='monospace',fontsize=10)
plt.show()
fig,ax1=plt.subplots(1,figsize=(10,8))
plot_data[[bench_column_name]].plot(c='r',ax=ax1)
plot_data[ref1_columns_name].plot(c='b',ax=ax1)
plot_data[ref2_columns_name[0:3]].plot(c='g',ax=ax1)
plot_data[ref2_columns_name[3:6]].plot(c='y',ax=ax1)
ax1.set_title(bench_mark+'_'+begin_date+'_'+over_date+" similar k lines")
#ax1.set_xticklabels(x_labels)
ax1.set_xlabel('date')
ax1.set_ylabel('net')
fig.savefig('pdf/'+'_new_algo_'+prefix+'_'+bench_mark+'_'+begin_date+'_'+over_date+'.pdf',dpi=1000,bbox_inches='tight')
plt.show()
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
labels=[u'big_big_down',u'big_down',u'down',u'up',u'big_up',u'big_big_up']
X=test_result[-1].reshape(-1,).tolist()
fig = plt.figure()
plt.pie(X,labels=labels,autopct='%1.2f%%') #画饼图（数据，数据对应的标签，百分数保留两位小数点）
plt.title(code+'_lstm_prob')
plt.savefig(code+'_lstm_prob'+'.pdf')
plt.show()
