from matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY,YEARLY
from matplotlib.finance import quotes_historical_yahoo_ohlc, candlestick_ohlc
import tushare as ts
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.pylab import date2num
import datetime
import numpy as np
from pandas import DataFrame
from numpy import row_stack,column_stack
df=ts.get_hist_data('601857',start='2016-06-15',end='2017-11-06')
dd=df[['open','high','low','close']]
dd1=dd .sort_index()
dd2=dd1.values.flatten()
g1=dd2[::-1]
g2=g1[0:120]
g3=g2[::-1]
gg=DataFrame(g3)
gg.T.to_excel('gg.xls')
g=dd2[0:140]
for i in range(dd.values.shape[0]-34):
s=dd2[i*4:i*4+140]
g=row_stack((g,s))
fg=DataFrame(g)
print(fg)
fg.to_excel('fg.xls')
import pandas as pd
inputfile1='fg.xls'
testoutputfile = 'test_output_data.xls'
data_train = pd.read_excel(inputfile1)
data_mean = data_train.mean()
data_std = data_train.std()
data_train1 = (data_train-data_mean)/5
y_train = data_train1.iloc[:,120:140].as_matrix()
x_train = data_train1.iloc[:,0:120].as_matrix()
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
model = Sequential()
model.add(Dense(input_dim = 120, output_dim = 240))
model.add(Activation('relu'))
model.add(Dense(input_dim = 240, output_dim = 120))
model.add(Activation('relu'))
model.add(Dense(input_dim = 120, output_dim = 120))
model.add(Activation('relu'))
model.add(Dense(input_dim = 120, output_dim = 20))
model.add(Activation('sigmoid'))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(x_train, y_train, nb_epoch = 100, batch_size = 8)
model.save_weights('net.model')
inputfile2='gg.xls'
pre = pd.read_excel(inputfile2)
pre_mean = data_mean[0:120]
pre_std = pre.std()
pre1 = (pre-pre_mean)/5
pre2 = pre1.iloc[:,0:120].as_matrix()
r = pd.DataFrame(model.predict(pre2))
rt=r*5+data_mean[120:140].as_matrix()
print(rt.round(2))
rt.to_excel('rt.xls')
a=list(df.index[0:-1])
b=a[0]
c= datetime.datetime.strptime(b,'%Y-%m-%d')
d = date2num(c)
c1=[d+i+1 for i in range(5)]
c2=np.array([c1])
r1=rt.values.flatten()
r2=r1[0:4]
for i in range(4):
r3=r1[i*4+4:i*4+8]
r2=row_stack((r2,r3))
c3=column_stack((c2.T,r2))
r5=DataFrame(c3)
if len(c3) == 0:
raise SystemExit
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
candlestick_ohlc(ax, c3, width=0.6, colorup='r', colordown='g')
ax.xaxis_date()
ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
ax.grid(True)
plt.show()
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