>>> import bulbea as bb
>>> share = bb.Share('YAHOO', 'GOOGL')
>>> share.data
# Open        High         Low       Close      Volume  \
# Date                                                                     
# 2004-08-19   99.999999  104.059999   95.959998  100.339998  44659000.0   
# 2004-08-20  101.010005  109.079998  100.500002  108.310002  22834300.0   
# 2004-08-23  110.750003  113.479998  109.049999  109.399998  18256100.0   
# 2004-08-24  111.239999  111.599998  103.570003  104.870002  15247300.0   
# 2004-08-25  104.960000  108.000002  103.880003  106.000005   9188600.0
...

>>> from bulbea.learn.evaluation import split
>>> Xtrain, Xtest, ytrain, ytest = split(share, 'Close', normalize = True)

>>> import numpy as np
>>> Xtrain = np.reshape(Xtrain, (Xtrain.shape[0], Xtrain.shape[1], 1))
>>> Xtest  = np.reshape( Xtest, ( Xtest.shape[0],  Xtest.shape[1], 1))

>>> from bulbea.learn.models import RNN
>>> rnn = RNN([1, 100, 100, 1]) # number of neurons in each layer
>>> rnn.fit(Xtrain, ytrain)
# Epoch 1/10
# 1877/1877 [==============================] - 6s - loss: 0.0039
# Epoch 2/10
# 1877/1877 [==============================] - 6s - loss: 0.0019
...

>>> from sklearn.metrics import mean_squared_error
>>> p = rnn.predict(Xtest)
>>> mean_squared_error(ytest, p)
0.00042927869370525931
>>> import matplotlib.pyplot as pplt
>>> pplt.plot(ytest)
>>> pplt.plot(p)
>>> pplt.show()

>>> bb.sentiment(share)
0.07580128205128206