In [1]:
import pandas as pd
import numpy as np
def f(row):
    if row['Adj Close'] - row['Open'] < 0: return -1
    else: return 1

In [2]:
df = pd.read_csv('data/GOOGLu.csv')
df['Utility'] = df.apply(f, axis=1)
df.head()


Out[2]:
Date Open High Low Close Adj Close Volume Utility
0 2004-08-19 50.050049 52.082081 48.028027 100.339996 50.220219 44659000 1
1 2004-08-20 50.555557 54.594593 50.300301 108.310005 54.209209 22834300 1
2 2004-08-23 55.430431 56.796795 54.579578 109.400002 54.754753 18256100 -1
3 2004-08-24 55.675674 55.855854 51.836838 104.870003 52.487488 15247300 -1
4 2004-08-25 52.532532 54.054054 51.991993 106.000008 53.053055 9188600 1

In [3]:
data = df.drop('Utility', 1)
data = data.drop('Date',1).values
target = df['Utility'].values

Playing with Stuff


In [ ]:
from random import shuffle
train_input = ['{0:020b}'.format(i) for i in range(2**20)]
shuffle(train_input)
train_input = [map(int,i) for i in train_input]

ti  = []
for i in train_input:
    temp_list = []
    for j in i:
        temp_list.append([j])
    ti.append(np.array(temp_list))
train_input = ti

In [ ]:
#print(train_input[0])
train_input[0].shape

In [ ]:
train_output = []
for i in train_input:
    count = 0
    for j in i:
        if j[0] == 1:
            count+=1
    temp_list = ([0]*21)
    temp_list[count]=1
    train_output.append(temp_list)

In [ ]:
train_output[0]

End Playing with Stuff


In [4]:
ti=[]
for i in data:
    temp_list = []
    for j in i:
        temp_list.append([j])
    ti.append(np.array(temp_list))
data2=ti

#print(data2[0])
data2[0].shape

labels = []
for i in target:
    labels.append([i])
#labels

In [5]:
data2


Out[5]:
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        [  5.20820810e+01],
        [  4.80280270e+01],
        [  1.00339996e+02],
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        [  4.46590000e+07]]), array([[  5.05555570e+01],
        [  5.45945930e+01],
        [  5.03003010e+01],
        [  1.08310005e+02],
        [  5.42092090e+01],
        [  2.28343000e+07]]), array([[  5.54304310e+01],
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        [  5.45795780e+01],
        [  1.09400002e+02],
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        [  1.82561000e+07]]), array([[  5.56756740e+01],
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        [  4.96900000e+06]]), array([[  2.34029022e+02],
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        [  7.16180000e+06]]), array([[  2.39424423e+02],
        [  2.45130127e+02],
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        [  4.86339996e+02],
        [  2.43413406e+02],
        [  6.74480000e+06]]), array([[  2.41241241e+02],
        [  2.42242249e+02],
        [  2.38443451e+02],
        [  4.79120026e+02],
        [  2.39799805e+02],
        [  5.54200000e+06]]), ...]

In [6]:
from lstm import LSTM

In [7]:
model = LSTM(data2, labels)


/Users/jarvis/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py:93: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
  "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
WARNING:tensorflow:From /Users/jarvis/GoogleDrive/education/UIUC_GRAD/coursework/INFO_597/summer-2017/INFO597-DeepLearning-GameTheory/applications/dailyStocks/lstm.py:33: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
Epoch:   0%|          | 0/50 [00:00<?, ?it/s]
Running Network
Epoch: 100%|██████████| 50/50 [02:14<00:00,  2.68s/it]

In [8]:
data = model.results

In [ ]:
len(data)

In [9]:
data[11111]


Out[9]:
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

In [ ]:
len(data[0])

In [ ]:
data2[0]

In [ ]:
labels[0]

In [ ]:
len(labels)

In [ ]: