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
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]
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],
[ 5.02202190e+01],
[ 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],
[ 5.67967950e+01],
[ 5.45795780e+01],
[ 1.09400002e+02],
[ 5.47547530e+01],
[ 1.82561000e+07]]), array([[ 5.56756740e+01],
[ 5.58558540e+01],
[ 5.18368380e+01],
[ 1.04870003e+02],
[ 5.24874880e+01],
[ 1.52473000e+07]]), array([[ 5.25325320e+01],
[ 5.40540540e+01],
[ 5.19919930e+01],
[ 1.06000008e+02],
[ 5.30530550e+01],
[ 9.18860000e+06]]), array([[ 5.25275270e+01],
[ 5.40290300e+01],
[ 5.23823810e+01],
[ 1.07910004e+02],
[ 5.40090100e+01],
[ 7.09480000e+06]]), array([[ 5.41041030e+01],
[ 5.43643650e+01],
[ 5.28979000e+01],
[ 1.06150002e+02],
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[ 6.21170000e+06]]), array([[ 5.26926920e+01],
[ 5.27977980e+01],
[ 5.10560570e+01],
[ 1.02010002e+02],
[ 5.10560570e+01],
[ 5.19670000e+06]]), array([[ 5.12012020e+01],
[ 5.19069060e+01],
[ 5.11311300e+01],
[ 1.02370003e+02],
[ 5.12362370e+01],
[ 4.91780000e+06]]), array([[ 5.14014020e+01],
[ 5.15365370e+01],
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[ 5.08058050e+01],
[ 1.51186000e+07]]), array([[ 5.05255240e+01],
[ 5.09209210e+01],
[ 4.97097090e+01],
[ 1.00010002e+02],
[ 5.00550540e+01],
[ 5.15240000e+06]]), array([[ 5.05555570e+01],
[ 5.10510520e+01],
[ 4.98548550e+01],
[ 1.01580002e+02],
[ 5.08408390e+01],
[ 5.84750000e+06]]), array([[ 5.04204220e+01],
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[ 6.01651650e+01],
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[ 1.69290000e+07]]), array([[ 6.34134140e+01],
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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 [ ]:
Content source: ikegwukc/INFO597-DeepLearning-GameTheory
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