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In [1]:



    


import pandas as pd
import numpy as np
import tensorflow as tf
from tqdm import tqdm

Formatting Data





In [2]:



    


def f(row):
    if row['Adj Close'] - row['Open'] < 0: return -1
    else: return 1



    











In [4]:



    


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



    


















    
Out[4]:











  
    

      
      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 [5]:



    


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



    











In [6]:



    


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

labels = []
for i in target:
    if i == -1: labels.append([1, 0])
    else: labels.append([0, 1])

LSTM





In [7]:



    


tf.reset_default_graph()
sess = tf.Session()
data = tf.placeholder(tf.float32, [None, 6, 1])
target = tf.placeholder(tf.float32, [None, 2])

num_hidden = 50
cell = tf.contrib.rnn.LSTMCell(num_hidden,state_is_tuple=True)
val, state = tf.nn.dynamic_rnn(cell, data, dtype=tf.float32)

val = tf.transpose(val, [1, 0, 2])
last = tf.gather(val, int(val.get_shape()[0]) - 1)

weight = tf.Variable(tf.truncated_normal([num_hidden, int(target.get_shape()[1])]))
bias = tf.Variable(tf.constant(0.1, shape=[target.get_shape()[1]]))

prediction = tf.nn.softmax(tf.matmul(last, weight) + bias)
_pred = tf.argmax(prediction, 1)
cross_entropy = -tf.reduce_sum(target * tf.log(tf.clip_by_value(prediction,1e-10,1.0)))

optimizer = tf.train.AdamOptimizer()
minimize = optimizer.minimize(cross_entropy)

mistakes = tf.not_equal(tf.argmax(target, 1), tf.argmax(prediction, 1))
error = tf.reduce_mean(tf.cast(mistakes, tf.float32))

init_op = tf.global_variables_initializer()
sess.run(init_op)
#sess.close()



    


















    






/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. "

















In [14]:



    


batch_size = 1000
print("Running Network")
no_of_batches = int(len(features)/batch_size)
epoch = 50000
results = []

for i in tqdm(range(epoch), total=epoch, desc='Epoch'):
    ptr = 0
    for j in range(no_of_batches):
        inp, out = features[ptr:ptr+batch_size], labels[ptr:ptr+batch_size]
        ptr+=batch_size
        sess.run(minimize, {data: inp, target: out})
        results.append(sess.run(_pred, {data: inp, target: out}))



    


















    






Epoch:   0%|          | 1/50000 [00:00<1:58:45,  7.02it/s]









    






Running Network










    






Epoch: 100%|██████████| 50000/50000 [1:30:13<00:00,  9.35it/s]

















In [15]:



    


results[100000]



    


















    
Out[15]:








array([1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
       1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1,
       0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1,
       1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0,
       0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1,
       1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0,
       0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1,
       0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0,
       1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1,
       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1,
       0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1,
       1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1,
       0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,
       1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0,
       1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0,
       1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1,
       0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
       1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0,
       0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,
       1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0,
       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0,
       1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0,
       1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1,
       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1,
       0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,
       0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1,
       1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1,
       1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0,
       1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
       1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1,
       0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,
       1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0,
       1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0,
       0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0,
       1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,
       1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
       1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0,
       1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0,
       1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0,
       1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1])

















In [16]:



    


sess.run(prediction, {data: inp, target: out})



    


















    
Out[16]:








array([[  6.79860357e-04,   9.99320149e-01],
       [  9.10495102e-01,   8.95049646e-02],
       [  9.67982858e-02,   9.03201759e-01],
       ..., 
       [  4.51701969e-01,   5.48298061e-01],
       [  4.33861226e-01,   5.66138744e-01],
       [  4.34459984e-01,   5.65540075e-01]], dtype=float32)

















In [17]:



    


sess.run(error, {data: inp, target: out})



    


















    
Out[17]:








0.27599999

















In [18]:



    


sess.run(minimize, {data: inp, target: out})



    











In [12]:



    


sess.close



    


















    
Out[12]:








<bound method BaseSession.close of <tensorflow.python.client.session.Session object at 0x117b18518>>

















In [ ]:

Content source: ikegwukc/INFO597-DeepLearning-GameTheory

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