lab-12-5-rnn_stock_prediction.py

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
tf.set_random_seed(777)  # reproducibility


def MinMaxScaler(data):
    numerator = data - np.min(data, 0)
    denominator = np.max(data, 0) - np.min(data, 0)
    # noise term prevents the zero division
    return numerator / (denominator + 1e-7)


timesteps = seq_length = 7
data_dim = 5
output_dim = 1

# Open, High, Low, Volume, Close
xy = np.loadtxt('data-02-stock_daily.csv', delimiter=',')
xy = xy[::-1]  # reverse order (chronically ordered)
xy = MinMaxScaler(xy)
x = xy
y = xy[:, [-1]]  # Close as label

dataX = []
dataY = []
for i in range(0, len(y) - seq_length):
    _x = x[i:i + seq_length]
    _y = y[i + seq_length]  # Next close price
    print(_x, "->", _y)
    dataX.append(_x)
    dataY.append(_y)

# split to train and testing
train_size = int(len(dataY) * 0.7)
test_size = len(dataY) - train_size
trainX, testX = np.array(dataX[0:train_size]), np.array(
    dataX[train_size:len(dataX)])
trainY, testY = np.array(dataY[0:train_size]), np.array(
    dataY[train_size:len(dataY)])

# input place holders
X = tf.placeholder(tf.float32, [None, seq_length, data_dim])
Y = tf.placeholder(tf.float32, [None, 1])

cell = tf.contrib.rnn.BasicLSTMCell(num_units=output_dim, state_is_tuple=True)
outputs, _states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)
Y_pred = outputs[:, -1]  # We use the last cell's output

print(outputs[:, -1])

# cost/loss
loss = tf.reduce_sum(tf.square(Y_pred - Y))  # sum of the squares
# optimizer
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)

# RMSE
targets = tf.placeholder(tf.float32, [None, 1])
predictions = tf.placeholder(tf.float32, [None, 1])
rmse = tf.sqrt(tf.reduce_mean(tf.square(targets - predictions)))

sess = tf.Session()
sess.run(tf.global_variables_initializer())

for i in range(500):
    _, step_loss = sess.run([train, loss], feed_dict={X: trainX, Y: trainY})
    print(i, step_loss)

testPredict = sess.run(Y_pred, feed_dict={X: testX})
print("RMSE", sess.run(rmse, feed_dict={
      targets: testY, predictions: testPredict}))
plt.plot(testY)
plt.plot(testPredict)
plt.xlabel("Time Period")
plt.ylabel("Stock Price")
plt.show()