AI实战-Tensorflow使用 LSTM 进行股票市场预测 (完整步骤+源码讲解)_lstm预测股价全过程

plt.figure(figsize = (18,9))
plt.plot(range(df.shape[0]),(df['Low']+df['High'])/2.0)
plt.xticks(range(0,df.shape[0],500),df['Date'].loc[::500],rotation=45)
plt.xlabel('Date',fontsize=18)
plt.ylabel('Mid Price',fontsize=18)
plt.show()

# First calculate the mid prices from the highest and lowest
high_prices = df.loc[:,'High'].as_matrix()
low_prices = df.loc[:,'Low'].as_matrix()
mid_prices = (high_prices+low_prices)/2.0

# Scale the data to be between 0 and 1
# When scaling remember! You normalize both test and train data with respect to training data
# Because you are not supposed to have access to test data
scaler = MinMaxScaler()
train_data = train_data.reshape(-1,1)
test_data = test_data.reshape(-1,1)

# Train the Scaler with training data and smooth data
smoothing_window_size = 2500
for di in range(0,10000,smoothing_window_size):
    scaler.fit(train_data[di:di+smoothing_window_size,:])
    train_data[di:di+smoothing_window_size,:] = scaler.transform(train_data[di:di+smoothing_window_size,:])
 
# You normalize the last bit of remaining data
scaler.fit(train_data[di+smoothing_window_size:,:])
train_data[di+smoothing_window_size:,:] = scaler.transform(train_data[di+smoothing_window_size:,:])

# Reshape both train and test data
train_data = train_data.reshape(-1)
 
# Normalize test data
test_data = scaler.transform(test_data).reshape(-1)

# Now perform exponential moving average smoothing
# So the data will have a smoother curve than the original ragged data
EMA = 0.0
gamma = 0.1
for ti in range(11000):
  EMA = gamma*train_data[ti] + (1-gamma)*EMA
  train_data[ti] = EMA
 
# Used for visualization and test purposes
all_mid_data = np.concatenate([train_data,test_data],axis=0)

D = 1 # Dimensionality of the data. Since your data is 1-D this would be 1
num_unrollings = 50 # Number of time steps you look into the future.
batch_size = 500 # Number of samples in a batch
num_nodes = [200,200,150] # Number of hidden nodes in each layer of the deep LSTM stack we're using
n_layers = len(num_nodes) # number of layers
dropout = 0.2 # dropout amount
 
tf.reset_default_graph() # This is important in case you run this multiple times

# Input data.
train_inputs, train_outputs = [],[]
 
# You unroll the input over time defining placeholders for each time step
for ui in range(num_unrollings):
    train_inputs.append(tf.placeholder(tf.float32, shape=[batch_size,D],name='train_inputs_%d'%ui))
    train_outputs.append(tf.placeholder(tf.float32, shape=[batch_size,1], name = 'train_outputs_%d'%ui))

lstm_cells = [
    tf.contrib.rnn.LSTMCell(num_units=num_nodes[li],
                            state_is_tuple=True,
                            initializer= tf.contrib.layers.xavier_initializer()
                           )
 for li in range(n_layers)]
 
drop_lstm_cells = [tf.contrib.rnn.DropoutWrapper(
    lstm, input_keep_prob=1.0,output_keep_prob=1.0-dropout, state_keep_prob=1.0-dropout
) for lstm in lstm_cells]
drop_multi_cell = tf.contrib.rnn.MultiRNNCell(drop_lstm_cells)
multi_cell = tf.contrib.rnn.MultiRNNCell(lstm_cells)
 
w = tf.get_variable('w',shape=[num_nodes[-1], 1], initializer=tf.contrib.layers.xavier_initializer())
b = tf.get_variable('b',initializer=tf.random_uniform([1],-0.1,0.1))

# Create cell state and hidden state variables to maintain the state of the LSTM
c, h = [],[]
initial_state = []
for li in range(n_layers):
  c.append(tf.Variable(tf.zeros([batch_size, num_nodes[li]]), trainable=False))
  h.append(tf.Variable(tf.zeros([batch_size, num_nodes[li]]), trainable=False))
  initial_state.append(tf.contrib.rnn.LSTMStateTuple(c[li], h[li]))
 
# Do several tensor transofmations, because the function dynamic_rnn requires the output to be of
# a specific format. Read more at: https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn
all_inputs = tf.concat([tf.expand_dims(t,0) for t in train_inputs],axis=0)
 
# all_outputs is [seq_length, batch_size, num_nodes]
all_lstm_outputs, state = tf.nn.dynamic_rnn(
    drop_multi_cell, all_inputs, initial_state=tuple(initial_state),
    time_major = True, dtype=tf.float32)
 
all_lstm_outputs = tf.reshape(all_lstm_outputs, [batch_size*num_unrollings,num_nodes[-1]])
 
all_outputs = tf.nn.xw_plus_b(all_lstm_outputs,w,b)
 
split_outputs = tf.split(all_outputs,num_unrollings,axis=0)

 When calculating the loss you need to be careful about the exact form, because you calculate
# loss of all the unrolled steps at the same time
# Therefore, take the mean error or each batch and get the sum of that over all the unrolled steps
 
print('Defining training Loss')
loss = 0.0
with tf.control_dependencies([tf.assign(c[li], state[li][0]) for li in range(n_layers)]+
                             [tf.assign(h[li], state[li][1]) for li in range(n_layers)]):
  for ui in range(num_unrollings):
    loss += tf.reduce_mean(0.5*(split_outputs[ui]-train_outputs[ui])**2)
 
print('Learning rate decay operations')
global_step = tf.Variable(0, trainable=False)
inc_gstep = tf.assign(global_step,global_step + 1)
tf_learning_rate = tf.placeholder(shape=None,dtype=tf.float32)
tf_min_learning_rate = tf.placeholder(shape=None,dtype=tf.float32)
 
learning_rate = tf.maximum(
    tf.train.exponential_decay(tf_learning_rate, global_step, decay_steps=1, decay_rate=0.5, staircase=True),
    tf_min_learning_rate)
 
# Optimizer.
print('TF Optimization operations')
optimizer = tf.train.AdamOptimizer(learning_rate)
gradients, v = zip(*optimizer.compute_gradients(loss))
gradients, _ = tf.clip_by_global_norm(gradients, 5.0)
optimizer = optimizer.apply_gradients(
    zip(gradients, v))
 
print('\tAll done')

print('Defining prediction related TF functions')
 
sample_inputs = tf.placeholder(tf.float32, shape=[1,D])
 
# Maintaining LSTM state for prediction stage
sample_c, sample_h, initial_sample_state = [],[],[]
for li in range(n_layers):
  sample_c.append(tf.Variable(tf.zeros([1, num_nodes[li]]), trainable=False))
  sample_h.append(tf.Variable(tf.zeros([1, num_nodes[li]]), trainable=False))
  initial_sample_state.append(tf.contrib.rnn.LSTMStateTuple(sample_c[li],sample_h[li]))
 
reset_sample_states = tf.group(*[tf.assign(sample_c[li],tf.zeros([1, num_nodes[li]])) for li in range(n_layers)],
                               *[tf.assign(sample_h[li],tf.zeros([1, num_nodes[li]])) for li in range(n_layers)])
 
sample_outputs, sample_state = tf.nn.dynamic_rnn(multi_cell, tf.expand_dims(sample_inputs,0),
                                   initial_state=tuple(initial_sample_state),
                                   time_major = True,
                                   dtype=tf.float32)
 
with tf.control_dependencies([tf.assign(sample_c[li],sample_state[li][0]) for li in range(n_layers)]+
                              [tf.assign(sample_h[li],sample_state[li][1]) for li in range(n_layers)]):  
  sample_prediction = tf.nn.xw_plus_b(tf.reshape(sample_outputs,[1,-1]), w, b)
 
print('\tAll done')