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21
README.md
21
README.md
@ -1,21 +0,0 @@
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## Predict stock with LSTM
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This project includes training and predicting processes with LSTM for stock data. The characteristics is as fellow:
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- Concise and modular
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- Support three mainstream deep learning frameworks of pytorch, keras and tensorflow
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- Parameters, models and frameworks can be highly customized and modified
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- Supports incremental training
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- Support predicting multiple indicators at the same time
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- Support predicting any number of days in the future
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Chinese introduction can refer to : <https://blog.csdn.net/songyunli1111/article/details/78513811>
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The simultaneous predict results for stock high and low price with pytorch show as follow:
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21
main.py
21
main.py
@ -4,8 +4,7 @@ import os
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from sklearn.model_selection import train_test_split
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frame = "tensorflow"
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from model.model_tensorflow import train, predict
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from model_tensorflow import train, predict
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class Config:
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feature_columns = list([2,5])
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@ -139,24 +138,6 @@ def draw(config, origin_data, predict_norm_data):
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print(label_data)
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print(predict_data)
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'''
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for i in range(label_column_num):
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plt.figure(i + 1)
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plt.plot(label_X, label_data[:, i], label='label')
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plt.plot(predict_X, predict_data[:, i], label='predict')
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plt.legend(loc='upper right')
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plt.xlabel("Day")
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plt.ylabel("Price")
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plt.title("Predict stock {} price with {}".format(label_name[i], config.used_frame))
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print("The predicted stock {} for the next {} day(s) is: ".format(label_name[i], config.predict_day),
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np.squeeze(predict_data[-config.predict_day:, i]))
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if config.do_figure_save:
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plt.savefig(config.figure_save_path + "{}predict_{}_with_{}.png".format(config.continue_flag, label_name[i],
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config.used_frame))
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plt.show()
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'''
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def main(config):
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np.random.seed(config.random_seed)
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@ -1,35 +0,0 @@
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from keras.layers import Input, Dense, LSTM
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from keras.models import Model
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from keras.callbacks import ModelCheckpoint, EarlyStopping
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def get_keras_model(config):
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input1 = Input(shape=(config.time_step, config.input_size))
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lstm = input1
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for i in range(config.lstm_layers):
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lstm = LSTM(units=config.hidden_size, dropout=config.dropout_rate, return_sequences=True)(lstm)
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output = Dense(config.output_size)(lstm)
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model = Model(input1, output)
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model.compile(loss='mse', optimizer='adam')
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return model
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def train(config, train_X, train_Y, valid_X, valid_Y):
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model = get_keras_model(config)
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model.summary()
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if config.add_train:
|
||||
model.load_weights(config.model_save_path + config.model_name)
|
||||
|
||||
check_point = ModelCheckpoint(filepath=config.model_save_path + config.model_name, monitor='val_loss',
|
||||
save_best_only=True, mode='auto')
|
||||
early_stop = EarlyStopping(monitor='val_loss', patience=config.patience, mode='auto')
|
||||
model.fit(train_X, train_Y, batch_size=config.batch_size, epochs=config.epoch, verbose=2,
|
||||
validation_data=(valid_X, valid_Y), callbacks=[check_point, early_stop])
|
||||
|
||||
|
||||
def predict(config, test_X):
|
||||
model = get_keras_model(config)
|
||||
model.load_weights(config.model_save_path + config.model_name)
|
||||
result = model.predict(test_X, batch_size=1)
|
||||
result = result.reshape((-1, config.output_size))
|
||||
return result
|
||||
@ -1,98 +0,0 @@
|
||||
import torch
|
||||
from torch.nn import Module, LSTM, Linear
|
||||
from torch.utils.data import DataLoader, TensorDataset
|
||||
import numpy as np
|
||||
|
||||
|
||||
class Net(Module):
|
||||
def __init__(self, config):
|
||||
super(Net, self).__init__()
|
||||
self.lstm = LSTM(input_size=config.input_size, hidden_size=config.hidden_size,
|
||||
num_layers=config.lstm_layers, batch_first=True, dropout=config.dropout_rate)
|
||||
self.linear = Linear(in_features=config.hidden_size, out_features=config.output_size)
|
||||
|
||||
def forward(self, x, hidden=None):
|
||||
lstm_out, hidden = self.lstm(x, hidden)
|
||||
linear_out = self.linear(lstm_out)
|
||||
return linear_out, hidden
|
||||
|
||||
|
||||
def train(config, train_X, train_Y, valid_X, valid_Y):
|
||||
train_X, train_Y = torch.from_numpy(train_X).float(), torch.from_numpy(train_Y).float()
|
||||
train_loader = DataLoader(TensorDataset(train_X, train_Y), batch_size=config.batch_size)
|
||||
|
||||
valid_X, valid_Y = torch.from_numpy(valid_X).float(), torch.from_numpy(valid_Y).float()
|
||||
valid_loader = DataLoader(TensorDataset(valid_X, valid_Y), batch_size=config.batch_size)
|
||||
|
||||
model = Net(config)
|
||||
if config.add_train:
|
||||
model.load_state_dict(torch.load(config.model_save_path + config.model_name))
|
||||
optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
|
||||
criterion = torch.nn.MSELoss()
|
||||
|
||||
valid_loss_min = float("inf")
|
||||
bad_epoch = 0
|
||||
for epoch in range(config.epoch):
|
||||
print("Epoch {}/{}".format(epoch, config.epoch))
|
||||
model.train()
|
||||
train_loss_array = []
|
||||
hidden_train = None
|
||||
for i, _data in enumerate(train_loader):
|
||||
_train_X, _train_Y = _data
|
||||
optimizer.zero_grad()
|
||||
pred_Y, hidden_train = model(_train_X, hidden_train)
|
||||
|
||||
if not config.do_continue_train:
|
||||
hidden_train = None
|
||||
else:
|
||||
h_0, c_0 = hidden_train
|
||||
h_0.detach_(), c_0.detach_()
|
||||
hidden_train = (h_0, c_0)
|
||||
loss = criterion(pred_Y, _train_Y)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
train_loss_array.append(loss.item())
|
||||
|
||||
model.eval()
|
||||
valid_loss_array = []
|
||||
hidden_valid = None
|
||||
for _valid_X, _valid_Y in valid_loader:
|
||||
pred_Y, hidden_valid = model(_valid_X, hidden_valid)
|
||||
if not config.do_continue_train: hidden_valid = None
|
||||
loss = criterion(pred_Y, _valid_Y)
|
||||
valid_loss_array.append(loss.item())
|
||||
|
||||
valid_loss_cur = np.mean(valid_loss_array)
|
||||
print("The train loss is {:.4f}. ".format(np.mean(train_loss_array)),
|
||||
"The valid loss is {:.4f}.".format(valid_loss_cur))
|
||||
|
||||
if valid_loss_cur < valid_loss_min:
|
||||
valid_loss_min = valid_loss_cur
|
||||
bad_epoch = 0
|
||||
torch.save(model.state_dict(), config.model_save_path + config.model_name)
|
||||
else:
|
||||
bad_epoch += 1
|
||||
if bad_epoch >= config.patience:
|
||||
print(" The training stops early in epoch {}".format(epoch))
|
||||
break
|
||||
|
||||
|
||||
def predict(config, test_X):
|
||||
test_X = torch.from_numpy(test_X).float()
|
||||
test_set = TensorDataset(test_X)
|
||||
test_loader = DataLoader(test_set, batch_size=1)
|
||||
|
||||
model = Net(config)
|
||||
model.load_state_dict(torch.load(config.model_save_path + config.model_name))
|
||||
|
||||
result = torch.Tensor()
|
||||
|
||||
model.eval()
|
||||
hidden_predict = None
|
||||
for _data in test_loader:
|
||||
data_X = _data[0]
|
||||
pred_X, hidden_predict = model(data_X, hidden_predict)
|
||||
cur_pred = torch.squeeze(pred_X, dim=0)
|
||||
result = torch.cat((result, cur_pred), dim=0)
|
||||
|
||||
return result.detach().numpy()
|
||||
@ -6,4 +6,3 @@ tensorflow==1.15.2
|
||||
matplotlib>=3.0.2
|
||||
numpy>=1.14.6
|
||||
scipy>=1.1.0
|
||||
torch>=1.1.0
|
||||
|
||||
156
serve.py
156
serve.py
@ -1,87 +1,89 @@
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
import tushare as ts
|
||||
import pandas as pd
|
||||
from sklearn.preprocessing import MinMaxScaler
|
||||
#!/usr/bin/python
|
||||
from threading import Thread
|
||||
from threading import Lock
|
||||
from http.server import BaseHTTPRequestHandler, HTTPServer
|
||||
import cgi
|
||||
import json
|
||||
from urllib import parse
|
||||
|
||||
timesteps = seq_length = 7
|
||||
data_dim = 5
|
||||
output_dim = 1
|
||||
PORT_NUMBER = 8000
|
||||
|
||||
stock_data = ts.get_k_data('600000', start='2015-01-01', end='2017-12-01')
|
||||
xy = stock_data[['open', 'close', 'high', 'low', 'volume']]
|
||||
|
||||
# xy_new = pd.DataFrame()
|
||||
# scaler = MinMaxScaler()
|
||||
|
||||
# scaler.fit(xy)
|
||||
# t = scaler.transform(xy)
|
||||
|
||||
# for col in xy.columns:
|
||||
# xy_new.ix[:, col] = t[col]
|
||||
|
||||
x = xy
|
||||
y = xy[['close']]
|
||||
dataX = []
|
||||
dataY = []
|
||||
for i in range(0, len(y) - seq_length):
|
||||
_x = x[i:i + seq_length]
|
||||
_y = y.loc[i + seq_length]
|
||||
#print(_x, "->", _y)
|
||||
dataX.append(_x)
|
||||
dataY.append(_y)
|
||||
|
||||
x_real = np.vstack(dataX).reshape(-1, seq_length, data_dim)
|
||||
y_real = np.vstack(dataY).reshape(-1, output_dim)
|
||||
print(x_real.shape)
|
||||
print(y_real.shape)
|
||||
dataX = x_real
|
||||
dataY = y_real
|
||||
|
||||
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)])
|
||||
|
||||
X = tf.placeholder(tf.float32, [None, seq_length, data_dim])
|
||||
Y = tf.placeholder(tf.float32, [None, 1])
|
||||
lock = Lock()
|
||||
|
||||
|
||||
def add_layer(inputs, in_size, out_size, activation_function=None):
|
||||
inputs = tf.reshape(inputs, [-1, in_size])
|
||||
Weights = tf.Variable(tf.random_normal([in_size, out_size]))
|
||||
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
|
||||
Wx_plus_b = tf.matmul(inputs, Weights) + biases
|
||||
if activation_function is None:
|
||||
outputs = Wx_plus_b
|
||||
def train_models():
|
||||
lock.acquire()
|
||||
|
||||
lock.release()
|
||||
|
||||
|
||||
class MyHandler(BaseHTTPRequestHandler):
|
||||
# Handler for the GET requests
|
||||
def do_GET(self):
|
||||
req = parse.urlparse(self.path)
|
||||
query = parse.parse_qs(req.query)
|
||||
|
||||
if req.path == "/ping":
|
||||
self.send_response(200)
|
||||
self.send_header('Content-type', 'application/json')
|
||||
self.end_headers()
|
||||
self.wfile.write(bytes("pong", "utf-8"))
|
||||
|
||||
elif req.path == "/predict":
|
||||
try:
|
||||
job = query.get('job')[0]
|
||||
gpu_model = query.get('gpu_model')[0]
|
||||
time = query.get('time')[0]
|
||||
msg = {'code': 1, 'error': "container not exist"}
|
||||
except Exception as e:
|
||||
msg = {'code': 2, 'error': str(e)}
|
||||
self.send_response(200)
|
||||
self.send_header('Content-type', 'application/json')
|
||||
self.end_headers()
|
||||
self.wfile.write(bytes(json.dumps(msg), "utf-8"))
|
||||
|
||||
else:
|
||||
outputs = activation_function(Wx_plus_b)
|
||||
return outputs
|
||||
self.send_error(404, 'File Not Found: %s' % self.path)
|
||||
|
||||
# Handler for the POST requests
|
||||
def do_POST(self):
|
||||
if self.path == "/train":
|
||||
form = cgi.FieldStorage(
|
||||
fp=self.rfile,
|
||||
headers=self.headers,
|
||||
environ={
|
||||
'REQUEST_METHOD': 'POST',
|
||||
'CONTENT_TYPE': self.headers['Content-Type'],
|
||||
})
|
||||
try:
|
||||
job = form.getvalue('job')[0]
|
||||
data = form.getvalue('records')[0]
|
||||
records = json.load(data)
|
||||
t = Thread(target=train_models(), name='train_models', args=(job, records,))
|
||||
t.start()
|
||||
msg = {"code": 0, "error": ""}
|
||||
except Exception as e:
|
||||
msg = {"code": 1, "error": str(e)}
|
||||
self.send_response(200)
|
||||
self.send_header('Content-type', 'application/json')
|
||||
self.end_headers()
|
||||
self.wfile.write(bytes(json.dumps(msg), "utf-8"))
|
||||
|
||||
else:
|
||||
self.send_error(404, 'File Not Found: %s' % self.path)
|
||||
|
||||
|
||||
outsize_first = 5
|
||||
l1 = add_layer(X, data_dim, outsize_first, activation_function=tf.nn.relu)
|
||||
l1_output = tf.reshape(l1, [-1, seq_length, outsize_first])
|
||||
if __name__ == '__main__':
|
||||
try:
|
||||
# Create a web server and define the handler to manage the
|
||||
# incoming request
|
||||
server = HTTPServer(('', PORT_NUMBER), MyHandler)
|
||||
print('Started http server on port ', PORT_NUMBER)
|
||||
|
||||
cell = tf.nn.rnn_cell.BasicLSTMCell(num_units=output_dim, state_is_tuple=True)
|
||||
outputs, _states = tf.nn.dynamic_rnn(cell, l1_output, dtype=tf.float32)
|
||||
Y_pred = outputs[:, -1]
|
||||
# Wait forever for incoming http requests
|
||||
server.serve_forever()
|
||||
|
||||
loss = tf.reduce_sum(tf.square(Y_pred - Y))
|
||||
except KeyboardInterrupt:
|
||||
print('^C received, shutting down the web server')
|
||||
|
||||
optimizer = tf.train.GradientDescentOptimizer(0.01)
|
||||
train = optimizer.minimize(loss)
|
||||
|
||||
sess = tf.Session()
|
||||
sess.run(tf.global_variables_initializer())
|
||||
for i in range(100):
|
||||
_, l = sess.run(
|
||||
[train, loss],
|
||||
feed_dict={X: trainX, Y: trainY}
|
||||
)
|
||||
#print(i, l)
|
||||
|
||||
testPredict = sess.run(Y_pred, feed_dict={X: testX})
|
||||
|
||||
print(testY)
|
||||
print(testPredict)
|
||||
server.socket.close()
|
||||
|
||||
111
test.py
111
test.py
@ -1,111 +0,0 @@
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
import tushare as ts
|
||||
|
||||
rnn_unit = 10
|
||||
input_size = 7
|
||||
output_size = 1
|
||||
lr = 0.0006
|
||||
|
||||
stock_data = ts.get_k_data('600000', start='2015-01-01', end='2017-12-01')
|
||||
data = stock_data.iloc[:, 2:10].values
|
||||
|
||||
|
||||
# ——————————获取训练集——————————
|
||||
def get_train_data(batch_size=60, time_step=20, train_begin=0, train_end=5800):
|
||||
batch_index = []
|
||||
data_train = data[train_begin:train_end]
|
||||
normalized_train_data = (data_train - np.mean(data_train, axis=0)) / np.std(data_train, axis=0) # 标准化
|
||||
train_x, train_y = [], [] # 训练集x和y初定义
|
||||
for i in range(len(normalized_train_data) - time_step):
|
||||
if i % batch_size == 0:
|
||||
batch_index.append(i)
|
||||
x = normalized_train_data[i:i + time_step, :7]
|
||||
y = normalized_train_data[i:i + time_step, 7, np.newaxis]
|
||||
train_x.append(x.tolist())
|
||||
train_y.append(y.tolist())
|
||||
batch_index.append((len(normalized_train_data) - time_step))
|
||||
return batch_index, train_x, train_y
|
||||
|
||||
|
||||
# ——————————获取测试集——————————
|
||||
def get_test_data(time_step=20, test_begin=5800):
|
||||
data_test = data[test_begin:]
|
||||
mean = np.mean(data_test, axis=0)
|
||||
std = np.std(data_test, axis=0)
|
||||
normalized_test_data = (data_test - mean) / std # 标准化
|
||||
size = (len(normalized_test_data) + time_step - 1) // time_step # 有size个sample
|
||||
test_x, test_y = [], []
|
||||
for i in range(size - 1):
|
||||
x = normalized_test_data[i * time_step:(i + 1) * time_step, :7]
|
||||
y = normalized_test_data[i * time_step:(i + 1) * time_step, 7]
|
||||
test_x.append(x.tolist())
|
||||
test_y.extend(y)
|
||||
test_x.append((normalized_test_data[(i + 1) * time_step:, :7]).tolist())
|
||||
test_y.extend((normalized_test_data[(i + 1) * time_step:, 7]).tolist())
|
||||
return mean, std, test_x, test_y
|
||||
|
||||
|
||||
# ——————————————————定义神经网络变量——————————————————
|
||||
def lstm(X):
|
||||
batch_size = tf.shape(X)[0]
|
||||
time_step = tf.shape(X)[1]
|
||||
w_in = weights['in']
|
||||
b_in = biases['in']
|
||||
input = tf.reshape(X, [-1, input_size]) # 需要将tensor转成2维进行计算，计算后的结果作为隐藏层的输入
|
||||
input_rnn = tf.matmul(input, w_in) + b_in
|
||||
input_rnn = tf.reshape(input_rnn, [-1, time_step, rnn_unit]) # 将tensor转成3维，作为lstm cell的输入
|
||||
cell = tf.nn.rnn_cell.BasicLSTMCell(rnn_unit)
|
||||
init_state = cell.zero_state(batch_size, dtype=tf.float32)
|
||||
output_rnn, final_states = tf.nn.dynamic_rnn(cell, input_rnn, initial_state=init_state,
|
||||
dtype=tf.float32) # output_rnn是记录lstm每个输出节点的结果，final_states是最后一个cell的结果
|
||||
output = tf.reshape(output_rnn, [-1, rnn_unit]) # 作为输出层的输入
|
||||
w_out = weights['out']
|
||||
b_out = biases['out']
|
||||
pred = tf.matmul(output, w_out) + b_out
|
||||
return pred, final_states
|
||||
|
||||
|
||||
# ——————————————————训练模型——————————————————
|
||||
def train_lstm(batch_size=80, time_step=15, train_begin=0, train_end=5800):
|
||||
X = tf.placeholder(tf.float32, shape=[None, time_step, input_size])
|
||||
Y = tf.placeholder(tf.float32, shape=[None, time_step, output_size])
|
||||
batch_index, train_x, train_y = get_train_data(batch_size, time_step, train_begin, train_end)
|
||||
pred, _ = lstm(X)
|
||||
# 损失函数
|
||||
loss = tf.reduce_mean(tf.square(tf.reshape(pred, [-1]) - tf.reshape(Y, [-1])))
|
||||
train_op = tf.train.AdamOptimizer(lr).minimize(loss)
|
||||
saver = tf.train.Saver(tf.global_variables(), max_to_keep=15)
|
||||
module_file = tf.train.latest_checkpoint()
|
||||
with tf.Session() as sess:
|
||||
# sess.run(tf.global_variables_initializer())
|
||||
saver.restore(sess, module_file)
|
||||
# 重复训练2000次
|
||||
for i in range(2000):
|
||||
for step in range(len(batch_index) - 1):
|
||||
_, loss_ = sess.run([train_op, loss], feed_dict={X: train_x[batch_index[step]:batch_index[step + 1]],
|
||||
Y: train_y[batch_index[step]:batch_index[step + 1]]})
|
||||
print(i, loss_)
|
||||
if i % 200 == 0:
|
||||
print("保存模型：", saver.save(sess, 'stock2.model', global_step=i))
|
||||
|
||||
|
||||
# ————————————————预测模型————————————————————
|
||||
def prediction(time_step=20):
|
||||
X = tf.placeholder(tf.float32, shape=[None, time_step, input_size])
|
||||
mean, std, test_x, test_y = get_test_data(time_step)
|
||||
pred, _ = lstm(X)
|
||||
saver = tf.train.Saver(tf.global_variables())
|
||||
with tf.Session() as sess:
|
||||
# 参数恢复
|
||||
module_file = tf.train.latest_checkpoint()
|
||||
saver.restore(sess, module_file)
|
||||
test_predict = []
|
||||
for step in range(len(test_x) - 1):
|
||||
prob = sess.run(pred, feed_dict={X: [test_x[step]]})
|
||||
predict = prob.reshape((-1))
|
||||
test_predict.extend(predict)
|
||||
test_y = np.array(test_y) * std[7] + mean[7]
|
||||
test_predict = np.array(test_predict) * std[7] + mean[7]
|
||||
acc = np.average(np.abs(test_predict - test_y[:len(test_predict)]) / test_y[:len(test_predict)])
|
||||
Loading…
Reference in New Issue
Block a user