1 !pip install tushare
2 import tushare as ts
3 import numpy as np
4 import tensorflow as tf
5 from tensorflow.keras.layers import Dropout, Dense, LSTM
6 import matplotlib.pyplot as plt
7 import os
8 import pandas as pd
9 from sklearn.preprocessing import MinMaxScaler
10 from sklearn.metrics import mean_squared_error, mean_absolute_error
11 import math
12
13
14 df1 = ts.get_k_data('600519', ktype='D', start='2004-01-01', end='2020-05-12')
15
16 datapath1 = "./SH600519.csv"
17 df1.to_csv(datapath1)
18
19
20 maotai = pd.read_csv("./SH600519.csv")
21
22 maotai.head()
23
24
25 maotai.tail()
26
27
28 training_set = maotai.iloc[0:3000, 2:3].values
29 test_set = maotai.iloc[3000:, 2:3].values
30
31 #归一化
32 sc = MinMaxScaler(feature_range = (0, 1))
33 training_set_scaled = sc.fit_transform(training_set)
34 test_set = sc.transform(test_set)
35
36 training_set_scaled.shape
37
38 test_set.shape
39
40 x_train = []
41 y_train = []
42
43 x_test = []
44 y_test = []
45
46
47 for i in range(60, len(training_set_scaled)):
48 x_train.append(training_set_scaled[i - 60:i, 0])
49 y_train.append(training_set_scaled[i, 0])
50
51 np.random.seed(7)
52 np.random.shuffle(x_train)
53 np.random.seed(7)
54 np.random.shuffle(y_train)
55 tf.random.set_seed(7)
56
57
58 x_train, y_train = np.array(x_train), np.array(y_train)
59
60 x_train.shape
61 y_train.shape
62
63
64 x_train = np.reshape(x_train, (x_train.shape[0], 60, 1))
65 for i in range(60, len(test_set)):
66 x_test.append(test_set[i-60:i, 0])
67 y_test.append(test_set[i, 0])
68
69 x_test, y_test = np.array(x_test), np.array(y_test)
70 x_test = np.reshape(x_test, (x_test.shape[0], 60, 1))
71
72
73 model = tf.keras.Sequential([
74 LSTM(80, return_sequences=True),
75 Dropout(0.2),
76 LSTM(100),
77 Dropout(0.2),
78 Dense(1)
79 ])
80
81 model.compile(optimizer=tf.keras.optimizers.Adam(0.0001),
82 loss='mean_squared_error')
83
84 checkpoint_save_path = "./checkpoint/LSTM_stock.ckpt"
85
86 if os.path.exists(checkpoint_save_path + '.index'):
87 print('-------------load the model-------------')
88 model.load_weights(checkpoint_save_path)
89
90 cp_callback = tf.keras.callbacks.ModelCheckpoint(
91 filepath=checkpoint_save_path,
92 save_weights_only=True,
93 save_best_only=True,
94 monitor='val_loss')
95
96 history = model.fit(x_train, y_train, batch_size=64, epochs=24,
97 validation_data=(x_test, y_test), validation_freq=1, callbacks=[cp_callback])
98
99 model.summary()
100
101
102
103 with open("./weights.txt", "w") as f:
104 for v in model.trainable_variables:
105 f.write(str(v.name) + '\n')
106 f.write(str(v.shape) + '\n')
107 f.write(str(v.numpy()) + '\n')
108
109
110 loss = history.history['loss']
111 val_loss = history.history['val_loss']
112
113 plt.plot(loss, label='Training Loss')
114 plt.plot(val_loss, label='Validation Loss')
115 plt.title('Training and Validation Loss')
116 plt.legend()
117 plt.show()
118
119
120 predicted_stock_price = model.predict(x_test)
121 predicted_stock_price = sc.inverse_transform(predicted_stock_price)
122 real_stock_price = sc.inverse_transform(test_set[60:])
123
124 plt.plot(real_stock_price, color='red', label='real_stock_price')
125 plt.plot(predicted_stock_price, color='blue', label='predicted_stock_price')
126 plt.title('Maotai Stock Price Prediction')
127 plt.xlabel('Time')
128 plt.ylabel('Maotai Stock Price')
129 plt.legend()
130 plt.show()
131
132
133 mse = mean_squared_error(predicted_stock_price, real_stock_price)
134 rmse = math.sqrt(mean_squared_error(predicted_stock_price, real_stock_price))
135 mae = mean_absolute_error(predicted_stock_price, real_stock_price)
136 print('均方误差： %.6f'%mse)
137 print('均方根误差： %.6f'%rmse)
138 print('平均绝对误差： %.6f'%mae)
