循环神经网络（RNN）实现股票预测
文章目录
- 一、前言
- 二、前期工作
-
- 1. 设置GPU（如果使用的是CPU可以忽略这步）
- 2. 导入数据
- 四、数据预处理
-
- 1.归一化
- 2.设置测试集训练集
- 五、构建模型
- 六、激活模型
- 七、训练模型
- 八、结果可视化
-
- 1.绘制loss图
- 2.预测
- 3.评估
一、前言
我的环境：
- 语言环境：Python3.6.5
- 编译器：jupyter notebook
- 深度学习环境：TensorFlow2.4.1
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来自专栏：机器学习与深度学习算法推荐
二、前期工作
1. 设置GPU（如果使用的是CPU可以忽略这步）
import tensorflow as tf
gpus = tf.config.list_physical_devices("GPU")
if gpus:
tf.config.experimental.set_memory_growth(gpus[0], True) #设置GPU显存用量按需使用
tf.config.set_visible_devices([gpus[0]],"GPU")
2. 导入数据
import os,math
from tensorflow.keras.layers import Dropout, Dense, SimpleRNN
from sklearn.preprocessing import MinMaxScaler
from sklearn import metrics
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
# 支持中文
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
data = pd.read_csv('./datasets/SH600519.csv') # 读取股票文件
data
| Unnamed: 0 | date | open | close | high | low | volume | code | |
|---|---|---|---|---|---|---|---|---|
| 0 | 74 | 2010-04-26 | 88.702 | 87.381 | 89.072 | 87.362 | 107036.13 | 600519 |
| 1 | 75 | 2010-04-27 | 87.355 | 84.841 | 87.355 | 84.681 | 58234.48 | 600519 |
| 2 | 76 | 2010-04-28 | 84.235 | 84.318 | 85.128 | 83.597 | 26287.43 | 600519 |
| 3 | 77 | 2010-04-29 | 84.592 | 85.671 | 86.315 | 84.592 | 34501.20 | 600519 |
| 4 | 78 | 2010-04-30 | 83.871 | 82.340 | 83.871 | 81.523 | 85566.70 | 600519 |
| … | … | … | … | … | … | … | … | … |
| 2421 | 2495 | 2020-04-20 | 1221.000 | 1227.300 | 1231.500 | 1216.800 | 24239.00 | 600519 |
| 2422 | 2496 | 2020-04-21 | 1221.020 | 1200.000 | 1223.990 | 1193.000 | 29224.00 | 600519 |
| 2423 | 2497 | 2020-04-22 | 1206.000 | 1244.500 | 1249.500 | 1202.220 | 44035.00 | 600519 |
| 2424 | 2498 | 2020-04-23 | 1250.000 | 1252.260 | 1265.680 | 1247.770 | 26899.00 | 600519 |
| 2425 | 2499 | 2020-04-24 | 1248.000 | 1250.560 | 1259.890 | 1235.180 | 19122.00 | 600519 |
2426 rows × 8 columns
training_set = data.iloc[0:2426 - 300, 2:3].values
test_set = data.iloc[2426 - 300:, 2:3].values
四、数据预处理
1.归一化
sc = MinMaxScaler(feature_range=(0, 1))
training_set = sc.fit_transform(training_set)
test_set = sc.transform(test_set)
2.设置测试集训练集
x_train = []
y_train = []
x_test = []
y_test = []
"""
使用前60天的开盘价作为输入特征x_train
第61天的开盘价作为输入标签y_train
for循环共构建2426-300-60=2066组训练数据。
共构建300-60=260组测试数据
"""
for i in range(60, len(training_set)):
x_train.append(training_set[i - 60:i, 0])
y_train.append(training_set[i, 0])
for i in range(60, len(test_set)):
x_test.append(test_set[i - 60:i, 0])
y_test.append(test_set[i, 0])
# 对训练集进行打乱
np.random.seed(7)
np.random.shuffle(x_train)
np.random.seed(7)
np.random.shuffle(y_train)
tf.random.set_seed(7)
"""
将训练数据调整为数组（array）
调整后的形状：
x_train:(2066, 60, 1)
y_train:(2066,)
x_test :(240, 60, 1)
y_test :(240,)
"""
x_train, y_train = np.array(x_train), np.array(y_train) # x_train形状为：(2066, 60, 1)
x_test, y_test = np.array(x_test), np.array(y_test)
"""
输入要求：[送入样本数， 循环核时间展开步数， 每个时间步输入特征个数]
"""
x_train = np.reshape(x_train, (x_train.shape[0], 60, 1))
x_test = np.reshape(x_test, (x_test.shape[0], 60, 1))
五、构建模型
model = tf.keras.Sequential([
SimpleRNN(80, return_sequences=True), #布尔值。是返回输出序列中的最后一个输出，还是全部序列。
Dropout(0.2), #防止过拟合
SimpleRNN(80),
Dropout(0.2),
Dense(1)
])
六、激活模型
# 该应用只观测loss数值，不观测准确率，所以删去metrics选项，一会在每个epoch迭代显示时只显示loss值
model.compile(optimizer=tf.keras.optimizers.Adam(0.001),
loss='mean_squared_error') # 损失函数用均方误差
七、训练模型
history = model.fit(x_train, y_train,
batch_size=64,
epochs=20,
validation_data=(x_test, y_test),
validation_freq=1) #测试的epoch间隔数
model.summary()
Epoch 1/20
33/33 [==============================] - 6s 123ms/step - loss: 0.1809 - val_loss: 0.0310
Epoch 2/20
33/33 [==============================] - 3s 105ms/step - loss: 0.0257 - val_loss: 0.0721
Epoch 3/20
33/33 [==============================] - 3s 85ms/step - loss: 0.0165 - val_loss: 0.0059
Epoch 4/20
33/33 [==============================] - 3s 85ms/step - loss: 0.0097 - val_loss: 0.0111
Epoch 5/20
33/33 [==============================] - 3s 90ms/step - loss: 0.0099 - val_loss: 0.0139
Epoch 6/20
33/33 [==============================] - 3s 105ms/step - loss: 0.0067 - val_loss: 0.0167
Epoch 7/20
33/33 [==============================] - 3s 86ms/step - loss: 0.0067 - val_loss: 0.0095
Epoch 8/20
33/33 [==============================] - 3s 91ms/step - loss: 0.0063 - val_loss: 0.0218
Epoch 9/20
33/33 [==============================] - 3s 99ms/step - loss: 0.0052 - val_loss: 0.0109
Epoch 10/20
33/33 [==============================] - 3s 99ms/step - loss: 0.0043 - val_loss: 0.0120
Epoch 11/20
33/33 [==============================] - 3s 92ms/step - loss: 0.0044 - val_loss: 0.0167
Epoch 12/20
33/33 [==============================] - 3s 89ms/step - loss: 0.0039 - val_loss: 0.0032
Epoch 13/20
33/33 [==============================] - 3s 88ms/step - loss: 0.0041 - val_loss: 0.0052
Epoch 14/20
33/33 [==============================] - 3s 93ms/step - loss: 0.0035 - val_loss: 0.0179
Epoch 15/20
33/33 [==============================] - 4s 110ms/step - loss: 0.0033 - val_loss: 0.0124
Epoch 16/20
33/33 [==============================] - 3s 95ms/step - loss: 0.0035 - val_loss: 0.0149
Epoch 17/20
33/33 [==============================] - 4s 111ms/step - loss: 0.0028 - val_loss: 0.0111
Epoch 18/20
33/33 [==============================] - 4s 110ms/step - loss: 0.0029 - val_loss: 0.0061
Epoch 19/20
33/33 [==============================] - 3s 104ms/step - loss: 0.0027 - val_loss: 0.0110
Epoch 20/20
33/33 [==============================] - 3s 90ms/step - loss: 0.0028 - val_loss: 0.0037
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
simple_rnn (SimpleRNN) (None, 60, 80) 6560
_________________________________________________________________
dropout (Dropout) (None, 60, 80) 0
_________________________________________________________________
simple_rnn_1 (SimpleRNN) (None, 80) 12880
_________________________________________________________________
dropout_1 (Dropout) (None, 80) 0
_________________________________________________________________
dense (Dense) (None, 1) 81
=================================================================
Total params: 19,521
Trainable params: 19,521
Non-trainable params: 0
_________________________________________________________________
八、结果可视化
1.绘制loss图
plt.plot(history.history['loss'] , label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.legend()
plt.show()
2.预测
predicted_stock_price = model.predict(x_test) # 测试集输入模型进行预测
predicted_stock_price = sc.inverse_transform(predicted_stock_price) # 对预测数据还原---从（0，1）反归一化到原始范围
real_stock_price = sc.inverse_transform(test_set[60:]) # 对真实数据还原---从（0，1）反归一化到原始范围
# 画出真实数据和预测数据的对比曲线
plt.plot(real_stock_price, color='red', label='Stock Price')
plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')
plt.title('Stock Price Prediction by K同学啊')
plt.xlabel('Time')
plt.ylabel('Stock Price')
plt.legend()
plt.show()
3.评估
MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)
RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5
MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)
R2 = metrics.r2_score(predicted_stock_price, real_stock_price)
print('均方误差: %.5f' % MSE)
print('均方根误差: %.5f' % RMSE)
print('平均绝对误差: %.5f' % MAE)
print('R2: %.5f' % R2)
均方误差: 1833.92534
均方根误差: 42.82435
平均绝对误差: 36.23424
R2: 0.72347