rnn神经网络模型 python股票代码
时间: 2023-06-11 12:06:15 浏览: 72
以下是一个简单的RNN神经网络模型的Python股票代码,用于预测某支股票的价格走势:
```
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM
# 读取股票数据
df = pd.read_csv('stock_data.csv')
# 选择需要预测的特征列
data = df.iloc[:, [2, 3, 4, 5]].values
# 对数据进行归一化处理
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))
data = scaler.fit_transform(data)
# 准备训练数据和测试数据
train_size = int(len(data) * 0.8)
test_size = len(data) - train_size
train, test = data[0:train_size,:], data[train_size:len(data),:]
# 转换成适合RNN输入的数据格式
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back), :]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return np.array(dataX), np.array(dataY)
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
# 创建RNN模型
model = Sequential()
model.add(LSTM(4, input_shape=(look_back, 4)))
model.add(Dropout(0.2))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
# 训练模型
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)
# 预测股票价格
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
# 反归一化处理
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
# 计算均方根误差
from sklearn.metrics import mean_squared_error
trainScore = np.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = np.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))
```
请注意,这只是一个简单的RNN模型代码示例,你需要根据你的数据集和需求进行调整和优化。