写出用LSTM模型预测股价的python代码

首先，我们需要导入必要的库，如Keras、numpy和pandas。

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import LSTM, Dense

然后，我们加载股价数据：

df = pd.read_csv("stock_data.csv")

接下来，我们将数据处理为用于训练LSTM模型的格式：

data = df.close.values
data = data.reshape(-1, 1)

# normalize the data
mean = np.mean(data)
std = np.std(data)
data = (data - mean) / std

接下来，我们需要准备训练数据和目标：

# split into training and test sets
train_size = int(len(data) * 0.67)
test_size = len(data) - train_size
train, test = data[0:train_size, :], data[train_size:len(data), :]

# convert an array of values into a dataset matrix
def create_dataset(dataset, look_back=1):
    dataX, dataY = [], []
    for i in range(len(dataset) - look_back):
        a = dataset[i:(i + look_back), 0]
        dataX.append(a)
        dataY.append(dataset[i + look_back, 0])
    return np.array(dataX), np.array(dataY)

# reshape into X=t and Y=t+1
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

然后，我们可以构建LSTM模型：

# create and fit the LSTM network
model = Sequential()
model.add(LSTM(4, input_shape=(None, 1)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)

最后，我们可以使用测试数据评估模型的性能：

# make predictions
trainPredict = model.predict(trainX)
testPredict = model.p