LSTM多股票价格预测用粒子群进行优化的代码示例

以下是一个使用LSTM网络和粒子群优化算法进行多股票价格预测的Python代码示例：

import numpy as np
import pandas as pd
import random

from keras.models import Sequential
from keras.layers import Dense, LSTM

from pyswarm import pso

# 读取数据
df = pd.read_csv('stock_prices.csv')

# 数据预处理
data = df.drop(['Date'], axis=1).values
data = data.astype('float32')
max_value = np.max(data)
min_value = np.min(data)
scalar = max_value - min_value
data = list(map(lambda x: x / scalar, data))

# 数据划分为训练集和测试集
train_size = int(len(data) * 0.7)
train_data = data[:train_size]
test_data = data[train_size:]

# 转换为监督学习数据集
def create_dataset(dataset, look_back=1):
    X, Y = [], []
    for i in range(len(dataset)-look_back):
        X.append(dataset[i:(i+look_back)])
        Y.append(dataset[i+look_back])
    return np.array(X), np.array(Y)

look_back = 3
trainX, trainY = create_dataset(train_data, look_back)
testX, testY = create_dataset(test_data, look_back)

# LSTM模型
model = Sequential()
model.add(LSTM(units=50, input_shape=(look_back, 1)))
model.add(Dense(units=1))
model.compile(optimizer='adam', loss='mse')

# 粒子群优化算法
def optimize(parameters):
    model.set_weights(parameters)
    model.fit(trainX, trainY, epochs=100, batch_size=32, verbose=0)
    testPredict = model.predict(testX)
    testPredict = testPredict * scalar
    testY = testY * scalar
    mse = ((testPredict - testY) ** 2).mean(axis=0)
    return mse

lb = [-1] * model.count_params()
ub = [1] * model.count_params()

xopt, fopt = pso(optimize, lb, ub, swarmsize=10, maxiter=50)

# 使用最优参数进行预测
model.set_weights(xopt)
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
trainPredict = trainPredict * scalar
trainY = trainY * scalar
testPredict = testPredict * scalar
testY = testY * scalar

# 输出结果
print("Train MSE:", ((trainPredict - trainY) ** 2).mean(axis=0))
print("Test MSE:", ((testPredict - testY) ** 2).mean(axis=0))

请注意，这只是一个简单的示例代码，实际应用中可能需要进行更多的数据预处理和调整模型参数。