transformer股票预测完整程序pytorch

以下是一个使用Transformer模型进行股票预测的完整程序，使用PyTorch框架实现：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

# 定义超参数
input_size = 1
hidden_size = 32
num_layers = 2
num_heads = 4
dropout = 0.2
lr = 0.0005
num_epochs = 200

# 读取数据
data = pd.read_csv('AAPL.csv')
data = data['Close'].values.reshape(-1, 1)

# 归一化
scaler = MinMaxScaler()
data = scaler.fit_transform(data)

# 划分训练集和测试集
train_size = int(len(data) * 0.8)
train_data = data[:train_size]
test_data = data[train_size:]

# 定义数据处理函数
def create_dataset(data, look_back=1):
    X, Y = [], []
    for i in range(len(data) - look_back):
        X.append(data[i:(i + look_back)])
        Y.append(data[i + look_back])
    return np.array(X), np.array(Y)

# 创建训练集和测试集
look_back = 7
train_X, train_Y = create_dataset(train_data, look_back)
test_X, test_Y = create_dataset(test_data, look_back)

# 转换为Tensor
train_X = torch.from_numpy(train_X).float()
train_Y = torch.from_numpy(train_Y).float()
test_X = torch.from_numpy(test_X).float()
test_Y = torch.from_numpy(test_Y).float()

# 定义模型
class TransformerModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_heads, dropout):
        super(TransformerModel, self).__init__()
        self.transformer = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(
                d_model=input_size,
                nhead=num_heads,
                dropout=dropout
            ),
            num_layers=num_layers
        )
        self.linear = nn.Linear(hidden_size, 1)
        
    def forward(self, x):
        x = self.transformer(x)
        x = self.linear(x)
        return x.squeeze()

# 定义模型、损失函数和优化器
model = TransformerModel(input_size, hidden_size, num_layers, num_heads, dropout)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=lr)

# 训练模型
for epoch in range(num_epochs):
    model.train()
    optimizer.zero_grad()
    outputs = model(train_X)
    loss = criterion(outputs, train_Y)
    loss.backward()
    optimizer.step()
    
    if (epoch + 1) % 10 == 0:
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch + 1, num_epochs, loss.item()))

# 测试模型
model.eval()
with torch.no_grad():
    train_predict = model(train_X)
    test_predict = model(test_X)
    
train_predict = scaler.inverse_transform(train_predict.numpy())
train_Y = scaler.inverse_transform(train_Y.numpy())
test_predict = scaler.inverse_transform(test_predict.numpy())
test_Y = scaler.inverse_transform(test_Y.numpy())

# 计算误差
train_rmse = np.sqrt(np.mean(np.square(train_predict - train_Y)))
test_rmse = np.sqrt(np.mean(np.square(test_predict - test_Y)))
print('Train RMSE:', train_rmse)
print('Test RMSE:', test_rmse)

这个程序使用Transformer模型来预测苹果公司股票的收盘价。首先读取数据，然后归一化。然后将数据划分为训练集和测试集，并创建数据集。接着定义模型、损失函数和优化器，使用训练集对模型进行训练。最后测试模型，并计算误差。