pytorch卷积股票预测 示例

PyTorch是一个流行的深度学习库，常用于金融领域，包括股票价格预测。卷积神经网络(CNN)可以应用于时间序列数据，如股票价格，因为它们擅长捕捉局部特征并处理一维输入。下面是一个简单的示例，展示如何使用PyTorch构建一个基本的卷积模型对股票价格进行预测：

# 导入所需的库
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader

class StockDataset(Dataset):
    # 假设我们有历史收盘价、开盘价、最高价、最低价等特征
    def __init__(self, prices, lookback_window, future_steps):
        self.prices = prices
        self.lookback_window = lookback_window
        self.future_steps = future_steps

    def __len__(self):
        return len(self.prices) - self.lookback_window - self.future_steps + 1

    def __getitem__(self, idx):
        start_idx = idx
        end_idx = idx + self.lookback_window
        X = self.prices[start_idx:end_idx]  # 输入序列
        y = self.prices[end_idx+1:end_idx+1+self.future_steps]  # 输出序列
        return torch.tensor(X), torch.tensor(y)

# 构建卷积神经网络模型
class ConvModel(nn.Module):
    def __init__(self, input_size, output_size):
        super(ConvModel, self).__init__()
        self.conv1d = nn.Conv1d(in_channels=1, out_channels=64, kernel_size=5)
        self.fc = nn.Linear(64, output_size)

    def forward(self, x):
        x = x.unsqueeze(1)  # 添加通道维度
        x = F.relu(self.conv1d(x))
        x = x.reshape(-1, x.size(2))  # 展平以便于全连接层
        x = self.fc(x)
        return x

# 示例设置
lookback_window = 20
future_steps = 5
input_size = (lookback_window, 1)  # 单一股票特征
output_size = future_steps

# 加载数据集（这里假设有一个名为stock_data.csv的数据文件）
dataset = StockDataset(load_stock_data(), lookback_window, future_steps)
data_loader = DataLoader(dataset, batch_size=32, shuffle=True)

# 创建模型实例
model = ConvModel(input_size[0], output_size)

# 编译模型（选择适当的损失函数和优化器）
optimizer = torch.optim.Adam(model.parameters())
criterion = nn.MSELoss()  # 对于回归任务通常使用均方误差

# 训练模型
for epoch in range(num_epochs):
    for batch_X, batch_y in data_loader:
        # ... 进行前向传播、计算损失、反向传播和优化步骤 ...

# 预测阶段
predictions = model(test_data_loader.dataset.X)