第7章：深度学习入门与应用

> 深度学习能够自动学习复杂特征，在时间序列预测中表现优异。

学习目标

• ✅ 理解神经网络的基本原理
• ✅ 掌握PyTorch基础
• ✅ 学会LSTM模型构建
• ✅ 实现股价预测模型

7.1 PyTorch基础

神经网络构建

import torch
import torch.nn as nn

class SimpleNet(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, output_size)
    
    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

7.2 LSTM模型

模型定义

class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super().__init__()
        self.lstm = nn.LSTM(
            input_size=input_size,
            hidden_size=hidden_size,
            num_layers=num_layers,
            batch_first=True,
            dropout=0.2
        )
        self.fc = nn.Linear(hidden_size, output_size)
    
    def forward(self, x):
        lstm_out, _ = self.lstm(x)
        out = lstm_out[:, -1, :]  # 取最后一个时间步
        out = self.fc(out)
        return out

训练流程

class Trainer:
    def __init__(self, model, device='cuda'):
        self.model = model.to(device)
        self.criterion = nn.MSELoss()
        self.optimizer = optim.Adam(model.parameters(), lr=0.001)
    
    def train_epoch(self, dataloader):
        self.model.train()
        for batch_x, batch_y in dataloader:
            self.optimizer.zero_grad()
            outputs = self.model(batch_x)
            loss = self.criterion(outputs, batch_y)
            loss.backward()
            self.optimizer.step()

7.3 股价预测实战

数据准备

class StockDataset(Dataset):
    def __init__(self, data, seq_len=20, prediction_horizon=5):
        self.seq_len = seq_len
        self.prediction_horizon = prediction_horizon
        
        # 准备特征
        df = data.copy()
        df['returns'] = df['close'].pct_change()
        df['ma5'] = df['close'].rolling(5).mean()
        df['rsi'] = calculate_rsi(df['close'])
        
        # 创建序列
        self.X, self.y = self._create_sequences(df)
    
    def __getitem__(self, idx):
        return torch.FloatTensor(self.X[idx]), torch.FloatTensor([self.y[idx]])

Attention LSTM

class AttentionLSTM(nn.Module):
    """带注意力机制的LSTM"""
    
    def __init__(self, input_size, hidden_size, num_layers):
        super().__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        
        # 注意力层
        self.attention = nn.Sequential(
            nn.Linear(hidden_size, hidden_size),
            nn.Tanh(),
            nn.Linear(hidden_size, 1)
        )
        self.fc = nn.Linear(hidden_size, 1)
    
    def forward(self, x):
        lstm_out, _ = self.lstm(x)
        attn_weights = torch.softmax(self.attention(lstm_out), dim=1)
        context = torch.sum(attn_weights * lstm_out, dim=1)
        return self.fc(context)

7.4 完整系统

class DeepLearningTradingSystem:
    """深度学习交易系统"""
    
    def __init__(self, model_type='lstm', seq_len=20):
        self.seq_len = seq_len
        self.model = LSTMModel(input_size=10, hidden_size=64, num_layers=2, output_size=1)
    
    def train(self, data, epochs=100):
        # 准备数据
        dataset = StockDataset(data, self.seq_len)
        dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
        
        # 训练
        trainer = Trainer(self.model)
        for epoch in range(epochs):
            trainer.train_epoch(dataloader)
    
    def predict(self, data):
        self.model.eval()
        # 预测逻辑...

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*本文节选自《AI量化交易从入门到精通》第7章* *完整内容请访问代码仓：book_writing/part2_core/part7_dl/README.md*