【书籍连载】AI量化交易从入门到精通 - 第7章：深度学习入门与应用

# 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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... ``` --- *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*