商品期货跨期套利策略：基于Attention-LSTM和网格策略的Python实现

以下是一个基于Attention-LSTM-网格策略的商品期货跨期套利的Python代码示例：\n\npython\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import Dataset, DataLoader\n\n# 定义Attention-LSTM模型\nclass AttentionLSTM(nn.Module):\n def __init__(self, input_size, hidden_size, output_size):\n super(AttentionLSTM, self).__init__()\n self.hidden_size = hidden_size\n self.lstm = nn.LSTM(input_size, hidden_size)\n self.attention = nn.Linear(hidden_size, output_size)\n \n def forward(self, input):\n output, _ = self.lstm(input)\n attention_weights = self.attention(output)\n attention_weights = torch.softmax(attention_weights, dim=0)\n output = torch.sum(output * attention_weights, dim=0)\n return output\n\n# 定义数据集类\nclass FuturesDataset(Dataset):\n def __init__(self, data):\n self.data = data\n \n def __len__(self):\n return len(self.data)\n \n def __getitem__(self, index):\n return self.data[index]\n \n# 加载数据\ndata = pd.read_csv('futures_data.csv')\ntrain_data = data.iloc[:-100]\ntest_data = data.iloc[-100:]\n\n# 准备训练数据\ntrain_dataset = FuturesDataset(train_data.values)\ntrain_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n\n# 准备测试数据\ntest_dataset = FuturesDataset(test_data.values)\ntest_loader = DataLoader(test_dataset, batch_size=1, shuffle=False)\n\n# 设置模型超参数\ninput_size = 10\nhidden_size = 32\noutput_size = 1\nlearning_rate = 0.001\nnum_epochs = 10\n\n# 创建模型和优化器\nmodel = AttentionLSTM(input_size, hidden_size, output_size)\noptimizer = optim.Adam(model.parameters(), lr=learning_rate)\ncriterion = nn.MSELoss()\n\n# 训练模型\nfor epoch in range(num_epochs):\n for batch_data in train_loader:\n inputs = batch_data[:, :-1]\n labels = batch_data[:, -1]\n \n optimizer.zero_grad()\n \n outputs = model(inputs.unsqueeze(0).float())\n loss = criterion(outputs, labels.float())\n \n loss.backward()\n optimizer.step()\n \n print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss.item()}')\n\n# 测试模型\nmodel.eval()\nwith torch.no_grad():\n test_loss = 0\n for batch_data in test_loader:\n inputs = batch_data[:, :-1]\n labels = batch_data[:, -1]\n \n outputs = model(inputs.unsqueeze(0).float())\n test_loss += criterion(outputs, labels.float()).item()\n \n avg_test_loss = test_loss / len(test_loader)\n print(f'Average Test Loss: {avg_test_loss}')\n\n# 使用模型进行套利策略\nmodel.eval()\nwith torch.no_grad():\n positions = []\n for batch_data in test_loader:\n inputs = batch_data[:, :-1]\n outputs = model(inputs.unsqueeze(0).float())\n \n if outputs > 0:\n positions.append(1) # 买入开仓\n else:\n positions.append(-1) # 卖出开仓\n \n # 根据持仓情况计算收益\n returns = []\n position = 0\n for i in range(len(test_data)):\n if positions[i] != position:\n if position == 0:\n returns.append(0) # 空仓\n else:\n returns.append(test_data['close'].iloc[i] - test_data['close'].iloc[i-1]) # 平仓\n position = positions[i]\n else:\n returns.append(0) # 保持持仓\n \n total_return = sum(returns)\n print(f'Total Return: {total_return}')\n\n\n请注意，上述代码只是一个示例，具体实现可能需要根据您的数据和策略进行一些调整和修改。此外，还需要确保数据文件futures_data.csv存在，并且格式正确。