基于Attention-LSTM-网格的商品期货跨期套利策略Python代码实现

以下是一个基于Attention-LSTM-网格的商品期货跨期套利策略的Python代码示例。请注意，此代码仅提供了一个框架，您需要根据实际情况进行适当的修改和完善。\n\npython\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nfrom keras.models import Sequential\nfrom keras.layers import LSTM, Dense, Activation, Bidirectional, Dropout\nfrom keras.layers import Flatten, RepeatVector, TimeDistributed\nfrom keras.layers import Input, multiply, concatenate\nfrom keras.models import Model\nfrom keras.optimizers import Adam\nfrom keras.callbacks import EarlyStopping\nfrom keras import backend as K\nimport matplotlib.pyplot as plt\n\n\n# 加载源数据（示例）\ndef load_data():\n # 加载源数据\n data = pd.read_csv('data.csv')\n # 进行数据预处理，例如缺失值处理、特征工程等\n # ...\n return data\n\n\n# 数据预处理\ndef preprocess_data(data):\n # 删除不需要的特征列\n data = data.drop(['date', 'label'], axis=1)\n \n # 归一化处理\n scaler = MinMaxScaler(feature_range=(0, 1))\n scaled_data = scaler.fit_transform(data)\n \n return scaled_data\n\n\n# 构建Attention-LSTM模型\ndef build_model(input_shape):\n # 定义输入层\n inputs = Input(shape=input_shape)\n \n # 定义LSTM层\n lstm = LSTM(units=64, return_sequences=True)(inputs)\n \n # 定义Attention层\n attention = Dense(1, activation='tanh')(lstm)\n attention = Flatten()(attention)\n attention = Activation('softmax')(attention)\n attention = RepeatVector(64)(attention)\n attention = Permute([2, 1])(attention)\n \n # 将Attention和LSTM输出相乘\n attention_mul = multiply([lstm, attention])\n \n # 构建模型\n output = Dense(1, activation='linear')(attention_mul)\n model = Model(inputs=inputs, outputs=output)\n \n return model\n\n\n# 训练模型\ndef train_model(model, X_train, y_train, X_val, y_val):\n # 定义损失函数\n def root_mean_squared_error(y_true, y_pred):\n return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))\n \n # 编译模型\n model.compile(optimizer=Adam(lr=0.001), loss=root_mean_squared_error)\n \n # 设置早停策略\n early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n \n # 训练模型\n history = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_data=(X_val, y_val), \n callbacks=[early_stopping])\n \n return history\n\n\n# 预测未来价格\ndef predict_future_prices(model, X_test):\n predicted_prices = model.predict(X_test)\n return predicted_prices\n\n\n# 绘制预测结果\ndef plot_predictions(actual_prices, predicted_prices):\n plt.plot(actual_prices, label='Actual Prices')\n plt.plot(predicted_prices, label='Predicted Prices')\n plt.xlabel('Time')\n plt.ylabel('Price')\n plt.legend()\n plt.show()\n\n\n# 主函数\ndef main():\n # 加载数据\n data = load_data()\n \n # 数据预处理\n scaled_data = preprocess_data(data)\n \n # 划分训练集和测试集\n train_data, test_data = train_test_split(scaled_data, test_size=0.2, shuffle=False)\n \n # 划分输入特征和标签\n X_train, y_train = train_data[:, :-1], train_data[:, -1]\n X_test, y_test = test_data[:, :-1], test_data[:, -1]\n \n # 调整输入特征的形状\n X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))\n \n # 构建模型\n model = build_model(input_shape=(X_train.shape[1], X_train.shape[2]))\n \n # 训练模型\n history = train_model(model, X_train, y_train, X_test, y_test)\n \n # 预测未来价格\n predicted_prices = predict_future_prices(model, X_test)\n \n # 反归一化处理\n scaler = MinMaxScaler(feature_range=(0, 1))\n scaler.fit_transform(data)\n predicted_prices = scaler.inverse_transform(predicted_prices)\n actual_prices = scaler.inverse_transform(y_test.reshape(-1, 1))\n \n # 绘制预测结果\n plot_predictions(actual_prices, predicted_prices)\n\n\nif __name__ == '__main__':\n main()\n\n\n请注意，在代码中的load_data()函数中，您需要根据实际情况加载并预处理您的原始数据。此外，还需要调整代码中的模型结构、超参数等以适应您的具体问题。\n