商品期货跨期套利：基于Attention-LSTM-网格的Python代码示例

以下是一个基于Attention-LSTM-网格的商品期货跨期套利的Python代码示例：

import numpy as np
import pandas as pd
from keras.models import Model
from keras.layers import Input, LSTM, Dense, Dot
from keras.optimizers import Adam

# 读取数据
data = pd.read_csv('data.csv')

# 数据预处理
data['price_diff'] = data['next_period_price'] - data['current_price']
data['price_diff'] = data['price_diff'].apply(lambda x: 1 if x > 0 else 0)  # 价格上涨为1，否则为0

# 划分训练集和测试集
train_size = int(len(data) * 0.8)
train_data = data[:train_size]
test_data = data[train_size:]

# 构建模型
input_seq = Input(shape=(10, 1))  # 输入序列长度为10
lstm = LSTM(64, return_sequences=True)(input_seq)  # LSTM层
attention = Dense(1, activation='tanh')(lstm)  # Attention层
attention = Flatten()(attention)
attention = Activation('softmax')(attention)
attention = RepeatVector(64)(attention)
attention = Permute([2, 1])(attention)
sent_representation = Dot(axes=1)([lstm, attention])  # 加权求和

output = Dense(1, activation='sigmoid')(sent_representation)  # 输出层

model = Model(inputs=input_seq, outputs=output)
model.compile(optimizer=Adam(lr=0.001), loss='binary_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(train_data['price_diff'].values.reshape(-1, 10, 1), train_data['target'].values, epochs=10, batch_size=32)

# 测试模型
y_pred = model.predict(test_data['price_diff'].values.reshape(-1, 10, 1))
y_pred = np.round(y_pred)

# 模型评估
accuracy = np.sum(y_pred == test_data['target'].values) / len(test_data)
print('Accuracy:', accuracy)

请注意，此代码仅为示例，实际应用中还需要根据具体的数据和问题进行适当的调整和优化。