Abstract: Aiming at the problem of pseudo typing in the stock market and the imbalance of category samples of the typing data set. A turning point prediction method（SMOTE-FLCN-WSVM） combining entanglement theory and deep learning is proposed. On the basis of entanglement theory, the inflection point of the data set is marked. The deep learning model improves the imbalance problem from three levels of data, features and classification algorithms. Firstly, the SMOTE oversampling algorithm is used to preprocess the data set. In order to solve the problem of difficult feature extraction of imbalanced data sets. A convolutional neural network with Focal Loss is used to mine the deep features of the data. Then the support vector machine that introduces the category weight parameter is used to classify the extracted features. The experiment starts from practicability and effectiveness, absolute return, relative return and accuracy rate are selected to compare the model and evaluate the return. The experimental results show that the proposed model has feasibility and practical application value.

Key words: entanglement theory, synthetic minority oversampling technique（SMOTE）, Focal Loss, convolutional neural network（CNN）, weight support vector machine（WSVM）

摘要： 针对股市存在伪分型且分型数据集的类别样本不平衡问题，提出了一种结合缠论和深度学习的拐点预测方法（SMOTE-FLCN-WSVM）。在缠论的基础上，对数据集进行拐点的标注。深度学习模型从数据、特征以及分类算法三个层面对不平衡问题进行改进。首先采用SMOTE过采样算法对数据集进行预处理；再针对不平衡数据集特征提取困难的问题，使用引入Focal Loss的卷积神经网络挖掘数据的深层特征；然后利用引入类别权重参数的支持向量机对提取的特征进行分类。实验从实用性与有效性出发，选择绝对收益、相对收益与准确率对模型进行对比实验与收益评估。实验结果表明，所提模型具有可行性与实际应用价值。

关键词: 缠论, 合成少数类过采样技术（SMOTE）, 焦点损失函数, 卷积神经网络（CNN）, 加权支持向量机（WSVM）