In recent decades, financial quantification has emerged and matured rapidly. For financial institutions such as funds, investment institutions are increasingly dissatisfied with the situation of passively constructing investment portfolios with average market returns, and are paying more and more attention to active quantitative strategy investment portfolios. This requires the introduction of active stock investment fund management models. Currently, in my country's stock fund investment market, there are many active quantitative investment strategies, and the algorithms used vary widely, such as SVM, random forest, RNN recurrent memory network, etc. This article focuses on this trend, using the emerging LSTM-GRU gate-controlled long short-term memory network model in the field of financial stock investment as a basis to build a set of active investment stock strategies, and combining it with SVM, which has been widely used in the field of quantitative stock investment. Comparing models such as RNN, theoretically speaking, compared to SVM that simply relies on kernel functions for high-order mapping and classification of data, neural network algorithms such as RNN and LSTM-GRU have better principles and are more suitable for processing financial stock data. Then, through multiple By comparison, it was finally found that the LSTM- GRU gate-controlled long short-term memory network has a better accuracy. By selecting the LSTM-GRU algorithm to construct a trading strategy based on the Shanghai and Shenzhen 300 Index constituent stocks, the parameters were adjusted and the neural layer connection was adjusted. Finally, It has significantly outperformed the benchmark index CSI 300 over the long term. The conclusion of this article is that the research results can provide certain quantitative strategy references for financial institutions to construct active stock investment portfolios.

翻译：近几十年来，金融量化迅速兴起并趋于成熟。对于基金等金融机构而言，投资机构日益不满于被动构建市场平均收益组合的现状，愈发关注主动量化策略投资组合。这要求引入主动型股票投资基金管理模式。当前我国股票基金投资市场中，主动量化投资策略众多，所采用的算法也千差万别，例如支持向量机（SVM）、随机森林、循环神经网络（RNN）等。本文顺应这一趋势，以金融股票投资领域新兴的LSTM-GRU门控长短期记忆网络模型为基础，构建了一套主动投资股票策略，并与已在量化股票投资领域广泛应用的SVM、RNN等模型进行对比。理论上，相比单纯依赖核函数对数据进行高阶映射与分类的SVM，RNN、LSTM-GRU等神经网络算法具有更优的原理，更适合处理金融股票数据。随后，通过多次比较发现，LSTM-GRU门控长短期记忆网络具有更优的准确率。通过选取LSTM-GRU算法，基于沪深300指数成分股构建交易策略，调整参数及神经层连接，最终在长期内显著跑赢基准指数沪深300。本文的结论是：研究成果可为金融机构构建主动型股票投资组合提供一定的量化策略参考。