基于均线与K线指标的量化投资策略

Quantitative Investment Strategies Based on Moving Averages and K-Line Indicators

摘要: 量化投资是一种基于大数据和数字化技术的投资理念，它通过利用计算机模型、复杂算法等技术手段，对市场数据进行预测和分析，制定自动化的投资策略。本文基于均线与K线两个基本的选股指标，构建均线回归策略与K线形态捕捉策略相结合的选股模型：当探测到某支股票的股价低于5日均价的0.95倍，且捕获到所设定的K线为上涨形态时就买入；当所持有的股票价格高于5日均价的1.05倍或者捕获到所设定的K线为下跌形态时就卖出。利用聚宽(JoinQuant)所提供的量化环境，对2022年1月1日至2022年12月31日一整年的A股市场进行回测，最终共交易138笔，实现超额收益19.12%。本文同时对策略回测结果进行了分析，并就单独个案进行了详细的阐释。文章最后提出了优化与改进策略的方法，为此策略应用于股票的预测与投资提供了参考。

Abstract: Quantitative investment is an investment concept based on big data and digital technology. It uses computer models, complex algorithms and other technical means to forecast and analyze market data and develop automated investment strategies. Based on two basic stock selection indicators, the paper constructs a stock selection model combining the regression strategy of the moving average and the K-line pattern capture strategy. When the stock price is lower than 0.95 times of the 5-day average price and the K-line rise pattern is captured, the stock will be bought. Sell when the stock price is higher than 1.05 times the 5-day average price or captures the set K-line decline pattern. Using the quantitative environment provided by JoinQuant, the A-share market for the whole year from January 1, 2022 to December 31, 2022 was backtested, with a total of 138 transactions and an excess return of 19.12%. At the same time, the results of the strategy backtest are analyzed, and the individual cases are explained in detail. Finally, the paper puts forward the method of optimizing and improving the strategy, which provides a reference for the application of the strategy to stock prediction and investment.