Using stochastics in stock market analysis is widely accepted for index estimation and ultra-high-frequency trading. However, previous studies linking index estimation to actual trading without applying low-frequency trading are limited. This study applied William%R to the existing research and used fixed parameters to remove noise from stochastics. We propose contributing to stock market stakeholders by finding an easy-to-apply algorithmic trading methodology for individual and pension fund investors. The algorithm constructed two oscillators with fixed parameters to identify when to enter and exit the index and achieved good results against the benchmark. We tested two ETFs, SPY (S&P 500) and EWY (MSCI Korea), from 2010 to 2022. Over the 12-year study period, our model showed it can outperform the benchmark index, having a high hit ratio of over 80%, a maximum drawdown in the low single digits, and a trading frequency of 1.5 trades per year. The results of our empirical research show that this methodology simplifies the process for investors to effectively implement market timing strategies in their investment decisions.

中文翻译：

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使用随机振荡器和 William%R 的基于算法的低频交易：美国和韩国指数的案例研究

在股票市场分析中使用随机指标被广泛接受用于指数估计和超高频交易。然而，之前在不应用低频交易的情况下将指数估计与实际交易联系起来的研究是有限的。本研究将William%R应用到现有研究中，使用固定参数去除随机噪声。我们建议通过为个人和养老基金投资者寻找易于应用的算法交易方法来为股票市场利益相关者做出贡献。该算法构建了两个具有固定参数的振荡器来识别何时进入和退出指数，并在基准测试中取得了良好的结果。我们从2010年到2022年测试了两只ETF，SPY（S&P 500）和EWY（MSCI韩国）。在12年的研究期间，我们的模型显示它可以跑赢基准指数，命中率高达80%以上，最大回撤为低个位数，交易频率为每年 1.5 次。我们的实证研究结果表明，这种方法简化了投资者在投资决策中有效实施市场时机策略的过程。