Algorithm-Based Low-Frequency Trading Using a Stochastic Oscillator, Williams%R, and Trading Volume for the S&P 500

ChanKyu Paik, Jinhee Choi () and Ivan Ureta Vaquero
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ChanKyu Paik: Seoul Business School, aSSIST University, Seoul 03767, Republic of Korea
Jinhee Choi: Seoul Business School, aSSIST University, Seoul 03767, Republic of Korea
Ivan Ureta Vaquero: Department of Business Economics, Health, and Social Care, The University of Applied Sciences and Arts of Southern Switzerland, 6928 Manno, Switzerland

JRFM, 2024, vol. 17, issue 11, 1-20

Abstract: Recent research in algorithmic trading has primarily focused on ultra-high-frequency strategies and index estimation. In response to the need for a low-frequency, real-world trading model, we developed an enhanced algorithm that builds on existing models with high hit ratios and low maximum drawdowns. We utilized established price indicators, including the stochastic oscillator and Williams %R, while introducing a volume factor to improve the model’s robustness and performance. The refined algorithm achieved superior returns while maintaining its high hit ratio and low maximum drawdown. Specifically, we leveraged 2X and 3X signals, incorporating volume data, the 52-week average, standard deviation, and other variables. The dataset comprised SPY ETF price and volume data spanning from 2010 to 2023, over 13 years. Our enhanced algorithmic model outperformed both the benchmark and previous iterations, achieving a hit rate of over 90%, a maximum drawdown of less than 1%, an average of 1.5 trades per year, a total return of 519.3%, and an annualized return (AnnR) of 15.1%. This analysis demonstrates that the model’s simplicity, ease of use, and interpretability provide valuable tools for investors, although it is important to note that past performance does not guarantee future returns.

Keywords: investment; trading; algorithm trading; market timing; ETF; S&P 500; low-frequency trading; hit ratio; volume; oversold (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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